diff --git a/.github/workflows/coverage.yaml b/.github/workflows/coverage.yaml new file mode 100644 index 000000000..9a7441058 --- /dev/null +++ b/.github/workflows/coverage.yaml @@ -0,0 +1,54 @@ +name: coverage + +on: + workflow_dispatch: + push: + branches: + - main + pull_request: + branches: + - "**" + paths-ignore: + - "docs/**" + +jobs: + coverage: + name: "Coverage" + runs-on: ubuntu-latest + steps: + - name: Checkout repo + uses: actions/checkout@v4 + - name: Set up Python + id: setup_python + uses: actions/setup-python@v4 + with: + python-version: "3.10" + - name: Cache virtual environment + uses: actions/cache@v3 + with: + # We are hashing dev-requirements.txt and test-requirements.txt which + # contain all dependencies needed to run the tests. + key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('dev-requirements.txt') }}-${{ hashFiles('test-requirements.txt') }} + path: .venv + - name: Install system packages + id: install_system_packages + run: | + sudo apt-get install -y portaudio19-dev + - name: Setup virtual environment + run: | + python -m venv .venv + - name: Install basic Python dependencies + run: | + source .venv/bin/activate + python -m pip install --upgrade pip + pip install -r dev-requirements.txt -r test-requirements.txt + - name: Run tests with coverage + run: | + source .venv/bin/activate + coverage run + coverage xml + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v5 + with: + token: ${{ secrets.CODECOV_TOKEN }} + slug: pipecat-ai/pipecat diff --git a/.gitignore b/.gitignore index 50944a3a8..1a11c0f2a 100644 --- a/.gitignore +++ b/.gitignore @@ -32,6 +32,21 @@ fly.toml # Example files pipecat/examples/twilio-chatbot/templates/streams.xml +pipecat/examples/bot-ready-signalling/client/react-native/node_modules/ +pipecat/examples/bot-ready-signalling/client/react-native/.expo/ +pipecat/examples/bot-ready-signalling/client/react-native/dist/ +pipecat/examples/bot-ready-signalling/client/react-native/npm-debug.* +pipecat/examples/bot-ready-signalling/client/react-native/*.jks +pipecat/examples/bot-ready-signalling/client/react-native/*.p8 +pipecat/examples/bot-ready-signalling/client/react-native/*.p12 +pipecat/examples/bot-ready-signalling/client/react-native/*.key +pipecat/examples/bot-ready-signalling/client/react-native/*.mobileprovision +pipecat/examples/bot-ready-signalling/client/react-native/*.orig.* +pipecat/examples/bot-ready-signalling/client/react-native/web-build/ + +# macOS +.DS_Store + # Documentation docs/api/_build/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6906b571b..d48d8bb8c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,7 +1,8 @@ repos: - - repo: local + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.9.7 hooks: - - id: ruff-format-hook - name: Check ruff formatting - entry: sh scripts/pre-commit.sh - language: system + - id: ruff + language_version: python3 + args: [ --select, I, ] + - id: ruff-format diff --git a/CHANGELOG.md b/CHANGELOG.md index 3fd1a86d5..a93901bd1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,10 +5,688 @@ All notable changes to **Pipecat** will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -## [Unreleased] +## [0.0.61] - 2025-03-26 ### Added +- Added a new frame, `LLMSetToolChoiceFrame`, which provides a mechanism + for modifying the `tool_choice` in the context. + +- Added `GroqTTSService` which provides text-to-speech functionality using + Groq's API. + +- Added support in `DailyTransport` for updating remote participants' + `canReceive` permission via the `update_remote_participants()` method, by + bumping the daily-python dependency to >= 0.16.0. + +- ElevenLabs TTS services now support a sample rate of 8000. + +- Added support for `instructions` in `OpenAITTSService` + +- Added support for `base_url` in `OpenAIImageGenService` and `OpenAITTSService` + +### Fixed + +- Fixed an issue in `RTVIObserver` that prevented handling of Google LLM + context messages. The observer now processes both OpenAI-style and + Google-style contexts. + +- Fixed an issue in Daily involving switching virtual devices, by bumping the + daily-python dependency to >= 0.16.1. + +- Fixed a `GoogleAssistantContextAggregator` issue where function calls + placeholders where not being updated when then function call result was + different from a string. + +- Fixed an issue that would cause `LLMAssistantContextAggregator` to block + processing more frames while processing a function call result. + +- Fixed an issue where the `RTVIObserver` would report two bot started and + stopped speaking events for each bot turn. + +- Fixed an issue in `UltravoxSTTService` that caused improper audio processing + and incorrect LLM frame output. + +### Other + +- Added `examples/foundational/07x-interruptible-local.py` to show how a local + transport can be used. + +## [0.0.60] - 2025-03-20 + +### Added + +- Added `default_headers` parameter to `BaseOpenAILLMService` constructor. + +### Changed + +- Rollback to `deepgram-sdk` 3.8.0 since 3.10.1 was causing connections issues. + +- Changed the default `InputAudioTranscription` model to `gpt-4o-transcribe` + for `OpenAIRealtimeBetaLLMService`. + +### Other + +- Update the `19-openai-realtime-beta.py` and `19a-azure-realtime-beta.py` + examples to use the FunctionSchema format. + +## [0.0.59] - 2025-03-20 + +### Added + +- When registering a function call it is now possible to indicate if you want + the function call to be cancelled if there's a user interruption via + `cancel_on_interruption` (defaults to False). This is now possible because + function calls are executed concurrently. + +- Added support for detecting idle pipelines. By default, if no activity has + been detected during 5 minutes, the `PipelineTask` will be automatically + cancelled. It is possible to override this behavior by passing + `cancel_on_idle_timeout=False`. It is also possible to change the default + timeout with `idle_timeout_secs` or the frames that prevent the pipeline from + being idle with `idle_timeout_frames`. Finally, an `on_idle_timeout` event + handler will be triggered if the idle timeout is reached (whether the pipeline + task is cancelled or not). + +- Added `FalSTTService`, which provides STT for Fal's Wizper API. + +- Added a `reconnect_on_error` parameter to websocket-based TTS services as well + as a `on_connection_error` event handler. The `reconnect_on_error` indicates + whether the TTS service should reconnect on error. The `on_connection_error` + will always get called if there's any error no matter the value of + `reconnect_on_error`. This allows, for example, to fallback to a different TTS + provider if something goes wrong with the current one. + +- Added new `SkipTagsAggregator` that extends `BaseTextAggregator` to aggregate + text and skips end of sentence matching if aggregated text is between + start/end tags. + +- Added new `PatternPairAggregator` that extends `BaseTextAggregator` to + identify content between matching pattern pairs in streamed text. This allows + for detection and processing of structured content like XML-style tags that + may span across multiple text chunks or sentence boundaries. + +- Added new `BaseTextAggregator`. Text aggregators are used by the TTS service + to aggregate LLM tokens and decide when the aggregated text should be pushed + to the TTS service. They also allow for the text to be manipulated while it's + being aggregated. A text aggregator can be passed via `text_aggregator` to the + TTS service. + +- Added new `sample_rate` constructor parameter to `TavusVideoService` to allow + changing the output sample rate. + +- Added new `NeuphonicTTSService`. + (see https://neuphonic.com) + +- Added new `UltravoxSTTService`. + (see https://github.com/fixie-ai/ultravox) + +- Added `on_frame_reached_upstream` and `on_frame_reached_downstream` event + handlers to `PipelineTask`. Those events will be called when a frame reaches + the beginning or end of the pipeline respectively. Note that by default, the + event handlers will not be called unless a filter is set with + `PipelineTask.set_reached_upstream_filter()` or + `PipelineTask.set_reached_downstream_filter()`. + +- Added support for Chirp voices in `GoogleTTSService`. + +- Added a `flush_audio()` method to `FishTTSService` and `LmntTTSService`. + +- Added a `set_language` convenience method for `GoogleSTTService`, allowing + you to set a single language. This is in addition to the `set_languages` + method which allows you to set a list of languages. + +- Added `on_user_turn_audio_data` and `on_bot_turn_audio_data` to + `AudioBufferProcessor`. This gives the ability to grab the audio of only that + turn for both the user and the bot. + +- Added new base class `BaseObject` which is now the base class of + `FrameProcessor`, `PipelineRunner`, `PipelineTask` and `BaseTransport`. The + new `BaseObject` adds supports for event handlers. + +- Added support for a unified format for specifying function calling across all + LLM services. + +```python + weather_function = FunctionSchema( + name="get_current_weather", + description="Get the current weather", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the user's location.", + }, + }, + required=["location"], + ) + tools = ToolsSchema(standard_tools=[weather_function]) +``` + +- Added `speech_threshold` parameter to `GladiaSTTService`. + +- Allow passing user (`user_kwargs`) and assistant (`assistant_kwargs`) context + aggregator parameters when using `create_context_aggregator()`. The values are + passed as a mapping that will then be converted to arguments. + +- Added `speed` as an `InputParam` for both `ElevenLabsTTSService` and + `ElevenLabsHttpTTSService`. + +- Added new `LLMFullResponseAggregator` to aggregate full LLM completions. At + every completion the `on_completion` event handler is triggered. + +- Added a new frame, `RTVIServerMessageFrame`, and RTVI message + `RTVIServerMessage` which provides a generic mechanism for sending custom + messages from server to client. The `RTVIServerMessageFrame` is processed by + the `RTVIObserver` and will be delivered to the client's `onServerMessage` + callback or `ServerMessage` event. + +- Added `GoogleLLMOpenAIBetaService` for Google LLM integration with an + OpenAI-compatible interface. Added foundational example + `14o-function-calling-gemini-openai-format.py`. + +- Added `AzureRealtimeBetaLLMService` to support Azure's OpeanAI Realtime API. Added + foundational example `19a-azure-realtime-beta.py`. + +- Introduced `GoogleVertexLLMService`, a new class for integrating with Vertex AI + Gemini models. Added foundational example + `14p-function-calling-gemini-vertex-ai.py`. + +- Added support in `OpenAIRealtimeBetaLLMService` for a slate of new features: + + - The `'gpt-4o-transcribe'` input audio transcription model, along + with new `language` and `prompt` options specific to that model. + - The `input_audio_noise_reduction` session property. + + ```python + session_properties = SessionProperties( + # ... + input_audio_noise_reduction=InputAudioNoiseReduction( + type="near_field" # also supported: "far_field" + ) + # ... + ) + ``` + + - The `'semantic_vad'` `turn_detection` session property value, a more + sophisticated model for detecting when the user has stopped speaking. + - `on_conversation_item_created` and `on_conversation_item_updated` + events to `OpenAIRealtimeBetaLLMService`. + + ```python + @llm.event_handler("on_conversation_item_created") + async def on_conversation_item_created(llm, item_id, item): + # ... + + @llm.event_handler("on_conversation_item_updated") + async def on_conversation_item_updated(llm, item_id, item): + # `item` may not always be available here + # ... + ``` + + - The `retrieve_conversation_item(item_id)` method for introspecting a + conversation item on the server. + + ```python + item = await llm.retrieve_conversation_item(item_id) + ``` + +### Changed + +- Updated `OpenAISTTService` to use `gpt-4o-transcribe` as the default + transcription model. + +- Updated `OpenAITTSService` to use `gpt-4o-mini-tts` as the default TTS model. + +- Function calls are now executed in tasks. This means that the pipeline will + not be blocked while the function call is being executed. + +- ⚠️ `PipelineTask` will now be automatically cancelled if no bot activity is + happening in the pipeline. There are a few settings to configure this + behavior, see `PipelineTask` documentation for more details. + +- All event handlers are now executed in separate tasks in order to prevent + blocking the pipeline. It is possible that event handlers take some time to + execute in which case the pipeline would be blocked waiting for the event + handler to complete. + +- Updated `TranscriptProcessor` to support text output from + `OpenAIRealtimeBetaLLMService`. + +- `OpenAIRealtimeBetaLLMService` and `GeminiMultimodalLiveLLMService` now push + a `TTSTextFrame`. + +- Updated the default mode for `CartesiaTTSService` and + `CartesiaHttpTTSService` to `sonic-2`. + +### Deprecated + +- Passing a `start_callback` to `LLMService.register_function()` is now + deprecated, simply move the code from the start callback to the function call. + +- `TTSService` parameter `text_filter` is now deprecated, use `text_filters` + instead which is now a list. This allows passing multiple filters that will be + executed in order. + +### Removed + +- Removed deprecated `audio.resample_audio()`, use `create_default_resampler()` + instead. + +- Removed deprecated`stt_service` parameter from `STTMuteFilter`. + +- Removed deprecated RTVI processors, use an `RTVIObserver` instead. + +- Removed deprecated `AWSTTSService`, use `PollyTTSService` instead. + +- Removed deprecated field `tier` from `DailyTranscriptionSettings`, use `model` + instead. + +- Removed deprecated `pipecat.vad` package, use `pipecat.audio.vad` instead. + +### Fixed + +- Fixed an assistant aggregator issue that could cause assistant text to be + split into multiple chunks during function calls. + +- Fixed an assistant aggregator issue that was causing assistant text to not be + added to the context during function calls. This could lead to duplications. + +- Fixed a `SegmentedSTTService` issue that was causing audio to be sent + prematurely to the STT service. Instead of analyzing the volume in this + service we rely on VAD events which use both VAD and volume. + +- Fixed a `GeminiMultimodalLiveLLMService` issue that was causing messages to be + duplicated in the context when pushing `LLMMessagesAppendFrame` frames. + +- Fixed an issue with `SegmentedSTTService` based services + (e.g. `GroqSTTService`) that was not allow audio to pass-through downstream. + +- Fixed a `CartesiaTTSService` and `RimeTTSService` issue that would consider + text between spelling out tags end of sentence. + +- Fixed a `match_endofsentence` issue that would result in floating point + numbers to be considered an end of sentence. + +- Fixed a `match_endofsentence` issue that would result in emails to be + considered an end of sentence. + +- Fixed an issue where the RTVI message `disconnect-bot` was pushing an + `EndFrame`, resulting in the pipeline not shutting down. It now pushes an + `EndTaskFrame` upstream to shutdown the pipeline. + +- Fixed an issue with the `GoogleSTTService` where stream timeouts during + periods of inactivity were causing connection failures. The service now + properly detects timeout errors and handles reconnection gracefully, + ensuring continuous operation even after periods of silence or when using an + `STTMuteFilter`. + +- Fixed an issue in `RimeTTSService` where the last line of text sent didn't + result in an audio output being generated. + +- Fixed `OpenAIRealtimeBetaLLMService` by adding proper handling for: + - The `conversation.item.input_audio_transcription.delta` server message, + which was added server-side at some point and not handled client-side. + - Errors reported by the `response.done` server message. + +### Other + +- Add foundational example `07w-interruptible-fal.py`, showing `FalSTTService`. + +- Added a new Ultravox example + `examples/foundational/07u-interruptible-ultravox.py`. + +- Added new Neuphonic examples + `examples/foundational/07v-interruptible-neuphonic.py` and + `examples/foundational/07v-interruptible-neuphonic-http.py`. + +- Added a new example `examples/foundational/36-user-email-gathering.py` to show + how to gather user emails. The example uses's Cartesia's `` + tags and Rime `spell()` function to spell out the emails for confirmation. + +- Update the `34-audio-recording.py` example to include an STT processor. + +- Added foundational example `35-voice-switching.py` showing how to use the new + `PatternPairAggregator`. This example shows how to encode information for the + LLM to instruct TTS voice changes, but this can be used to encode any + information into the LLM response, which you want to parse and use in other + parts of your application. + +- Added a Pipecat Cloud deployment example to the `examples` directory. + +- Removed foundational examples 28b and 28c as the TranscriptProcessor no + longer has an LLM depedency. Renamed foundational example 28a to + `28-transcript-processor.py`. + +## [0.0.58] - 2025-02-26 + +### Added + +- Added track-specific audio event `on_track_audio_data` to + `AudioBufferProcessor` for accessing separate input and output audio tracks. + +- Pipecat version will now be logged on every application startup. This will + help us identify what version we are running in case of any issues. + +- Added a new `StopFrame` which can be used to stop a pipeline task while + keeping the frame processors running. The frame processors could then be used + in a different pipeline. The difference between a `StopFrame` and a + `StopTaskFrame` is that, as with `EndFrame` and `EndTaskFrame`, the + `StopFrame` is pushed from the task and the `StopTaskFrame` is pushed upstream + inside the pipeline by any processor. + +- Added a new `PipelineTask` parameter `observers` that replaces the previous + `PipelineParams.observers`. + +- Added a new `PipelineTask` parameter `check_dangling_tasks` to enable or + disable checking for frame processors' dangling tasks when the Pipeline + finishes running. + +- Added new `on_completion_timeout` event for LLM services (all OpenAI-based + services, Anthropic and Google). Note that this event will only get triggered + if LLM timeouts are setup and if the timeout was reached. It can be useful to + retrigger another completion and see if the timeout was just a blip. + +- Added new log observers `LLMLogObserver` and `TranscriptionLogObserver` that + can be useful for debugging your pipelines. + +- Added `room_url` property to `DailyTransport`. + +- Added `addons` argument to `DeepgramSTTService`. + +- Added `exponential_backoff_time()` to `utils.network` module. + +### Changed + +- ⚠️ `PipelineTask` now requires keyword arguments (except for the first one for + the pipeline). + +- Updated `PlayHTHttpTTSService` to take a `voice_engine` and `protocol` input + in the constructor. The previous method of providing a `voice_engine` input + that contains the engine and protocol is deprecated by PlayHT. + +- The base `TTSService` class now strips leading newlines before sending text + to the TTS provider. This change is to solve issues where some TTS providers, + like Azure, would not output text due to newlines. + +- `GrokLLMSService` now uses `grok-2` as the default model. + +- `AnthropicLLMService` now uses `claude-3-7-sonnet-20250219` as the default + model. + +- `RimeHttpTTSService` needs an `aiohttp.ClientSession` to be passed to the + constructor as all the other HTTP-based services. + +- `RimeHttpTTSService` doesn't use a default voice anymore. + +- `DeepgramSTTService` now uses the new `nova-3` model by default. If you want + to use the previous model you can pass `LiveOptions(model="nova-2-general")`. + (see https://deepgram.com/learn/introducing-nova-3-speech-to-text-api) + +```python +stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general")) +``` + +### Deprecated + +- `PipelineParams.observers` is now deprecated, you the new `PipelineTask` + parameter `observers`. + +### Removed + +- Remove `TransportParams.audio_out_is_live` since it was not being used at all. + +### Fixed + +- Fixed an issue that would cause undesired interruptions via + `EmulateUserStartedSpeakingFrame`. + +- Fixed a `GoogleLLMService` that was causing an exception when sending inline + audio in some cases. + +- Fixed an `AudioContextWordTTSService` issue that would cause an `EndFrame` to + disconnect from the TTS service before audio from all the contexts was + received. This affected services like Cartesia and Rime. + +- Fixed an issue that was not allowing to pass an `OpenAILLMContext` to create + `GoogleLLMService`'s context aggregators. + +- Fixed a `ElevenLabsTTSService`, `FishAudioTTSService`, `LMNTTTSService` and + `PlayHTTTSService` issue that was resulting in audio requested before an + interruption being played after an interruption. + +- Fixed `match_endofsentence` support for ellipses. + +- Fixed an issue where `EndTaskFrame` was not triggering + `on_client_disconnected` or closing the WebSocket in FastAPI. + +- Fixed an issue in `DeepgramSTTService` where the `sample_rate` passed to the + `LiveOptions` was not being used, causing the service to use the default + sample rate of pipeline. + +- Fixed a context aggregator issue that would not append the LLM text response + to the context if a function call happened in the same LLM turn. + +- Fixed an issue that was causing HTTP TTS services to push `TTSStoppedFrame` + more than once. + +- Fixed a `FishAudioTTSService` issue where `TTSStoppedFrame` was not being + pushed. + +- Fixed an issue that `start_callback` was not invoked for some LLM services. + +- Fixed an issue that would cause `DeepgramSTTService` to stop working after an + error occurred (e.g. sudden network loss). If the network recovered we would + not reconnect. + +- Fixed a `STTMuteFilter` issue that would not mute user audio frames causing + transcriptions to be generated by the STT service. + +### Other + +- Added Gemini support to `examples/phone-chatbot`. + +- Added foundational example `34-audio-recording.py` showing how to use the + AudioBufferProcessor callbacks to save merged and track recordings. + +## [0.0.57] - 2025-02-14 + +### Added + +- Added new `AudioContextWordTTSService`. This is a TTS base class for TTS + services that handling multiple separate audio requests. + +- Added new frames `EmulateUserStartedSpeakingFrame` and + `EmulateUserStoppedSpeakingFrame` which can be used to emulated VAD behavior + without VAD being present or not being triggered. + +- Added a new `audio_in_stream_on_start` field to `TransportParams`. + +- Added a new method `start_audio_in_streaming` in the `BaseInputTransport`. + + - This method should be used to start receiving the input audio in case the + field `audio_in_stream_on_start` is set to `false`. + +- Added support for the `RTVIProcessor` to handle buffered audio in `base64` + format, converting it into InputAudioRawFrame for transport. + +- Added support for the `RTVIProcessor` to trigger `start_audio_in_streaming` + only after the `client-ready` message. + +- Added new `MUTE_UNTIL_FIRST_BOT_COMPLETE` strategy to `STTMuteStrategy`. This + strategy starts muted and remains muted until the first bot speech completes, + ensuring the bot's first response cannot be interrupted. This complements the + existing `FIRST_SPEECH` strategy which only mutes during the first detected + bot speech. + +- Added support for Google Cloud Speech-to-Text V2 through `GoogleSTTService`. + +- Added `RimeTTSService`, a new `WordTTSService`. Updated the foundational + example `07q-interruptible-rime.py` to use `RimeTTSService`. + +- Added support for Groq's Whisper API through the new `GroqSTTService` and + OpenAI's Whisper API through the new `OpenAISTTService`. Introduced a new + base class `BaseWhisperSTTService` to handle common Whisper API + functionality. + +- Added `PerplexityLLMService` for Perplexity NIM API integration, with an + OpenAI-compatible interface. Also, added foundational example + `14n-function-calling-perplexity.py`. + +- Added `DailyTransport.update_remote_participants()`. This allows you to update + remote participant's settings, like their permissions or which of their + devices are enabled. Requires that the local participant have participant + admin permission. + +### Changed + +- We don't consider a colon `:` and end of sentence any more. + +- Updated `DailyTransport` to respect the `audio_in_stream_on_start` field, + ensuring it only starts receiving the audio input if it is enabled. + +- Updated `FastAPIWebsocketOutputTransport` to send `TransportMessageFrame` and + `TransportMessageUrgentFrame` to the serializer. + +- Updated `WebsocketServerOutputTransport` to send `TransportMessageFrame` and + `TransportMessageUrgentFrame` to the serializer. + +- Enhanced `STTMuteConfig` to validate strategy combinations, preventing + `MUTE_UNTIL_FIRST_BOT_COMPLETE` and `FIRST_SPEECH` from being used together + as they handle first bot speech differently. + +- Updated foundational example `07n-interruptible-google.py` to use all Google + services. + +- `RimeHttpTTSService` now uses the `mistv2` model by default. + +- Improved error handling in `AzureTTSService` to properly detect and log + synthesis cancellation errors. + +- Enhanced `WhisperSTTService` with full language support and improved model + documentation. + +- Updated foundation example `14f-function-calling-groq.py` to use + `GroqSTTService` for transcription. + +- Updated `GroqLLMService` to use `llama-3.3-70b-versatile` as the default + model. + +- `RTVIObserver` doesn't handle `LLMSearchResponseFrame` frames anymore. For + now, to handle those frames you need to create a `GoogleRTVIObserver` + instead. + +### Deprecated + +- `STTMuteFilter` constructor's `stt_service` parameter is now deprecated and + will be removed in a future version. The filter now manages mute state + internally instead of querying the STT service. + +- `RTVI.observer()` is now deprecated, instantiate an `RTVIObserver` directly + instead. + +- All RTVI frame processors (e.g. `RTVISpeakingProcessor`, + `RTVIBotLLMProcessor`) are now deprecated, instantiate an `RTVIObserver` + instead. + +### Fixed + +- Fixed a `FalImageGenService` issue that was causing the event loop to be + blocked while loading the downloadded image. + +- Fixed a `CartesiaTTSService` service issue that would cause audio overlapping + in some cases. + +- Fixed a websocket-based service issue (e.g. `CartesiaTTSService`) that was + preventing a reconnection after the server disconnected cleanly, which was + causing an inifite loop instead. + +- Fixed a `BaseOutputTransport` issue that was causing upstream frames to no be + pushed upstream. + +- Fixed multiple issue where user transcriptions where not being handled + properly. It was possible for short utterances to not trigger VAD which would + cause user transcriptions to be ignored. It was also possible for one or more + transcriptions to be generated after VAD in which case they would also be + ignored. + +- Fixed an issue that was causing `BotStoppedSpeakingFrame` to be generated too + late. This could then cause issues unblocking `STTMuteFilter` later than + desired. + +- Fixed an issue that was causing `AudioBufferProcessor` to not record + synchronized audio. + +- Fixed an `RTVI` issue that was causing `bot-tts-text` messages to be sent + before being processed by the output transport. + +- Fixed an issue[#1192] in 11labs where we are trying to reconnect/disconnect + the websocket connection even when the connection is already closed. + +- Fixed an issue where `has_regular_messages` condition was always true in + `GoogleLLMContext` due to `Part` having `function_call` & `function_response` + with `None` values. + +### Other + +- Added new `instant-voice` example. This example showcases how to enable + instant voice communication as soon as a user connects. + +- Added new `local-input-select-stt` example. This examples allows you to play + with local audio inputs by slecting them through a nice text interface. + +## [0.0.56] - 2025-02-06 + +### Changed + +- Use `gemini-2.0-flash-001` as the default model for `GoogleLLMSerivce`. + +- Improved foundational examples 22b, 22c, and 22d to support function calling. + With these base examples, `FunctionCallInProgressFrame` and + `FunctionCallResultFrame` will no longer be blocked by the gates. + +### Fixed + +- Fixed a `TkLocalTransport` and `LocalAudioTransport` issues that was causing + errors on cleanup. + +- Fixed an issue that was causing `tests.utils` import to fail because of + logging setup. + +- Fixed a `SentryMetrics` issue that was preventing any metrics to be sent to + Sentry and also was preventing from metrics frames to be pushed to the + pipeline. + +- Fixed an issue in `BaseOutputTransport` where incoming audio would not be + resampled to the desired output sample rate. + +- Fixed an issue with the `TwilioFrameSerializer` and `TelnyxFrameSerializer` + where `twilio_sample_rate` and `telnyx_sample_rate` were incorrectly + initialized to `audio_in_sample_rate`. Those values currently default to 8000 + and should be set manually from the serializer constructor if a different + value is needed. + +### Other + +- Added a new `sentry-metrics` example. + +## [0.0.55] - 2025-02-05 + +### Added + +- Added a new `start_metadata` field to `PipelineParams`. The provided metadata + will be set to the initial `StartFrame` being pushed from the `PipelineTask`. + +- Added new fields to `PipelineParams` to control audio input and output sample + rates for the whole pipeline. This allows controlling sample rates from a + single place instead of having to specify sample rates in each + service. Setting a sample rate to a service is still possible and will + override the value from `PipelineParams`. + - Introduce audio resamplers (`BaseAudioResampler`). This is just a base class to implement audio resamplers. Currently, two implementations are provided `SOXRAudioResampler` and `ResampyResampler`. A new @@ -74,7 +752,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - `AudioBufferProcessor.reset_audio_buffers()` has been removed, use `AudioBufferProcessor.start_recording()` and - ``AudioBufferProcessor.stop_recording()` instead. + `AudioBufferProcessor.stop_recording()` instead. ### Fixed @@ -101,6 +779,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Other +- Improved Unit Test `run_test()` to use `PipelineTask` and + `PipelineRunner`. There's now also some control around `StartFrame` and + `EndFrame`. The `EndTaskFrame` has been removed since it doesn't seem + necessary with this new approach. + - Updated `twilio-chatbot` with a few new features: use 8000 sample rate and avoid resampling, a new client useful for stress testing and testing locally without the need to make phone calls. Also, added audio recording on both the @@ -135,7 +818,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added `enable_recording` and `geo` parameters to `DailyRoomProperties`. -- Added `RecordingsBucketConfig` to `DailyRoomProperties` to upload recordings to a custom AWS bucket. +- Added `RecordingsBucketConfig` to `DailyRoomProperties` to upload recordings + to a custom AWS bucket. ### Changed @@ -1441,7 +2125,7 @@ async def on_connected(processor): completed. If a task is never ran `has_finished()` will return False. - `PipelineRunner` now supports SIGTERM. If received, the runner will be - canceled. + cancelled. ### Fixed diff --git a/README.md b/README.md index 5a915041d..f28005618 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@  pipecat -[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) +[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![codecov](https://codecov.io/gh/pipecat-ai/pipecat/graph/badge.svg?token=LNVUIVO4Y9)](https://codecov.io/gh/pipecat-ai/pipecat) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences. @@ -55,17 +55,17 @@ pip install "pipecat-ai[option,...]" ### Available services -| Category | Services | Install Command Example | -| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | -| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) | `pip install "pipecat-ai[deepgram]"` | -| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"` | -| Text-to-Speech | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"` | -| Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) | `pip install "pipecat-ai[openai]"` | -| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local | `pip install "pipecat-ai[daily]"` | -| Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) | `pip install "pipecat-ai[tavus,simli]"` | -| Vision & Image | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal) | `pip install "pipecat-ai[moondream]"` | -| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter) | `pip install "pipecat-ai[silero]"` | -| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) | `pip install "pipecat-ai[canonical]"` | +| Category | Services | Install Command Example | +| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | +| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) | `pip install "pipecat-ai[deepgram]"` | +| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"` | +| Text-to-Speech | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"` | +| Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) | `pip install "pipecat-ai[google]"` | +| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local | `pip install "pipecat-ai[daily]"` | +| Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) | `pip install "pipecat-ai[tavus,simli]"` | +| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) | `pip install "pipecat-ai[moondream]"` | +| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter) | `pip install "pipecat-ai[silero]"` | +| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) | `pip install "pipecat-ai[canonical]"` | 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services) @@ -149,36 +149,40 @@ Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https:// ## Hacking on the framework itself -_Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_ +_Note: You may need to set up a virtual environment before following these instructions. From the root of the repo:_ ```shell python3 -m venv venv source venv/bin/activate ``` -From the root of this repo, run the following: +Install the development dependencies: ```shell pip install -r dev-requirements.txt ``` -This will install the necessary development dependencies. Also, make sure you install the git pre-commit hooks: +Install the git pre-commit hooks (these help ensure your code follows project rules): ```shell pre-commit install ``` -The hooks will just save you time when you submit a PR by making sure your code follows the project rules. - -To use the package locally (e.g. to run sample files), run: +Install the `pipecat-ai` package locally in editable mode: ```shell -pip install --editable ".[option,...]" +pip install -e . ``` -The `--editable` option makes sure you don't have to run `pip install` again and you can just edit the project files locally. +The `-e` or `--editable` option allows you to modify the code without reinstalling. -If you want to use this package from another directory, you can run: +To include optional dependencies, add them to the install command. For example: + +```shell +pip install -e ".[daily,deepgram,cartesia,openai,silero]" # Updated for the services you're using +``` + +If you want to use this package from another directory: ```shell pip install "path_to_this_repo[option,...]" diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 000000000..372cb800c --- /dev/null +++ b/codecov.yml @@ -0,0 +1,11 @@ +coverage: + range: 50..90 # coverage lower than 50 is red, higher than 90 green, between color code + + status: + project: + default: + target: auto # auto % coverage target + threshold: 5% # allow for 5% reduction of coverage without failing + + # do not run coverage on patch nor changes + patch: false diff --git a/dev-requirements.txt b/dev-requirements.txt index e3f52f9cd..e65c2755c 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -1,11 +1,12 @@ build~=1.2.2 -grpcio-tools~=1.69.0 +coverage~=7.6.12 +grpcio-tools~=1.67.1 pip-tools~=7.4.1 pre-commit~=4.0.1 -pyright~=1.1.392 +pyright~=1.1.397 pytest~=8.3.4 -pytest-asyncio~=0.25.2 -ruff~=0.9.1 -setuptools~=75.8.0 +pytest-asyncio~=0.25.3 +ruff~=0.11.1 +setuptools~=70.0.0 setuptools_scm~=8.1.0 python-dotenv~=1.0.1 diff --git a/dot-env.template b/dot-env.template index 1e902be60..2da20fc0b 100644 --- a/dot-env.template +++ b/dot-env.template @@ -18,6 +18,9 @@ AZURE_DALLE_API_KEY=... AZURE_DALLE_ENDPOINT=https://... AZURE_DALLE_MODEL=... +# Cartesia +CARTESIA_API_KEY=... + # Daily DAILY_API_KEY=... DAILY_SAMPLE_ROOM_URL=https://... @@ -26,6 +29,9 @@ DAILY_SAMPLE_ROOM_URL=https://... ELEVENLABS_API_KEY=... ELEVENLABS_VOICE_ID=... +# Neuphonic +NEUPHONIC_API_KEY=... + # Fal FAL_KEY=... diff --git a/examples/bot-ready-signalling/client/javascript/package-lock.json b/examples/bot-ready-signalling/client/javascript/package-lock.json index f10bc8528..0f23ca233 100644 --- a/examples/bot-ready-signalling/client/javascript/package-lock.json +++ b/examples/bot-ready-signalling/client/javascript/package-lock.json @@ -12,7 +12,7 @@ "@daily-co/daily-js": "0.74.0" }, "devDependencies": { - "vite": "^6.0.2" + "vite": "^6.0.9" } }, "node_modules/@babel/runtime": { @@ -1007,15 +1007,14 @@ } }, "node_modules/vite": { - "version": "6.0.7", - "resolved": "https://registry.npmjs.org/vite/-/vite-6.0.7.tgz", - "integrity": "sha512-RDt8r/7qx9940f8FcOIAH9PTViRrghKaK2K1jY3RaAURrEUbm9Du1mJ72G+jlhtG3WwodnfzY8ORQZbBavZEAQ==", + "version": "6.1.0", + "resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz", + "integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==", "dev": true, - "license": "MIT", "dependencies": { "esbuild": "^0.24.2", - "postcss": "^8.4.49", - "rollup": "^4.23.0" + "postcss": "^8.5.1", + "rollup": "^4.30.1" }, "bin": { "vite": "bin/vite.js" diff --git a/examples/bot-ready-signalling/client/javascript/package.json b/examples/bot-ready-signalling/client/javascript/package.json index 53b28499b..d85baff59 100644 --- a/examples/bot-ready-signalling/client/javascript/package.json +++ b/examples/bot-ready-signalling/client/javascript/package.json @@ -12,7 +12,7 @@ "license": "ISC", "description": "", "devDependencies": { - "vite": "^6.0.2" + "vite": "^6.0.9" }, "dependencies": { "@daily-co/daily-js": "0.74.0" diff --git a/examples/bot-ready-signalling/client/react-native/.nvmrc b/examples/bot-ready-signalling/client/react-native/.nvmrc new file mode 100644 index 000000000..744ca17ec --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/.nvmrc @@ -0,0 +1 @@ +22.14 diff --git a/examples/bot-ready-signalling/client/react-native/README.md b/examples/bot-ready-signalling/client/react-native/README.md new file mode 100644 index 000000000..5da0c67be --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/README.md @@ -0,0 +1,60 @@ +# React Native Implementation + +Basic implementation using the [Pipecat React Native SDK](https://docs.pipecat.ai/client/react-native/introduction). + +## Usage + +### Expo requirements + +This project cannot be used with an [Expo Go](https://docs.expo.dev/workflow/expo-go/) app because [it requires custom native code](https://docs.expo.io/workflow/customizing/). + +When a project requires custom native code or a config plugin, we need to transition from using [Expo Go](https://docs.expo.dev/workflow/expo-go/) +to a [development build](https://docs.expo.dev/development/introduction/). + +More details about the custom native code used by this demo can be found in [rn-daily-js-expo-config-plugin](https://github.com/daily-co/rn-daily-js-expo-config-plugin). + +### Building remotely + +If you do not have experience with Xcode and Android Studio builds or do not have them installed locally on your computer, you will need to follow [this guide from Expo to use EAS Build](https://docs.expo.dev/development/create-development-builds/#create-and-install-eas-build). + +### Building locally + +You will need to have installed locally on your computer: +- [Xcode](https://developer.apple.com/xcode/) to build for iOS; +- [Android Studio](https://developer.android.com/studio) to build for Android; + +#### Install the demo dependencies + +```bash +# Use the version of node specified in .nvmrc +nvm i + +# Install dependencies +npm i + +# Before a native app can be compiled, the native source code must be generated. +npx expo prebuild + +# Configure the environment variable to connect to the local server +cp env.example .env +# edit .env and add your local ip address, for example: http://192.168.1.16:7860 +``` + +#### Running on Android + +After plugging in an Android device [configured for debugging](https://developer.android.com/studio/debug/dev-options), run the following command: + +``` +npm run android +``` + +#### Running on iOS + +Run the following command: + +``` +npm run ios +``` + +#### Connect to the server +Use the http://localhost:5173 in your app. diff --git a/examples/bot-ready-signalling/client/react-native/app.json b/examples/bot-ready-signalling/client/react-native/app.json new file mode 100644 index 000000000..fb9394e96 --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/app.json @@ -0,0 +1,75 @@ +{ + "expo": { + "name": "bot-ready-rn", + "slug": "bot-ready-rn", + "version": "1.0.0", + "orientation": "portrait", + "icon": "./assets/icon.png", + "userInterfaceStyle": "light", + "splash": { + "image": "./assets/splash.png", + "resizeMode": "contain", + "backgroundColor": "#ffffff" + }, + "updates": { + "fallbackToCacheTimeout": 0 + }, + "assetBundlePatterns": [ + "**/*" + ], + "ios": { + "supportsTablet": true, + "bitcode": false, + "bundleIdentifier": "co.daily.expo.BotReady", + "infoPlist": { + "UIBackgroundModes": [ + "voip" + ] + }, + "appleTeamId": "EEBGKV9N3N" + }, + "android": { + "adaptiveIcon": { + "foregroundImage": "./assets/adaptive-icon.png", + "backgroundColor": "#FFFFFF" + }, + "package": "co.daily.expo.BotReady", + "permissions": [ + "android.permission.ACCESS_NETWORK_STATE", + "android.permission.BLUETOOTH", + "android.permission.CAMERA", + "android.permission.INTERNET", + "android.permission.MODIFY_AUDIO_SETTINGS", + "android.permission.RECORD_AUDIO", + "android.permission.SYSTEM_ALERT_WINDOW", + "android.permission.WAKE_LOCK", + "android.permission.FOREGROUND_SERVICE", + "android.permission.FOREGROUND_SERVICE_CAMERA", + "android.permission.FOREGROUND_SERVICE_MICROPHONE", + "android.permission.FOREGROUND_SERVICE_MEDIA_PROJECTION", + "android.permission.POST_NOTIFICATIONS" + ] + }, + "web": { + "favicon": "./assets/favicon.png" + }, + "plugins": [ + "@config-plugins/react-native-webrtc", + "@daily-co/config-plugin-rn-daily-js", + [ + "expo-build-properties", + { + "android": { + "minSdkVersion": 24, + "compileSdkVersion": 35, + "targetSdkVersion": 34, + "buildToolsVersion": "35.0.0" + }, + "ios": { + "deploymentTarget": "15.1" + } + } + ] + ] + } +} diff --git a/examples/bot-ready-signalling/client/react-native/assets/adaptive-icon.png b/examples/bot-ready-signalling/client/react-native/assets/adaptive-icon.png new file mode 100644 index 000000000..03d6f6b6c Binary files /dev/null and b/examples/bot-ready-signalling/client/react-native/assets/adaptive-icon.png differ diff --git a/examples/bot-ready-signalling/client/react-native/assets/favicon.png b/examples/bot-ready-signalling/client/react-native/assets/favicon.png new file mode 100644 index 000000000..e75f697b1 Binary files /dev/null and b/examples/bot-ready-signalling/client/react-native/assets/favicon.png differ diff --git a/examples/bot-ready-signalling/client/react-native/assets/icon.png b/examples/bot-ready-signalling/client/react-native/assets/icon.png new file mode 100644 index 000000000..a0b1526fc Binary files /dev/null and b/examples/bot-ready-signalling/client/react-native/assets/icon.png differ diff --git a/examples/bot-ready-signalling/client/react-native/assets/splash.png b/examples/bot-ready-signalling/client/react-native/assets/splash.png new file mode 100644 index 000000000..0e89705a9 Binary files /dev/null and b/examples/bot-ready-signalling/client/react-native/assets/splash.png differ diff --git a/examples/bot-ready-signalling/client/react-native/babel.config.js b/examples/bot-ready-signalling/client/react-native/babel.config.js new file mode 100644 index 000000000..81d4cb0da --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/babel.config.js @@ -0,0 +1,7 @@ +module.exports = function(api) { + api.cache(true); + return { + presets: ['babel-preset-expo'], + plugins: [["module:react-native-dotenv"]], + }; +}; diff --git a/examples/bot-ready-signalling/client/react-native/env.example b/examples/bot-ready-signalling/client/react-native/env.example new file mode 100644 index 000000000..4c242c880 --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/env.example @@ -0,0 +1 @@ +API_BASE_URL=http://YOUR_LOCAL_IP:7860 diff --git a/examples/bot-ready-signalling/client/react-native/index.js b/examples/bot-ready-signalling/client/react-native/index.js new file mode 100644 index 000000000..9d93586db --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/index.js @@ -0,0 +1,7 @@ +import { registerRootComponent } from "expo"; + +import App from "./src/App"; + +// registerRootComponent calls AppRegistry.registerComponent('main', () => App); +// It also ensures that the environment is set up appropriately +registerRootComponent(App); diff --git a/examples/bot-ready-signalling/client/react-native/metro.config.js b/examples/bot-ready-signalling/client/react-native/metro.config.js new file mode 100644 index 000000000..9430b0f9b --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/metro.config.js @@ -0,0 +1,4 @@ +// Learn more https://docs.expo.io/guides/customizing-metro +const { getDefaultConfig } = require('expo/metro-config'); + +module.exports = getDefaultConfig(__dirname); diff --git a/examples/bot-ready-signalling/client/react-native/package-lock.json b/examples/bot-ready-signalling/client/react-native/package-lock.json new file mode 100644 index 000000000..c8cff3517 --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/package-lock.json @@ -0,0 +1,10983 @@ +{ + "name": "bot-ready-rn", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "bot-ready-rn", + "version": "1.0.0", + "dependencies": { + "@config-plugins/react-native-webrtc": 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"@daily-co/config-plugin-rn-daily-js": "0.0.7", + "@daily-co/react-native-daily-js": "^0.70.0", + "@daily-co/react-native-webrtc": "^118.0.3-daily.2", + "@react-native-async-storage/async-storage": "1.23.1", + "expo": "^52.0.0", + "expo-build-properties": "~0.13.1", + "expo-dev-client": "~5.0.5", + "expo-splash-screen": "~0.29.16", + "expo-status-bar": "~2.0.0", + "react": "18.3.1", + "react-native": "0.76.3", + "react-native-background-timer": "^2.4.1", + "react-native-dotenv": "^3.4.11", + "react-native-get-random-values": "^1.11.0" + }, + "devDependencies": { + "@babel/core": "^7.12.9" + }, + "private": true +} diff --git a/examples/bot-ready-signalling/client/react-native/src/App.js b/examples/bot-ready-signalling/client/react-native/src/App.js new file mode 100644 index 000000000..46b4c1e91 --- /dev/null +++ b/examples/bot-ready-signalling/client/react-native/src/App.js @@ -0,0 +1,121 @@ +import React, { useState, useEffect } from 'react'; +import {SafeAreaView, View, Text, Button, StyleSheet, ScrollView} from 'react-native'; +import Daily from "@daily-co/react-native-daily-js"; +import { API_BASE_URL } from "@env"; + +const CallScreen = () => { + const [connectionStatus, setConnectionStatus] = useState('Disconnected'); + const [isConnected, setIsConnected] = useState(false); + const [callObject, setCallObject] = useState(null); + const [logs, setLogs] = useState([]); + + useEffect(() => { + if (callObject) { + setupTrackListeners(callObject); + } + }, [callObject]); + + const log = (message) => { + setLogs((prevLogs) => [...prevLogs, `${new Date().toISOString()} - ${message}`]); + console.log(message); + }; + + const setupTrackListeners = (callObject) => { + callObject.on("joined-meeting", () => { + setConnectionStatus('Connected'); + setIsConnected(true); + log('Client connected'); + }); + callObject.on("left-meeting", () => { + setConnectionStatus('Disconnected'); + setIsConnected(false); + log('Client disconnected'); + }); + callObject.on("participant-left", () => { + // When the bot leaves, we are also disconnecting from the call + disconnect().catch((err) => { + log(`Failed to disconnect ${err}`); + }) + }); + // Trigger so the bot can start sending audio + callObject.on("track-started", (evt) => { + if (evt.track.kind === "audio" && evt.participant.local === false) { + handleEventToConsole(evt) + log("Sending the message that will trigger the bot to play the audio.") + callObject.sendAppMessage("playable") + } + }); + callObject.on("error", (evt) => log(`Error: ${evt.error}`)); + // Other events just for awareness + callObject.on("track-stopped", handleEventToConsole); + callObject.on("participant-joined", handleEventToConsole); + callObject.on("participant-updated", handleEventToConsole); + }; + + const handleEventToConsole = (evt) => { + log(`Received event: ${evt.action}`); + }; + + const connect = async () => { + try { + const callObject = Daily.createCallObject({ subscribeToTracksAutomatically: true }); + setCallObject(callObject); + const connectionUrl = `${API_BASE_URL}/connect` + const res = await fetch(connectionUrl, { method: "POST", headers: { "Content-Type": "application/json" } }); + const roomInfo = await res.json(); + await callObject.join({ url: roomInfo.room_url }); + } catch (error) { + log(`Error connecting: ${error.message}`); + } + }; + + const disconnect = async () => { + if (callObject) { + try { + await callObject.leave(); + await callObject.destroy(); + setCallObject(null); + } catch (error) { + log(`Error disconnecting: ${error.message}`); + } + } + }; + + return ( + + + + Status: {connectionStatus} + + + + + + + + +
+

Debug Info

+
+
+ + + + + + + diff --git a/examples/instant-voice/client/javascript/package-lock.json b/examples/instant-voice/client/javascript/package-lock.json new file mode 100644 index 000000000..54ed9131c --- /dev/null +++ b/examples/instant-voice/client/javascript/package-lock.json @@ -0,0 +1,1449 @@ +{ + "name": "client", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "client", + "version": "1.0.0", + "license": "ISC", + "dependencies": { + "@pipecat-ai/client-js": "^0.3.2", + "@pipecat-ai/daily-transport": "^0.3.5" + }, + "devDependencies": { + "@types/node": "^22.13.1", + "@vitejs/plugin-react-swc": "^3.7.2", + "typescript": "^5.7.3", + "vite": "^6.0.2" + } + }, + "node_modules/@babel/runtime": { + "version": "7.26.0", + "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.26.0.tgz", + "integrity": "sha512-FDSOghenHTiToteC/QRlv2q3DhPZ/oOXTBoirfWNx1Cx3TMVcGWQtMMmQcSvb/JjpNeGzx8Pq/b4fKEJuWm1sw==", + "license": "MIT", + 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new file mode 100644 index 000000000..9c3afb89a --- /dev/null +++ b/examples/instant-voice/client/javascript/package.json @@ -0,0 +1,24 @@ +{ + "name": "client", + "version": "1.0.0", + "main": "index.js", + "scripts": { + "dev": "vite", + "build": "tsc && vite build", + "preview": "vite preview" + }, + "keywords": [], + "author": "", + "license": "ISC", + "description": "", + "devDependencies": { + "@types/node": "^22.13.1", + "@vitejs/plugin-react-swc": "^3.7.2", + "typescript": "^5.7.3", + "vite": "^6.0.2" + }, + "dependencies": { + "@pipecat-ai/client-js": "^0.3.2", + "@pipecat-ai/daily-transport": "^0.3.5" + } +} diff --git a/examples/instant-voice/client/javascript/src/app.ts b/examples/instant-voice/client/javascript/src/app.ts new file mode 100644 index 000000000..bb587307c --- /dev/null +++ b/examples/instant-voice/client/javascript/src/app.ts @@ -0,0 +1,268 @@ +/** + * Copyright (c) 2024–2025, Daily + * + * SPDX-License-Identifier: BSD 2-Clause License + */ + +/** + * RTVI Client Implementation + * + * This client connects to an RTVI-compatible bot server using WebRTC (via Daily). + * It handles audio/video streaming and manages the connection lifecycle. + * + * Requirements: + * - A running RTVI bot server (defaults to http://localhost:7860) + * - The server must implement the /connect endpoint that returns Daily.co room credentials + * - Browser with WebRTC support + */ + +import { + Participant, + RTVIClient, + RTVIClientOptions, + RTVIEvent, +} from '@pipecat-ai/client-js'; +import { DailyTransport } from '@pipecat-ai/daily-transport'; +import SoundUtils from "./util/soundUtils"; +import { InstantVoiceHelper } from "./util/instantVoiceHelper"; + +/** + * InstantVoiceClient handles the connection and media management for a real-time + * voice and video interaction with an AI bot. + */ +class InstantVoiceClient { + + private declare rtviClient: RTVIClient; + private connectBtn: HTMLButtonElement | null = null; + private disconnectBtn: HTMLButtonElement | null = null; + private statusSpan: HTMLElement | null = null; + private bufferingAudioSpan: HTMLElement | null = null; + private debugLog: HTMLElement | null = null; + private botAudio: HTMLAudioElement; + private declare startTime: number; + + constructor() { + this.botAudio = document.createElement('audio'); + this.botAudio.autoplay = true; + document.body.appendChild(this.botAudio); + this.setupDOMElements(); + this.setupEventListeners(); + this.initializeRTVIClient(); + } + + /** + * Set up references to DOM elements and create necessary media elements + */ + private setupDOMElements(): void { + this.connectBtn = document.getElementById('connect-btn') as HTMLButtonElement; + this.disconnectBtn = document.getElementById('disconnect-btn') as HTMLButtonElement; + this.statusSpan = document.getElementById('connection-status'); + this.bufferingAudioSpan = document.getElementById('buffering-status'); + this.debugLog = document.getElementById('debug-log'); + } + + /** + * Set up event listeners for connect/disconnect buttons + */ + private setupEventListeners(): void { + this.connectBtn?.addEventListener('click', () => this.connect()); + this.disconnectBtn?.addEventListener('click', () => this.disconnect()); + } + + private initializeRTVIClient(): void { + const transport = new DailyTransport({ + bufferLocalAudioUntilBotReady: true + }); + const RTVIConfig: RTVIClientOptions = { + transport, + params: { + // The baseURL and endpoint of your bot server that the client will connect to + baseUrl: 'http://localhost:7860', + endpoints: { connect: '/connect' }, + }, + enableMic: true, + enableCam: false, + callbacks: { + onConnected: () => { + this.updateStatus('Connected'); + if (this.connectBtn) this.connectBtn.disabled = true; + if (this.disconnectBtn) this.disconnectBtn.disabled = false; + }, + onDisconnected: () => { + this.updateStatus('Disconnected'); + this.updateBufferingStatus('No'); + if (this.connectBtn) this.connectBtn.disabled = false; + if (this.disconnectBtn) this.disconnectBtn.disabled = true; + this.log('Client disconnected'); + }, + onBotConnected: (participant: Participant) => { + this.log(`onBotConnected, timeTaken: ${Date.now() - this.startTime}`); + }, + onBotReady: (data) => { + this.log(`onBotReady, timeTaken: ${Date.now() - this.startTime}`); + this.log(`Bot ready: ${JSON.stringify(data)}`); + this.setupMediaTracks(); + }, + onUserTranscript: (data) => { + if (data.final) { + this.log(`User: ${data.text}`); + } + }, + onBotTranscript: (data) => this.log(`Bot: ${data.text}`), + onMessageError: (error) => console.error('Message error:', error), + onError: (error) => console.error('Error:', error), + }, + } + + this.rtviClient = new RTVIClient(RTVIConfig); + this.rtviClient.registerHelper("transport", new InstantVoiceHelper({ + callbacks: { + onAudioBufferingStarted: () => { + SoundUtils.beep() + this.updateBufferingStatus('Yes'); + this.log(`onMicCaptureStarted, timeTaken: ${Date.now() - this.startTime}`); + }, + onAudioBufferingStopped: () => { + this.updateBufferingStatus('No'); + this.log(`onMicCaptureStopped, timeTaken: ${Date.now() - this.startTime}`); + } + } + } + )); + this.setupTrackListeners(); + } + + /** + * Add a timestamped message to the debug log + */ + private log(message: string): void { + if (!this.debugLog) return; + const entry = document.createElement('div'); + entry.textContent = `${new Date().toISOString()} - ${message}`; + if (message.startsWith('User: ')) { + entry.style.color = '#2196F3'; + } else if (message.startsWith('Bot: ')) { + entry.style.color = '#4CAF50'; + } + this.debugLog.appendChild(entry); + this.debugLog.scrollTop = this.debugLog.scrollHeight; + console.log(message); + } + + /** + * Update the connection status display + */ + private updateStatus(status: string): void { + if (this.statusSpan) { + this.statusSpan.textContent = status; + } + this.log(`Status: ${status}`); + } + + /** + * Update the connection status display + */ + private updateBufferingStatus(status: string): void { + if (this.bufferingAudioSpan) { + this.bufferingAudioSpan.textContent = status; + } + this.log(`BufferingStatus: ${status}`); + } + + /** + * Check for available media tracks and set them up if present + * This is called when the bot is ready or when the transport state changes to ready + */ + setupMediaTracks() { + if (!this.rtviClient) return; + const tracks = this.rtviClient.tracks(); + if (tracks.bot?.audio) { + this.setupAudioTrack(tracks.bot.audio); + } + } + + /** + * Set up listeners for track events (start/stop) + * This handles new tracks being added during the session + */ + setupTrackListeners() { + if (!this.rtviClient) return; + + // Listen for new tracks starting + this.rtviClient.on(RTVIEvent.TrackStarted, (track, participant) => { + // Only handle non-local (bot) tracks + if (!participant?.local && track.kind === 'audio') { + this.setupAudioTrack(track); + } + }); + + // Listen for tracks stopping + this.rtviClient.on(RTVIEvent.TrackStopped, (track, participant) => { + this.log(`Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`); + }); + } + + /** + * Set up an audio track for playback + * Handles both initial setup and track updates + */ + private setupAudioTrack(track: MediaStreamTrack): void { + this.log('Setting up audio track'); + if (this.botAudio.srcObject && "getAudioTracks" in this.botAudio.srcObject) { + const oldTrack = this.botAudio.srcObject.getAudioTracks()[0]; + if (oldTrack?.id === track.id) return; + } + this.botAudio.srcObject = new MediaStream([track]); + } + + /** + * Initialize and connect to the bot + * This sets up the RTVI client, initializes devices, and establishes the connection + */ + public async connect(): Promise { + try { + this.startTime = Date.now(); + this.log('Connecting to bot...'); + await this.rtviClient.connect(); + } catch (error) { + this.log(`Error connecting: ${(error as Error).message}`); + this.updateStatus('Error'); + this.updateBufferingStatus('No'); + + // Clean up if there's an error + if (this.rtviClient) { + try { + await this.rtviClient.disconnect(); + } catch (disconnectError) { + this.log(`Error during disconnect: ${disconnectError}`); + } + } + } + } + + /** + * Disconnect from the bot and clean up media resources + */ + public async disconnect(): Promise { + try { + await this.rtviClient.disconnect(); + if (this.botAudio.srcObject && "getAudioTracks" in this.botAudio.srcObject) { + this.botAudio.srcObject.getAudioTracks().forEach((track) => track.stop()); + this.botAudio.srcObject = null; + } + } catch (error) { + this.log(`Error disconnecting: ${(error as Error).message}`); + } + } +} + +declare global { + interface Window { + InstantVoiceClient: typeof InstantVoiceClient; + } +} + +window.addEventListener('DOMContentLoaded', () => { + window.InstantVoiceClient = InstantVoiceClient; + new InstantVoiceClient(); +}); diff --git a/examples/instant-voice/client/javascript/src/style.css b/examples/instant-voice/client/javascript/src/style.css new file mode 100644 index 000000000..9c147266e --- /dev/null +++ b/examples/instant-voice/client/javascript/src/style.css @@ -0,0 +1,98 @@ +body { + margin: 0; + padding: 20px; + font-family: Arial, sans-serif; + background-color: #f0f0f0; +} + +.container { + max-width: 1200px; + margin: 0 auto; +} + +.status-bar { + display: flex; + justify-content: space-between; + align-items: center; + padding: 10px; + background-color: #fff; + border-radius: 8px; + margin-bottom: 20px; +} + +.controls button { + padding: 8px 16px; + margin-left: 10px; + border: none; + border-radius: 4px; + cursor: pointer; +} + +#connect-btn { + background-color: #4caf50; + color: white; +} + +#disconnect-btn { + background-color: #f44336; + color: white; +} + +button:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +.main-content { + background-color: #fff; + border-radius: 8px; + padding: 20px; + margin-bottom: 20px; +} + +.bot-container { + display: flex; + flex-direction: column; + align-items: center; +} + +#bot-video-container { + width: 640px; + height: 360px; + background-color: #e0e0e0; + border-radius: 8px; + margin: 20px auto; + overflow: hidden; + display: flex; + align-items: center; + justify-content: center; +} + +#bot-video-container video { + width: 100%; + height: 100%; + object-fit: cover; +} + +.debug-panel { + background-color: #fff; + border-radius: 8px; + padding: 20px; +} + +.debug-panel h3 { + margin: 0 0 10px 0; + font-size: 16px; + font-weight: bold; +} + +#debug-log { + height: 500px; + overflow-y: auto; + background-color: #f8f8f8; + padding: 10px; + border-radius: 4px; + font-family: monospace; + font-size: 12px; + line-height: 1.4; +} diff --git a/examples/instant-voice/client/javascript/src/util/instantVoiceHelper.ts b/examples/instant-voice/client/javascript/src/util/instantVoiceHelper.ts new file mode 100644 index 000000000..2ce3a15ce --- /dev/null +++ b/examples/instant-voice/client/javascript/src/util/instantVoiceHelper.ts @@ -0,0 +1,39 @@ +import {RTVIClientHelper, RTVIClientHelperOptions, RTVIMessage} from "@pipecat-ai/client-js"; +import {DailyRTVIMessageType} from '@pipecat-ai/daily-transport'; + +export type InstantVoiceHelperCallbacks = Partial<{ + onAudioBufferingStarted: () => void; + onAudioBufferingStopped: () => void; +}>; + +// --- Interface and class +export interface InstantVoiceHelperOptions extends RTVIClientHelperOptions { + callbacks?: InstantVoiceHelperCallbacks; +} +export class InstantVoiceHelper extends RTVIClientHelper { + + protected declare _options: InstantVoiceHelperOptions; + + constructor(options: InstantVoiceHelperOptions) { + super(options); + } + + handleMessage(rtviMessage: RTVIMessage): void { + switch (rtviMessage.type) { + case DailyRTVIMessageType.AUDIO_BUFFERING_STARTED: + if (this._options.callbacks?.onAudioBufferingStarted) { + this._options.callbacks?.onAudioBufferingStarted() + } + break; + case DailyRTVIMessageType.AUDIO_BUFFERING_STOPPED: + if (this._options.callbacks?.onAudioBufferingStopped) { + this._options.callbacks?.onAudioBufferingStopped() + } + break; + } + } + + getMessageTypes(): string[] { + return [DailyRTVIMessageType.AUDIO_BUFFERING_STARTED, DailyRTVIMessageType.AUDIO_BUFFERING_STOPPED]; + } +} diff --git a/examples/instant-voice/client/javascript/src/util/soundUtils.ts b/examples/instant-voice/client/javascript/src/util/soundUtils.ts new file mode 100644 index 000000000..996185f40 --- /dev/null +++ b/examples/instant-voice/client/javascript/src/util/soundUtils.ts @@ -0,0 +1,8 @@ +class SoundUtils { + static beep() { + const snd = new Audio("data:audio/wav;base64,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"); + void snd.play(); + } +} + +export default SoundUtils; diff --git a/examples/instant-voice/client/javascript/tsconfig.json b/examples/instant-voice/client/javascript/tsconfig.json new file mode 100644 index 000000000..c9c555d96 --- /dev/null +++ b/examples/instant-voice/client/javascript/tsconfig.json @@ -0,0 +1,111 @@ +{ + "compilerOptions": { + /* Visit https://aka.ms/tsconfig to read more about this file */ + + /* Projects */ + // "incremental": true, /* Save .tsbuildinfo files to allow for incremental compilation of projects. */ + // "composite": true, /* Enable constraints that allow a TypeScript project to be used with project references. */ + // "tsBuildInfoFile": "./.tsbuildinfo", /* Specify the path to .tsbuildinfo incremental compilation file. */ + // "disableSourceOfProjectReferenceRedirect": true, /* Disable preferring source files instead of declaration files when referencing composite projects. */ + // "disableSolutionSearching": true, /* Opt a project out of multi-project reference checking when editing. */ + // "disableReferencedProjectLoad": true, /* Reduce the number of projects loaded automatically by TypeScript. */ + + /* Language and Environment */ + "target": "es2016", /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */ + // "lib": [], /* Specify a set of bundled library declaration files that describe the target runtime environment. */ + // "jsx": "preserve", /* Specify what JSX code is generated. */ + // "experimentalDecorators": true, /* Enable experimental support for legacy experimental decorators. */ + // "emitDecoratorMetadata": true, /* Emit design-type metadata for decorated declarations in source files. */ + // "jsxFactory": "", /* Specify the JSX factory function used when targeting React JSX emit, e.g. 'React.createElement' or 'h'. */ + // "jsxFragmentFactory": "", /* Specify the JSX Fragment reference used for fragments when targeting React JSX emit e.g. 'React.Fragment' or 'Fragment'. */ + // "jsxImportSource": "", /* Specify module specifier used to import the JSX factory functions when using 'jsx: react-jsx*'. */ + // "reactNamespace": "", /* Specify the object invoked for 'createElement'. This only applies when targeting 'react' JSX emit. */ + // "noLib": true, /* Disable including any library files, including the default lib.d.ts. */ + // "useDefineForClassFields": true, /* Emit ECMAScript-standard-compliant class fields. */ + // "moduleDetection": "auto", /* Control what method is used to detect module-format JS files. */ + + /* Modules */ + "module": "commonjs", /* Specify what module code is generated. */ + // "rootDir": "./", /* Specify the root folder within your source files. */ + // "moduleResolution": "node10", /* Specify how TypeScript looks up a file from a given module specifier. */ + // "baseUrl": "./", /* Specify the base directory to resolve non-relative module names. */ + // "paths": {}, /* Specify a set of entries that re-map imports to additional lookup locations. */ + // "rootDirs": [], /* Allow multiple folders to be treated as one when resolving modules. */ + // "typeRoots": [], /* Specify multiple folders that act like './node_modules/@types'. */ + // "types": [], /* Specify type package names to be included without being referenced in a source file. */ + // "allowUmdGlobalAccess": true, /* Allow accessing UMD globals from modules. */ + // "moduleSuffixes": [], /* List of file name suffixes to search when resolving a module. */ + // "allowImportingTsExtensions": true, /* Allow imports to include TypeScript file extensions. Requires '--moduleResolution bundler' and either '--noEmit' or '--emitDeclarationOnly' to be set. */ + // "rewriteRelativeImportExtensions": true, /* Rewrite '.ts', '.tsx', '.mts', and '.cts' file extensions in relative import paths to their JavaScript equivalent in output files. */ + // "resolvePackageJsonExports": true, /* Use the package.json 'exports' field when resolving package imports. */ + // "resolvePackageJsonImports": true, /* Use the package.json 'imports' field when resolving imports. */ + // "customConditions": [], /* Conditions to set in addition to the resolver-specific defaults when resolving imports. */ + // "noUncheckedSideEffectImports": true, /* Check side effect imports. */ + // "resolveJsonModule": true, /* Enable importing .json files. */ + // "allowArbitraryExtensions": true, /* Enable importing files with any extension, provided a declaration file is present. */ + // "noResolve": true, /* Disallow 'import's, 'require's or ''s from expanding the number of files TypeScript should add to a project. */ + + /* JavaScript Support */ + // "allowJs": true, /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */ + // "checkJs": true, /* Enable error reporting in type-checked JavaScript files. */ + // "maxNodeModuleJsDepth": 1, /* Specify the maximum folder depth used for checking JavaScript files from 'node_modules'. Only applicable with 'allowJs'. */ + + /* Emit */ + // "declaration": true, /* Generate .d.ts files from TypeScript and JavaScript files in your project. */ + // "declarationMap": true, /* Create sourcemaps for d.ts files. */ + // "emitDeclarationOnly": true, /* Only output d.ts files and not JavaScript files. */ + // "sourceMap": true, /* Create source map files for emitted JavaScript files. */ + // "inlineSourceMap": true, /* Include sourcemap files inside the emitted JavaScript. */ + // "noEmit": true, /* Disable emitting files from a compilation. */ + // "outFile": "./", /* Specify a file that bundles all outputs into one JavaScript file. If 'declaration' is true, also designates a file that bundles all .d.ts output. */ + // "outDir": "./", /* Specify an output folder for all emitted files. */ + // "removeComments": true, /* Disable emitting comments. */ + // "importHelpers": true, /* Allow importing helper functions from tslib once per project, instead of including them per-file. */ + // "downlevelIteration": true, /* Emit more compliant, but verbose and less performant JavaScript for iteration. */ + // "sourceRoot": "", /* Specify the root path for debuggers to find the reference source code. */ + // "mapRoot": "", /* Specify the location where debugger should locate map files instead of generated locations. */ + // "inlineSources": true, /* Include source code in the sourcemaps inside the emitted JavaScript. */ + // "emitBOM": true, /* Emit a UTF-8 Byte Order Mark (BOM) in the beginning of output files. */ + // "newLine": "crlf", /* Set the newline character for emitting files. */ + // "stripInternal": true, /* Disable emitting declarations that have '@internal' in their JSDoc comments. */ + // "noEmitHelpers": true, /* Disable generating custom helper functions like '__extends' in compiled output. */ + // "noEmitOnError": true, /* Disable emitting files if any type checking errors are reported. */ + // "preserveConstEnums": true, /* Disable erasing 'const enum' declarations in generated code. */ + // "declarationDir": "./", /* Specify the output directory for generated declaration files. */ + + /* Interop Constraints */ + // "isolatedModules": true, /* Ensure that each file can be safely transpiled without relying on other imports. */ + // "verbatimModuleSyntax": true, /* Do not transform or elide any imports or exports not marked as type-only, ensuring they are written in the output file's format based on the 'module' setting. */ + // "isolatedDeclarations": true, /* Require sufficient annotation on exports so other tools can trivially generate declaration files. */ + // "allowSyntheticDefaultImports": true, /* Allow 'import x from y' when a module doesn't have a default export. */ + "esModuleInterop": true, /* Emit additional JavaScript to ease support for importing CommonJS modules. This enables 'allowSyntheticDefaultImports' for type compatibility. */ + // "preserveSymlinks": true, /* Disable resolving symlinks to their realpath. This correlates to the same flag in node. */ + "forceConsistentCasingInFileNames": true, /* Ensure that casing is correct in imports. */ + + /* Type Checking */ + "strict": true, /* Enable all strict type-checking options. */ + // "noImplicitAny": true, /* Enable error reporting for expressions and declarations with an implied 'any' type. */ + // "strictNullChecks": true, /* When type checking, take into account 'null' and 'undefined'. */ + // "strictFunctionTypes": true, /* When assigning functions, check to ensure parameters and the return values are subtype-compatible. */ + // "strictBindCallApply": true, /* Check that the arguments for 'bind', 'call', and 'apply' methods match the original function. */ + // "strictPropertyInitialization": true, /* Check for class properties that are declared but not set in the constructor. */ + // "strictBuiltinIteratorReturn": true, /* Built-in iterators are instantiated with a 'TReturn' type of 'undefined' instead of 'any'. */ + // "noImplicitThis": true, /* Enable error reporting when 'this' is given the type 'any'. */ + // "useUnknownInCatchVariables": true, /* Default catch clause variables as 'unknown' instead of 'any'. */ + // "alwaysStrict": true, /* Ensure 'use strict' is always emitted. */ + // "noUnusedLocals": true, /* Enable error reporting when local variables aren't read. */ + // "noUnusedParameters": true, /* Raise an error when a function parameter isn't read. */ + // "exactOptionalPropertyTypes": true, /* Interpret optional property types as written, rather than adding 'undefined'. */ + // "noImplicitReturns": true, /* Enable error reporting for codepaths that do not explicitly return in a function. */ + // "noFallthroughCasesInSwitch": true, /* Enable error reporting for fallthrough cases in switch statements. */ + // "noUncheckedIndexedAccess": true, /* Add 'undefined' to a type when accessed using an index. */ + // "noImplicitOverride": true, /* Ensure overriding members in derived classes are marked with an override modifier. */ + // "noPropertyAccessFromIndexSignature": true, /* Enforces using indexed accessors for keys declared using an indexed type. */ + // "allowUnusedLabels": true, /* Disable error reporting for unused labels. */ + // "allowUnreachableCode": true, /* Disable error reporting for unreachable code. */ + + /* Completeness */ + // "skipDefaultLibCheck": true, /* Skip type checking .d.ts files that are included with TypeScript. */ + "skipLibCheck": true /* Skip type checking all .d.ts files. */ + } +} diff --git a/examples/instant-voice/client/javascript/vite.config.js b/examples/instant-voice/client/javascript/vite.config.js new file mode 100644 index 000000000..daf85167d --- /dev/null +++ b/examples/instant-voice/client/javascript/vite.config.js @@ -0,0 +1,15 @@ +import { defineConfig } from 'vite'; +import react from '@vitejs/plugin-react-swc'; + +export default defineConfig({ + plugins: [react()], + server: { + proxy: { + // Proxy /api requests to the backend server + '/connect': { + target: 'http://0.0.0.0:7860', // Replace with your backend URL + changeOrigin: true, + }, + }, + }, +}); diff --git a/examples/instant-voice/client/javascript/yarn.lock b/examples/instant-voice/client/javascript/yarn.lock new file mode 100644 index 000000000..e74aef8b7 --- /dev/null +++ b/examples/instant-voice/client/javascript/yarn.lock @@ -0,0 +1,339 @@ +# THIS IS AN AUTOGENERATED FILE. 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index 000000000..7dd76e98c --- /dev/null +++ b/examples/instant-voice/server/env.example @@ -0,0 +1,3 @@ +DAILY_API_KEY= +GOOGLE_API_KEY= +DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev) \ No newline at end of file diff --git a/examples/instant-voice/server/pyproject.toml b/examples/instant-voice/server/pyproject.toml new file mode 100644 index 000000000..2c2ce8e48 --- /dev/null +++ b/examples/instant-voice/server/pyproject.toml @@ -0,0 +1,11 @@ +[tool.ruff] +exclude = [".git", "*_pb2.py"] +line-length = 100 + +[tool.ruff.lint] +select = [ + "I", # Import rules +] + +[tool.ruff.lint.pydocstyle] +convention = "google" \ No newline at end of file diff --git a/examples/instant-voice/server/requirements.txt b/examples/instant-voice/server/requirements.txt new file mode 100644 index 000000000..292de88c2 --- /dev/null +++ b/examples/instant-voice/server/requirements.txt @@ -0,0 +1,4 @@ +python-dotenv +fastapi[all] +uvicorn +pipecat-ai[openai,silero,websocket,google,daily] diff --git a/examples/instant-voice/server/src/server.py b/examples/instant-voice/server/src/server.py new file mode 100644 index 000000000..0777a3730 --- /dev/null +++ b/examples/instant-voice/server/src/server.py @@ -0,0 +1,200 @@ +# +# Copyright (c) 2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# +import asyncio +import os +from contextlib import asynccontextmanager +from typing import Any, Dict, List + +import aiohttp +import uvicorn +from dotenv import load_dotenv +from fastapi import FastAPI, HTTPException, Request +from fastapi.middleware.cors import CORSMiddleware +from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams + +# Load environment variables +load_dotenv(override=True) + +NUMBER_OF_ROOMS = 1 + + +class RoomPool: + """Manages a pool of pre-created rooms for quick allocation.""" + + def __init__(self, daily_rest_helper: DailyRESTHelper): + self.daily_rest_helper = daily_rest_helper + self.pool: List[Dict[str, str]] = [] + self.lock = asyncio.Lock() + + async def fill_pool(self, count: int): + """Fills the pool with `count` new rooms.""" + for _ in range(count): + await self.add_room() + + async def add_room(self): + """Creates a new room and adds it to the pool.""" + try: + room = await self.daily_rest_helper.create_room(DailyRoomParams()) + if not room.url: + raise HTTPException(status_code=500, detail="Failed to create room") + + user_token = await self.daily_rest_helper.get_token(room.url) + if not user_token: + raise HTTPException(status_code=500, detail="Failed to get user token") + + bot_token = await self.daily_rest_helper.get_token(room.url) + if not bot_token: + raise HTTPException(status_code=500, detail="Failed to get bot token") + + async with self.lock: + self.pool.append( + {"room_url": room.url, "user_token": user_token, "bot_token": bot_token} + ) + + except Exception as e: + print(f"Error adding room to pool: {e}") + + async def get_room(self) -> Dict[str, str]: + """Retrieves a room from the pool and requests a new one to maintain the size.""" + async with self.lock: + if not self.pool: + raise HTTPException(status_code=503, detail="No available rooms") + + room = self.pool.pop(0) # Get first available room + + # Start a background task to replenish the pool + asyncio.create_task(self.add_room()) + + return room + + async def delete_room(self, room_url: str): + """Deletes a room when it is not needed anymore""" + await self.daily_rest_helper.delete_room_by_url(room_url) + + async def cleanup(self): + for rooms in self.pool: + room_url = rooms["room_url"] + await self.delete_room(room_url) + + +class BotManager: + """Manages bot subprocesses asynchronously.""" + + def __init__(self): + self.bot_procs: Dict[int, asyncio.subprocess.Process] = {} + self.room_mappings: Dict[int, str] = {} # Maps process ID to room URL + + async def start_bot(self, room_url: str, token: str) -> int: + bot_file = "single_bot" + command = f"python3 -m {bot_file} -u {room_url} -t {token}" + + try: + proc = await asyncio.create_subprocess_shell( + command, + cwd=os.path.dirname(os.path.abspath(__file__)), + ) + if proc.pid is None: + raise HTTPException(status_code=500, detail="Failed to get subprocess PID") + + self.bot_procs[proc.pid] = proc + self.room_mappings[proc.pid] = room_url + # Monitor the process and delete the room when it exits + asyncio.create_task(self._monitor_process(proc.pid)) + + return proc.pid + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}") + + async def _monitor_process(self, pid: int): + """Monitors a bot process and deletes the associated room when it exits.""" + proc = self.bot_procs.get(pid) + if proc: + await proc.wait() # Wait for the process to exit + room_url = self.room_mappings.pop(pid, None) + + if room_url: + await room_pool.delete_room(room_url) + print(f"Deleted room: {room_url}") + + del self.bot_procs[pid] + + async def cleanup(self): + """Terminates all running bot processes and deletes associated rooms.""" + for pid, proc in list(self.bot_procs.items()): + try: + proc.terminate() + await asyncio.wait_for(proc.wait(), timeout=5) + + room_url = self.room_mappings.pop(pid, None) + if room_url: + await room_pool.delete_room(room_url) # Delete room when process terminates + print(f"Deleted room: {room_url}") + + except asyncio.TimeoutError: + print(f"Process {pid} did not terminate in time.") + except Exception as e: + print(f"Error terminating process {pid}: {e}") + + # Clear remaining mappings + self.bot_procs.clear() + self.room_mappings.clear() + + +# Global instances +bot_manager = BotManager() +room_pool: RoomPool # Will be initialized in lifespan + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Handles FastAPI startup and shutdown.""" + global room_pool + aiohttp_session = aiohttp.ClientSession() + daily_rest_helper = DailyRESTHelper( + daily_api_key=os.getenv("DAILY_API_KEY", ""), + daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"), + aiohttp_session=aiohttp_session, + ) + + room_pool = RoomPool(daily_rest_helper) + await room_pool.fill_pool(NUMBER_OF_ROOMS) # Fill pool on startup + + yield # Run app + + await bot_manager.cleanup() + await room_pool.cleanup() + await aiohttp_session.close() + + +# Initialize FastAPI app with lifespan manager +app = FastAPI(lifespan=lifespan) + +# Configure CORS to allow requests from any origin +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.post("/connect") +async def bot_connect(request: Request) -> Dict[Any, Any]: + try: + room = await room_pool.get_room() + await bot_manager.start_bot(room["room_url"], room["bot_token"]) + except HTTPException as e: + return {"error": str(e)} + + return { + "room_url": room["room_url"], + "token": room["user_token"], + } + + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=7860) diff --git a/examples/instant-voice/server/src/single_bot.py b/examples/instant-voice/server/src/single_bot.py new file mode 100644 index 000000000..55ba20377 --- /dev/null +++ b/examples/instant-voice/server/src/single_bot.py @@ -0,0 +1,118 @@ +# +# Copyright (c) 2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# +import argparse +import asyncio +import os +import sys + +from dotenv import load_dotenv +from loguru import logger +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext +from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor +from pipecat.services.gemini_multimodal_live import GeminiMultimodalLiveLLMService +from pipecat.transports.services.daily import DailyParams, DailyTransport + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + +SYSTEM_INSTRUCTION = f""" +"You are Gemini Chatbot, a friendly, helpful robot. + +Your goal is to demonstrate your capabilities in a succinct way. + +Your output will be converted to audio so don't include special characters in your answers. + +Respond to what the user said in a creative and helpful way. Keep your responses brief. One or two sentences at most. +""" + + +def extract_arguments(): + parser = argparse.ArgumentParser(description="Instant Voice Example") + parser.add_argument( + "-u", "--url", type=str, required=True, help="URL of the Daily room to join" + ) + parser.add_argument( + "-t", "--token", type=str, required=False, help="Token of the Daily room to join" + ) + args, unknown = parser.parse_known_args() + url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL") + token = args.token + return url, token + + +async def main(): + room_url, token = extract_arguments() + print(f"room_url: {room_url}") + + daily_transport = DailyTransport( + room_url, + token, + "Instant voice Chatbot", + DailyParams( + audio_out_enabled=True, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + vad_audio_passthrough=True, + ), + ) + + llm = GeminiMultimodalLiveLLMService( + api_key=os.getenv("GOOGLE_API_KEY"), + voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck + transcribe_user_audio=True, + transcribe_model_audio=True, + system_instruction=SYSTEM_INSTRUCTION, + ) + + context = OpenAILLMContext() + context_aggregator = llm.create_context_aggregator(context) + + # RTVI events for Pipecat client UI + rtvi = RTVIProcessor(config=RTVIConfig(config=[]), transport=daily_transport) + + pipeline = Pipeline( + [ + daily_transport.input(), + context_aggregator.user(), + rtvi, + llm, # LLM + daily_transport.output(), + context_aggregator.assistant(), + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams(allow_interruptions=True), + observers=[rtvi.observer()], + ) + + @rtvi.event_handler("on_client_ready") + async def on_client_ready(rtvi): + await rtvi.set_bot_ready() + + @daily_transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + @daily_transport.event_handler("on_participant_left") + async def on_participant_left(transport, participant, reason): + print(f"Participant left: {participant}") + await task.cancel() + + runner = PipelineRunner(handle_sigint=False) + + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/local-input-select-stt/README.md b/examples/local-input-select-stt/README.md new file mode 100644 index 000000000..c5aa58b35 --- /dev/null +++ b/examples/local-input-select-stt/README.md @@ -0,0 +1,88 @@ + +# Pipecat Audio Transcription Example 🚀🎙️ + +Welcome to the **Pipecat Audio Transcription Example**! + +This project showcases how to integrate the awesome [pipecat](https://github.com/pipecat-ai/pipecat) library with a neat textual interface (powered by [Textual](https://github.com/Textualize/textual)) to select audio devices, perform real-time speech-to-text (STT) transcription using [Whisper](https://github.com/openai/whisper). + +> **Note:** Although the script allows you to select both input and output audio devices, this example only utilizes the audio **input** for transcription. + +--- + +## 🎉 Features + +- **Interactive Audio Device Selection:** + Choose your preferred audio input device using a cool, textual UI. + +- **State-of-the-Art Transcription:** + Leverage Whisper's large model (running on CUDA) for high-quality, real-time STT. + +- **Live Transcription Logging:** + Watch your spoken words transform into text on your console instantly. + +- **Easy Setup:** + Everything you need is in the [`requirements.txt`](./requirements.txt). + +--- + +## 🎥 Demo + +Get a quick glimpse of the app in action! +*(Don't worry – I'll be adding a GIF demo here soon!)* + +![Demo GIF](demo.gif) + +--- + +## 🔧 Installation + + +Install Dependencies: + +```bash +pip install -r requirements.txt +``` + + +--- + +## 🚀 Usage + +Run the main script: + +```bash +python bot.py +``` + +When the app launches, you'll see a textual interface that lets you select your audio input device. Once selected, the app will begin capturing audio, transcribing it using Whisper. + +--- + +## ⚙️ How It Works + + +1. **LocalAudioTransport:** + Captures audio from your chosen input device. + +2. **WhisperSTTService:** + Processes the audio stream using Whisper's large model for speech-to-text conversion. + +3. **TranscriptionLogger:** + Logs the transcribed text to the console as soon as it's processed. + + +--- + +## 📦 Dependencies + +The project relies on: + +- [pipecat](https://github.com/yourusername/pipecat) – For building the audio processing pipeline. +- [Textual](https://github.com/Textualize/textual) – For the interactive terminal UI. +- [Whisper](https://github.com/openai/whisper) – For state-of-the-art STT transcription. + +--- + +## Example improvements: + +I plan to improve this example with local LLM calls and audio output. diff --git a/examples/local-input-select-stt/bot.py b/examples/local-input-select-stt/bot.py new file mode 100644 index 000000000..f472152b9 --- /dev/null +++ b/examples/local-input-select-stt/bot.py @@ -0,0 +1,65 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import sys +from typing import Tuple + +from dotenv import load_dotenv +from loguru import logger +from select_audio_device import AudioDevice, run_device_selector + +from pipecat.frames.frames import Frame, TranscriptionFrame +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineTask +from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.services.whisper import Model, WhisperSTTService +from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +class TranscriptionLogger(FrameProcessor): + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + if isinstance(frame, TranscriptionFrame): + print(f"Transcription: {frame.text}") + + +async def main(input_device: int, output_device: int): + transport = LocalAudioTransport( + LocalAudioTransportParams( + audio_in_enabled=True, + audio_out_enabled=False, + input_device_index=input_device, + output_device_index=output_device, + ) + ) + + stt = WhisperSTTService(device="cuda", model=Model.LARGE, no_speech_prob=0.3) + + tl = TranscriptionLogger() + + pipeline = Pipeline([transport.input(), stt, tl]) + + task = PipelineTask(pipeline) + + runner = PipelineRunner(handle_sigint=False if sys.platform == "win32" else True) + + await asyncio.gather(runner.run(task)) + + +if __name__ == "__main__": + res: Tuple[AudioDevice, AudioDevice, int] = asyncio.run( + run_device_selector() # runs the textual app that allows to select input device + ) + + asyncio.run(main(res[0].index, res[1].index)) diff --git a/examples/local-input-select-stt/demo.gif b/examples/local-input-select-stt/demo.gif new file mode 100644 index 000000000..f4beb2cb7 Binary files /dev/null and b/examples/local-input-select-stt/demo.gif differ diff --git a/examples/local-input-select-stt/requirements.txt b/examples/local-input-select-stt/requirements.txt new file mode 100644 index 000000000..9e2f3e592 --- /dev/null +++ b/examples/local-input-select-stt/requirements.txt @@ -0,0 +1,8 @@ +--extra-index-url https://download.pytorch.org/whl/cu124 +torch==2.5.0+cu124 +torchvision +torchaudio +pipecat[whisper, openai] +textual==1.0.0 +pydantic-settings==2.7.1 +pyaudio==0.2.14 diff --git a/examples/local-input-select-stt/select_audio_device.py b/examples/local-input-select-stt/select_audio_device.py new file mode 100644 index 000000000..2993eeafc --- /dev/null +++ b/examples/local-input-select-stt/select_audio_device.py @@ -0,0 +1,247 @@ +from typing import List, Optional, Tuple + +import pyaudio +from pydantic import BaseModel, ConfigDict, Field +from pydantic_settings import BaseSettings +from textual.app import App, ComposeResult +from textual.containers import Container +from textual.widgets import Footer, Header, Label, ListItem, ListView, Select +from textual.widgets.option_list import Option + +# ─── DATA MODELS ─────────────────────────────────────────────────────────────── + + +class HostApi(BaseModel): + index: int + struct_version: int = Field(..., alias="structVersion") + type: int + name: str + device_count: int = Field(..., alias="deviceCount") + default_input_device: int = Field(..., alias="defaultInputDevice") + default_output_device: int = Field(..., alias="defaultOutputDevice") + + +class AudioDevice(BaseModel): + model_config = ConfigDict(populate_by_name=True) + index: int + struct_version: int = Field(..., alias="structVersion") + name: str + host_api: int = Field(..., alias="hostApi") + max_input_channels: int = Field(..., alias="maxInputChannels") + max_output_channels: int = Field(..., alias="maxOutputChannels") + default_low_input_latency: float = Field(..., alias="defaultLowInputLatency") + default_low_output_latency: float = Field(..., alias="defaultLowOutputLatency") + default_high_input_latency: float = Field(..., alias="defaultHighInputLatency") + default_high_output_latency: float = Field(..., alias="defaultHighOutputLatency") + default_sample_rate: float = Field(..., alias="defaultSampleRate") + + +# ─── SETTINGS MODEL ─────────────────────────────────────────────────────────── + + +class AudioSettings(BaseSettings): # to save settings to a file + host_api: Optional[int] = None + input_device: Optional[AudioDevice] = None + output_device: Optional[AudioDevice] = None + + class Config: + env_file = "settings.env" # or adjust as needed + + def save_to_json(self, filepath: str) -> None: + with open(filepath, "w") as f: + f.write(self.model_dump_json(indent=2)) + + +# ─── TEXTUAL APP ────────────────────────────────────────────────────────────── + + +class AudioDeviceSelectorApp(App): + CSS = """ + Screen { + align: center middle; + } + #container { + width: 80%; + border: round green; + padding: 1 2; + } + """ + + def __init__( + self, + default_host_api: Optional[int] = None, + default_input_device: Optional[AudioDevice] = None, + default_output_device: Optional[AudioDevice] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + # Save defaults passed from settings. + self.default_host_api: Optional[int] = default_host_api + self.default_input_device: Optional[AudioDevice] = default_input_device + self.default_output_device: Optional[AudioDevice] = default_output_device + + self.pyaudio_instance = pyaudio.PyAudio() + + # Static datastructures: host APIs and devices as well‐typed models. + self.host_apis: List[HostApi] = [] + self.current_host_api: Optional[int] = None + + self.all_input_devices: List[AudioDevice] = [] + self.all_output_devices: List[AudioDevice] = [] + self.input_devices: List[AudioDevice] = [] + self.output_devices: List[AudioDevice] = [] + + # Stage management: first select input, then output. + self.stage: str = "input" + self.selected_input_device: Optional[AudioDevice] = None + self.selected_output_device: Optional[AudioDevice] = None + host_api_count: int = self.pyaudio_instance.get_host_api_count() + for i in range(host_api_count): + raw_api = self.pyaudio_instance.get_host_api_info_by_index(i) + # Inject the index (if not already present) + raw_api["index"] = i + try: + api = HostApi.parse_obj(raw_api) + self.host_apis.append(api) + except Exception as e: + # Skip APIs that don't conform. + continue + + def compose(self) -> ComposeResult: + options: List[Tuple[str, Option]] = [ + ( + api.name, + Option( + prompt=str(api.name) if api.name else f"Host API {api.index}", + id=str(api.index), + ), + ) + for api in self.host_apis + ] + + yield Header() + + yield Footer() + with Container(id="container"): + yield Label("Select Host API:", id="host-api-label") + # Create the Select widget with no options initially. + self.host_api_select: Select[HostApi] = Select(options=options, id="host-api-select") + yield self.host_api_select + self.prompt = Label("Select Input Audio Device:", id="prompt") + yield self.prompt + self.list_view = ListView(id="device-list") + yield self.list_view + + def on_mount(self) -> None: + # Populate host APIs from PyAudio. + + # Build the dropdown options. + + self.host_api_select.refresh() # Force a redraw + + # Determine the default host API. + if self.default_host_api is not None: + self.current_host_api = self.default_host_api + else: + default_api_info = self.pyaudio_instance.get_default_host_api_info() + self.current_host_api = default_api_info["index"] + + # Delay setting the dropdown's value until the widget is fully initialized. + self.set_timer( + 0, + lambda: setattr(self.host_api_select, "value", str(self.current_host_api)), + ) + + # Load all devices and parse them into AudioDevice objects. + device_count: int = self.pyaudio_instance.get_device_count() + for i in range(device_count): + raw_device = self.pyaudio_instance.get_device_info_by_index(i) + raw_device["index"] = i + try: + device = AudioDevice.parse_obj(raw_device) + except Exception as e: + # Skip devices missing required fields. + continue + if device.max_input_channels > 0: + self.all_input_devices.append(device) + if device.max_output_channels > 0: + self.all_output_devices.append(device) + + self.filter_devices() + self.populate_list(self.input_devices) + if self.default_input_device: + self._select_default_in_list(self.default_input_device) + + def filter_devices(self) -> None: + """Filter devices based on the selected host API.""" + self.input_devices = [ + d for d in self.all_input_devices if d.host_api == self.current_host_api + ] + self.output_devices = [ + d for d in self.all_output_devices if d.host_api == self.current_host_api + ] + + def populate_list(self, devices: List[AudioDevice]) -> None: + """Populate the ListView with a list of AudioDevice objects.""" + self.list_view.clear() + for dev in devices: + item_text: str = f"{dev.name} (Index: {dev.index})" + item = ListItem(Label(item_text)) + # Attach the AudioDevice instance to the widget. + item.device_info = dev # type: ignore + self.list_view.append(item) + + def _select_default_in_list(self, default_device: AudioDevice) -> None: + """Pre-select the default device if present in the current list.""" + for idx, item in enumerate(self.list_view.children): + if hasattr(item, "device_info") and item.device_info.index == default_device.index: + self.list_view.index = idx + break + + async def on_select_changed(self, event: Select.Changed) -> None: + """Handle changes in the host API dropdown.""" + if event.select.id == "host-api-select": + self.current_host_api = int(event.value.id) + self.filter_devices() + if self.stage == "input": + self.populate_list(self.input_devices) + if self.default_input_device: + self._select_default_in_list(self.default_input_device) + elif self.stage == "output": + self.populate_list(self.output_devices) + if self.default_output_device: + self._select_default_in_list(self.default_output_device) + + async def on_list_view_selected(self, message: ListView.Selected) -> None: + """Record device selection and switch stages.""" + selected_item = message.item + device_info: AudioDevice = selected_item.device_info # type: ignore + if self.stage == "input": + self.selected_input_device = device_info + self.stage = "output" + self.prompt.update("Select Output Audio Device:") + self.populate_list(self.output_devices) + if self.default_output_device: + self._select_default_in_list(self.default_output_device) + elif self.stage == "output": + self.selected_output_device = device_info + await self.action_quit() + + +# ─── HELPER FUNCTIONS ───────────────────────────────────────────────────────── + + +async def run_device_selector( + default_host_api: Optional[int] = None, + default_input_device: Optional[AudioDevice] = None, + default_output_device: Optional[AudioDevice] = None, +) -> Tuple[AudioDevice, AudioDevice, int]: + app = AudioDeviceSelectorApp( + default_host_api=default_host_api, + default_input_device=default_input_device, + default_output_device=default_output_device, + ) + await app.run_async() + + # The current_host_api is guaranteed to be set. + return app.selected_input_device, app.selected_output_device, app.current_host_api # type: ignore diff --git a/examples/moondream-chatbot/bot.py b/examples/moondream-chatbot/bot.py index 4669607a7..6639c08c1 100644 --- a/examples/moondream-chatbot/bot.py +++ b/examples/moondream-chatbot/bot.py @@ -23,7 +23,6 @@ from pipecat.frames.frames import ( OutputImageRawFrame, SpriteFrame, TextFrame, - UserImageRawFrame, UserImageRequestFrame, ) from pipecat.pipeline.parallel_pipeline import ParallelPipeline @@ -106,8 +105,8 @@ class UserImageRequester(FrameProcessor): UserImageRequestFrame(self.participant_id), FrameDirection.UPSTREAM ) await self.push_frame(TextFrame("Describe the image in a short sentence.")) - elif isinstance(frame, UserImageRawFrame): - await self.push_frame(frame) + else: + await self.push_frame(frame, direction) class TextFilterProcessor(FrameProcessor): @@ -154,7 +153,7 @@ async def main(): tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), - voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady ) llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") diff --git a/examples/news-chatbot/client/javascript/package-lock.json b/examples/news-chatbot/client/javascript/package-lock.json index 7204924e7..2c51d3cc7 100644 --- a/examples/news-chatbot/client/javascript/package-lock.json +++ b/examples/news-chatbot/client/javascript/package-lock.json @@ -13,7 +13,7 @@ "@pipecat-ai/daily-transport": "^0.3.4" }, 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"^4.30.1" }, "bin": { "vite": "bin/vite.js" diff --git a/examples/news-chatbot/client/javascript/package.json b/examples/news-chatbot/client/javascript/package.json index ad216dc7c..84a4c3fb6 100644 --- a/examples/news-chatbot/client/javascript/package.json +++ b/examples/news-chatbot/client/javascript/package.json @@ -12,7 +12,7 @@ "license": "ISC", "description": "", "devDependencies": { - "vite": "^6.0.2" + "vite": "^6.0.9" }, "dependencies": { "@pipecat-ai/client-js": "^0.3.2", diff --git a/examples/news-chatbot/server/news_bot.py b/examples/news-chatbot/server/news_bot.py index f1c83c2fd..b9f60200f 100644 --- a/examples/news-chatbot/server/news_bot.py +++ b/examples/news-chatbot/server/news_bot.py @@ -23,7 +23,7 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor from pipecat.services.cartesia import CartesiaTTSService from pipecat.services.deepgram import DeepgramSTTService -from pipecat.services.google import GoogleLLMService, LLMSearchResponseFrame +from pipecat.services.google import GoogleLLMService, GoogleRTVIObserver, LLMSearchResponseFrame from pipecat.transports.services.daily import DailyParams, DailyTransport from pipecat.utils.text.markdown_text_filter import MarkdownTextFilter @@ -96,12 +96,13 @@ async def main(): tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), - voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady - text_filter=MarkdownTextFilter(), + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + text_filters=[MarkdownTextFilter()], ) llm = GoogleLLMService( api_key=os.getenv("GOOGLE_API_KEY"), + model="gemini-1.5-flash-002", system_instruction=system_instruction, tools=tools, ) @@ -139,10 +140,8 @@ async def main(): task = PipelineTask( pipeline, - PipelineParams( - allow_interruptions=True, - observers=[rtvi.observer()], - ), + params=PipelineParams(allow_interruptions=True), + observers=[GoogleRTVIObserver(rtvi)], ) @rtvi.event_handler("on_client_ready") diff --git a/examples/news-chatbot/server/runner.py b/examples/news-chatbot/server/runner.py index c1556e8a4..04157d549 100644 --- a/examples/news-chatbot/server/runner.py +++ b/examples/news-chatbot/server/runner.py @@ -6,6 +6,7 @@ import argparse import os +from typing import Optional import aiohttp @@ -18,7 +19,7 @@ async def configure(aiohttp_session: aiohttp.ClientSession): async def configure_with_args( - aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None + aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None ): if not parser: parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample") diff --git a/examples/patient-intake/bot.py b/examples/patient-intake/bot.py index 9efff46d3..de6ad2051 100644 --- a/examples/patient-intake/bot.py +++ b/examples/patient-intake/bot.py @@ -142,7 +142,9 @@ class IntakeProcessor: ] ) - async def start_prescriptions(self, function_name, llm, context): + async def list_prescriptions( + self, function_name, tool_call_id, args, llm, context, result_callback + ): print(f"!!! doing start prescriptions") # Move on to allergies context.set_tools( @@ -182,9 +184,12 @@ class IntakeProcessor: print(f"!!! about to await llm process frame in start prescrpitions") await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM) print(f"!!! past await process frame in start prescriptions") + await self.save_data(args, result_callback) - async def start_allergies(self, function_name, llm, context): - print("!!! doing start allergies") + async def list_allergies( + self, function_name, tool_call_id, args, llm, context, result_callback + ): + print("!!! doing list allergies") # Move on to conditions context.set_tools( [ @@ -221,8 +226,11 @@ class IntakeProcessor: } ) await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM) + await self.save_data(args, result_callback) - async def start_conditions(self, function_name, llm, context): + async def list_conditions( + self, function_name, tool_call_id, args, llm, context, result_callback + ): print("!!! doing start conditions") # Move on to visit reasons context.set_tools( @@ -260,8 +268,11 @@ class IntakeProcessor: } ) await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM) + await self.save_data(args, result_callback) - async def start_visit_reasons(self, function_name, llm, context): + async def list_visit_reasons( + self, function_name, tool_call_id, args, llm, context, result_callback + ): print("!!! doing start visit reasons") # move to finish call context.set_tools([]) @@ -269,8 +280,9 @@ class IntakeProcessor: {"role": "system", "content": "Now, thank the user and end the conversation."} ) await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM) + await self.save_data(args, result_callback) - async def save_data(self, function_name, tool_call_id, args, llm, context, result_callback): + async def save_data(self, args, result_callback): logger.info(f"!!! Saving data: {args}") # Since this is supposed to be "async", returning None from the callback # will prevent adding anything to context or re-prompting @@ -303,7 +315,7 @@ async def main(): tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), - voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady ) # tts = CartesiaTTSService( @@ -319,18 +331,10 @@ async def main(): intake = IntakeProcessor(context) llm.register_function("verify_birthday", intake.verify_birthday) - llm.register_function( - "list_prescriptions", intake.save_data, start_callback=intake.start_prescriptions - ) - llm.register_function( - "list_allergies", intake.save_data, start_callback=intake.start_allergies - ) - llm.register_function( - "list_conditions", intake.save_data, start_callback=intake.start_conditions - ) - llm.register_function( - "list_visit_reasons", intake.save_data, start_callback=intake.start_visit_reasons - ) + llm.register_function("list_prescriptions", intake.list_prescriptions) + llm.register_function("list_allergies", intake.list_allergies) + llm.register_function("list_conditions", intake.list_conditions) + llm.register_function("list_visit_reasons", intake.list_visit_reasons) fl = FrameLogger("LLM Output") @@ -346,7 +350,7 @@ async def main(): ] ) - task = PipelineTask(pipeline, PipelineParams(allow_interruptions=False)) + task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=False)) @transport.event_handler("on_first_participant_joined") async def on_first_participant_joined(transport, participant): diff --git a/examples/phone-chatbot/README.md b/examples/phone-chatbot/README.md index f207ebd9d..040fb7038 100644 --- a/examples/phone-chatbot/README.md +++ b/examples/phone-chatbot/README.md @@ -1,3 +1,5 @@ + +
 pipecat
@@ -104,6 +106,41 @@ curl -X POST "http://localhost:7860/daily_start_bot" \ -d '{"dialoutNumber": "+18057145330", "detectVoicemail": true}' ``` +### New! Using Gemini 2.0 Flash Lite with Daily + +We have introduced support for Google's Gemini 2.0 Flash Lite model in this example. This lightweight model offers faster response times and reduced costs while maintaining good conversational capabilities. + +**Quick Start** +To use the Gemini-based bot instead of OpenAI: + +```shell +curl -X POST "http://localhost:7860/daily_gemini_start_bot" \ py pipecat + -H "Content-Type: application/json" \ + -d '{"detectVoicemail": true}' +``` + +All request body parameters supported by /daily_start_bot (such as detectVoicemail, dialoutNumber, etc.) are also compatible with /daily_gemini_start_bot. + +This example uses context switching to help steer the bot in the right direction. As Flash Lite is a smaller model, breaking the prompt down into smaller piece helps to improve the bot's accuracy. + +For example, instead of giving one large prompt like: + +```python +system_instruction="""You are a chatbot that needs to detect if you're talking to a voicemail system or human, then either leave a message or have a conversation. If it's voicemail, say "Hello, this is a message..." and hang up. If it's a human, introduce yourself and be helpful until they say goodbye.""" +``` + +We break it into stages: + +First prompt focuses only on detection: "Determine if this is voicemail or human" +After detection, we switch to a new context: either "Leave this specific voicemail message" or "Have a conversation with the human". + +**Implementation Details** +The implementation is available in bot_daily_gemini.py and features: + +- Staged prompting approach: Breaking down complex tasks into smaller, more focused prompts to improve the lightweight model's performance +- Dynamic context switching: The bot can change its behavior in real-time based on what it detects (voicemail vs. human caller) +- Function-based architecture: Uses function calling to trigger context switches and call termination + ### More information For more configuration options, please consult [Daily's API documentation](https://docs.daily.co). diff --git a/examples/phone-chatbot/bot_daily.py b/examples/phone-chatbot/bot_daily.py index fac3b37b2..c3468bbed 100644 --- a/examples/phone-chatbot/bot_daily.py +++ b/examples/phone-chatbot/bot_daily.py @@ -2,13 +2,14 @@ import argparse import asyncio import os import sys +from typing import Optional from dotenv import load_dotenv from loguru import logger from openai.types.chat import ChatCompletionToolParam from pipecat.audio.vad.silero import SileroVADAnalyzer -from pipecat.frames.frames import EndFrame, EndTaskFrame +from pipecat.frames.frames import EndTaskFrame from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask @@ -42,13 +43,17 @@ async def main( callId: str, callDomain: str, detect_voicemail: bool, - dialout_number: str | None, + dialout_number: Optional[str], ): # dialin_settings are only needed if Daily's SIP URI is used # If you are handling this via Twilio, Telnyx, set this to None # and handle call-forwarding when on_dialin_ready fires. - dialin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain) + # We don't want to specify dial-in settings if we're not dialing in + dialin_settings = None + if callId and callDomain: + dialin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain) + transport = DailyTransport( room_url, token, @@ -95,21 +100,31 @@ async def main( - **"Please leave a message after the beep."** - **"No one is available to take your call."** - **"Record your message after the tone."** + - **"Please leave a message after the beep"** + - **"You have reached voicemail for..."** + - **"You have reached [phone number]"** + - **"[phone number] is unavailable"** + - **"The person you are trying to reach..."** + - **"The number you have dialed..."** + - **"Your call has been forwarded to an automated voice messaging system"** - **Any phrase that suggests an answering machine or voicemail.** - **ASSUME IT IS A VOICEMAIL. DO NOT WAIT FOR MORE CONFIRMATION.** + - **IF THE CALL SAYS "PLEASE LEAVE A MESSAGE AFTER THE BEEP", WAIT FOR THE BEEP BEFORE LEAVING A MESSAGE.** #### **Step 2: Leave a Voicemail Message** - - Immediately say: + - Immediately say: *"Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you."* - **IMMEDIATELY AFTER LEAVING THE MESSAGE, CALL `terminate_call`.** - **DO NOT SPEAK AFTER CALLING `terminate_call`.** - **FAILURE TO CALL `terminate_call` IMMEDIATELY IS A MISTAKE.** #### **Step 3: If Speaking to a Human** - - If the call is answered by a human, say: + - If the call is answered by a human, say: *"Oh, hello! I'm a friendly chatbot. Is there anything I can help you with?"* - Keep responses **brief and helpful**. - - If the user no longer needs assistance, **call `terminate_call` immediately.** + - If the user no longer needs assistance, say: + *"Okay, thank you! Have a great day!"* + -**Then call `terminate_call` immediately.** --- @@ -135,7 +150,7 @@ async def main( ] ) - task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True)) + task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True)) if dialout_number: logger.debug("dialout number detected; doing dialout") diff --git a/examples/phone-chatbot/bot_daily_gemini.py b/examples/phone-chatbot/bot_daily_gemini.py new file mode 100644 index 000000000..4972cd3ee --- /dev/null +++ b/examples/phone-chatbot/bot_daily_gemini.py @@ -0,0 +1,464 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# +import argparse +import asyncio +import os +import sys +from typing import Optional + +from dotenv import load_dotenv +from loguru import logger + +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.frames.frames import ( + EndFrame, + EndTaskFrame, + InputAudioRawFrame, + StopTaskFrame, + TranscriptionFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, +) +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.services.ai_services import LLMService +from pipecat.services.deepgram import DeepgramSTTService +from pipecat.services.elevenlabs import ElevenLabsTTSService +from pipecat.services.google import GoogleLLMService +from pipecat.services.google.google import GoogleLLMContext +from pipecat.transports.services.daily import ( + DailyDialinSettings, + DailyParams, + DailyTransport, +) + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +daily_api_key = os.getenv("DAILY_API_KEY", "") +daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1") + +system_message = None + + +class UserAudioCollector(FrameProcessor): + """This FrameProcessor collects audio frames in a buffer, then adds them to the + LLM context when the user stops speaking. + """ + + def __init__(self, context, user_context_aggregator): + super().__init__() + self._context = context + self._user_context_aggregator = user_context_aggregator + self._audio_frames = [] + self._start_secs = 0.2 # this should match VAD start_secs (hardcoding for now) + self._user_speaking = False + + async def process_frame(self, frame, direction): + await super().process_frame(frame, direction) + + if isinstance(frame, TranscriptionFrame): + # We could gracefully handle both audio input and text/transcription input ... + # but let's leave that as an exercise to the reader. :-) + return + if isinstance(frame, UserStartedSpeakingFrame): + self._user_speaking = True + elif isinstance(frame, UserStoppedSpeakingFrame): + self._user_speaking = False + self._context.add_audio_frames_message(audio_frames=self._audio_frames) + await self._user_context_aggregator.push_frame( + self._user_context_aggregator.get_context_frame() + ) + elif isinstance(frame, InputAudioRawFrame): + if self._user_speaking: + self._audio_frames.append(frame) + else: + # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest + # frames as necessary. Assume all audio frames have the same duration. + self._audio_frames.append(frame) + frame_duration = len(frame.audio) / 16 * frame.num_channels / frame.sample_rate + buffer_duration = frame_duration * len(self._audio_frames) + while buffer_duration > self._start_secs: + self._audio_frames.pop(0) + buffer_duration -= frame_duration + + await self.push_frame(frame, direction) + + +class ContextSwitcher: + def __init__(self, llm, context_aggregator): + self._llm = llm + self._context_aggregator = context_aggregator + + async def switch_context(self, system_instruction): + """Switch the context to a new system instruction based on what the bot hears.""" + # Create messages with updated system instruction + messages = [ + { + "role": "system", + "content": system_instruction, + } + ] + + # Update context with new messages + self._context_aggregator.set_messages(messages) + # Get the context frame with the updated messages + context_frame = self._context_aggregator.get_context_frame() + # Trigger LLM response by pushing a context frame + await self._llm.push_frame(context_frame) + + +class FunctionHandlers: + def __init__(self, context_switcher): + self.context_switcher = context_switcher + + async def voicemail_response( + self, + function_name, + tool_call_id, + args, + llm: LLMService, + context, + result_callback, + ): + """Function the bot can call to leave a voicemail message.""" + message = """You are Chatbot leaving a voicemail message. Say EXACTLY this message and nothing else: + + "Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you." + + After saying this message, call the terminate_call function.""" + + await self.context_switcher.switch_context(system_instruction=message) + await result_callback("Leaving a voicemail message") + + async def human_conversation( + self, + function_name, + tool_call_id, + args, + llm: LLMService, + context, + result_callback, + ): + """Function the bot can when it detects it's talking to a human.""" + await llm.push_frame(StopTaskFrame(), FrameDirection.UPSTREAM) + + +async def terminate_call( + function_name, + tool_call_id, + args, + llm: LLMService, + context, + result_callback, + call_state=None, +): + """Function the bot can call to terminate the call upon completion of the call.""" + if call_state: + call_state.bot_terminated_call = True + await llm.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM) + + +async def main( + room_url: str, + token: str, + callId: Optional[str], + callDomain: Optional[str], + detect_voicemail: bool, + dialout_number: Optional[str], +): + dialin_settings = None + if callId and callDomain: + dialin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain) + transport_params = DailyParams( + api_url=daily_api_url, + api_key=daily_api_key, + dialin_settings=dialin_settings, + audio_in_enabled=True, + audio_out_enabled=True, + camera_out_enabled=False, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + vad_audio_passthrough=True, + ) + else: + transport_params = DailyParams( + api_url=daily_api_url, + api_key=daily_api_key, + audio_in_enabled=True, + audio_out_enabled=True, + camera_out_enabled=False, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + vad_audio_passthrough=True, + ) + + class CallState: + participant_left_early = False + bot_terminated_call = False + + call_state = CallState() + + transport = DailyTransport( + room_url, + token, + "Chatbot", + transport_params, + ) + + tts = ElevenLabsTTSService( + api_key=os.getenv("ELEVENLABS_API_KEY", ""), + voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""), + ) + + stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) + + ### VOICEMAIL PIPELINE + + tools = [ + { + "function_declarations": [ + { + "name": "switch_to_voicemail_response", + "description": "Call this function when you detect this is a voicemail system.", + }, + { + "name": "switch_to_human_conversation", + "description": "Call this function when you detect this is a human.", + }, + { + "name": "terminate_call", + "description": "Call this function to terminate the call.", + }, + ] + } + ] + + system_instruction = """You are Chatbot trying to determine if this is a voicemail system or a human. + + If you hear any of these phrases (or very similar ones): + - "Please leave a message after the beep" + - "No one is available to take your call" + - "Record your message after the tone" + - "You have reached voicemail for..." + - "You have reached [phone number]" + - "[phone number] is unavailable" + - "The person you are trying to reach..." + - "The number you have dialed..." + - "Your call has been forwarded to an automated voice messaging system" + + Then call the function switch_to_voicemail_response. + + If it sounds like a human (saying hello, asking questions, etc.), call the function switch_to_human_conversation. + + DO NOT say anything until you've determined if this is a voicemail or human.""" + + voicemail_detection_llm = GoogleLLMService( + model="models/gemini-2.0-flash-lite", + api_key=os.getenv("GOOGLE_API_KEY"), + system_instruction=system_instruction, + tools=tools, + ) + + voicemail_detection_context = GoogleLLMContext() + voicemail_detection_context_aggregator = voicemail_detection_llm.create_context_aggregator( + voicemail_detection_context + ) + context_switcher = ContextSwitcher( + voicemail_detection_llm, voicemail_detection_context_aggregator.user() + ) + handlers = FunctionHandlers(context_switcher) + + voicemail_detection_llm.register_function( + "switch_to_voicemail_response", handlers.voicemail_response + ) + voicemail_detection_llm.register_function( + "switch_to_human_conversation", handlers.human_conversation + ) + voicemail_detection_llm.register_function( + "terminate_call", + lambda *args, **kwargs: terminate_call(*args, **kwargs, call_state=call_state), + ) + + voicemail_detection_audio_collector = UserAudioCollector( + voicemail_detection_context, voicemail_detection_context_aggregator.user() + ) + + voicemail_detection_pipeline = Pipeline( + [ + transport.input(), # Transport user input + voicemail_detection_audio_collector, # Collect audio frames + voicemail_detection_context_aggregator.user(), # User responses + voicemail_detection_llm, # LLM + tts, # TTS + transport.output(), # Transport bot output + voicemail_detection_context_aggregator.assistant(), # Assistant spoken responses + ] + ) + voicemail_detection_pipeline_task = PipelineTask( + voicemail_detection_pipeline, + params=PipelineParams(allow_interruptions=True), + ) + + if dialout_number: + logger.debug("dialout number detected; doing dialout") + + # Configure some handlers for dialing out + @transport.event_handler("on_joined") + async def on_joined(transport, data): + logger.debug(f"Joined; starting dialout to: {dialout_number}") + await transport.start_dialout({"phoneNumber": dialout_number}) + + @transport.event_handler("on_dialout_connected") + async def on_dialout_connected(transport, data): + logger.debug(f"Dial-out connected: {data}") + + @transport.event_handler("on_dialout_answered") + async def on_dialout_answered(transport, data): + logger.debug(f"Dial-out answered: {data}") + + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + await transport.capture_participant_transcription(participant["id"]) + # unlike the dialin case, for the dialout case, the caller will speak first. Presumably + # they will answer the phone and say "Hello?" Since we've captured their transcript, + # That will put a frame into the pipeline and prompt an LLM completion, which is how the + # bot will then greet the user. + elif detect_voicemail: + logger.debug("Detect voicemail example. You can test this in example in Daily Prebuilt") + + # For the voicemail detection case, we do not want the bot to answer the phone. We want it to wait for the voicemail + # machine to say something like 'Leave a message after the beep', or for the user to say 'Hello?'. + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + logger.debug("Detect voicemail; capturing participant transcription") + await transport.capture_participant_transcription(participant["id"]) + else: + logger.debug("+++++ No dialout number; assuming dialin") + + # Different handlers for dialin + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + # This event is not firing for some reason + await transport.capture_participant_transcription(participant["id"]) + dialin_instructions = """Always call the function switch_to_human_conversation""" + messages = [ + { + "role": "system", + "content": dialin_instructions, + } + ] + voicemail_detection_context_aggregator.user().set_messages(messages) + await voicemail_detection_pipeline_task.queue_frames( + [voicemail_detection_context_aggregator.user().get_context_frame()] + ) + + runner = PipelineRunner() + + @transport.event_handler("on_participant_left") + async def on_participant_left(transport, participant, reason): + call_state.participant_left_early = True + await voicemail_detection_pipeline_task.queue_frame(EndFrame()) + + print("!!! starting voicemail detection pipeline") + await runner.run(voicemail_detection_pipeline_task) + print("!!! Done with voicemail detection pipeline") + + if call_state.participant_left_early or call_state.bot_terminated_call: + if call_state.participant_left_early: + print("!!! Participant left early; terminating call") + elif call_state.bot_terminated_call: + print("!!! Bot terminated call; not proceeding to human conversation") + return + + ### HUMAN CONVERSATION PIPELINE + + human_conversation_system_instruction = """You are Chatbot talking to a human. Be friendly and helpful. + + Start with: "Hello! I'm a friendly chatbot. How can I help you today?" + + Keep your responses brief and to the point. Listen to what the person says. + + When the person indicates they're done with the conversation by saying something like: + - "Goodbye" + - "That's all" + - "I'm done" + - "Thank you, that's all I needed" + + THEN say: "Thank you for chatting. Goodbye!" and call the terminate_call function.""" + + human_conversation_llm = GoogleLLMService( + model="models/gemini-2.0-flash-001", + api_key=os.getenv("GOOGLE_API_KEY"), + system_instruction=human_conversation_system_instruction, + tools=tools, + ) + human_conversation_context = GoogleLLMContext() + + human_conversation_context_aggregator = human_conversation_llm.create_context_aggregator( + human_conversation_context + ) + + human_conversation_llm.register_function( + "terminate_call", + lambda *args, **kwargs: terminate_call(*args, **kwargs, call_state=call_state), + ) + + human_conversation_pipeline = Pipeline( + [ + transport.input(), # Transport user input + stt, + human_conversation_context_aggregator.user(), # User responses + human_conversation_llm, # LLM + tts, # TTS + transport.output(), # Transport bot output + human_conversation_context_aggregator.assistant(), # Assistant spoken responses + ] + ) + + human_conversation_pipeline_task = PipelineTask( + human_conversation_pipeline, + params=PipelineParams(allow_interruptions=True), + ) + + @transport.event_handler("on_participant_left") + async def on_participant_left(transport, participant, reason): + await voicemail_detection_pipeline_task.queue_frame(EndFrame()) + await human_conversation_pipeline_task.queue_frame(EndFrame()) + + print("!!! starting human conversation pipeline") + human_conversation_context_aggregator.user().set_messages( + [ + { + "role": "system", + "content": human_conversation_system_instruction, + } + ] + ) + await human_conversation_pipeline_task.queue_frames( + [human_conversation_context_aggregator.user().get_context_frame()] + ) + await runner.run(human_conversation_pipeline_task) + + print("!!! Done with human conversation pipeline") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Pipecat Simple ChatBot") + parser.add_argument("-u", type=str, help="Room URL") + parser.add_argument("-t", type=str, help="Token") + parser.add_argument("-i", type=str, help="Call ID") + parser.add_argument("-d", type=str, help="Call Domain") + parser.add_argument("-v", action="store_true", help="Detect voicemail") + parser.add_argument("-o", type=str, help="Dialout number", default=None) + config = parser.parse_args() + + asyncio.run(main(config.u, config.t, config.i, config.d, config.v, config.o)) diff --git a/examples/phone-chatbot/bot_runner.py b/examples/phone-chatbot/bot_runner.py index 03962b8b1..da64805e3 100644 --- a/examples/phone-chatbot/bot_runner.py +++ b/examples/phone-chatbot/bot_runner.py @@ -110,10 +110,15 @@ async def _create_daily_room( # Spawn a new agent, and join the user session # Note: this is mostly for demonstration purposes (refer to 'deployment' in docs) + print(f"Vendor: {vendor}") if vendor == "daily": bot_proc = f"python3 -m bot_daily -u {room.url} -t {token} -i {callId} -d {callDomain}{' -v' if detect_voicemail else ''}" if dialoutNumber: bot_proc += f" -o {dialoutNumber}" + elif vendor == "daily-gemini": + bot_proc = f"python3 -m bot_daily_gemini -u {room.url} -t {token} -i {callId} -d {callDomain}{' -v' if detect_voicemail else ''}" + if dialoutNumber: + bot_proc += f" -o {dialoutNumber}" else: bot_proc = f"python3 -m bot_twilio -u {room.url} -t {token} -i {callId} -s {room.config.sip_endpoint}" @@ -201,6 +206,38 @@ async def daily_start_bot(request: Request) -> JSONResponse: return JSONResponse({"room_url": room.url, "sipUri": room.config.sip_endpoint}) +@app.post("/daily_gemini_start_bot") +async def daily_gemini_start_bot(request: Request) -> JSONResponse: + # The /daily_start_bot is invoked when a call is received on Daily's SIP URI + # daily_start_bot will create the room, put the call on hold until + # the bot and sip worker are ready. Daily will automatically + # forward the call to the SIP URi when dialin_ready fires. + + # Use specified room URL, or create a new one if not specified + room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", None) + # Get the dial-in properties from the request + try: + data = await request.json() + if "test" in data: + # Pass through any webhook checks + return JSONResponse({"test": True}) + detect_voicemail = data.get("detectVoicemail", False) + callId = data.get("callId", None) + callDomain = data.get("callDomain", None) + dialoutNumber = data.get("dialoutNumber", None) + except Exception: + raise HTTPException( + status_code=500, detail="Missing properties 'callId', 'callDomain', or 'dialoutNumber'" + ) + + room: DailyRoomObject = await _create_daily_room( + room_url, callId, callDomain, dialoutNumber, "daily-gemini", detect_voicemail + ) + + # Grab a token for the user to join with + return JSONResponse({"room_url": room.url, "sipUri": room.config.sip_endpoint}) + + # ----------------- Main ----------------- # diff --git a/examples/phone-chatbot/bot_twilio.py b/examples/phone-chatbot/bot_twilio.py index 9da28dfa1..3aab6a6d8 100644 --- a/examples/phone-chatbot/bot_twilio.py +++ b/examples/phone-chatbot/bot_twilio.py @@ -77,7 +77,7 @@ async def main(room_url: str, token: str, callId: str, sipUri: str): ] ) - task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True)) + task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True)) @transport.event_handler("on_first_participant_joined") async def on_first_participant_joined(transport, participant): diff --git a/examples/sentry-metrics/.gitignore b/examples/sentry-metrics/.gitignore new file mode 100644 index 000000000..2bc1403d1 --- /dev/null +++ b/examples/sentry-metrics/.gitignore @@ -0,0 +1,161 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ +runpod.toml diff --git a/examples/sentry-metrics/Dockerfile b/examples/sentry-metrics/Dockerfile new file mode 100644 index 000000000..419adca34 --- /dev/null +++ b/examples/sentry-metrics/Dockerfile @@ -0,0 +1,15 @@ +FROM python:3.10-bullseye + +RUN mkdir /app +RUN mkdir /app/assets +RUN mkdir /app/utils +COPY *.py /app/ +COPY requirements.txt /app/ + + +WORKDIR /app +RUN pip3 install -r requirements.txt + +EXPOSE 7860 + +CMD ["python3", "server.py"] diff --git a/examples/sentry-metrics/README.md b/examples/sentry-metrics/README.md new file mode 100644 index 000000000..f2ba13109 --- /dev/null +++ b/examples/sentry-metrics/README.md @@ -0,0 +1,29 @@ +# Sentry Metrics + +This app connects you to a chatbot powered by GPT-4. It provides TTFB (Time-To-First-Byte) and processing metrics to Sentry. + +## Get started + +```python +python3 -m venv venv +source venv/bin/activate +pip install -r requirements.txt + +cp env.example .env # and add your credentials + +``` + +## Run the server + +```bash +python server.py +``` + +Then, visit `http://localhost:7860/` in your browser to start a chatbot session. + +## Build and test the Docker image + +``` +docker build -t chatbot . +docker run --env-file .env -p 7860:7860 chatbot +``` diff --git a/examples/sentry-metrics/bot.py b/examples/sentry-metrics/bot.py new file mode 100644 index 000000000..89cd8dcf3 --- /dev/null +++ b/examples/sentry-metrics/bot.py @@ -0,0 +1,112 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import os +import sys + +import aiohttp +import sentry_sdk +from dotenv import load_dotenv +from loguru import logger +from runner import configure + +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext +from pipecat.processors.metrics.sentry import SentryMetrics +from pipecat.services.elevenlabs import ElevenLabsTTSService +from pipecat.services.openai import OpenAILLMService +from pipecat.transports.services.daily import DailyParams, DailyTransport + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +async def main(): + async with aiohttp.ClientSession() as session: + (room_url, token) = await configure(session) + + transport = DailyTransport( + room_url, + token, + "Chatbot", + DailyParams( + audio_out_enabled=True, + audio_in_enabled=True, + camera_out_enabled=False, + vad_enabled=True, + vad_audio_passthrough=True, + vad_analyzer=SileroVADAnalyzer(), + transcription_enabled=True, + ), + ) + + # Initialize Sentry + sentry_sdk.init( + dsn="your-project-dsn", + traces_sample_rate=1.0, + ) + + tts = ElevenLabsTTSService( + api_key=os.getenv("ELEVENLABS_API_KEY"), + voice_id="cgSgspJ2msm6clMCkdW9", + metrics=SentryMetrics(), + ) + + llm = OpenAILLMService( + api_key=os.getenv("OPENAI_API_KEY"), + model="gpt-4o", + metrics=SentryMetrics(), + ) + + messages = [ + { + "role": "system", + "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself.", + }, + ] + + context = OpenAILLMContext(messages) + context_aggregator = llm.create_context_aggregator(context) + + pipeline = Pipeline( + [ + transport.input(), # microphone + context_aggregator.user(), + llm, + tts, + transport.output(), + context_aggregator.assistant(), + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams(allow_interruptions=True, enable_metrics=True), + ) + + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + await transport.capture_participant_transcription(participant["id"]) + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + @transport.event_handler("on_participant_left") + async def on_participant_left(transport, participant, reason): + print(f"Participant left: {participant}") + await task.cancel() + + runner = PipelineRunner() + + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/sentry-metrics/env.example b/examples/sentry-metrics/env.example new file mode 100644 index 000000000..d368ae510 --- /dev/null +++ b/examples/sentry-metrics/env.example @@ -0,0 +1,4 @@ +DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev) +DAILY_API_KEY=7df... +OPENAI_API_KEY=sk-PL... +ELEVENLABS_API_KEY=aeb... \ No newline at end of file diff --git a/examples/sentry-metrics/requirements.txt b/examples/sentry-metrics/requirements.txt new file mode 100644 index 000000000..21ea7c996 --- /dev/null +++ b/examples/sentry-metrics/requirements.txt @@ -0,0 +1,4 @@ +python-dotenv +fastapi[all] +uvicorn +pipecat-ai[daily,openai,sentry,silero,elevenlabs] diff --git a/examples/sentry-metrics/runner.py b/examples/sentry-metrics/runner.py new file mode 100644 index 000000000..50743fd09 --- /dev/null +++ b/examples/sentry-metrics/runner.py @@ -0,0 +1,56 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import argparse +import os + +import aiohttp + +from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper + + +async def configure(aiohttp_session: aiohttp.ClientSession): + parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample") + parser.add_argument( + "-u", "--url", type=str, required=False, help="URL of the Daily room to join" + ) + parser.add_argument( + "-k", + "--apikey", + type=str, + required=False, + help="Daily API Key (needed to create an owner token for the room)", + ) + + args, unknown = parser.parse_known_args() + + url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL") + key = args.apikey or os.getenv("DAILY_API_KEY") + + if not url: + raise Exception( + "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL." + ) + + if not key: + raise Exception( + "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers." + ) + + daily_rest_helper = DailyRESTHelper( + daily_api_key=key, + daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"), + aiohttp_session=aiohttp_session, + ) + + # Create a meeting token for the given room with an expiration 1 hour in + # the future. + expiry_time: float = 60 * 60 + + token = await daily_rest_helper.get_token(url, expiry_time) + + return (url, token) + return (url, token) diff --git a/examples/sentry-metrics/server.py b/examples/sentry-metrics/server.py new file mode 100644 index 000000000..a0f38854c --- /dev/null +++ b/examples/sentry-metrics/server.py @@ -0,0 +1,139 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import argparse +import os +import subprocess +from contextlib import asynccontextmanager + +import aiohttp +from dotenv import load_dotenv +from fastapi import FastAPI, HTTPException, Request +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import JSONResponse, RedirectResponse + +from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams + +MAX_BOTS_PER_ROOM = 1 + +# Bot sub-process dict for status reporting and concurrency control +bot_procs = {} + +daily_helpers = {} + +load_dotenv(override=True) + + +def cleanup(): + # Clean up function, just to be extra safe + for entry in bot_procs.values(): + proc = entry[0] + proc.terminate() + proc.wait() + + +@asynccontextmanager +async def lifespan(app: FastAPI): + aiohttp_session = aiohttp.ClientSession() + daily_helpers["rest"] = DailyRESTHelper( + daily_api_key=os.getenv("DAILY_API_KEY", ""), + daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"), + aiohttp_session=aiohttp_session, + ) + yield + await aiohttp_session.close() + cleanup() + + +app = FastAPI(lifespan=lifespan) + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.get("/") +async def start_agent(request: Request): + print(f"!!! Creating room") + room = await daily_helpers["rest"].create_room(DailyRoomParams()) + print(f"!!! Room URL: {room.url}") + # Ensure the room property is present + if not room.url: + raise HTTPException( + status_code=500, + detail="Missing 'room' property in request data. Cannot start agent without a target room!", + ) + + # Check if there is already an existing process running in this room + num_bots_in_room = sum( + 1 for proc in bot_procs.values() if proc[1] == room.url and proc[0].poll() is None + ) + if num_bots_in_room >= MAX_BOTS_PER_ROOM: + raise HTTPException(status_code=500, detail=f"Max bot limited reach for room: {room.url}") + + # Get the token for the room + token = await daily_helpers["rest"].get_token(room.url) + + if not token: + raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}") + + # Spawn a new agent, and join the user session + # Note: this is mostly for demonstration purposes (refer to 'deployment' in README) + try: + proc = subprocess.Popen( + [f"python3 -m bot -u {room.url} -t {token}"], + shell=True, + bufsize=1, + cwd=os.path.dirname(os.path.abspath(__file__)), + ) + bot_procs[proc.pid] = (proc, room.url) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}") + + return RedirectResponse(room.url) + + +@app.get("/status/{pid}") +def get_status(pid: int): + # Look up the subprocess + proc = bot_procs.get(pid) + + # If the subprocess doesn't exist, return an error + if not proc: + raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found") + + # Check the status of the subprocess + if proc[0].poll() is None: + status = "running" + else: + status = "finished" + + return JSONResponse({"bot_id": pid, "status": status}) + + +if __name__ == "__main__": + import uvicorn + + default_host = os.getenv("HOST", "0.0.0.0") + default_port = int(os.getenv("FAST_API_PORT", "7860")) + + parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server") + parser.add_argument("--host", type=str, default=default_host, help="Host address") + parser.add_argument("--port", type=int, default=default_port, help="Port number") + parser.add_argument("--reload", action="store_true", help="Reload code on change") + + config = parser.parse_args() + + uvicorn.run( + "server:app", + host=config.host, + port=config.port, + reload=config.reload, + ) diff --git a/examples/simple-chatbot/client/javascript/package-lock.json b/examples/simple-chatbot/client/javascript/package-lock.json index 7204924e7..2c51d3cc7 100644 --- a/examples/simple-chatbot/client/javascript/package-lock.json +++ b/examples/simple-chatbot/client/javascript/package-lock.json @@ -13,7 +13,7 @@ "@pipecat-ai/daily-transport": "^0.3.4" }, "devDependencies": { - 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"license": "BSD-3-Clause", "engines": { "node": ">=0.10.0" } @@ -3395,15 +3378,14 @@ } }, "node_modules/vite": { - "version": "6.0.2", - "resolved": "https://registry.npmjs.org/vite/-/vite-6.0.2.tgz", - "integrity": "sha512-XdQ+VsY2tJpBsKGs0wf3U/+azx8BBpYRHFAyKm5VeEZNOJZRB63q7Sc8Iup3k0TrN3KO6QgyzFf+opSbfY1y0g==", + "version": "6.1.0", + "resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz", + "integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==", "dev": true, - "license": "MIT", "dependencies": { - "esbuild": "^0.24.0", - "postcss": "^8.4.49", - "rollup": "^4.23.0" + "esbuild": "^0.24.2", + "postcss": "^8.5.1", + "rollup": "^4.30.1" }, "bin": { "vite": "bin/vite.js" diff --git a/examples/simple-chatbot/client/react/package.json b/examples/simple-chatbot/client/react/package.json index cc5a0c82b..03ec2536f 100644 --- a/examples/simple-chatbot/client/react/package.json +++ b/examples/simple-chatbot/client/react/package.json @@ -27,6 +27,6 @@ "globals": "^15.12.0", "typescript": "~5.6.2", "typescript-eslint": "^8.15.0", - "vite": "^6.0.1" + "vite": "^6.0.9" } } diff --git a/examples/simple-chatbot/server/bot-gemini.py b/examples/simple-chatbot/server/bot-gemini.py index 7999b1bfc..753cb2af7 100644 --- a/examples/simple-chatbot/server/bot-gemini.py +++ b/examples/simple-chatbot/server/bot-gemini.py @@ -40,7 +40,7 @@ from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor +from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService from pipecat.transports.services.daily import DailyParams, DailyTransport @@ -121,8 +121,6 @@ async def main(): token, "Chatbot", DailyParams( - audio_in_sample_rate=16000, - audio_out_sample_rate=24000, audio_out_enabled=True, camera_out_enabled=True, camera_out_width=1024, @@ -174,12 +172,12 @@ async def main(): task = PipelineTask( pipeline, - PipelineParams( + params=PipelineParams( allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True, - observers=[rtvi.observer()], ), + observers=[RTVIObserver(rtvi)], ) await task.queue_frame(quiet_frame) diff --git a/examples/simple-chatbot/server/bot-openai.py b/examples/simple-chatbot/server/bot-openai.py index 0d30a5d87..a5e4312ab 100644 --- a/examples/simple-chatbot/server/bot-openai.py +++ b/examples/simple-chatbot/server/bot-openai.py @@ -40,7 +40,7 @@ from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor +from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor from pipecat.services.elevenlabs import ElevenLabsTTSService from pipecat.services.openai import OpenAILLMService from pipecat.transports.services.daily import DailyParams, DailyTransport @@ -198,12 +198,12 @@ async def main(): task = PipelineTask( pipeline, - PipelineParams( + params=PipelineParams( allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True, - observers=[rtvi.observer()], ), + observers=[RTVIObserver(rtvi)], ) await task.queue_frame(quiet_frame) diff --git a/examples/storytelling-chatbot/README.md b/examples/storytelling-chatbot/README.md index 8230deba8..08da91044 100644 --- a/examples/storytelling-chatbot/README.md +++ b/examples/storytelling-chatbot/README.md @@ -74,6 +74,8 @@ If you'd like to run a custom domain or port: ➡️ Open the host URL in your browser `http://localhost:7860` +If you've run previous versions of the demo, make sure to set `ENV=dev`, and remove the `RUN_AS_VM` line from the .env file. + --- ## Improvements to make diff --git a/examples/storytelling-chatbot/env.example b/examples/storytelling-chatbot/env.example index 3b6e66316..513715897 100644 --- a/examples/storytelling-chatbot/env.example +++ b/examples/storytelling-chatbot/env.example @@ -3,6 +3,4 @@ DAILY_SAMPLE_ROOM_URL= ELEVENLABS_API_KEY= ELEVENLABS_VOICE_ID= GOOGLE_API_KEY= - -ENV= # dev | production -RUN_AS_VM= # Set this if you want to run bots on process (not launch a new VM) \ No newline at end of file +ENV=dev diff --git a/examples/storytelling-chatbot/requirements.txt b/examples/storytelling-chatbot/requirements.txt index c8cd82646..354e169f0 100644 --- a/examples/storytelling-chatbot/requirements.txt +++ b/examples/storytelling-chatbot/requirements.txt @@ -2,5 +2,4 @@ async_timeout fastapi uvicorn python-dotenv --e "../..[daily,silero,openai,fal,cartesia,google]" --e "../../../python-genai" +pipecat-ai[daily,silero,openai,cartesia,google] \ No newline at end of file diff --git a/examples/storytelling-chatbot/src/bot.py b/examples/storytelling-chatbot/src/bot.py index 05f8974bb..92ab7d617 100644 --- a/examples/storytelling-chatbot/src/bot.py +++ b/examples/storytelling-chatbot/src/bot.py @@ -23,8 +23,7 @@ from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.elevenlabs import ElevenLabsTTSService -from pipecat.services.fal import FalImageGenService -from pipecat.services.google import GoogleLLMService +from pipecat.services.google import GoogleImageGenService, GoogleLLMService from pipecat.transports.services.daily import ( DailyParams, DailyTransport, @@ -105,7 +104,7 @@ async def main(room_url, token=None): main_task = PipelineTask( main_pipeline, - PipelineParams( + params=PipelineParams( allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True, diff --git a/examples/studypal/requirements.txt b/examples/studypal/requirements.txt index 2d6e21042..a2e7b803d 100644 --- a/examples/studypal/requirements.txt +++ b/examples/studypal/requirements.txt @@ -1,5 +1,5 @@ beautifulsoup4==4.12.3 pypdf==4.3.1 tiktoken==0.7.0 -pipecat-ai[daily,cartesia,openai,silero]==0.0.40 +pipecat-ai[daily,cartesia,openai,silero] python-dotenv==1.0.1 diff --git a/examples/studypal/runner.py b/examples/studypal/runner.py index c1556e8a4..04157d549 100644 --- a/examples/studypal/runner.py +++ b/examples/studypal/runner.py @@ -6,6 +6,7 @@ import argparse import os +from typing import Optional import aiohttp @@ -18,7 +19,7 @@ async def configure(aiohttp_session: aiohttp.ClientSession): async def configure_with_args( - aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None + aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None ): if not parser: parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample") diff --git a/examples/studypal/studypal.py b/examples/studypal/studypal.py index b4691fc5f..1ec1d4aa4 100644 --- a/examples/studypal/studypal.py +++ b/examples/studypal/studypal.py @@ -112,7 +112,6 @@ async def main(): token, "studypal", DailyParams( - audio_out_sample_rate=44100, audio_out_enabled=True, transcription_enabled=True, vad_enabled=True, @@ -124,7 +123,6 @@ async def main(): api_key=os.getenv("CARTESIA_API_KEY"), voice_id=os.getenv("CARTESIA_VOICE_ID", "4d2fd738-3b3d-4368-957a-bb4805275bd9"), # British Narration Lady: 4d2fd738-3b3d-4368-957a-bb4805275bd9 - sample_rate=44100, ) llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini") @@ -155,7 +153,14 @@ Your task is to help the user understand and learn from this article in 2 senten ] ) - task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True)) + task = PipelineTask( + pipeline, + params=PipelineParams( + audio_out_sample_rate=44100, + allow_interruptions=True, + enable_metrics=True, + ), + ) @transport.event_handler("on_first_participant_joined") async def on_first_participant_joined(transport, participant): diff --git a/examples/telnyx-chatbot/bot.py b/examples/telnyx-chatbot/bot.py index ce0cf052b..94f44e2b1 100644 --- a/examples/telnyx-chatbot/bot.py +++ b/examples/telnyx-chatbot/bot.py @@ -11,14 +11,13 @@ from dotenv import load_dotenv from loguru import logger from pipecat.audio.vad.silero import SileroVADAnalyzer -from pipecat.frames.frames import EndFrame from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.serializers.telnyx import TelnyxFrameSerializer +from pipecat.services.cartesia import CartesiaTTSService from pipecat.services.deepgram import DeepgramSTTService -from pipecat.services.elevenlabs import ElevenLabsTTSService, Language from pipecat.services.openai import OpenAILLMService from pipecat.transports.network.fastapi_websocket import ( FastAPIWebsocketParams, @@ -31,7 +30,12 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def run_bot(websocket_client, stream_id, outbound_encoding, inbound_encoding): +async def run_bot( + websocket_client, + stream_id: str, + outbound_encoding: str, + inbound_encoding: str, +): transport = FastAPIWebsocketTransport( websocket=websocket_client, params=FastAPIWebsocketParams( @@ -48,11 +52,9 @@ async def run_bot(websocket_client, stream_id, outbound_encoding, inbound_encodi stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) - tts = ElevenLabsTTSService( - api_key=os.getenv("ELEVENLABS_API_KEY"), - voice_id="CwhRBWXzGAHq8TQ4Fs17", - output_format="pcm_24000", - params=ElevenLabsTTSService.InputParams(language=Language.EN), + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady ) messages = [ @@ -77,7 +79,14 @@ async def run_bot(websocket_client, stream_id, outbound_encoding, inbound_encodi ] ) - task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True)) + task = PipelineTask( + pipeline, + params=PipelineParams( + audio_in_sample_rate=8000, + audio_out_sample_rate=8000, + allow_interruptions=True, + ), + ) @transport.event_handler("on_client_connected") async def on_client_connected(transport, client): @@ -87,7 +96,7 @@ async def run_bot(websocket_client, stream_id, outbound_encoding, inbound_encodi @transport.event_handler("on_client_disconnected") async def on_client_disconnected(transport, client): - await task.queue_frames([EndFrame()]) + await task.cancel() runner = PipelineRunner(handle_sigint=False) diff --git a/examples/telnyx-chatbot/env.example b/examples/telnyx-chatbot/env.example index 5de3bace2..1da398649 100644 --- a/examples/telnyx-chatbot/env.example +++ b/examples/telnyx-chatbot/env.example @@ -1,3 +1,3 @@ OPENAI_API_KEY= DEEPGRAM_API_KEY= -ELEVENLABS_API_KEY= +CARTESIA_API_KEY= diff --git a/examples/telnyx-chatbot/requirements.txt b/examples/telnyx-chatbot/requirements.txt index 630a98dd6..e103e438d 100644 --- a/examples/telnyx-chatbot/requirements.txt +++ b/examples/telnyx-chatbot/requirements.txt @@ -1,4 +1,4 @@ -pipecat-ai[openai,silero,deepgram,elevenlabs] +pipecat-ai[openai,silero,deepgram,cartesia] fastapi uvicorn python-dotenv diff --git a/examples/translation-chatbot/bot.py b/examples/translation-chatbot/bot.py index d35cf7af2..3443f3370 100644 --- a/examples/translation-chatbot/bot.py +++ b/examples/translation-chatbot/bot.py @@ -24,17 +24,15 @@ from pipecat.frames.frames import ( ) from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner -from pipecat.pipeline.task import PipelineTask +from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.processors.frameworks.rtvi import RTVIObserver, RTVIProcessor from pipecat.processors.transcript_processor import TranscriptProcessor from pipecat.services.cartesia import CartesiaTTSService from pipecat.services.deepgram import DeepgramSTTService from pipecat.services.openai import OpenAILLMService -from pipecat.transports.services.daily import ( - DailyParams, - DailyTransport, -) +from pipecat.transports.services.daily import DailyParams, DailyTransport load_dotenv(override=True) @@ -146,7 +144,7 @@ async def main(): tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="34dbb662-8e98-413c-a1ef-1a3407675fe7", # Spanish Narrator Man - model="sonic-multilingual", + model="sonic-2", ) in_language = "English" @@ -166,21 +164,32 @@ async def main(): async def on_transcript_update(processor, frame): await transcript_handler.on_transcript_update(processor, frame) + rtvi = RTVIProcessor() + pipeline = Pipeline( [ transport.input(), + rtvi, stt, transcript.user(), # User transcripts tp, llm, tts, transport.output(), + transcript.assistant(), context_aggregator.assistant(), - transcript.assistant(), # Assistant transcripts ] ) - task = PipelineTask(pipeline) + task = PipelineTask( + pipeline, + params=PipelineParams( + allow_interruptions=False, # We don't want to interrupt the translator bot + enable_metrics=True, + enable_usage_metrics=True, + ), + observers=[RTVIObserver(rtvi)], + ) @transport.event_handler("on_first_participant_joined") async def on_first_participant_joined(transport, participant): diff --git a/examples/translation-chatbot/requirements.txt b/examples/translation-chatbot/requirements.txt index 6f05d3425..da1cbf0af 100644 --- a/examples/translation-chatbot/requirements.txt +++ b/examples/translation-chatbot/requirements.txt @@ -1,4 +1,4 @@ python-dotenv fastapi[all] -pipecat-ai[daily,openai,azure] +pipecat-ai[cartesia,daily,deepgram,openai,silero] aiohttp diff --git a/examples/twilio-chatbot/bot.py b/examples/twilio-chatbot/bot.py index 64c3c9cb5..7b05e6dfe 100644 --- a/examples/twilio-chatbot/bot.py +++ b/examples/twilio-chatbot/bot.py @@ -11,7 +11,6 @@ import sys import wave import aiofiles -from deepgram import LiveOptions from dotenv import load_dotenv from fastapi import WebSocket from loguru import logger @@ -36,8 +35,6 @@ load_dotenv(override=True) logger.remove(0) logger.add(sys.stderr, level="DEBUG") -SAMPLE_RATE = 8000 - async def save_audio(server_name: str, audio: bytes, sample_rate: int, num_channels: int): if len(audio) > 0: @@ -63,29 +60,21 @@ async def run_bot(websocket_client: WebSocket, stream_sid: str, testing: bool): params=FastAPIWebsocketParams( audio_in_enabled=True, audio_out_enabled=True, - audio_out_sample_rate=SAMPLE_RATE, add_wav_header=False, vad_enabled=True, - vad_analyzer=SileroVADAnalyzer(sample_rate=SAMPLE_RATE), + vad_analyzer=SileroVADAnalyzer(), vad_audio_passthrough=True, - serializer=TwilioFrameSerializer( - stream_sid, TwilioFrameSerializer.InputParams(sample_rate=SAMPLE_RATE) - ), + serializer=TwilioFrameSerializer(stream_sid), ), ) llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") - stt = DeepgramSTTService( - api_key=os.getenv("DEEPGRAM_API_KEY"), - live_options=LiveOptions(sample_rate=SAMPLE_RATE), - audio_passthrough=True, - ) + stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True) tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), - voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady - sample_rate=SAMPLE_RATE, + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady push_silence_after_stop=testing, ) @@ -101,7 +90,7 @@ async def run_bot(websocket_client: WebSocket, stream_sid: str, testing: bool): # NOTE: Watch out! This will save all the conversation in memory. You can # pass `buffer_size` to get periodic callbacks. - audiobuffer = AudioBufferProcessor(sample_rate=SAMPLE_RATE) + audiobuffer = AudioBufferProcessor(user_continuous_stream=not testing) pipeline = Pipeline( [ @@ -116,7 +105,14 @@ async def run_bot(websocket_client: WebSocket, stream_sid: str, testing: bool): ] ) - task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True)) + task = PipelineTask( + pipeline, + params=PipelineParams( + audio_in_sample_rate=8000, + audio_out_sample_rate=8000, + allow_interruptions=True, + ), + ) @transport.event_handler("on_client_connected") async def on_client_connected(transport, client): diff --git a/examples/twilio-chatbot/client.py b/examples/twilio-chatbot/client.py index fa710773b..c4b02c0d2 100644 --- a/examples/twilio-chatbot/client.py +++ b/examples/twilio-chatbot/client.py @@ -16,7 +16,6 @@ from uuid import uuid4 import aiofiles import aiohttp -from deepgram import LiveOptions from dotenv import load_dotenv from loguru import logger @@ -44,7 +43,6 @@ logger.add(sys.stderr, level="DEBUG") DEFAULT_CLIENT_DURATION = 30 -SAMPLE_RATE = 8000 async def download_twiml(server_url: str) -> str: @@ -92,15 +90,10 @@ async def run_client(client_name: str, server_url: str, duration_secs: int): params=WebsocketClientParams( audio_in_enabled=True, audio_out_enabled=True, - audio_out_sample_rate=SAMPLE_RATE, add_wav_header=False, - serializer=TwilioFrameSerializer( - stream_sid, params=TwilioFrameSerializer.InputParams(sample_rate=SAMPLE_RATE) - ), + serializer=TwilioFrameSerializer(stream_sid), vad_enabled=True, - vad_analyzer=SileroVADAnalyzer( - params=VADParams(stop_secs=1.5), sample_rate=SAMPLE_RATE - ), + vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=1.5)), vad_audio_passthrough=True, ), ) @@ -110,14 +103,12 @@ async def run_client(client_name: str, server_url: str, duration_secs: int): # We let the audio passthrough so we can record the conversation. stt = DeepgramSTTService( api_key=os.getenv("DEEPGRAM_API_KEY"), - live_options=LiveOptions(sample_rate=SAMPLE_RATE), audio_passthrough=True, ) tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="e13cae5c-ec59-4f71-b0a6-266df3c9bb8e", # Madame Mischief - sample_rate=SAMPLE_RATE, push_silence_after_stop=True, ) @@ -133,7 +124,7 @@ async def run_client(client_name: str, server_url: str, duration_secs: int): # NOTE: Watch out! This will save all the conversation in memory. You can # pass `buffer_size` to get periodic callbacks. - audiobuffer = AudioBufferProcessor(sample_rate=SAMPLE_RATE) + audiobuffer = AudioBufferProcessor(user_continuous_stream=False) pipeline = Pipeline( [ @@ -148,7 +139,14 @@ async def run_client(client_name: str, server_url: str, duration_secs: int): ] ) - task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True)) + task = PipelineTask( + pipeline, + params=PipelineParams( + audio_in_sample_rate=8000, + audio_out_sample_rate=8000, + allow_interruptions=True, + ), + ) @transport.event_handler("on_connected") async def on_connected(transport: WebsocketClientTransport, client): diff --git a/examples/websocket-server/bot.py b/examples/websocket-server/bot.py index a36339fff..489605aef 100644 --- a/examples/websocket-server/bot.py +++ b/examples/websocket-server/bot.py @@ -17,6 +17,7 @@ from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext +from pipecat.serializers.protobuf import ProtobufFrameSerializer from pipecat.services.cartesia import CartesiaTTSService from pipecat.services.deepgram import DeepgramSTTService from pipecat.services.openai import OpenAILLMService @@ -80,7 +81,7 @@ class SessionTimeoutHandler: async def main(): transport = WebsocketServerTransport( params=WebsocketServerParams( - audio_out_sample_rate=16000, + serializer=ProtobufFrameSerializer(), audio_out_enabled=True, add_wav_header=True, vad_enabled=True, @@ -96,8 +97,7 @@ async def main(): tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), - voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady - sample_rate=16000, + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady ) messages = [ @@ -122,7 +122,14 @@ async def main(): ] ) - task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True)) + task = PipelineTask( + pipeline, + params=PipelineParams( + audio_in_sample_rate=16000, + audio_out_sample_rate=16000, + allow_interruptions=True, + ), + ) @transport.event_handler("on_client_connected") async def on_client_connected(transport, client): diff --git a/pyproject.toml b/pyproject.toml index ee911098f..0f2e993ae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,20 +20,18 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence" ] dependencies = [ - "aiohttp~=3.11.11", + "aiohttp~=3.11.13", "audioop-lts~=0.2.1; python_version>='3.13'", - # We need an older version of `httpx` that doesn't remove the deprecated - # `proxies` argument. This is necessary for Azure and Anthropic clients. - "httpx~=0.27.2", "loguru~=0.7.3", "Markdown~=3.7", "numpy~=1.26.4", "Pillow~=11.1.0", "protobuf~=5.29.3", - "pydantic~=2.10.5", + "pydantic~=2.10.6", "pyloudnorm~=0.1.1", "resampy~=0.4.3", - "soxr~=0.5.0" + "soxr~=0.5.0", + "openai~=1.67.0" ] [project.urls] @@ -41,45 +39,51 @@ Source = "https://github.com/pipecat-ai/pipecat" Website = "https://pipecat.ai" [project.optional-dependencies] -anthropic = [ "anthropic~=0.39.0" ] -assemblyai = [ "assemblyai~=0.36.0" ] -aws = [ "boto3~=1.35.99" ] -azure = [ "azure-cognitiveservices-speech~=1.42.0", "openai~=1.59.6" ] +anthropic = [ "anthropic~=0.49.0" ] +assemblyai = [ "assemblyai~=0.37.0" ] +aws = [ "boto3~=1.37.16" ] +azure = [ "azure-cognitiveservices-speech~=1.42.0"] canonical = [ "aiofiles~=24.1.0" ] -cartesia = [ "cartesia~=1.3.1", "websockets~=13.1" ] -cerebras = [ "openai~=1.59.6" ] -deepseek = [ "openai~=1.59.6" ] -daily = [ "daily-python~=0.14.2" ] +cartesia = [ "cartesia~=1.4.0", "websockets~=13.1" ] +neuphonic = [ "pyneuphonic~=1.5.13", "websockets~=13.1" ] +cerebras = [] +deepseek = [] +daily = [ "daily-python~=0.16.1" ] deepgram = [ "deepgram-sdk~=3.8.0" ] elevenlabs = [ "websockets~=13.1" ] -fal = [ "fal-client~=0.5.6" ] +fal = [ "fal-client~=0.5.9" ] fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ] gladia = [ "websockets~=13.1" ] -google = [ "google-generativeai~=0.8.3", "google-cloud-texttospeech~=2.24.0", "google-genai~=0.7.0" ] -grok = [ "openai~=1.59.6" ] -groq = [ "openai~=1.59.6" ] +google = [ "google-cloud-speech~=2.31.1", "google-cloud-texttospeech~=2.25.1", "google-genai~=1.7.0", "google-generativeai~=0.8.4" ] +grok = [] +groq = [ "groq~=0.20.0" ] gstreamer = [ "pygobject~=3.50.0" ] -fireworks = [ "openai~=1.59.6" ] +fireworks = [] krisp = [ "pipecat-ai-krisp~=0.3.0" ] koala = [ "pvkoala~=2.0.3" ] -langchain = [ "langchain~=0.3.14", "langchain-community~=0.3.14", "langchain-openai~=0.3.0" ] -livekit = [ "livekit~=0.19.1", "livekit-api~=0.8.1", "tenacity~=9.0.0" ] +langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-openai~=0.3.9" ] +livekit = [ "livekit~=0.22.0", "livekit-api~=0.8.2", "tenacity~=9.0.0" ] lmnt = [ "websockets~=13.1" ] local = [ "pyaudio~=0.2.14" ] moondream = [ "einops~=0.8.0", "timm~=1.0.13", "transformers~=4.48.0" ] -nim = [ "openai~=1.59.6" ] +nim = [] noisereduce = [ "noisereduce~=3.0.3" ] -openai = [ "openai~=1.59.6", "websockets~=13.1", "python-deepcompare~=2.1.0" ] -openpipe = [ "openpipe~=4.43.0" ] -playht = [ "pyht~=0.1.6", "websockets~=13.1" ] -riva = [ "nvidia-riva-client~=2.18.0" ] +openai = [ "websockets~=13.1" ] +openpipe = [ "openpipe~=4.48.0" ] +openrouter = [] +perplexity = [] +playht = [ "pyht~=0.1.12", "websockets~=13.1" ] +rime = [ "websockets~=13.1" ] +riva = [ "nvidia-riva-client~=2.19.0" ] +sentry = [ "sentry-sdk~=2.23.1" ] silero = [ "onnxruntime~=1.20.1" ] simli = [ "simli-ai~=0.1.10"] soundfile = [ "soundfile~=0.13.0" ] -together = [ "openai~=1.59.6" ] +tavus=[] +together = [] +ultravox = [ "transformers~=4.48.0", "vllm~=0.7.3" ] websocket = [ "websockets~=13.1", "fastapi~=0.115.6" ] whisper = [ "faster-whisper~=1.1.1" ] -openrouter = [ "openai~=1.59.6" ] [tool.setuptools.packages.find] # All the following settings are optional: @@ -110,3 +114,8 @@ select = [ [tool.ruff.lint.pydocstyle] convention = "google" + +[tool.coverage.run] +command_line = "--module pytest" +source = ["src"] +omit = ["*/tests/*"] diff --git a/scripts/fix-ruff.sh b/scripts/fix-ruff.sh new file mode 100755 index 000000000..892f6d405 --- /dev/null +++ b/scripts/fix-ruff.sh @@ -0,0 +1,4 @@ +ruff format src +ruff format examples +ruff format tests +ruff check --select I --fix \ No newline at end of file diff --git a/src/pipecat/__init__.py b/src/pipecat/__init__.py index e69de29bb..b1a9c7413 100644 --- a/src/pipecat/__init__.py +++ b/src/pipecat/__init__.py @@ -0,0 +1,13 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from importlib.metadata import version + +from loguru import logger + +__version__ = version("pipecat-ai") + +logger.info(f"ᓚᘏᗢ Pipecat {__version__} ᓚᘏᗢ") diff --git a/src/pipecat/vad/__init__.py b/src/pipecat/adapters/__init__.py similarity index 100% rename from src/pipecat/vad/__init__.py rename to src/pipecat/adapters/__init__.py diff --git a/src/pipecat/adapters/base_llm_adapter.py b/src/pipecat/adapters/base_llm_adapter.py new file mode 100644 index 000000000..c26722604 --- /dev/null +++ b/src/pipecat/adapters/base_llm_adapter.py @@ -0,0 +1,22 @@ +from abc import ABC, abstractmethod +from typing import Any, List, Union, cast + +from loguru import logger + +from pipecat.adapters.schemas.tools_schema import ToolsSchema + + +class BaseLLMAdapter(ABC): + @abstractmethod + def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Any]: + """Converts tools to the provider's format.""" + pass + + def from_standard_tools(self, tools: Any) -> List[Any]: + if isinstance(tools, ToolsSchema): + logger.debug(f"Retrieving the tools using the adapter: {type(self)}") + return self.to_provider_tools_format(tools) + # Fallback to return the same tools in case they are not in a standard format + return tools + + # TODO: we can move the logic to also handle the Messages here diff --git a/src/pipecat/adapters/schemas/__init__.py b/src/pipecat/adapters/schemas/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/pipecat/adapters/schemas/function_schema.py b/src/pipecat/adapters/schemas/function_schema.py new file mode 100644 index 000000000..f6e59cef1 --- /dev/null +++ b/src/pipecat/adapters/schemas/function_schema.py @@ -0,0 +1,55 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import Any, Dict, List + + +class FunctionSchema: + def __init__( + self, name: str, description: str, properties: Dict[str, Any], required: List[str] + ) -> None: + """Standardized function schema representation. + + :param name: Name of the function. + :param description: Description of the function. + :param properties: Dictionary defining properties types and descriptions. + :param required: List of required parameters. + """ + self._name = name + self._description = description + self._properties = properties + self._required = required + + def to_default_dict(self) -> Dict[str, Any]: + """Converts the function schema to a dictionary. + + :return: Dictionary representation of the function schema. + """ + return { + "name": self._name, + "description": self._description, + "parameters": { + "type": "object", + "properties": self._properties, + "required": self._required, + }, + } + + @property + def name(self) -> str: + return self._name + + @property + def description(self) -> str: + return self._description + + @property + def properties(self) -> Dict[str, Any]: + return self._properties + + @property + def required(self) -> List[str]: + return self._required diff --git a/src/pipecat/adapters/schemas/tools_schema.py b/src/pipecat/adapters/schemas/tools_schema.py new file mode 100644 index 000000000..5720535c5 --- /dev/null +++ b/src/pipecat/adapters/schemas/tools_schema.py @@ -0,0 +1,43 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from enum import Enum +from typing import Any, Dict, List + +from pipecat.adapters.schemas.function_schema import FunctionSchema + + +class AdapterType(Enum): + GEMINI = "gemini" # that is the only service where we are able to add custom tools for now + + +class ToolsSchema: + def __init__( + self, + standard_tools: List[FunctionSchema], + custom_tools: Dict[AdapterType, List[Dict[str, Any]]] = None, + ) -> None: + """ + A schema for tools that includes both standardized function schemas + and custom tools that do not follow the FunctionSchema format. + + :param standard_tools: List of tools following FunctionSchema. + :param custom_tools: List of tools in a custom format (e.g., search_tool). + """ + self._standard_tools = standard_tools + self._custom_tools = custom_tools + + @property + def standard_tools(self) -> List[FunctionSchema]: + return self._standard_tools + + @property + def custom_tools(self) -> Dict[AdapterType, List[Dict[str, Any]]]: + return self._custom_tools + + @custom_tools.setter + def custom_tools(self, value: Dict[AdapterType, List[Dict[str, Any]]]) -> None: + self._custom_tools = value diff --git a/src/pipecat/adapters/services/__init__.py b/src/pipecat/adapters/services/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/pipecat/adapters/services/anthropic_adapter.py b/src/pipecat/adapters/services/anthropic_adapter.py new file mode 100644 index 000000000..a699469d3 --- /dev/null +++ b/src/pipecat/adapters/services/anthropic_adapter.py @@ -0,0 +1,34 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import Any, Dict, List, Union + +from pipecat.adapters.base_llm_adapter import BaseLLMAdapter +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema + + +class AnthropicLLMAdapter(BaseLLMAdapter): + @staticmethod + def _to_anthropic_function_format(function: FunctionSchema) -> Dict[str, Any]: + return { + "name": function.name, + "description": function.description, + "input_schema": { + "type": "object", + "properties": function.properties, + "required": function.required, + }, + } + + def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Dict[str, Any]]: + """Converts function schemas to Anthropic's function-calling format. + + :return: Anthropic formatted function call definition. + """ + + functions_schema = tools_schema.standard_tools + return [self._to_anthropic_function_format(func) for func in functions_schema] diff --git a/src/pipecat/adapters/services/gemini_adapter.py b/src/pipecat/adapters/services/gemini_adapter.py new file mode 100644 index 000000000..8efca5189 --- /dev/null +++ b/src/pipecat/adapters/services/gemini_adapter.py @@ -0,0 +1,28 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import Any, Dict, List, Union + +from pipecat.adapters.base_llm_adapter import BaseLLMAdapter +from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema + + +class GeminiLLMAdapter(BaseLLMAdapter): + def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Dict[str, Any]]: + """Converts function schemas to Gemini's function-calling format. + + :return: Gemini formatted function call definition. + """ + + functions_schema = tools_schema.standard_tools + formatted_standard_tools = [ + {"function_declarations": [func.to_default_dict() for func in functions_schema]} + ] + custom_gemini_tools = [] + if tools_schema.custom_tools: + custom_gemini_tools = tools_schema.custom_tools.get(AdapterType.GEMINI, []) + + return formatted_standard_tools + custom_gemini_tools diff --git a/src/pipecat/adapters/services/open_ai_adapter.py b/src/pipecat/adapters/services/open_ai_adapter.py new file mode 100644 index 000000000..909e5103a --- /dev/null +++ b/src/pipecat/adapters/services/open_ai_adapter.py @@ -0,0 +1,24 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# +from typing import List + +from openai.types.chat import ChatCompletionToolParam + +from pipecat.adapters.base_llm_adapter import BaseLLMAdapter +from pipecat.adapters.schemas.tools_schema import ToolsSchema + + +class OpenAILLMAdapter(BaseLLMAdapter): + def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[ChatCompletionToolParam]: + """Converts function schemas to OpenAI's function-calling format. + + :return: OpenAI formatted function call definition. + """ + functions_schema = tools_schema.standard_tools + return [ + ChatCompletionToolParam(type="function", function=func.to_default_dict()) + for func in functions_schema + ] diff --git a/src/pipecat/adapters/services/open_ai_realtime_adapter.py b/src/pipecat/adapters/services/open_ai_realtime_adapter.py new file mode 100644 index 000000000..b7eafaa81 --- /dev/null +++ b/src/pipecat/adapters/services/open_ai_realtime_adapter.py @@ -0,0 +1,34 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# +from typing import Any, Dict, List, Union + +from pipecat.adapters.base_llm_adapter import BaseLLMAdapter +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema + + +class OpenAIRealtimeLLMAdapter(BaseLLMAdapter): + @staticmethod + def _to_openai_realtime_function_format(function: FunctionSchema) -> Dict[str, Any]: + return { + "type": "function", + "name": function.name, + "description": function.description, + "parameters": { + "type": "object", + "properties": function.properties, + "required": function.required, + }, + } + + def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Dict[str, Any]]: + """Converts function schemas to Openai Realtime function-calling format. + + :return: Openai Realtime formatted function call definition. + """ + + functions_schema = tools_schema.standard_tools + return [self._to_openai_realtime_function_format(func) for func in functions_schema] diff --git a/src/pipecat/audio/filters/krisp_filter.py b/src/pipecat/audio/filters/krisp_filter.py index 194dd848d..b23a46b65 100644 --- a/src/pipecat/audio/filters/krisp_filter.py +++ b/src/pipecat/audio/filters/krisp_filter.py @@ -20,6 +20,22 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") +class KrispProcessorManager: + """ + Ensures that only one KrispAudioProcessor instance exists for the entire program. + """ + + _krisp_instance = None + + @classmethod + def get_processor(cls, sample_rate: int, sample_type: str, channels: int, model_path: str): + if cls._krisp_instance is None: + cls._krisp_instance = KrispAudioProcessor( + sample_rate, sample_type, channels, model_path + ) + return cls._krisp_instance + + class KrispFilter(BaseAudioFilter): def __init__( self, sample_type: str = "PCM_16", channels: int = 1, model_path: str = None @@ -48,7 +64,7 @@ class KrispFilter(BaseAudioFilter): async def start(self, sample_rate: int): self._sample_rate = sample_rate - self._krisp_processor = KrispAudioProcessor( + self._krisp_processor = KrispProcessorManager.get_processor( self._sample_rate, self._sample_type, self._channels, self._model_path ) diff --git a/src/pipecat/audio/utils.py b/src/pipecat/audio/utils.py index 7c4c25fcc..1f7db648f 100644 --- a/src/pipecat/audio/utils.py +++ b/src/pipecat/audio/utils.py @@ -18,23 +18,6 @@ def create_default_resampler(**kwargs) -> BaseAudioResampler: return SOXRAudioResampler(**kwargs) -def resample_audio(audio: bytes, original_rate: int, target_rate: int) -> bytes: - import warnings - - with warnings.catch_warnings(): - warnings.simplefilter("always") - warnings.warn( - "'resample_audio()' is deprecated, use 'create_default_resampler()' instead.", - DeprecationWarning, - ) - - if original_rate == target_rate: - return audio - audio_data = np.frombuffer(audio, dtype=np.int16) - resampled_audio = soxr.resample(audio_data, original_rate, target_rate) - return resampled_audio.astype(np.int16).tobytes() - - def mix_audio(audio1: bytes, audio2: bytes) -> bytes: data1 = np.frombuffer(audio1, dtype=np.int16) data2 = np.frombuffer(audio2, dtype=np.int16) diff --git a/src/pipecat/audio/vad/silero.py b/src/pipecat/audio/vad/silero.py index 00a1358a5..a5db2a0ae 100644 --- a/src/pipecat/audio/vad/silero.py +++ b/src/pipecat/audio/vad/silero.py @@ -5,6 +5,7 @@ # import time +from typing import Optional import numpy as np from loguru import logger @@ -104,11 +105,8 @@ class SileroOnnxModel: class SileroVADAnalyzer(VADAnalyzer): - def __init__(self, *, sample_rate: int = 16000, params: VADParams = VADParams()): - super().__init__(sample_rate=sample_rate, num_channels=1, params=params) - - if sample_rate != 16000 and sample_rate != 8000: - raise ValueError("Silero VAD sample rate needs to be 16000 or 8000") + def __init__(self, *, sample_rate: Optional[int] = None, params: VADParams = VADParams()): + super().__init__(sample_rate=sample_rate, params=params) logger.debug("Loading Silero VAD model...") @@ -138,6 +136,12 @@ class SileroVADAnalyzer(VADAnalyzer): # VADAnalyzer # + def set_sample_rate(self, sample_rate: int): + if sample_rate != 16000 and sample_rate != 8000: + raise ValueError("Silero VAD sample rate needs to be 16000 or 8000") + + super().set_sample_rate(sample_rate) + def num_frames_required(self) -> int: return 512 if self.sample_rate == 16000 else 256 diff --git a/src/pipecat/audio/vad/vad_analyzer.py b/src/pipecat/audio/vad/vad_analyzer.py index a6c4f70a1..3ca21a208 100644 --- a/src/pipecat/audio/vad/vad_analyzer.py +++ b/src/pipecat/audio/vad/vad_analyzer.py @@ -4,8 +4,9 @@ # SPDX-License-Identifier: BSD 2-Clause License # -from abc import abstractmethod +from abc import ABC, abstractmethod from enum import Enum +from typing import Optional from loguru import logger from pydantic import BaseModel @@ -32,12 +33,12 @@ class VADParams(BaseModel): min_volume: float = VAD_MIN_VOLUME -class VADAnalyzer: - def __init__(self, *, sample_rate: int, num_channels: int, params: VADParams): - self._sample_rate = sample_rate - self._num_channels = num_channels - - self.set_params(params) +class VADAnalyzer(ABC): + def __init__(self, *, sample_rate: Optional[int] = None, params: VADParams): + self._init_sample_rate = sample_rate + self._sample_rate = 0 + self._params = params + self._num_channels = 1 self._vad_buffer = b"" @@ -65,13 +66,17 @@ class VADAnalyzer: def voice_confidence(self, buffer) -> float: pass + def set_sample_rate(self, sample_rate: int): + self._sample_rate = self._init_sample_rate or sample_rate + self.set_params(self._params) + def set_params(self, params: VADParams): logger.info(f"Setting VAD params to: {params}") self._params = params self._vad_frames = self.num_frames_required() self._vad_frames_num_bytes = self._vad_frames * self._num_channels * 2 - vad_frames_per_sec = self._vad_frames / self._sample_rate + vad_frames_per_sec = self._vad_frames / self.sample_rate self._vad_start_frames = round(self._params.start_secs / vad_frames_per_sec) self._vad_stop_frames = round(self._params.stop_secs / vad_frames_per_sec) @@ -80,7 +85,7 @@ class VADAnalyzer: self._vad_state: VADState = VADState.QUIET def _get_smoothed_volume(self, audio: bytes) -> float: - volume = calculate_audio_volume(audio, self._sample_rate) + volume = calculate_audio_volume(audio, self.sample_rate) return exp_smoothing(volume, self._prev_volume, self._smoothing_factor) def analyze_audio(self, buffer) -> VADState: diff --git a/src/pipecat/frames/frames.py b/src/pipecat/frames/frames.py index 0038fcea9..11cd17801 100644 --- a/src/pipecat/frames/frames.py +++ b/src/pipecat/frames/frames.py @@ -6,13 +6,24 @@ from dataclasses import dataclass, field from enum import Enum -from typing import TYPE_CHECKING, Any, Awaitable, Callable, List, Literal, Mapping, Optional, Tuple +from typing import ( + TYPE_CHECKING, + Any, + Awaitable, + Callable, + Dict, + List, + Literal, + Mapping, + Optional, + Tuple, +) from pipecat.audio.vad.vad_analyzer import VADParams from pipecat.clocks.base_clock import BaseClock from pipecat.metrics.metrics import MetricsData from pipecat.transcriptions.language import Language -from pipecat.utils.asyncio import TaskManager +from pipecat.utils.asyncio import BaseTaskManager from pipecat.utils.time import nanoseconds_to_str from pipecat.utils.utils import obj_count, obj_id @@ -37,7 +48,7 @@ class KeypadEntry(str, Enum): STAR = "*" -def format_pts(pts: int | None): +def format_pts(pts: Optional[int]): return nanoseconds_to_str(pts) if pts else None @@ -48,13 +59,13 @@ class Frame: id: int = field(init=False) name: str = field(init=False) pts: Optional[int] = field(init=False) - metadata: dict = field(init=False) + metadata: Dict[str, Any] = field(init=False) def __post_init__(self): self.id: int = obj_id() self.name: str = f"{self.__class__.__name__}#{obj_count(self)}" self.pts: Optional[int] = None - self.metadata: dict = {} + self.metadata: Dict[str, Any] = {} def __str__(self): return self.name @@ -115,7 +126,7 @@ class ImageRawFrame: image: bytes size: Tuple[int, int] - format: str | None + format: Optional[str] # @@ -165,7 +176,7 @@ class URLImageRawFrame(OutputImageRawFrame): """ - url: str | None + url: Optional[str] def __str__(self): pts = format_pts(self.pts) @@ -224,7 +235,7 @@ class TranscriptionFrame(TextFrame): user_id: str timestamp: str - language: Language | None = None + language: Optional[Language] = None def __str__(self): return f"{self.name}(user: {self.user_id}, text: [{self.text}], language: {self.language}, timestamp: {self.timestamp})" @@ -239,7 +250,7 @@ class InterimTranscriptionFrame(TextFrame): text: str user_id: str timestamp: str - language: Language | None = None + language: Optional[Language] = None def __str__(self): return f"{self.name}(user: {self.user_id}, text: [{self.text}], language: {self.language}, timestamp: {self.timestamp})" @@ -261,7 +272,7 @@ class TranscriptionMessage: role: Literal["user", "assistant"] content: str - timestamp: str | None = None + timestamp: Optional[str] = None @dataclass @@ -352,6 +363,13 @@ class LLMSetToolsFrame(DataFrame): tools: List[dict] +@dataclass +class LLMSetToolChoiceFrame(DataFrame): + """A frame containing a tool choice for an LLM to use for function calling.""" + + tool_choice: Literal["none", "auto", "required"] | dict + + @dataclass class LLMEnablePromptCachingFrame(DataFrame): """A frame to enable/disable prompt caching in certain LLMs.""" @@ -373,7 +391,7 @@ class FunctionCallResultFrame(DataFrame): function_name: str tool_call_id: str - arguments: str + arguments: Any result: Any properties: Optional[FunctionCallResultProperties] = None @@ -427,12 +445,14 @@ class StartFrame(SystemFrame): """This is the first frame that should be pushed down a pipeline.""" clock: BaseClock - task_manager: TaskManager + task_manager: BaseTaskManager + audio_in_sample_rate: int = 16000 + audio_out_sample_rate: int = 24000 allow_interruptions: bool = False enable_metrics: bool = False enable_usage_metrics: bool = False - report_only_initial_ttfb: bool = False observer: Optional["BaseObserver"] = None + report_only_initial_ttfb: bool = False @dataclass @@ -500,9 +520,9 @@ class CancelTaskFrame(SystemFrame): @dataclass class StopTaskFrame(SystemFrame): - """Indicates that a pipeline task should be stopped but that the pipeline - processors should be kept in a running state. This is normally queued from - the pipeline task. + """This is used to notify the pipeline task that it should be stopped as + soon as possible (flushing all the queued frames) but that the pipeline + processors should be kept in a running state. """ @@ -552,6 +572,24 @@ class UserStoppedSpeakingFrame(SystemFrame): pass +@dataclass +class EmulateUserStartedSpeakingFrame(SystemFrame): + """Emitted by internal processors upstream to emulate VAD behavior when a + user starts speaking. + """ + + pass + + +@dataclass +class EmulateUserStoppedSpeakingFrame(SystemFrame): + """Emitted by internal processors upstream to emulate VAD behavior when a + user stops speaking. + """ + + pass + + @dataclass class BotInterruptionFrame(SystemFrame): """Emitted by when the bot should be interrupted. This will mainly cause the @@ -602,7 +640,23 @@ class FunctionCallInProgressFrame(SystemFrame): function_name: str tool_call_id: str - arguments: str + arguments: Any + cancel_on_interruption: bool = False + + +@dataclass +class FunctionCallCancelFrame(SystemFrame): + """A frame to signal a function call has been cancelled.""" + + function_name: str + tool_call_id: str + + +@dataclass +class STTMuteFrame(SystemFrame): + """System frame to mute/unmute the STT service.""" + + mute: bool @dataclass @@ -615,13 +669,19 @@ class TransportMessageUrgentFrame(SystemFrame): @dataclass class UserImageRequestFrame(SystemFrame): - """A frame user to request an image from the given user.""" + """A frame to request an image from the given user. The frame might be + generated by a function call in which case the corresponding fields will be + properly set. + + """ user_id: str context: Optional[Any] = None + function_name: Optional[str] = None + tool_call_id: Optional[str] = None def __str__(self): - return f"{self.name}, user: {self.user_id}" + return f"{self.name}(user: {self.user_id}, function: {self.function_name}, request: {self.tool_call_id})" @dataclass @@ -651,17 +711,18 @@ class UserImageRawFrame(InputImageRawFrame): """An image associated to a user.""" user_id: str + request: Optional[UserImageRequestFrame] = None def __str__(self): pts = format_pts(self.pts) - return f"{self.name}(pts: {pts}, user: {self.user_id}, size: {self.size}, format: {self.format})" + return f"{self.name}(pts: {pts}, user: {self.user_id}, size: {self.size}, format: {self.format}, request: {self.request})" @dataclass class VisionImageRawFrame(InputImageRawFrame): """An image with an associated text to ask for a description of it.""" - text: str | None + text: Optional[str] def __str__(self): pts = format_pts(self.pts) @@ -686,6 +747,17 @@ class EndFrame(ControlFrame): pass +@dataclass +class StopFrame(ControlFrame): + """Indicates that a pipeline should be stopped but that the pipeline + processors should be kept in a running state. This is normally queued from + the pipeline task. + + """ + + pass + + @dataclass class LLMFullResponseStartFrame(ControlFrame): """Used to indicate the beginning of an LLM response. Following by one or @@ -739,13 +811,6 @@ class TTSUpdateSettingsFrame(ServiceUpdateSettingsFrame): pass -@dataclass -class STTMuteFrame(ControlFrame): - """Control frame to mute/unmute the STT service.""" - - mute: bool - - @dataclass class STTUpdateSettingsFrame(ServiceUpdateSettingsFrame): pass diff --git a/src/pipecat/observers/loggers/__init__.py b/src/pipecat/observers/loggers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/pipecat/observers/loggers/llm_log_observer.py b/src/pipecat/observers/loggers/llm_log_observer.py new file mode 100644 index 000000000..907dce70b --- /dev/null +++ b/src/pipecat/observers/loggers/llm_log_observer.py @@ -0,0 +1,85 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from loguru import logger + +from pipecat.frames.frames import ( + Frame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, + LLMFullResponseEndFrame, + LLMFullResponseStartFrame, + LLMMessagesFrame, + LLMTextFrame, +) +from pipecat.observers.base_observer import BaseObserver +from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame +from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.services.ai_services import LLMService + + +class LLMLogObserver(BaseObserver): + """Observer to log LLM activity to the console. + + Logs all frame instances (only from/to LLM service) of: + + - LLMFullResponseStartFrame + - LLMFullResponseEndFrame + - LLMTextFrame + - FunctionCallInProgressFrame + - LLMMessagesFrame + - OpenAILLMContextFrame + + This allows you to track when the LLM starts responding, what it generates, + and when it finishes. + + """ + + async def on_push_frame( + self, + src: FrameProcessor, + dst: FrameProcessor, + frame: Frame, + direction: FrameDirection, + timestamp: int, + ): + if not isinstance(src, LLMService) and not isinstance(dst, LLMService): + return + + time_sec = timestamp / 1_000_000_000 + + arrow = "→" + + # Log LLM start/end frames (output) + if isinstance(frame, (LLMFullResponseStartFrame, LLMFullResponseEndFrame)): + event = "START" if isinstance(frame, LLMFullResponseStartFrame) else "END" + logger.debug(f"🧠 {src} {arrow} LLM {event} RESPONSE at {time_sec:.2f}s") + # Log all LLMTextFrames (output) + elif isinstance(frame, LLMTextFrame): + logger.debug(f"🧠 {src} {arrow} LLM GENERATING: {frame.text!r} at {time_sec:.2f}s") + # Log function calling (output) + elif ( + isinstance(frame, FunctionCallInProgressFrame) + and direction != FrameDirection.DOWNSTREAM + ): + logger.debug( + f"🧠 {src} {arrow} LLM FUNCTION CALL ({frame.tool_call_id}): {frame.function_name!r}({frame.arguments}) at {time_sec:.2f}s" + ) + # Log LLMMessagesFrame (input) + elif isinstance(frame, LLMMessagesFrame): + logger.debug( + f"🧠 {arrow} {dst} LLM MESSAGES FRAME: {frame.messages} at {time_sec:.2f}s" + ) + # Log OpenAILLMContextFrame (input) + elif isinstance(frame, OpenAILLMContextFrame): + logger.debug( + f"🧠 {arrow} {dst} LLM CONTEXT FRAME: {frame.context.messages} at {time_sec:.2f}s" + ) + # Log function call result (input) + elif isinstance(frame, FunctionCallResultFrame): + logger.debug( + f"🧠 {arrow} {src} LLM FUNCTION CALL RESULT ({frame.tool_call_id}): {frame.result} at {time_sec:.2f}s" + ) diff --git a/src/pipecat/observers/loggers/transcription_log_observer.py b/src/pipecat/observers/loggers/transcription_log_observer.py new file mode 100644 index 000000000..630f7ab33 --- /dev/null +++ b/src/pipecat/observers/loggers/transcription_log_observer.py @@ -0,0 +1,54 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from loguru import logger + +from pipecat.frames.frames import ( + Frame, + InterimTranscriptionFrame, + TranscriptionFrame, +) +from pipecat.observers.base_observer import BaseObserver +from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.services.ai_services import STTService + + +class TranscriptionLogObserver(BaseObserver): + """Observer to log transcription activity to the console. + + Logs all frame instances (only from STT service) of: + + - TranscriptionFrame + - InterimTranscriptionFrame + + This allows you to track when the LLM starts responding, what it generates, + and when it finishes. + + """ + + async def on_push_frame( + self, + src: FrameProcessor, + dst: FrameProcessor, + frame: Frame, + direction: FrameDirection, + timestamp: int, + ): + if not isinstance(src, STTService): + return + + time_sec = timestamp / 1_000_000_000 + + arrow = "→" + + if isinstance(frame, TranscriptionFrame): + logger.debug( + f"💬 {src} {arrow} TRANSCRIPTION: {frame.text!r} from {frame.user_id!r} at {time_sec:.2f}s" + ) + elif isinstance(frame, InterimTranscriptionFrame): + logger.debug( + f"💬 {src} {arrow} INTERIM TRANSCRIPTION: {frame.text!r} from {frame.user_id!r} at {time_sec:.2f}s" + ) diff --git a/src/pipecat/pipeline/base_task.py b/src/pipecat/pipeline/base_task.py index b8622bac2..278709b7e 100644 --- a/src/pipecat/pipeline/base_task.py +++ b/src/pipecat/pipeline/base_task.py @@ -5,25 +5,14 @@ # import asyncio -from abc import ABC, abstractmethod +from abc import abstractmethod from typing import AsyncIterable, Iterable from pipecat.frames.frames import Frame +from pipecat.utils.base_object import BaseObject -class BaseTask(ABC): - @property - @abstractmethod - def id(self) -> int: - """Returns the unique indetifier for this task.""" - pass - - @property - @abstractmethod - def name(self) -> str: - """Returns the name of this task.""" - pass - +class BaseTask(BaseObject): @abstractmethod def set_event_loop(self, loop: asyncio.AbstractEventLoop): """Sets the event loop that this task will run on.""" diff --git a/src/pipecat/pipeline/parallel_pipeline.py b/src/pipecat/pipeline/parallel_pipeline.py index f77d09cc6..c6f4de2df 100644 --- a/src/pipecat/pipeline/parallel_pipeline.py +++ b/src/pipecat/pipeline/parallel_pipeline.py @@ -81,6 +81,8 @@ class ParallelPipeline(BasePipeline): self._seen_ids = set() self._endframe_counter: Dict[int, int] = {} + self._up_task = None + self._down_task = None self._up_queue = asyncio.Queue() self._down_queue = asyncio.Queue() @@ -150,19 +152,30 @@ class ParallelPipeline(BasePipeline): await self._create_tasks() async def _stop(self): - # The up task doesn't receive an EndFrame, so we just cancel it. - await self.cancel_task(self._up_task) - # The down tasks waits for the last EndFrame sent by the internal - # pipelines. - await self._down_task + if self._up_task: + # The up task doesn't receive an EndFrame, so we just cancel it. + await self.cancel_task(self._up_task) + self._up_task = None + + if self._down_task: + # The down tasks waits for the last EndFrame sent by the internal + # pipelines. + await self._down_task + self._down_task = None async def _cancel(self): - await self.cancel_task(self._up_task) - await self.cancel_task(self._down_task) + if self._up_task: + await self.cancel_task(self._up_task) + self._up_task = None + if self._down_task: + await self.cancel_task(self._down_task) + self._down_task = None async def _create_tasks(self): - self._up_task = self.create_task(self._process_up_queue()) - self._down_task = self.create_task(self._process_down_queue()) + if not self._up_task: + self._up_task = self.create_task(self._process_up_queue()) + if not self._down_task: + self._down_task = self.create_task(self._process_down_queue()) async def _drain_queues(self): while not self._up_queue.empty: diff --git a/src/pipecat/pipeline/runner.py b/src/pipecat/pipeline/runner.py index e74c491e8..7ac07064f 100644 --- a/src/pipecat/pipeline/runner.py +++ b/src/pipecat/pipeline/runner.py @@ -12,20 +12,19 @@ from typing import Optional from loguru import logger from pipecat.pipeline.task import PipelineTask -from pipecat.utils.utils import obj_count, obj_id +from pipecat.utils.base_object import BaseObject -class PipelineRunner: +class PipelineRunner(BaseObject): def __init__( self, *, - name: str | None = None, + name: Optional[str] = None, handle_sigint: bool = True, force_gc: bool = False, loop: Optional[asyncio.AbstractEventLoop] = None, ): - self.id: int = obj_id() - self.name: str = name or f"{self.__class__.__name__}#{obj_count(self)}" + super().__init__(name=name) self._tasks = {} self._sig_task = None @@ -41,12 +40,18 @@ class PipelineRunner: task.set_event_loop(self._loop) await task.run() del self._tasks[task.name] + + # Cleanup base object. + await self.cleanup() + # If we are cancelling through a signal, make sure we wait for it so # everything gets cleaned up nicely. if self._sig_task: await self._sig_task + if self._force_gc: self._gc_collect() + logger.debug(f"Runner {self} finished running {task}") async def stop_when_done(self): @@ -74,6 +79,3 @@ class PipelineRunner: collected = gc.collect() logger.debug(f"Garbage collector: collected {collected} objects.") logger.debug(f"Garbage collector: uncollectable objects {gc.garbage}") - - def __str__(self): - return self.name diff --git a/src/pipecat/pipeline/task.py b/src/pipecat/pipeline/task.py index 6f9c95bb0..8279373cb 100644 --- a/src/pipecat/pipeline/task.py +++ b/src/pipecat/pipeline/task.py @@ -5,7 +5,8 @@ # import asyncio -from typing import AsyncIterable, Iterable, List +import time +from typing import Any, AsyncIterable, Dict, Iterable, List, Optional, Tuple, Type from loguru import logger from pydantic import BaseModel, ConfigDict @@ -13,6 +14,7 @@ from pydantic import BaseModel, ConfigDict from pipecat.clocks.base_clock import BaseClock from pipecat.clocks.system_clock import SystemClock from pipecat.frames.frames import ( + BotSpeakingFrame, CancelFrame, CancelTaskFrame, EndFrame, @@ -20,8 +22,10 @@ from pipecat.frames.frames import ( ErrorFrame, Frame, HeartbeatFrame, + LLMFullResponseEndFrame, MetricsFrame, StartFrame, + StopFrame, StopTaskFrame, ) from pipecat.metrics.metrics import ProcessingMetricsData, TTFBMetricsData @@ -30,32 +34,55 @@ from pipecat.pipeline.base_pipeline import BasePipeline from pipecat.pipeline.base_task import BaseTask from pipecat.pipeline.task_observer import TaskObserver from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -from pipecat.utils.asyncio import TaskManager -from pipecat.utils.utils import obj_count, obj_id +from pipecat.utils.asyncio import BaseTaskManager, TaskManager HEARTBEAT_SECONDS = 1.0 HEARTBEAT_MONITOR_SECONDS = HEARTBEAT_SECONDS * 5 class PipelineParams(BaseModel): + """Configuration parameters for pipeline execution. + + Attributes: + allow_interruptions: Whether to allow pipeline interruptions. + audio_in_sample_rate: Input audio sample rate in Hz. + audio_out_sample_rate: Output audio sample rate in Hz. + enable_heartbeats: Whether to enable heartbeat monitoring. + enable_metrics: Whether to enable metrics collection. + enable_usage_metrics: Whether to enable usage metrics. + heartbeats_period_secs: Period between heartbeats in seconds. + observers: List of observers for monitoring pipeline execution. + report_only_initial_ttfb: Whether to report only initial time to first byte. + send_initial_empty_metrics: Whether to send initial empty metrics. + start_metadata: Additional metadata for pipeline start. + """ + model_config = ConfigDict(arbitrary_types_allowed=True) allow_interruptions: bool = False + audio_in_sample_rate: int = 16000 + audio_out_sample_rate: int = 24000 enable_heartbeats: bool = False enable_metrics: bool = False enable_usage_metrics: bool = False - send_initial_empty_metrics: bool = True - report_only_initial_ttfb: bool = False - observers: List[BaseObserver] = [] heartbeats_period_secs: float = HEARTBEAT_SECONDS + observers: List[BaseObserver] = [] + report_only_initial_ttfb: bool = False + send_initial_empty_metrics: bool = True + start_metadata: Dict[str, Any] = {} class PipelineTaskSource(FrameProcessor): - """This is the source processor that is linked at the beginning of the + """Source processor for pipeline tasks that handles frame routing. + + This is the source processor that is linked at the beginning of the pipeline given to the pipeline task. It allows us to easily push frames downstream to the pipeline and also receive upstream frames coming from the pipeline. + Args: + up_queue: Queue for upstream frame processing. + """ def __init__(self, up_queue: asyncio.Queue, **kwargs): @@ -73,10 +100,14 @@ class PipelineTaskSource(FrameProcessor): class PipelineTaskSink(FrameProcessor): - """This is the sink processor that is linked at the end of the pipeline + """Sink processor for pipeline tasks that handles final frame processing. + + This is the sink processor that is linked at the end of the pipeline given to the pipeline task. It allows us to receive downstream frames and act on them, for example, waiting to receive an EndFrame. + Args: + down_queue: Queue for downstream frame processing. """ def __init__(self, down_queue: asyncio.Queue, **kwargs): @@ -89,18 +120,80 @@ class PipelineTaskSink(FrameProcessor): class PipelineTask(BaseTask): + """Manages the execution of a pipeline, handling frame processing and task lifecycle. + + It has a couple of event handlers `on_frame_reached_upstream` and + `on_frame_reached_downstream` that are called when upstream frames or + downstream frames reach both ends of pipeline. By default, the events + handlers will not be called unless some filters are set using + `set_reached_upstream_filter` and `set_reached_downstream_filter`. + + @task.event_handler("on_frame_reached_upstream") + async def on_frame_reached_upstream(task, frame): + ... + + @task.event_handler("on_frame_reached_downstream") + async def on_frame_reached_downstream(task, frame): + ... + + It also has an event handler that detects when the pipeline is idle. By + default, a pipeline is idle if no `BotSpeakingFrame` or + `LLMFullResponseEndFrame` are received within `idle_timeout_secs`. + + @task.event_handler("on_idle_timeout") + async def on_idle_timeout(task): + ... + + Args: + pipeline: The pipeline to execute. + params: Configuration parameters for the pipeline. + observers: List of observers for monitoring pipeline execution. + clock: Clock implementation for timing operations. + check_dangling_tasks: Whether to check for processors' tasks finishing properly. + idle_timeout_secs: Timeout (in seconds) to consider pipeline idle or + None. If a pipeline is idle the pipeline task will be cancelled + automatically. + idle_timeout_frames: A tuple with the frames that should trigger an idle + timeout if not received withing `idle_timeout_seconds`. + cancel_on_idle_timeout: Whether the pipeline task should be cancelled if + the idle timeout is reached. + + """ + def __init__( self, pipeline: BasePipeline, + *, params: PipelineParams = PipelineParams(), + observers: List[BaseObserver] = [], clock: BaseClock = SystemClock(), + task_manager: Optional[BaseTaskManager] = None, + check_dangling_tasks: bool = True, + idle_timeout_secs: Optional[float] = 300, + idle_timeout_frames: Tuple[Type[Frame], ...] = ( + BotSpeakingFrame, + LLMFullResponseEndFrame, + ), + cancel_on_idle_timeout: bool = True, ): - self._id: int = obj_id() - self._name: str = f"{self.__class__.__name__}#{obj_count(self)}" - + super().__init__() self._pipeline = pipeline self._clock = clock self._params = params + self._check_dangling_tasks = check_dangling_tasks + self._idle_timeout_secs = idle_timeout_secs + self._idle_timeout_frames = idle_timeout_frames + self._cancel_on_idle_timeout = cancel_on_idle_timeout + if self._params.observers: + import warnings + + with warnings.catch_warnings(): + warnings.simplefilter("always") + warnings.warn( + "Field 'observers' is deprecated, use the 'observers' parameter instead.", + DeprecationWarning, + ) + observers = self._params.observers self._finished = False # This queue receives frames coming from the pipeline upstream. @@ -112,33 +205,69 @@ class PipelineTask(BaseTask): # This is the heartbeat queue. When a heartbeat frame is received in the # down queue we add it to the heartbeat queue for processing. self._heartbeat_queue = asyncio.Queue() - # This event is used to indicate an EndFrame has been received in the - # down queue. - self._endframe_event = asyncio.Event() + # This is the idle queue. When frames are received downstream they are + # put in the queue. If no frame is received the pipeline is considered + # idle. + self._idle_queue = asyncio.Queue() + # This event is used to indicate a finalize frame (e.g. EndFrame, + # StopFrame) has been received in the down queue. + self._pipeline_end_event = asyncio.Event() + # This is a source processor that we connect to the provided + # pipeline. This source processor allows up to receive and react to + # upstream frames. self._source = PipelineTaskSource(self._up_queue) self._source.link(pipeline) + # This is a sink processor that we connect to the provided + # pipeline. This sink processor allows up to receive and react to + # downstream frames. self._sink = PipelineTaskSink(self._down_queue) pipeline.link(self._sink) - self._task_manager = TaskManager() + # This task maneger will handle all the asyncio tasks created by this + # PipelineTask and its frame processors. + self._task_manager = task_manager or TaskManager() - self._observer = TaskObserver(observers=params.observers, task_manager=self._task_manager) + # The task observer acts as a proxy to the provided observers. This way, + # we only need to pass a single observer (using the StartFrame) which + # then just acts as a proxy. + self._observer = TaskObserver(observers=observers, task_manager=self._task_manager) + + # These events can be used to check which frames make it to the source + # or sink processors. Instead of calling the event handlers for every + # frame the user needs to specify which events they are interested + # in. This is mainly for efficiency reason because each event handler + # creates a task and most likely you only care about one or two frame + # types. + self._reached_upstream_types: Tuple[Type[Frame], ...] = () + self._reached_downstream_types: Tuple[Type[Frame], ...] = () + self._register_event_handler("on_frame_reached_upstream") + self._register_event_handler("on_frame_reached_downstream") + self._register_event_handler("on_idle_timeout") @property - def id(self) -> int: - """Returns the unique indetifier for this task.""" - return self._id - - @property - def name(self) -> str: - """Returns the name of this task.""" - return self._name + def params(self) -> PipelineParams: + """Returns the pipeline parameters of this task.""" + return self._params def set_event_loop(self, loop: asyncio.AbstractEventLoop): self._task_manager.set_event_loop(loop) + def set_reached_upstream_filter(self, types: Tuple[Type[Frame], ...]): + """Sets which frames will be checked before calling the + on_frame_reached_upstream event handler. + + """ + self._reached_upstream_types = types + + def set_reached_downstream_filter(self, types: Tuple[Type[Frame], ...]): + """Sets which frames will be checked before calling the + on_frame_reached_downstream event handler. + + """ + self._reached_downstream_types = types + def has_finished(self) -> bool: """Indicates whether the tasks has finished. That is, all processors have stopped. @@ -155,9 +284,7 @@ class PipelineTask(BaseTask): await self.queue_frame(EndFrame()) async def cancel(self): - """ - Stops the running pipeline immediately. - """ + """Stops the running pipeline immediately.""" logger.debug(f"Canceling pipeline task {self}") # Make sure everything is cleaned up downstream. This is sent # out-of-band from the main streaming task which is what we want since @@ -167,14 +294,15 @@ class PipelineTask(BaseTask): await self._task_manager.cancel_task(self._process_push_task) async def run(self): - """ - Starts running the given pipeline. - """ + """Starts and manages the pipeline execution until completion or cancellation.""" if self.has_finished(): return + cleanup_pipeline = True try: push_task = await self._create_tasks() await self._task_manager.wait_for_task(push_task) + # We have already cleaned up the pipeline inside the task. + cleanup_pipeline = False except asyncio.CancelledError: # We are awaiting on the push task and it might be cancelled # (e.g. Ctrl-C). This means we will get a CancelledError here as @@ -182,19 +310,24 @@ class PipelineTask(BaseTask): # awaiting a task. pass await self._cancel_tasks() - await self._cleanup() - self._print_dangling_tasks() + await self._cleanup(cleanup_pipeline) + if self._check_dangling_tasks: + self._print_dangling_tasks() self._finished = True async def queue_frame(self, frame: Frame): - """ - Queue a frame to be pushed down the pipeline. + """Queue a single frame to be pushed down the pipeline. + + Args: + frame: The frame to be processed. """ await self._push_queue.put(frame) async def queue_frames(self, frames: Iterable[Frame] | AsyncIterable[Frame]): - """ - Queues multiple frames to be pushed down the pipeline. + """Queues multiple frames to be pushed down the pipeline. + + Args: + frames: An iterable or async iterable of frames to be processed. """ if isinstance(frames, AsyncIterable): async for frame in frames: @@ -227,19 +360,30 @@ class PipelineTask(BaseTask): self._heartbeat_monitor_handler(), f"{self}::_heartbeat_monitor_handler" ) + def _maybe_start_idle_task(self): + if self._idle_timeout_secs: + self._idle_monitor_task = self._task_manager.create_task( + self._idle_monitor_handler(), f"{self}::_idle_monitor_handler" + ) + async def _cancel_tasks(self): - await self._maybe_cancel_heartbeat_tasks() + await self._observer.stop() await self._task_manager.cancel_task(self._process_up_task) await self._task_manager.cancel_task(self._process_down_task) - await self._observer.stop() + await self._maybe_cancel_heartbeat_tasks() + await self._maybe_cancel_idle_task() async def _maybe_cancel_heartbeat_tasks(self): if self._params.enable_heartbeats: await self._task_manager.cancel_task(self._heartbeat_push_task) await self._task_manager.cancel_task(self._heartbeat_monitor_task) + async def _maybe_cancel_idle_task(self): + if self._idle_timeout_secs: + await self._task_manager.cancel_task(self._idle_monitor_task) + def _initial_metrics_frame(self) -> MetricsFrame: processors = self._pipeline.processors_with_metrics() data = [] @@ -248,52 +392,59 @@ class PipelineTask(BaseTask): data.append(ProcessingMetricsData(processor=p.name, value=0.0)) return MetricsFrame(data=data) - async def _wait_for_endframe(self): - await self._endframe_event.wait() - self._endframe_event.clear() + async def _wait_for_pipeline_end(self): + await self._pipeline_end_event.wait() + self._pipeline_end_event.clear() - async def _cleanup(self): + async def _cleanup(self, cleanup_pipeline: bool): + # Cleanup base object. + await self.cleanup() + + # Cleanup pipeline processors. await self._source.cleanup() - await self._pipeline.cleanup() + if cleanup_pipeline: + await self._pipeline.cleanup() await self._sink.cleanup() async def _process_push_queue(self): """This is the task that runs the pipeline for the first time by sending a StartFrame and by pushing any other frames queued by the user. It runs - until the tasks is canceled or stopped (e.g. with an EndFrame). + until the tasks is cancelled or stopped (e.g. with an EndFrame). """ self._clock.start() self._maybe_start_heartbeat_tasks() + self._maybe_start_idle_task() start_frame = StartFrame( clock=self._clock, task_manager=self._task_manager, allow_interruptions=self._params.allow_interruptions, + audio_in_sample_rate=self._params.audio_in_sample_rate, + audio_out_sample_rate=self._params.audio_out_sample_rate, enable_metrics=self._params.enable_metrics, enable_usage_metrics=self._params.enable_usage_metrics, - report_only_initial_ttfb=self._params.report_only_initial_ttfb, observer=self._observer, + report_only_initial_ttfb=self._params.report_only_initial_ttfb, ) + start_frame.metadata = self._params.start_metadata await self._source.queue_frame(start_frame, FrameDirection.DOWNSTREAM) if self._params.enable_metrics and self._params.send_initial_empty_metrics: await self._source.queue_frame(self._initial_metrics_frame(), FrameDirection.DOWNSTREAM) running = True - should_cleanup = True + cleanup_pipeline = True while running: frame = await self._push_queue.get() await self._source.queue_frame(frame, FrameDirection.DOWNSTREAM) - if isinstance(frame, EndFrame): - await self._wait_for_endframe() - running = not isinstance(frame, (CancelFrame, EndFrame, StopTaskFrame)) - should_cleanup = not isinstance(frame, StopTaskFrame) + if isinstance(frame, (EndFrame, StopFrame)): + await self._wait_for_pipeline_end() + running = not isinstance(frame, (CancelFrame, EndFrame, StopFrame)) + cleanup_pipeline = not isinstance(frame, StopFrame) self._push_queue.task_done() - # Cleanup only if we need to. - if should_cleanup: - await self._cleanup() + await self._cleanup(cleanup_pipeline) async def _process_up_queue(self): """This is the task that processes frames coming upstream from the @@ -304,6 +455,10 @@ class PipelineTask(BaseTask): """ while True: frame = await self._up_queue.get() + + if isinstance(frame, self._reached_upstream_types): + await self._call_event_handler("on_frame_reached_upstream", frame) + if isinstance(frame, EndTaskFrame): # Tell the task we should end nicely. await self.queue_frame(EndFrame()) @@ -311,14 +466,17 @@ class PipelineTask(BaseTask): # Tell the task we should end right away. await self.queue_frame(CancelFrame()) elif isinstance(frame, StopTaskFrame): - await self.queue_frame(StopTaskFrame()) + # Tell the task we should stop nicely. + await self.queue_frame(StopFrame()) elif isinstance(frame, ErrorFrame): - logger.error(f"Error running app: {frame}") if frame.fatal: + logger.error(f"A fatal error occurred: {frame}") # Cancel all tasks downstream. await self.queue_frame(CancelFrame()) # Tell the task we should stop. await self.queue_frame(StopTaskFrame()) + else: + logger.warning(f"Something went wrong: {frame}") self._up_queue.task_done() async def _process_down_queue(self): @@ -330,16 +488,22 @@ class PipelineTask(BaseTask): """ while True: frame = await self._down_queue.get() - if isinstance(frame, EndFrame): - self._endframe_event.set() + + # Queue received frame to the idle queue so we can monitor idle + # pipelines. + await self._idle_queue.put(frame) + + if isinstance(frame, self._reached_downstream_types): + await self._call_event_handler("on_frame_reached_downstream", frame) + + if isinstance(frame, (EndFrame, StopFrame)): + self._pipeline_end_event.set() elif isinstance(frame, HeartbeatFrame): await self._heartbeat_queue.put(frame) self._down_queue.task_done() async def _heartbeat_push_handler(self): - """ - This tasks pushes a heartbeat frame every heartbeat period. - """ + """This tasks pushes a heartbeat frame every heartbeat period.""" while True: # Don't use `queue_frame()` because if an EndFrame is queued the # task will just stop waiting for the pipeline to finish not @@ -366,10 +530,49 @@ class PipelineTask(BaseTask): f"{self}: heartbeat frame not received for more than {wait_time} seconds" ) + async def _idle_monitor_handler(self): + """This tasks monitors activity in the pipeline. If no frames are + received (heartbeats don't count) the pipeline is considered idle. + + """ + running = True + last_frame_time = 0 + while running: + try: + frame = await asyncio.wait_for( + self._idle_queue.get(), timeout=self._idle_timeout_secs + ) + + if isinstance(frame, StartFrame) or isinstance(frame, self._idle_timeout_frames): + # If we find a StartFrame or one of the frames that prevents a + # time out we update the time. + last_frame_time = time.time() + else: + # If we find any other frame we check if the pipeline is + # idle by checking the last time we received one of the + # valid frames. + diff_time = time.time() - last_frame_time + if diff_time >= self._idle_timeout_secs: + running = await self._idle_timeout_detected() + + self._idle_queue.task_done() + except asyncio.TimeoutError: + running = await self._idle_timeout_detected() + + async def _idle_timeout_detected(self) -> bool: + """Logic for when the pipeline is idle. + + Returns: + bool: Whther the pipeline task is being cancelled or not. + """ + await self._call_event_handler("on_idle_timeout") + if self._cancel_on_idle_timeout: + logger.warning(f"Idle pipeline detected, cancelling pipeline task...") + await self.cancel() + return False + return True + def _print_dangling_tasks(self): tasks = [t.get_name() for t in self._task_manager.current_tasks()] if tasks: logger.warning(f"Dangling tasks detected: {tasks}") - - def __str__(self): - return self.name diff --git a/src/pipecat/pipeline/task_observer.py b/src/pipecat/pipeline/task_observer.py index abcbf513b..dd805032c 100644 --- a/src/pipecat/pipeline/task_observer.py +++ b/src/pipecat/pipeline/task_observer.py @@ -12,8 +12,7 @@ from attr import dataclass from pipecat.frames.frames import Frame from pipecat.observers.base_observer import BaseObserver from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -from pipecat.utils.asyncio import TaskManager -from pipecat.utils.utils import obj_count, obj_id +from pipecat.utils.asyncio import BaseTaskManager @dataclass @@ -55,21 +54,11 @@ class TaskObserver(BaseObserver): """ - def __init__(self, *, observers: List[BaseObserver] = [], task_manager: TaskManager): - self._id: int = obj_id() - self._name: str = f"{self.__class__.__name__}#{obj_count(self)}" + def __init__(self, *, observers: List[BaseObserver] = [], task_manager: BaseTaskManager): self._observers = observers self._task_manager = task_manager self._proxies: List[Proxy] = [] - @property - def id(self) -> int: - return self._id - - @property - def name(self) -> str: - return self._name - async def start(self): """Starts all proxy observer tasks.""" self._proxies = self._create_proxies(self._observers) @@ -100,7 +89,7 @@ class TaskObserver(BaseObserver): queue = asyncio.Queue() task = self._task_manager.create_task( self._proxy_task_handler(queue, observer), - f"{self}::{observer.__class__.__name__}::_proxy_task_handler", + f"TaskObserver::{observer.__class__.__name__}::_proxy_task_handler", ) proxy = Proxy(queue=queue, task=task, observer=observer) proxies.append(proxy) @@ -112,6 +101,3 @@ class TaskObserver(BaseObserver): await observer.on_push_frame( data.src, data.dst, data.frame, data.direction, data.timestamp ) - - def __str__(self): - return self.name diff --git a/src/pipecat/processors/aggregators/gated_openai_llm_context.py b/src/pipecat/processors/aggregators/gated_openai_llm_context.py index f3ae78121..9973e3d02 100644 --- a/src/pipecat/processors/aggregators/gated_openai_llm_context.py +++ b/src/pipecat/processors/aggregators/gated_openai_llm_context.py @@ -21,6 +21,7 @@ class GatedOpenAILLMContextAggregator(FrameProcessor): self._notifier = notifier self._start_open = start_open self._last_context_frame = None + self._gate_task = None async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) @@ -41,10 +42,13 @@ class GatedOpenAILLMContextAggregator(FrameProcessor): await self.push_frame(frame, direction) async def _start(self): - self._gate_task = self.create_task(self._gate_task_handler()) + if not self._gate_task: + self._gate_task = self.create_task(self._gate_task_handler()) async def _stop(self): - await self.cancel_task(self._gate_task) + if self._gate_task: + await self.cancel_task(self._gate_task) + self._gate_task = None async def _gate_task_handler(self): while True: diff --git a/src/pipecat/processors/aggregators/llm_response.py b/src/pipecat/processors/aggregators/llm_response.py index 4157166aa..af8bf1a2e 100644 --- a/src/pipecat/processors/aggregators/llm_response.py +++ b/src/pipecat/processors/aggregators/llm_response.py @@ -4,20 +4,37 @@ # SPDX-License-Identifier: BSD 2-Clause License # -from typing import List, Type +import asyncio +from abc import abstractmethod +from typing import Dict, List, Literal, Set + +from loguru import logger from pipecat.frames.frames import ( + BotStoppedSpeakingFrame, + CancelFrame, + EmulateUserStartedSpeakingFrame, + EmulateUserStoppedSpeakingFrame, + EndFrame, Frame, + FunctionCallCancelFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, InterimTranscriptionFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesAppendFrame, LLMMessagesFrame, LLMMessagesUpdateFrame, + LLMSetToolChoiceFrame, LLMSetToolsFrame, + LLMTextFrame, + OpenAILLMContextAssistantTimestampFrame, + StartFrame, StartInterruptionFrame, TextFrame, TranscriptionFrame, + UserImageRawFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) @@ -26,236 +43,152 @@ from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContextFrame, ) from pipecat.processors.frame_processor import FrameDirection, FrameProcessor - - -class LLMResponseAggregator(FrameProcessor): - def __init__( - self, - *, - messages: List[dict], - role: str, - start_frame, - end_frame, - accumulator_frame: Type[TextFrame], - interim_accumulator_frame: Type[TextFrame] | None = None, - handle_interruptions: bool = False, - expect_stripped_words: bool = True, # if True, need to add spaces between words - ): - super().__init__() - - self._messages = messages - self._role = role - self._start_frame = start_frame - self._end_frame = end_frame - self._accumulator_frame = accumulator_frame - self._interim_accumulator_frame = interim_accumulator_frame - self._handle_interruptions = handle_interruptions - self._expect_stripped_words = expect_stripped_words - - # Reset our accumulator state. - self._reset() - - @property - def messages(self): - return self._messages - - @property - def role(self): - return self._role - - # - # Frame processor - # - - # Use cases implemented: - # - # S: Start, E: End, T: Transcription, I: Interim, X: Text - # - # S E -> None - # S T E -> X - # S I T E -> X - # S I E T -> X - # S I E I T -> X - # S E T -> X - # S E I T -> X - # - # The following case would not be supported: - # - # S I E T1 I T2 -> X - # - # and T2 would be dropped. - - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - send_aggregation = False - - if isinstance(frame, self._start_frame): - self._aggregation = "" - self._aggregating = True - self._seen_start_frame = True - self._seen_end_frame = False - self._seen_interim_results = False - await self.push_frame(frame, direction) - elif isinstance(frame, self._end_frame): - self._seen_end_frame = True - self._seen_start_frame = False - - # We might have received the end frame but we might still be - # aggregating (i.e. we have seen interim results but not the final - # text). - self._aggregating = self._seen_interim_results or len(self._aggregation) == 0 - - # Send the aggregation if we are not aggregating anymore (i.e. no - # more interim results received). - send_aggregation = not self._aggregating - await self.push_frame(frame, direction) - elif isinstance(frame, self._accumulator_frame): - if self._aggregating: - if self._expect_stripped_words: - self._aggregation += f" {frame.text}" if self._aggregation else frame.text - else: - self._aggregation += frame.text - # We have recevied a complete sentence, so if we have seen the - # end frame and we were still aggregating, it means we should - # send the aggregation. - send_aggregation = self._seen_end_frame - - # We just got our final result, so let's reset interim results. - self._seen_interim_results = False - elif self._interim_accumulator_frame and isinstance(frame, self._interim_accumulator_frame): - self._seen_interim_results = True - elif self._handle_interruptions and isinstance(frame, StartInterruptionFrame): - await self._push_aggregation() - # Reset anyways - self._reset() - await self.push_frame(frame, direction) - elif isinstance(frame, LLMMessagesAppendFrame): - self._add_messages(frame.messages) - elif isinstance(frame, LLMMessagesUpdateFrame): - self._set_messages(frame.messages) - elif isinstance(frame, LLMSetToolsFrame): - self._set_tools(frame.tools) - else: - await self.push_frame(frame, direction) - - if send_aggregation: - await self._push_aggregation() - - async def _push_aggregation(self): - if len(self._aggregation) > 0: - self._messages.append({"role": self._role, "content": self._aggregation}) - - # Reset the aggregation. Reset it before pushing it down, otherwise - # if the tasks gets cancelled we won't be able to clear things up. - self._aggregation = "" - - frame = LLMMessagesFrame(self._messages) - await self.push_frame(frame) - - # TODO-CB: Types - def _add_messages(self, messages): - self._messages.extend(messages) - - def _set_messages(self, messages): - self._reset() - self._messages.clear() - self._messages.extend(messages) - - def _set_tools(self, tools): - # noop in the base class - pass - - def _reset(self): - self._aggregation = "" - self._aggregating = False - self._seen_start_frame = False - self._seen_end_frame = False - self._seen_interim_results = False - - -class LLMAssistantResponseAggregator(LLMResponseAggregator): - def __init__(self, messages: List[dict] = []): - super().__init__( - messages=messages, - role="assistant", - start_frame=LLMFullResponseStartFrame, - end_frame=LLMFullResponseEndFrame, - accumulator_frame=TextFrame, - handle_interruptions=True, - ) - - -class LLMUserResponseAggregator(LLMResponseAggregator): - def __init__(self, messages: List[dict] = []): - super().__init__( - messages=messages, - role="user", - start_frame=UserStartedSpeakingFrame, - end_frame=UserStoppedSpeakingFrame, - accumulator_frame=TranscriptionFrame, - interim_accumulator_frame=InterimTranscriptionFrame, - ) +from pipecat.utils.time import time_now_iso8601 class LLMFullResponseAggregator(FrameProcessor): - """This class aggregates Text frames until it receives a - LLMFullResponseEndFrame, then emits the concatenated text as - a single text frame. + """This is an LLM aggregator that aggregates a full LLM completion. It + aggregates LLM text frames (tokens) received between + `LLMFullResponseStartFrame` and `LLMFullResponseEndFrame`. Every full + completion is returned via the "on_completion" event handler: - given the following frames: + @aggregator.event_handler("on_completion") + async def on_completion( + aggregator: LLMFullResponseAggregator, + completion: str, + completed: bool, + ) - TextFrame("Hello,") - TextFrame(" world.") - TextFrame(" I am") - TextFrame(" an LLM.") - LLMFullResponseEndFrame()] - - this processor will yield nothing for the first 4 frames, then - - TextFrame("Hello, world. I am an LLM.") - LLMFullResponseEndFrame() - - when passed the last frame. - - >>> async def print_frames(aggregator, frame): - ... async for frame in aggregator.process_frame(frame): - ... if isinstance(frame, TextFrame): - ... print(frame.text) - ... else: - ... print(frame.__class__.__name__) - - >>> aggregator = LLMFullResponseAggregator() - >>> asyncio.run(print_frames(aggregator, TextFrame("Hello,"))) - >>> asyncio.run(print_frames(aggregator, TextFrame(" world."))) - >>> asyncio.run(print_frames(aggregator, TextFrame(" I am"))) - >>> asyncio.run(print_frames(aggregator, TextFrame(" an LLM."))) - >>> asyncio.run(print_frames(aggregator, LLMFullResponseEndFrame())) - Hello, world. I am an LLM. - LLMFullResponseEndFrame """ - def __init__(self): - super().__init__() + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._aggregation = "" + self._started = False + + self._register_event_handler("on_completion") async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) - if isinstance(frame, TextFrame): - self._aggregation += frame.text - elif isinstance(frame, LLMFullResponseEndFrame): - await self.push_frame(TextFrame(self._aggregation)) - await self.push_frame(frame) + if isinstance(frame, StartInterruptionFrame): + await self._call_event_handler("on_completion", self._aggregation, False) self._aggregation = "" - else: - await self.push_frame(frame, direction) + self._started = False + elif isinstance(frame, LLMFullResponseStartFrame): + await self._handle_llm_start(frame) + elif isinstance(frame, LLMFullResponseEndFrame): + await self._handle_llm_end(frame) + elif isinstance(frame, LLMTextFrame): + await self._handle_llm_text(frame) + + await self.push_frame(frame, direction) + + async def _handle_llm_start(self, _: LLMFullResponseStartFrame): + self._started = True + + async def _handle_llm_end(self, _: LLMFullResponseEndFrame): + await self._call_event_handler("on_completion", self._aggregation, True) + self._started = False + self._aggregation = "" + + async def _handle_llm_text(self, frame: TextFrame): + if not self._started: + return + self._aggregation += frame.text -class LLMContextAggregator(LLMResponseAggregator): - def __init__(self, *, context: OpenAILLMContext, **kwargs): +class BaseLLMResponseAggregator(FrameProcessor): + """This is the base class for all LLM response aggregators. These + aggregators process incoming frames and aggregate content until they are + ready to push the aggregation. In the case of a user, an aggregation might + be a full transcription received from the STT service. + + The LLM response aggregators also keep a store (e.g. a message list or an + LLM context) of the current conversation, that is, it stores the messages + said by the user or by the bot. + + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + @property + @abstractmethod + def messages(self) -> List[dict]: + """Returns the messages from the current conversation.""" + pass + + @property + @abstractmethod + def role(self) -> str: + """Returns the role (e.g. user, assistant...) for this aggregator.""" + pass + + @abstractmethod + def add_messages(self, messages): + """Add the given messages to the conversation.""" + pass + + @abstractmethod + def set_messages(self, messages): + """Reset the conversation with the given messages.""" + pass + + @abstractmethod + def set_tools(self, tools): + """Set LLM tools to be used in the current conversation.""" + pass + + @abstractmethod + def set_tool_choice(self, tool_choice): + """Set the tool choice. This should modify the LLM context.""" + pass + + @abstractmethod + def reset(self): + """Reset the internals of this aggregator. This should not modify the + internal messages.""" + pass + + @abstractmethod + async def handle_aggregation(self, aggregation: str): + """Adds the given aggregation to the aggregator. The aggregator can use + a simple list of message or a context. It doesn't not push any frames. + + """ + pass + + @abstractmethod + async def push_aggregation(self): + """Pushes the current aggregation. For example, iN the case of context + aggregation this might push a new context frame. + + """ + pass + + +class LLMContextResponseAggregator(BaseLLMResponseAggregator): + """This is a base LLM aggregator that uses an LLM context to store the + conversation. It pushes `OpenAILLMContextFrame` as an aggregation frame. + + """ + + def __init__(self, *, context: OpenAILLMContext, role: str, **kwargs): super().__init__(**kwargs) self._context = context + self._role = role + + self._aggregation = "" + + @property + def messages(self) -> List[dict]: + return self._context.get_messages() + + @property + def role(self) -> str: + return self._role @property def context(self): @@ -264,57 +197,397 @@ class LLMContextAggregator(LLMResponseAggregator): def get_context_frame(self) -> OpenAILLMContextFrame: return OpenAILLMContextFrame(context=self._context) - async def push_context_frame(self): + async def push_context_frame(self, direction: FrameDirection = FrameDirection.DOWNSTREAM): frame = self.get_context_frame() - await self.push_frame(frame) + await self.push_frame(frame, direction) - # TODO-CB: Types - def _add_messages(self, messages): + def add_messages(self, messages): self._context.add_messages(messages) - def _set_messages(self, messages): + def set_messages(self, messages): self._context.set_messages(messages) - def _set_tools(self, tools: List): + def set_tools(self, tools: List): self._context.set_tools(tools) - async def _push_aggregation(self): + def set_tool_choice(self, tool_choice: Literal["none", "auto", "required"] | dict): + self._context.set_tool_choice(tool_choice) + + def reset(self): + self._aggregation = "" + + +class LLMUserContextAggregator(LLMContextResponseAggregator): + """This is a user LLM aggregator that uses an LLM context to store the + conversation. It aggregates transcriptions from the STT service and it has + logic to handle multiple scenarios where transcriptions are received between + VAD events (`UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame`) or + even outside or no VAD events at all. + + """ + + def __init__( + self, + context: OpenAILLMContext, + aggregation_timeout: float = 1.0, + **kwargs, + ): + super().__init__(context=context, role="user", **kwargs) + self._aggregation_timeout = aggregation_timeout + + self._seen_interim_results = False + self._user_speaking = False + self._emulating_vad = False + self._waiting_for_aggregation = False + + self._aggregation_event = asyncio.Event() + self._aggregation_task = None + + def reset(self): + super().reset() + self._seen_interim_results = False + self._waiting_for_aggregation = False + + async def handle_aggregation(self, aggregation: str): + self._context.add_message({"role": self.role, "content": self._aggregation}) + + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + if isinstance(frame, StartFrame): + # Push StartFrame before start(), because we want StartFrame to be + # processed by every processor before any other frame is processed. + await self.push_frame(frame, direction) + await self._start(frame) + elif isinstance(frame, EndFrame): + # Push EndFrame before stop(), because stop() waits on the task to + # finish and the task finishes when EndFrame is processed. + await self.push_frame(frame, direction) + await self._stop(frame) + elif isinstance(frame, CancelFrame): + await self._cancel(frame) + await self.push_frame(frame, direction) + elif isinstance(frame, UserStartedSpeakingFrame): + await self._handle_user_started_speaking(frame) + await self.push_frame(frame, direction) + elif isinstance(frame, UserStoppedSpeakingFrame): + await self._handle_user_stopped_speaking(frame) + await self.push_frame(frame, direction) + elif isinstance(frame, TranscriptionFrame): + await self._handle_transcription(frame) + elif isinstance(frame, InterimTranscriptionFrame): + await self._handle_interim_transcription(frame) + elif isinstance(frame, LLMMessagesAppendFrame): + self.add_messages(frame.messages) + elif isinstance(frame, LLMMessagesUpdateFrame): + self.set_messages(frame.messages) + elif isinstance(frame, LLMSetToolsFrame): + self.set_tools(frame.tools) + elif isinstance(frame, LLMSetToolChoiceFrame): + self.set_tool_choice(frame.tool_choice) + else: + await self.push_frame(frame, direction) + + async def push_aggregation(self): if len(self._aggregation) > 0: - self._context.add_message({"role": self._role, "content": self._aggregation}) + await self.handle_aggregation(self._aggregation) # Reset the aggregation. Reset it before pushing it down, otherwise # if the tasks gets cancelled we won't be able to clear things up. - self._aggregation = "" + self.reset() frame = OpenAILLMContextFrame(self._context) await self.push_frame(frame) - # Reset our accumulator state. - self._reset() + async def _start(self, frame: StartFrame): + self._create_aggregation_task() + + async def _stop(self, frame: EndFrame): + await self._cancel_aggregation_task() + + async def _cancel(self, frame: CancelFrame): + await self._cancel_aggregation_task() + + async def _handle_user_started_speaking(self, _: UserStartedSpeakingFrame): + self._user_speaking = True + self._waiting_for_aggregation = True + + async def _handle_user_stopped_speaking(self, _: UserStoppedSpeakingFrame): + self._user_speaking = False + # We just stopped speaking. Let's see if there's some aggregation to + # push. If the last thing we saw is an interim transcription, let's wait + # pushing the aggregation as we will probably get a final transcription. + if not self._seen_interim_results: + await self.push_aggregation() + + async def _handle_transcription(self, frame: TranscriptionFrame): + text = frame.text + + # Make sure we really have some text. + if not text.strip(): + return + + self._aggregation += f" {text}" if self._aggregation else text + # We just got a final result, so let's reset interim results. + self._seen_interim_results = False + # Reset aggregation timer. + self._aggregation_event.set() + + async def _handle_interim_transcription(self, _: InterimTranscriptionFrame): + self._seen_interim_results = True + + def _create_aggregation_task(self): + if not self._aggregation_task: + self._aggregation_task = self.create_task(self._aggregation_task_handler()) + + async def _cancel_aggregation_task(self): + if self._aggregation_task: + await self.cancel_task(self._aggregation_task) + self._aggregation_task = None + + async def _aggregation_task_handler(self): + while True: + try: + await asyncio.wait_for(self._aggregation_event.wait(), self._aggregation_timeout) + await self._maybe_push_bot_interruption() + except asyncio.TimeoutError: + if not self._user_speaking: + await self.push_aggregation() + + # If we are emulating VAD we still need to send the user stopped + # speaking frame. + if self._emulating_vad: + await self.push_frame( + EmulateUserStoppedSpeakingFrame(), FrameDirection.UPSTREAM + ) + self._emulating_vad = False + finally: + self._aggregation_event.clear() + + async def _maybe_push_bot_interruption(self): + """If the user stopped speaking a while back and we got a transcription + frame we might want to interrupt the bot. + + """ + if not self._user_speaking and not self._waiting_for_aggregation: + # If we reach this case we received a transcription but VAD was not + # able to detect voice (e.g. when you whisper a short + # utterance). So, we need to emulate VAD (i.e. user start/stopped + # speaking). + await self.push_frame(EmulateUserStartedSpeakingFrame(), FrameDirection.UPSTREAM) + self._emulating_vad = True -class LLMAssistantContextAggregator(LLMContextAggregator): - def __init__(self, context: OpenAILLMContext, *, expect_stripped_words: bool = True): - super().__init__( - messages=[], - context=context, - role="assistant", - start_frame=LLMFullResponseStartFrame, - end_frame=LLMFullResponseEndFrame, - accumulator_frame=TextFrame, - handle_interruptions=True, - expect_stripped_words=expect_stripped_words, +class LLMAssistantContextAggregator(LLMContextResponseAggregator): + """This is an assistant LLM aggregator that uses an LLM context to store the + conversation. It aggregates text frames received between + `LLMFullResponseStartFrame` and `LLMFullResponseEndFrame`. + + """ + + def __init__(self, context: OpenAILLMContext, *, expect_stripped_words: bool = True, **kwargs): + super().__init__(context=context, role="assistant", **kwargs) + self._expect_stripped_words = expect_stripped_words + + self._started = 0 + self._function_calls_in_progress: Dict[str, FunctionCallInProgressFrame] = {} + self._context_updated_tasks: Set[asyncio.Task] = set() + + async def handle_aggregation(self, aggregation: str): + self._context.add_message({"role": "assistant", "content": aggregation}) + + async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + pass + + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + pass + + async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + pass + + async def handle_user_image_frame(self, frame: UserImageRawFrame): + pass + + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + if isinstance(frame, StartInterruptionFrame): + await self._handle_interruptions(frame) + await self.push_frame(frame, direction) + elif isinstance(frame, LLMFullResponseStartFrame): + await self._handle_llm_start(frame) + elif isinstance(frame, LLMFullResponseEndFrame): + await self._handle_llm_end(frame) + elif isinstance(frame, TextFrame): + await self._handle_text(frame) + elif isinstance(frame, LLMMessagesAppendFrame): + self.add_messages(frame.messages) + elif isinstance(frame, LLMMessagesUpdateFrame): + self.set_messages(frame.messages) + elif isinstance(frame, LLMSetToolsFrame): + self.set_tools(frame.tools) + elif isinstance(frame, LLMSetToolChoiceFrame): + self.set_tool_choice(frame.tool_choice) + elif isinstance(frame, FunctionCallInProgressFrame): + await self._handle_function_call_in_progress(frame) + elif isinstance(frame, FunctionCallResultFrame): + await self._handle_function_call_result(frame) + elif isinstance(frame, FunctionCallCancelFrame): + await self._handle_function_call_cancel(frame) + elif isinstance(frame, UserImageRawFrame) and frame.request and frame.request.tool_call_id: + await self._handle_user_image_frame(frame) + elif isinstance(frame, BotStoppedSpeakingFrame): + await self.push_aggregation() + else: + await self.push_frame(frame, direction) + + async def push_aggregation(self): + if not self._aggregation: + return + + aggregation = self._aggregation.strip() + self.reset() + + if aggregation: + await self.handle_aggregation(aggregation) + + # Push context frame + await self.push_context_frame() + + # Push timestamp frame with current time + timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601()) + await self.push_frame(timestamp_frame) + + async def _handle_interruptions(self, frame: StartInterruptionFrame): + await self.push_aggregation() + self._started = 0 + self.reset() + + async def _handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + logger.debug( + f"{self} FunctionCallInProgressFrame: [{frame.function_name}:{frame.tool_call_id}]" + ) + await self.handle_function_call_in_progress(frame) + self._function_calls_in_progress[frame.tool_call_id] = frame + + async def _handle_function_call_result(self, frame: FunctionCallResultFrame): + logger.debug( + f"{self} FunctionCallResultFrame: [{frame.function_name}:{frame.tool_call_id}]" + ) + if frame.tool_call_id not in self._function_calls_in_progress: + logger.warning( + f"FunctionCallResultFrame tool_call_id [{frame.tool_call_id}] is not running" + ) + return + + del self._function_calls_in_progress[frame.tool_call_id] + + properties = frame.properties + + await self.handle_function_call_result(frame) + + # Run inference if the function call result requires it. + if frame.result: + run_llm = False + + if properties and properties.run_llm is not None: + # If the tool call result has a run_llm property, use it + run_llm = properties.run_llm + else: + # Default behavior is to run the LLM if there are no function calls in progress + run_llm = not bool(self._function_calls_in_progress) + + if run_llm: + await self.push_context_frame(FrameDirection.UPSTREAM) + + # Call the `on_context_updated` callback once the function call result + # is added to the context. Also, run this in a separate task to make + # sure we don't block the pipeline. + if properties and properties.on_context_updated: + task_name = f"{frame.function_name}:{frame.tool_call_id}:on_context_updated" + task = self.create_task(properties.on_context_updated(), task_name) + self._context_updated_tasks.add(task) + task.add_done_callback(self._context_updated_task_finished) + + async def _handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + logger.debug( + f"{self} FunctionCallCancelFrame: [{frame.function_name}:{frame.tool_call_id}]" + ) + if frame.tool_call_id not in self._function_calls_in_progress: + return + + if self._function_calls_in_progress[frame.tool_call_id].cancel_on_interruption: + await self.handle_function_call_cancel(frame) + del self._function_calls_in_progress[frame.tool_call_id] + + async def _handle_user_image_frame(self, frame: UserImageRawFrame): + logger.debug( + f"{self} UserImageRawFrame: [{frame.request.function_name}:{frame.request.tool_call_id}]" ) + if frame.request.tool_call_id not in self._function_calls_in_progress: + logger.warning( + f"UserImageRawFrame tool_call_id [{frame.request.tool_call_id}] is not running" + ) + return -class LLMUserContextAggregator(LLMContextAggregator): - def __init__(self, context: OpenAILLMContext): - super().__init__( - messages=[], - context=context, - role="user", - start_frame=UserStartedSpeakingFrame, - end_frame=UserStoppedSpeakingFrame, - accumulator_frame=TranscriptionFrame, - interim_accumulator_frame=InterimTranscriptionFrame, - ) + del self._function_calls_in_progress[frame.request.tool_call_id] + + await self.handle_user_image_frame(frame) + await self.push_aggregation() + await self.push_context_frame(FrameDirection.UPSTREAM) + + async def _handle_llm_start(self, _: LLMFullResponseStartFrame): + self._started += 1 + + async def _handle_llm_end(self, _: LLMFullResponseEndFrame): + self._started -= 1 + await self.push_aggregation() + + async def _handle_text(self, frame: TextFrame): + if not self._started: + return + + if self._expect_stripped_words: + self._aggregation += f" {frame.text}" if self._aggregation else frame.text + else: + self._aggregation += frame.text + + def _context_updated_task_finished(self, task: asyncio.Task): + self._context_updated_tasks.discard(task) + # The task is finished so this should exit immediately. We need to do + # this because otherwise the task manager would report a dangling task + # if we don't remove it. + asyncio.run_coroutine_threadsafe(self.wait_for_task(task), self.get_event_loop()) + + +class LLMUserResponseAggregator(LLMUserContextAggregator): + def __init__(self, messages: List[dict] = [], **kwargs): + super().__init__(context=OpenAILLMContext(messages), **kwargs) + + async def push_aggregation(self): + if len(self._aggregation) > 0: + await self.handle_aggregation(self._aggregation) + + # Reset the aggregation. Reset it before pushing it down, otherwise + # if the tasks gets cancelled we won't be able to clear things up. + self.reset() + + frame = LLMMessagesFrame(self._context.messages) + await self.push_frame(frame) + + +class LLMAssistantResponseAggregator(LLMAssistantContextAggregator): + def __init__(self, messages: List[dict] = [], **kwargs): + super().__init__(context=OpenAILLMContext(messages), **kwargs) + + async def push_aggregation(self): + if len(self._aggregation) > 0: + await self.handle_aggregation(self._aggregation) + + # Reset the aggregation. Reset it before pushing it down, otherwise + # if the tasks gets cancelled we won't be able to clear things up. + self.reset() + + frame = LLMMessagesFrame(self._context.messages) + await self.push_frame(frame) diff --git a/src/pipecat/processors/aggregators/openai_llm_context.py b/src/pipecat/processors/aggregators/openai_llm_context.py index ae11a94d3..948e3e101 100644 --- a/src/pipecat/processors/aggregators/openai_llm_context.py +++ b/src/pipecat/processors/aggregators/openai_llm_context.py @@ -9,33 +9,21 @@ import copy import io import json from dataclasses import dataclass -from typing import Any, Awaitable, Callable, List, Optional +from typing import Any, List, Optional -from loguru import logger +from openai._types import NOT_GIVEN, NotGiven +from openai.types.chat import ( + ChatCompletionMessageParam, + ChatCompletionToolChoiceOptionParam, + ChatCompletionToolParam, +) from PIL import Image -from pipecat.frames.frames import ( - AudioRawFrame, - Frame, - FunctionCallInProgressFrame, - FunctionCallResultFrame, -) +from pipecat.adapters.base_llm_adapter import BaseLLMAdapter +from pipecat.adapters.schemas.tools_schema import ToolsSchema +from pipecat.frames.frames import AudioRawFrame, Frame from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -try: - from openai._types import NOT_GIVEN, NotGiven - from openai.types.chat import ( - ChatCompletionMessageParam, - ChatCompletionToolChoiceOptionParam, - ChatCompletionToolParam, - ) -except ModuleNotFoundError as e: - logger.error(f"Exception: {e}") - logger.error( - "In order to use OpenAI, you need to `pip install pipecat-ai[openai]`. Also, set `OPENAI_API_KEY` environment variable." - ) - raise Exception(f"Missing module: {e}") - # JSON custom encoder to handle bytes arrays so that we can log contexts # with images to the console. @@ -51,14 +39,20 @@ class CustomEncoder(json.JSONEncoder): class OpenAILLMContext: def __init__( self, - messages: List[ChatCompletionMessageParam] | None = None, - tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN, + messages: Optional[List[ChatCompletionMessageParam]] = None, + tools: List[ChatCompletionToolParam] | NotGiven | ToolsSchema = NOT_GIVEN, tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN, ): self._messages: List[ChatCompletionMessageParam] = messages if messages else [] self._tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = tool_choice - self._tools: List[ChatCompletionToolParam] | NotGiven = tools - self._user_image_request_context = {} + self._tools: List[ChatCompletionToolParam] | NotGiven | ToolsSchema = tools + self._llm_adapter: Optional[BaseLLMAdapter] = None + + def get_llm_adapter(self) -> Optional[BaseLLMAdapter]: + return self._llm_adapter + + def set_llm_adapter(self, llm_adapter: BaseLLMAdapter): + self._llm_adapter = llm_adapter @staticmethod def from_messages(messages: List[dict]) -> "OpenAILLMContext": @@ -75,7 +69,9 @@ class OpenAILLMContext: return self._messages @property - def tools(self) -> List[ChatCompletionToolParam] | NotGiven: + def tools(self) -> List[ChatCompletionToolParam] | NotGiven | List[Any]: + if self._llm_adapter: + return self._llm_adapter.from_standard_tools(self._tools) return self._tools @property @@ -160,8 +156,8 @@ class OpenAILLMContext: def set_tool_choice(self, tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven): self._tool_choice = tool_choice - def set_tools(self, tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN): - if tools != NOT_GIVEN and len(tools) == 0: + def set_tools(self, tools: List[ChatCompletionToolParam] | NotGiven | ToolsSchema = NOT_GIVEN): + if tools != NOT_GIVEN and isinstance(tools, list) and len(tools) == 0: tools = NOT_GIVEN self._tools = tools @@ -184,61 +180,6 @@ class OpenAILLMContext: # todo: implement for OpenAI models and others pass - async def call_function( - self, - f: Callable[ - [str, str, Any, FrameProcessor, "OpenAILLMContext", Callable[[Any], Awaitable[None]]], - Awaitable[None], - ], - *, - function_name: str, - tool_call_id: str, - arguments: str, - llm: FrameProcessor, - run_llm: bool = True, - ) -> None: - logger.info(f"Calling function {function_name} with arguments {arguments}") - # Push a SystemFrame downstream. This frame will let our assistant context aggregator - # know that we are in the middle of a function call. Some contexts/aggregators may - # not need this. But some definitely do (Anthropic, for example). - # Also push a SystemFrame upstream for use by other processors, like STTMuteFilter. - progress_frame_downstream = FunctionCallInProgressFrame( - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - ) - progress_frame_upstream = FunctionCallInProgressFrame( - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - ) - - # Push frame both downstream and upstream - await llm.push_frame(progress_frame_downstream, FrameDirection.DOWNSTREAM) - await llm.push_frame(progress_frame_upstream, FrameDirection.UPSTREAM) - - # Define a callback function that pushes a FunctionCallResultFrame upstream & downstream. - async def function_call_result_callback(result, *, properties=None): - result_frame_downstream = FunctionCallResultFrame( - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - result=result, - properties=properties, - ) - result_frame_upstream = FunctionCallResultFrame( - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - result=result, - properties=properties, - ) - - await llm.push_frame(result_frame_downstream, FrameDirection.DOWNSTREAM) - await llm.push_frame(result_frame_upstream, FrameDirection.UPSTREAM) - - await f(function_name, tool_call_id, arguments, llm, self, function_call_result_callback) - def create_wav_header(self, sample_rate, num_channels, bits_per_sample, data_size): # RIFF chunk descriptor header = bytearray() diff --git a/src/pipecat/processors/aggregators/user_response.py b/src/pipecat/processors/aggregators/user_response.py index 616a1c060..6998fe200 100644 --- a/src/pipecat/processors/aggregators/user_response.py +++ b/src/pipecat/processors/aggregators/user_response.py @@ -4,129 +4,15 @@ # SPDX-License-Identifier: BSD 2-Clause License # -from pipecat.frames.frames import ( - Frame, - InterimTranscriptionFrame, - StartInterruptionFrame, - TextFrame, - TranscriptionFrame, - UserStartedSpeakingFrame, - UserStoppedSpeakingFrame, -) -from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.frames.frames import TextFrame +from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator -class ResponseAggregator(FrameProcessor): - """This frame processor aggregates frames between a start and an end frame - into complete text frame sentences. +class UserResponseAggregator(LLMUserResponseAggregator): + def __init__(self, **kwargs): + super().__init__(**kwargs) - For example, frame input/output: - UserStartedSpeakingFrame() -> None - TranscriptionFrame("Hello,") -> None - TranscriptionFrame(" world.") -> None - UserStoppedSpeakingFrame() -> TextFrame("Hello world.") - - Doctest: FIXME to work with asyncio - >>> async def print_frames(aggregator, frame): - ... async for frame in aggregator.process_frame(frame): - ... if isinstance(frame, TextFrame): - ... print(frame.text) - - >>> aggregator = ResponseAggregator(start_frame = UserStartedSpeakingFrame, - ... end_frame=UserStoppedSpeakingFrame, - ... accumulator_frame=TranscriptionFrame, - ... pass_through=False) - >>> asyncio.run(print_frames(aggregator, UserStartedSpeakingFrame())) - >>> asyncio.run(print_frames(aggregator, TranscriptionFrame("Hello,", 1, 1))) - >>> asyncio.run(print_frames(aggregator, TranscriptionFrame("world.", 1, 2))) - >>> asyncio.run(print_frames(aggregator, UserStoppedSpeakingFrame())) - Hello, world. - - """ - - def __init__( - self, - *, - start_frame, - end_frame, - accumulator_frame: TextFrame, - interim_accumulator_frame: TextFrame | None = None, - ): - super().__init__() - - self._start_frame = start_frame - self._end_frame = end_frame - self._accumulator_frame = accumulator_frame - self._interim_accumulator_frame = interim_accumulator_frame - - # Reset our accumulator state. - self._reset() - - # - # Frame processor - # - - # Use cases implemented: - # - # S: Start, E: End, T: Transcription, I: Interim, X: Text - # - # S E -> None - # S T E -> X - # S I T E -> X - # S I E T -> X - # S I E I T -> X - # S E T -> X - # S E I T -> X - # - # The following case would not be supported: - # - # S I E T1 I T2 -> X - # - # and T2 would be dropped. - - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - send_aggregation = False - - if isinstance(frame, self._start_frame): - self._aggregating = True - self._seen_start_frame = True - self._seen_end_frame = False - self._seen_interim_results = False - await self.push_frame(frame, direction) - elif isinstance(frame, self._end_frame): - self._seen_end_frame = True - self._seen_start_frame = False - - # We might have received the end frame but we might still be - # aggregating (i.e. we have seen interim results but not the final - # text). - self._aggregating = self._seen_interim_results or len(self._aggregation) == 0 - - # Send the aggregation if we are not aggregating anymore (i.e. no - # more interim results received). - send_aggregation = not self._aggregating - await self.push_frame(frame, direction) - elif isinstance(frame, self._accumulator_frame): - if self._aggregating: - self._aggregation += f" {frame.text}" - # We have recevied a complete sentence, so if we have seen the - # end frame and we were still aggregating, it means we should - # send the aggregation. - send_aggregation = self._seen_end_frame - - # We just got our final result, so let's reset interim results. - self._seen_interim_results = False - elif self._interim_accumulator_frame and isinstance(frame, self._interim_accumulator_frame): - self._seen_interim_results = True - else: - await self.push_frame(frame, direction) - - if send_aggregation: - await self._push_aggregation() - - async def _push_aggregation(self): + async def push_aggregation(self): if len(self._aggregation) > 0: frame = TextFrame(self._aggregation.strip()) @@ -137,21 +23,4 @@ class ResponseAggregator(FrameProcessor): await self.push_frame(frame) # Reset our accumulator state. - self._reset() - - def _reset(self): - self._aggregation = "" - self._aggregating = False - self._seen_start_frame = False - self._seen_end_frame = False - self._seen_interim_results = False - - -class UserResponseAggregator(ResponseAggregator): - def __init__(self): - super().__init__( - start_frame=UserStartedSpeakingFrame, - end_frame=UserStoppedSpeakingFrame, - accumulator_frame=TranscriptionFrame, - interim_accumulator_frame=InterimTranscriptionFrame, - ) + self.reset() diff --git a/src/pipecat/processors/audio/audio_buffer_processor.py b/src/pipecat/processors/audio/audio_buffer_processor.py index d56604330..c1b2eb810 100644 --- a/src/pipecat/processors/audio/audio_buffer_processor.py +++ b/src/pipecat/processors/audio/audio_buffer_processor.py @@ -5,44 +5,85 @@ # import time +from typing import Optional from pipecat.audio.utils import create_default_resampler, interleave_stereo_audio, mix_audio from pipecat.frames.frames import ( AudioRawFrame, + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, CancelFrame, EndFrame, Frame, InputAudioRawFrame, OutputAudioRawFrame, + StartFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, ) from pipecat.processors.frame_processor import FrameDirection, FrameProcessor class AudioBufferProcessor(FrameProcessor): - """This processor buffers audio raw frames (input and output). The mixed - audio can be obtained by calling `get_audio()` (if `buffer_size` is 0) or by - registering an "on_audio_data" event handler. The event handler will be - called every time `buffer_size` is reached. + """Processes and buffers audio frames from both input (user) and output (bot) sources. - You can provide the desired output `sample_rate` and incoming audio frames - will resampled to match it. Also, you can provide the number of channels, 1 - for mono and 2 for stereo. With mono audio user and bot audio will be mixed, - in the case of stereo the left channel will be used for the user's audio and - the right channel for the bot. + This processor manages audio buffering and synchronization, providing both merged and + track-specific audio access through event handlers. It supports various audio configurations + including sample rate conversion and mono/stereo output. + Events: + on_audio_data: Triggered when buffer_size is reached, providing merged audio + on_track_audio_data: Triggered when buffer_size is reached, providing separate tracks + on_user_turn_audio_data: Triggered when user turn has ended, providing that user turn's audio + on_bot_turn_audio_data: Triggered when bot turn has ended, providing that bot turn's audio + + Args: + sample_rate (Optional[int]): Desired output sample rate. If None, uses source rate + num_channels (int): Number of channels (1 for mono, 2 for stereo). Defaults to 1 + buffer_size (int): Size of buffer before triggering events. 0 for no buffering + user_continuous_stream (bool): Whether user audio is continuous or speech-only + enable_turn_audio (bool): Whether turn audio event handlers should be triggered + + Audio handling: + - Mono output (num_channels=1): User and bot audio are mixed + - Stereo output (num_channels=2): User audio on left, bot audio on right + - Automatic resampling of incoming audio to match desired sample_rate + - Silence insertion for non-continuous audio streams + - Buffer synchronization between user and bot audio + + Note: + When user_continuous_stream is False, the processor expects only speech + segments and will handle silence insertion between segments automatically. """ def __init__( - self, *, sample_rate: int = 24000, num_channels: int = 1, buffer_size: int = 0, **kwargs + self, + *, + sample_rate: Optional[int] = None, + num_channels: int = 1, + buffer_size: int = 0, + user_continuous_stream: bool = True, + enable_turn_audio: bool = False, + **kwargs, ): super().__init__(**kwargs) - self._sample_rate = sample_rate + self._init_sample_rate = sample_rate + self._sample_rate = 0 + self._audio_buffer_size_1s = 0 self._num_channels = num_channels self._buffer_size = buffer_size + self._user_continuous_stream = user_continuous_stream + self._enable_turn_audio = enable_turn_audio self._user_audio_buffer = bytearray() self._bot_audio_buffer = bytearray() + self._user_speaking = False + self._bot_speaking = False + self._user_turn_audio_buffer = bytearray() + self._bot_turn_audio_buffer = bytearray() + + # Intermittent (non continous user stream variables) self._last_user_frame_at = 0 self._last_bot_frame_at = 0 @@ -51,21 +92,47 @@ class AudioBufferProcessor(FrameProcessor): self._resampler = create_default_resampler() self._register_event_handler("on_audio_data") + self._register_event_handler("on_track_audio_data") + self._register_event_handler("on_user_turn_audio_data") + self._register_event_handler("on_bot_turn_audio_data") @property def sample_rate(self) -> int: + """Current sample rate of the audio processor. + + Returns: + int: The sample rate in Hz + """ return self._sample_rate @property def num_channels(self) -> int: + """Number of channels in the audio output. + + Returns: + int: Number of channels (1 for mono, 2 for stereo) + """ return self._num_channels def has_audio(self) -> bool: + """Check if both user and bot audio buffers contain data. + + Returns: + bool: True if both buffers contain audio data + """ return self._buffer_has_audio(self._user_audio_buffer) and self._buffer_has_audio( self._bot_audio_buffer ) def merge_audio_buffers(self) -> bytes: + """Merge user and bot audio buffers into a single audio stream. + + For mono output, audio is mixed. For stereo output, user audio is placed + on the left channel and bot audio on the right channel. + + Returns: + bytes: Mixed audio data + """ if self._num_channels == 1: return mix_audio(bytes(self._user_audio_buffer), bytes(self._bot_audio_buffer)) elif self._num_channels == 2: @@ -76,17 +143,103 @@ class AudioBufferProcessor(FrameProcessor): return b"" async def start_recording(self): + """Start recording audio from both user and bot. + + Initializes recording state and resets audio buffers. + """ self._recording = True self._reset_recording() async def stop_recording(self): + """Stop recording and trigger final audio data handlers. + + Calls audio handlers with any remaining buffered audio before stopping. + """ await self._call_on_audio_data_handler() self._recording = False async def process_frame(self, frame: Frame, direction: FrameDirection): + """Process incoming audio frames and manage audio buffers.""" await super().process_frame(frame, direction) - if self._recording and isinstance(frame, InputAudioRawFrame): + # Update output sample rate if necessary. + if isinstance(frame, StartFrame): + self._update_sample_rate(frame) + + if self._recording: + await self._process_recording(frame) + if self._enable_turn_audio: + await self._process_turn_recording(frame) + + if isinstance(frame, (CancelFrame, EndFrame)): + await self.stop_recording() + + await self.push_frame(frame, direction) + + def _update_sample_rate(self, frame: StartFrame): + self._sample_rate = self._init_sample_rate or frame.audio_out_sample_rate + self._audio_buffer_size_1s = self._sample_rate * 2 + + async def _process_recording(self, frame: Frame): + if self._user_continuous_stream: + await self._handle_continuous_stream(frame) + else: + await self._handle_intermittent_stream(frame) + + if self._buffer_size > 0 and len(self._user_audio_buffer) > self._buffer_size: + await self._call_on_audio_data_handler() + + async def _process_turn_recording(self, frame: Frame): + if isinstance(frame, UserStartedSpeakingFrame): + self._user_speaking = True + elif isinstance(frame, UserStoppedSpeakingFrame): + await self._call_event_handler( + "on_user_turn_audio_data", self._user_turn_audio_buffer, self.sample_rate, 1 + ) + self._user_speaking = False + self._user_turn_audio_buffer = bytearray() + elif isinstance(frame, BotStartedSpeakingFrame): + self._bot_speaking = True + elif isinstance(frame, BotStoppedSpeakingFrame): + await self._call_event_handler( + "on_bot_turn_audio_data", self._bot_turn_audio_buffer, self.sample_rate, 1 + ) + self._bot_speaking = False + self._bot_turn_audio_buffer = bytearray() + + if isinstance(frame, InputAudioRawFrame): + resampled = await self._resample_audio(frame) + self._user_turn_audio_buffer += resampled + # In the case of the user, we need to keep a short buffer of audio + # since VAD notification of when the user starts speaking comes + # later. + if ( + not self._user_speaking + and len(self._user_turn_audio_buffer) > self._audio_buffer_size_1s + ): + discarded = len(self._user_turn_audio_buffer) - self._audio_buffer_size_1s + self._user_turn_audio_buffer = self._user_turn_audio_buffer[discarded:] + elif self._bot_speaking and isinstance(frame, OutputAudioRawFrame): + resampled = await self._resample_audio(frame) + self._bot_turn_audio_buffer += resampled + + async def _handle_continuous_stream(self, frame: Frame): + if isinstance(frame, InputAudioRawFrame): + # Add user audio. + resampled = await self._resample_audio(frame) + self._user_audio_buffer.extend(resampled) + # Sync the bot's buffer to the user's buffer by adding silence if needed + if len(self._user_audio_buffer) > len(self._bot_audio_buffer): + silence_size = len(self._user_audio_buffer) - len(self._bot_audio_buffer) + silence = b"\x00" * silence_size + self._bot_audio_buffer.extend(silence) + elif self._recording and isinstance(frame, OutputAudioRawFrame): + # Add bot audio. + resampled = await self._resample_audio(frame) + self._bot_audio_buffer.extend(resampled) + + async def _handle_intermittent_stream(self, frame: Frame): + if isinstance(frame, InputAudioRawFrame): # Add silence if we need to. silence = self._compute_silence(self._last_user_frame_at) self._user_audio_buffer.extend(silence) @@ -105,22 +258,25 @@ class AudioBufferProcessor(FrameProcessor): # Save time of frame so we can compute silence. self._last_bot_frame_at = time.time() - if self._buffer_size > 0 and len(self._user_audio_buffer) > self._buffer_size: - await self._call_on_audio_data_handler() - - if isinstance(frame, (CancelFrame, EndFrame)): - await self.stop_recording() - - await self.push_frame(frame, direction) - async def _call_on_audio_data_handler(self): if not self.has_audio() or not self._recording: return + # Call original handler with merged audio merged_audio = self.merge_audio_buffers() await self._call_event_handler( "on_audio_data", merged_audio, self._sample_rate, self._num_channels ) + + # Call new handler with separate tracks + await self._call_event_handler( + "on_track_audio_data", + bytes(self._user_audio_buffer), + bytes(self._bot_audio_buffer), + self._sample_rate, + self._num_channels, + ) + self._reset_audio_buffers() def _buffer_has_audio(self, buffer: bytearray) -> bool: @@ -134,16 +290,17 @@ class AudioBufferProcessor(FrameProcessor): def _reset_audio_buffers(self): self._user_audio_buffer = bytearray() self._bot_audio_buffer = bytearray() + self._user_turn_audio_buffer = bytearray() + self._bot_turn_audio_buffer = bytearray() async def _resample_audio(self, frame: AudioRawFrame) -> bytes: return await self._resampler.resample(frame.audio, frame.sample_rate, self._sample_rate) def _compute_silence(self, from_time: float) -> bytes: quiet_time = time.time() - from_time - # We should get audio frames very frequently. We pick 100ms because - # that's big enough, but it could be even a bit slower since we usually - # do 20ms audio frames. - if from_time == 0 or quiet_time < 0.1: + # We should get audio frames very frequently. We introduce silence only + # if there's a big enough gap of 1s. + if from_time == 0 or quiet_time < 1.0: return b"" num_bytes = int(quiet_time * self._sample_rate) * 2 silence = b"\x00" * num_bytes diff --git a/src/pipecat/processors/audio/vad/silero.py b/src/pipecat/processors/audio/vad/silero.py index 2d6619a0c..edfe484ba 100644 --- a/src/pipecat/processors/audio/vad/silero.py +++ b/src/pipecat/processors/audio/vad/silero.py @@ -4,6 +4,8 @@ # SPDX-License-Identifier: BSD 2-Clause License # +from typing import Optional + from loguru import logger from pipecat.audio.vad.silero import SileroVADAnalyzer @@ -11,6 +13,7 @@ from pipecat.audio.vad.vad_analyzer import VADParams, VADState from pipecat.frames.frames import ( AudioRawFrame, Frame, + StartFrame, StartInterruptionFrame, StopInterruptionFrame, UserStartedSpeakingFrame, @@ -23,7 +26,7 @@ class SileroVAD(FrameProcessor): def __init__( self, *, - sample_rate: int = 16000, + sample_rate: Optional[int] = None, vad_params: VADParams = VADParams(), audio_passthrough: bool = False, ): @@ -41,6 +44,9 @@ class SileroVAD(FrameProcessor): async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) + if isinstance(frame, StartFrame): + self._vad_analyzer.set_sample_rate(frame.audio_in_sample_rate) + if isinstance(frame, AudioRawFrame): await self._analyze_audio(frame) if self._audio_passthrough: diff --git a/src/pipecat/processors/filters/stt_mute_filter.py b/src/pipecat/processors/filters/stt_mute_filter.py index 1a276df18..cce087f22 100644 --- a/src/pipecat/processors/filters/stt_mute_filter.py +++ b/src/pipecat/processors/filters/stt_mute_filter.py @@ -23,6 +23,8 @@ from pipecat.frames.frames import ( Frame, FunctionCallInProgressFrame, FunctionCallResultFrame, + InputAudioRawFrame, + StartFrame, StartInterruptionFrame, StopInterruptionFrame, STTMuteFrame, @@ -30,23 +32,24 @@ from pipecat.frames.frames import ( UserStoppedSpeakingFrame, ) from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -from pipecat.services.ai_services import STTService class STTMuteStrategy(Enum): """Strategies determining when STT should be muted. Attributes: - FIRST_SPEECH: Mute only during first bot speech + FIRST_SPEECH: Mute only during first detected bot speech + MUTE_UNTIL_FIRST_BOT_COMPLETE: Start muted and remain muted until first bot speech completes FUNCTION_CALL: Mute during function calls ALWAYS: Mute during all bot speech CUSTOM: Allow custom logic via callback """ - FIRST_SPEECH = "first_speech" # Mute only during first bot speech - FUNCTION_CALL = "function_call" # Mute during function calls - ALWAYS = "always" # Mute during all bot speech - CUSTOM = "custom" # Allow custom logic via callback + FIRST_SPEECH = "first_speech" + MUTE_UNTIL_FIRST_BOT_COMPLETE = "mute_until_first_bot_complete" + FUNCTION_CALL = "function_call" + ALWAYS = "always" + CUSTOM = "custom" @dataclass @@ -57,12 +60,25 @@ class STTMuteConfig: strategies: Set of muting strategies to apply should_mute_callback: Optional callback for custom muting logic. Only required when using STTMuteStrategy.CUSTOM + + Note: + MUTE_UNTIL_FIRST_BOT_COMPLETE and FIRST_SPEECH strategies should not be used together + as they handle the first bot speech differently. """ strategies: set[STTMuteStrategy] # Optional callback for custom muting logic should_mute_callback: Optional[Callable[["STTMuteFilter"], Awaitable[bool]]] = None + def __post_init__(self): + if ( + STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE in self.strategies + and STTMuteStrategy.FIRST_SPEECH in self.strategies + ): + raise ValueError( + "MUTE_UNTIL_FIRST_BOT_COMPLETE and FIRST_SPEECH strategies should not be used together" + ) + class STTMuteFilter(FrameProcessor): """A processor that handles STT muting and interruption control. @@ -71,28 +87,29 @@ class STTMuteFilter(FrameProcessor): feature. When STT is muted, interruptions are automatically disabled. Args: - stt_service: Service handling speech-to-text functionality config: Configuration specifying muting strategies + stt_service: STT service instance (deprecated, will be removed in future version) **kwargs: Additional arguments passed to parent class """ - def __init__(self, stt_service: STTService, config: STTMuteConfig, **kwargs): + def __init__(self, *, config: STTMuteConfig, **kwargs): super().__init__(**kwargs) - self._stt_service = stt_service self._config = config self._first_speech_handled = False self._bot_is_speaking = False self._function_call_in_progress = False + self._is_muted = False # Initialize as unmuted, will set state on StartFrame if needed @property def is_muted(self) -> bool: """Returns whether STT is currently muted.""" - return self._stt_service.is_muted + return self._is_muted async def _handle_mute_state(self, should_mute: bool): """Handles both STT muting and interruption control.""" if should_mute != self.is_muted: logger.debug(f"STT {'muting' if should_mute else 'unmuting'}") + self._is_muted = should_mute await self.push_frame(STTMuteFrame(mute=should_mute)) async def _should_mute(self) -> bool: @@ -112,6 +129,10 @@ class STTMuteFilter(FrameProcessor): self._first_speech_handled = True return True + case STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE: + if not self._first_speech_handled: + return True + case STTMuteStrategy.CUSTOM: if self._bot_is_speaking and self._config.should_mute_callback: should_mute = await self._config.should_mute_callback(self) @@ -121,25 +142,31 @@ class STTMuteFilter(FrameProcessor): return False async def process_frame(self, frame: Frame, direction: FrameDirection): + """Processes incoming frames and manages muting state.""" await super().process_frame(frame, direction) - """Processes incoming frames and manages muting state.""" - # Handle function call state changes - if isinstance(frame, FunctionCallInProgressFrame): + # Determine if we need to change mute state based on frame type + should_mute = None + + # Process frames to determine mute state + if isinstance(frame, StartFrame): + should_mute = await self._should_mute() + elif isinstance(frame, FunctionCallInProgressFrame): self._function_call_in_progress = True - await self._handle_mute_state(await self._should_mute()) + should_mute = await self._should_mute() elif isinstance(frame, FunctionCallResultFrame): self._function_call_in_progress = False - await self._handle_mute_state(await self._should_mute()) - # Handle bot speaking state changes + should_mute = await self._should_mute() elif isinstance(frame, BotStartedSpeakingFrame): self._bot_is_speaking = True - await self._handle_mute_state(await self._should_mute()) + should_mute = await self._should_mute() elif isinstance(frame, BotStoppedSpeakingFrame): self._bot_is_speaking = False - await self._handle_mute_state(await self._should_mute()) + if not self._first_speech_handled: + self._first_speech_handled = True + should_mute = await self._should_mute() - # Handle frame propagation + # Then push the original frame if isinstance( frame, ( @@ -147,13 +174,18 @@ class STTMuteFilter(FrameProcessor): StopInterruptionFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, + InputAudioRawFrame, ), ): # Only pass VAD-related frames when not muted if not self.is_muted: await self.push_frame(frame, direction) else: - logger.debug(f"{frame.__class__.__name__} suppressed - STT currently muted") + logger.trace(f"{frame.__class__.__name__} suppressed - STT currently muted") else: # Pass all other frames through await self.push_frame(frame, direction) + + # Finally handle mute state change if needed + if should_mute is not None and should_mute != self.is_muted: + await self._handle_mute_state(should_mute) diff --git a/src/pipecat/processors/frame_processor.py b/src/pipecat/processors/frame_processor.py index 260e53ef2..590698e7f 100644 --- a/src/pipecat/processors/frame_processor.py +++ b/src/pipecat/processors/frame_processor.py @@ -5,7 +5,6 @@ # import asyncio -import inspect from enum import Enum from typing import Awaitable, Callable, Coroutine, Optional @@ -23,8 +22,8 @@ from pipecat.frames.frames import ( ) from pipecat.metrics.metrics import LLMTokenUsage, MetricsData from pipecat.processors.metrics.frame_processor_metrics import FrameProcessorMetrics -from pipecat.utils.asyncio import TaskManager -from pipecat.utils.utils import obj_count, obj_id +from pipecat.utils.asyncio import BaseTaskManager +from pipecat.utils.base_object import BaseObject class FrameDirection(Enum): @@ -32,7 +31,7 @@ class FrameDirection(Enum): UPSTREAM = 2 -class FrameProcessor: +class FrameProcessor(BaseObject): def __init__( self, *, @@ -40,19 +39,16 @@ class FrameProcessor: metrics: Optional[FrameProcessorMetrics] = None, **kwargs, ): - self._id: int = obj_id() - self._name = name or f"{self.__class__.__name__}#{obj_count(self)}" + super().__init__(name=name) self._parent: Optional["FrameProcessor"] = None self._prev: Optional["FrameProcessor"] = None self._next: Optional["FrameProcessor"] = None - self._event_handlers: dict = {} - # Clock self._clock: Optional[BaseClock] = None # Task Manager - self._task_manager: Optional[TaskManager] = None + self._task_manager: Optional[BaseTaskManager] = None # Other properties self._allow_interruptions = False @@ -73,10 +69,11 @@ class FrameProcessor: self._metrics.set_processor_name(self.name) # Processors have an input queue. The input queue will be processed - # immediately (default) or it will block if `pause_processing_frames()` is - # called. To resume processing frames we need to call - # `resume_processing_frames()`. + # immediately (default) or it will block if `pause_processing_frames()` + # is called. To resume processing frames we need to call + # `resume_processing_frames()` which will wake up the event. self.__should_block_frames = False + self.__input_event = asyncio.Event() self.__input_frame_task: Optional[asyncio.Task] = None # Every processor in Pipecat should only output frames from a single @@ -150,10 +147,13 @@ class FrameProcessor: await self.stop_ttfb_metrics() await self.stop_processing_metrics() - def create_task(self, coroutine: Coroutine) -> asyncio.Task: + def create_task(self, coroutine: Coroutine, name: Optional[str] = None) -> asyncio.Task: if not self._task_manager: raise Exception(f"{self} TaskManager is still not initialized.") - name = f"{self}::{coroutine.cr_code.co_name}" + if name: + name = f"{self}::{name}" + else: + name = f"{self}::{coroutine.cr_code.co_name}" return self._task_manager.create_task(coroutine, name) async def cancel_task(self, task: asyncio.Task, timeout: Optional[float] = None): @@ -167,6 +167,7 @@ class FrameProcessor: await self._task_manager.wait_for_task(task, timeout) async def cleanup(self): + await super().cleanup() await self.__cancel_input_task() await self.__cancel_push_task() @@ -191,7 +192,7 @@ class FrameProcessor: raise Exception(f"{self} Clock is still not initialized.") return self._clock - def get_task_manager(self) -> TaskManager: + def get_task_manager(self) -> BaseTaskManager: if not self._task_manager: raise Exception(f"{self} TaskManager is still not initialized.") return self._task_manager @@ -239,38 +240,29 @@ class FrameProcessor: elif isinstance(frame, StopInterruptionFrame): self._should_report_ttfb = True elif isinstance(frame, CancelFrame): - self._cancelling = True + await self.__cancel(frame) async def push_error(self, error: ErrorFrame): await self.push_frame(error, FrameDirection.UPSTREAM) async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM): + if not self._check_ready(frame): + return + if isinstance(frame, SystemFrame): await self.__internal_push_frame(frame, direction) else: await self.__push_queue.put((frame, direction)) - def event_handler(self, event_name: str): - def decorator(handler): - self.add_event_handler(event_name, handler) - return handler - - return decorator - - def add_event_handler(self, event_name: str, handler): - if event_name not in self._event_handlers: - raise Exception(f"Event handler {event_name} not registered") - self._event_handlers[event_name].append(handler) - - def _register_event_handler(self, event_name: str): - if event_name in self._event_handlers: - raise Exception(f"Event handler {event_name} already registered") - self._event_handlers[event_name] = [] - async def __start(self, frame: StartFrame): self.__create_input_task() self.__create_push_task() + async def __cancel(self, frame: CancelFrame): + self._cancelling = True + await self.__cancel_input_task() + await self.__cancel_push_task() + # # Handle interruptions # @@ -319,11 +311,21 @@ class FrameProcessor: await self.push_error(ErrorFrame(str(e))) raise + def _check_ready(self, frame: Frame): + # If we are trying to push a frame but we still have no clock, it means + # we didn't process a StartFrame. + if not self._clock: + logger.error( + f"{self} not properly initialized, missing super().process_frame(frame, direction)?" + ) + return False + return True + def __create_input_task(self): if not self.__input_frame_task: self.__should_block_frames = False + self.__input_event.clear() self.__input_queue = asyncio.Queue() - self.__input_event = asyncio.Event() self.__input_frame_task = self.create_task(self.__input_frame_task_handler()) async def __cancel_input_task(self): @@ -366,16 +368,3 @@ class FrameProcessor: (frame, direction) = await self.__push_queue.get() await self.__internal_push_frame(frame, direction) self.__push_queue.task_done() - - async def _call_event_handler(self, event_name: str, *args, **kwargs): - try: - for handler in self._event_handlers[event_name]: - if inspect.iscoroutinefunction(handler): - await handler(self, *args, **kwargs) - else: - handler(self, *args, **kwargs) - except Exception as e: - logger.exception(f"Exception in event handler {event_name}: {e}") - - def __str__(self): - return self.name diff --git a/src/pipecat/processors/frameworks/langchain.py b/src/pipecat/processors/frameworks/langchain.py index 9553b954f..dee197a51 100644 --- a/src/pipecat/processors/frameworks/langchain.py +++ b/src/pipecat/processors/frameworks/langchain.py @@ -4,7 +4,7 @@ # SPDX-License-Identifier: BSD 2-Clause License # -from typing import Union +from typing import Optional, Union from loguru import logger @@ -30,7 +30,7 @@ class LangchainProcessor(FrameProcessor): super().__init__() self._chain = chain self._transcript_key = transcript_key - self._participant_id: str | None = None + self._participant_id: Optional[str] = None def set_participant_id(self, participant_id: str): self._participant_id = participant_id diff --git a/src/pipecat/processors/frameworks/rtvi.py b/src/pipecat/processors/frameworks/rtvi.py index 90495f8c7..eec07a29f 100644 --- a/src/pipecat/processors/frameworks/rtvi.py +++ b/src/pipecat/processors/frameworks/rtvi.py @@ -5,6 +5,7 @@ # import asyncio +import base64 from dataclasses import dataclass from typing import ( Any, @@ -28,9 +29,11 @@ from pipecat.frames.frames import ( CancelFrame, DataFrame, EndFrame, + EndTaskFrame, ErrorFrame, Frame, FunctionCallResultFrame, + InputAudioRawFrame, InterimTranscriptionFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, @@ -58,7 +61,9 @@ from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContextFrame, ) from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -from pipecat.services.google.frames import LLMSearchOrigin, LLMSearchResponseFrame +from pipecat.transports.base_input import BaseInputTransport +from pipecat.transports.base_output import BaseOutputTransport +from pipecat.transports.base_transport import BaseTransport from pipecat.utils.string import match_endofsentence RTVI_PROTOCOL_VERSION = "0.3.0" @@ -296,12 +301,6 @@ class RTVITextMessageData(BaseModel): text: str -class RTVISearchResponseMessageData(BaseModel): - search_result: Optional[str] - rendered_content: Optional[str] - origins: List[LLMSearchOrigin] - - class RTVIBotTranscriptionMessage(BaseModel): label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL type: Literal["bot-transcription"] = "bot-transcription" @@ -314,12 +313,6 @@ class RTVIBotLLMTextMessage(BaseModel): data: RTVITextMessageData -class RTVIBotLLMSearchResponseMessage(BaseModel): - label: Literal["rtvi-ai"] = "rtvi-ai" - type: Literal["bot-llm-search-response"] = "bot-llm-search-response" - data: RTVISearchResponseMessageData - - class RTVIBotTTSTextMessage(BaseModel): label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL type: Literal["bot-tts-text"] = "bot-tts-text" @@ -383,209 +376,35 @@ class RTVIMetricsMessage(BaseModel): data: Mapping[str, Any] -class RTVIFrameProcessor(FrameProcessor): - def __init__(self, direction: FrameDirection = FrameDirection.DOWNSTREAM, **kwargs): - super().__init__(**kwargs) - self._direction = direction - - async def _push_transport_message_urgent(self, model: BaseModel, exclude_none: bool = True): - frame = TransportMessageUrgentFrame(message=model.model_dump(exclude_none=exclude_none)) - await self.push_frame(frame, self._direction) +class RTVIServerMessage(BaseModel): + label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL + type: Literal["server-message"] = "server-message" + data: Any -class RTVISpeakingProcessor(RTVIFrameProcessor): - def __init__(self, **kwargs): - super().__init__(**kwargs) +@dataclass +class RTVIServerMessageFrame(SystemFrame): + """A frame for sending server messages to the client.""" - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) + data: Any - await self.push_frame(frame, direction) - - if isinstance(frame, (UserStartedSpeakingFrame, UserStoppedSpeakingFrame)): - await self._handle_interruptions(frame) - elif isinstance(frame, (BotStartedSpeakingFrame, BotStoppedSpeakingFrame)): - await self._handle_bot_speaking(frame) - - async def _handle_interruptions(self, frame: Frame): - message = None - if isinstance(frame, UserStartedSpeakingFrame): - message = RTVIUserStartedSpeakingMessage() - elif isinstance(frame, UserStoppedSpeakingFrame): - message = RTVIUserStoppedSpeakingMessage() - - if message: - await self._push_transport_message_urgent(message) - - async def _handle_bot_speaking(self, frame: Frame): - message = None - if isinstance(frame, BotStartedSpeakingFrame): - message = RTVIBotStartedSpeakingMessage() - elif isinstance(frame, BotStoppedSpeakingFrame): - message = RTVIBotStoppedSpeakingMessage() - - if message: - await self._push_transport_message_urgent(message) - - -class RTVIUserTranscriptionProcessor(RTVIFrameProcessor): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - await self.push_frame(frame, direction) - - if isinstance(frame, (TranscriptionFrame, InterimTranscriptionFrame)): - await self._handle_user_transcriptions(frame) - - async def _handle_user_transcriptions(self, frame: Frame): - message = None - if isinstance(frame, TranscriptionFrame): - message = RTVIUserTranscriptionMessage( - data=RTVIUserTranscriptionMessageData( - text=frame.text, user_id=frame.user_id, timestamp=frame.timestamp, final=True - ) - ) - elif isinstance(frame, InterimTranscriptionFrame): - message = RTVIUserTranscriptionMessage( - data=RTVIUserTranscriptionMessageData( - text=frame.text, user_id=frame.user_id, timestamp=frame.timestamp, final=False - ) - ) - - if message: - await self._push_transport_message_urgent(message) - - -class RTVIUserLLMTextProcessor(RTVIFrameProcessor): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - await self.push_frame(frame, direction) - - if isinstance(frame, OpenAILLMContextFrame): - await self._handle_context(frame) - - async def _handle_context(self, frame: OpenAILLMContextFrame): - messages = frame.context.messages - if len(messages) > 0: - message = messages[-1] - if message["role"] == "user": - content = message["content"] - if isinstance(content, list): - text = " ".join(item["text"] for item in content if "text" in item) - else: - text = content - rtvi_message = RTVIUserLLMTextMessage(data=RTVITextMessageData(text=text)) - await self._push_transport_message_urgent(rtvi_message) - - -class RTVIBotTranscriptionProcessor(RTVIFrameProcessor): - def __init__(self): - super().__init__() - self._aggregation = "" - - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - await self.push_frame(frame, direction) - - if isinstance(frame, UserStartedSpeakingFrame): - await self._push_aggregation() - elif isinstance(frame, LLMTextFrame): - self._aggregation += frame.text - if match_endofsentence(self._aggregation): - await self._push_aggregation() - - async def _push_aggregation(self): - if len(self._aggregation) > 0: - message = RTVIBotTranscriptionMessage(data=RTVITextMessageData(text=self._aggregation)) - await self._push_transport_message_urgent(message) - self._aggregation = "" - - -class RTVIBotLLMProcessor(RTVIFrameProcessor): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - await self.push_frame(frame, direction) - - if isinstance(frame, LLMFullResponseStartFrame): - await self._push_transport_message_urgent(RTVIBotLLMStartedMessage()) - elif isinstance(frame, LLMFullResponseEndFrame): - await self._push_transport_message_urgent(RTVIBotLLMStoppedMessage()) - elif isinstance(frame, LLMTextFrame): - message = RTVIBotLLMTextMessage(data=RTVITextMessageData(text=frame.text)) - await self._push_transport_message_urgent(message) - - -class RTVIBotTTSProcessor(RTVIFrameProcessor): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - await self.push_frame(frame, direction) - - if isinstance(frame, TTSStartedFrame): - await self._push_transport_message_urgent(RTVIBotTTSStartedMessage()) - elif isinstance(frame, TTSStoppedFrame): - await self._push_transport_message_urgent(RTVIBotTTSStoppedMessage()) - elif isinstance(frame, TTSTextFrame): - message = RTVIBotTTSTextMessage(data=RTVITextMessageData(text=frame.text)) - await self._push_transport_message_urgent(message) - - -class RTVIMetricsProcessor(RTVIFrameProcessor): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - await self.push_frame(frame, direction) - - if isinstance(frame, MetricsFrame): - await self._handle_metrics(frame) - - async def _handle_metrics(self, frame: MetricsFrame): - metrics = {} - for d in frame.data: - if isinstance(d, TTFBMetricsData): - if "ttfb" not in metrics: - metrics["ttfb"] = [] - metrics["ttfb"].append(d.model_dump(exclude_none=True)) - elif isinstance(d, ProcessingMetricsData): - if "processing" not in metrics: - metrics["processing"] = [] - metrics["processing"].append(d.model_dump(exclude_none=True)) - elif isinstance(d, LLMUsageMetricsData): - if "tokens" not in metrics: - metrics["tokens"] = [] - metrics["tokens"].append(d.value.model_dump(exclude_none=True)) - elif isinstance(d, TTSUsageMetricsData): - if "characters" not in metrics: - metrics["characters"] = [] - metrics["characters"].append(d.model_dump(exclude_none=True)) - - message = RTVIMetricsMessage(data=metrics) - await self._push_transport_message_urgent(message) + def __str__(self): + return f"{self.name}(data: {self.data})" class RTVIObserver(BaseObserver): - """This is a pipeline frame observer that is used to send RTVI server - messages to clients. The observer does not handle incoming RTVI client - messages, which is done by the RTVIProcessor. + """Pipeline frame observer for RTVI server message handling. + This observer monitors pipeline frames and converts them into appropriate RTVI messages + for client communication. It handles various frame types including speech events, + transcriptions, LLM responses, and TTS events. + + Note: + This observer only handles outgoing messages. Incoming RTVI client messages + are handled by the RTVIProcessor. + + Args: + rtvi (FrameProcessor): The RTVI processor to push frames to. """ def __init__(self, rtvi: FrameProcessor): @@ -602,14 +421,28 @@ class RTVIObserver(BaseObserver): direction: FrameDirection, timestamp: int, ): + """Process a frame being pushed through the pipeline. + + Args: + src: Source processor pushing the frame + dst: Destination processor receiving the frame + frame: The frame being pushed + direction: Direction of frame flow in pipeline + timestamp: Time when frame was pushed + """ # If we have already seen this frame, let's skip it. if frame.id in self._frames_seen: return - self._frames_seen.add(frame.id) + + # This tells whether the frame is already processed. If false, we will try + # again the next time we see the frame. + mark_as_seen = True if isinstance(frame, (UserStartedSpeakingFrame, UserStoppedSpeakingFrame)): await self._handle_interruptions(frame) - elif isinstance(frame, (BotStartedSpeakingFrame, BotStoppedSpeakingFrame)): + elif isinstance(frame, (BotStartedSpeakingFrame, BotStoppedSpeakingFrame)) and ( + direction == FrameDirection.UPSTREAM + ): await self._handle_bot_speaking(frame) elif isinstance(frame, (TranscriptionFrame, InterimTranscriptionFrame)): await self._handle_user_transcriptions(frame) @@ -618,24 +451,37 @@ class RTVIObserver(BaseObserver): elif isinstance(frame, UserStartedSpeakingFrame): await self._push_bot_transcription() elif isinstance(frame, LLMFullResponseStartFrame): - await self._push_transport_message_urgent(RTVIBotLLMStartedMessage()) + await self.push_transport_message_urgent(RTVIBotLLMStartedMessage()) elif isinstance(frame, LLMFullResponseEndFrame): - await self._push_transport_message_urgent(RTVIBotLLMStoppedMessage()) + await self.push_transport_message_urgent(RTVIBotLLMStoppedMessage()) elif isinstance(frame, LLMTextFrame): await self._handle_llm_text_frame(frame) - elif isinstance(frame, LLMSearchResponseFrame): - await self._handle_llm_search_response_frame(frame) elif isinstance(frame, TTSStartedFrame): - await self._push_transport_message_urgent(RTVIBotTTSStartedMessage()) + await self.push_transport_message_urgent(RTVIBotTTSStartedMessage()) elif isinstance(frame, TTSStoppedFrame): - await self._push_transport_message_urgent(RTVIBotTTSStoppedMessage()) + await self.push_transport_message_urgent(RTVIBotTTSStoppedMessage()) elif isinstance(frame, TTSTextFrame): - message = RTVIBotTTSTextMessage(data=RTVITextMessageData(text=frame.text)) - await self._push_transport_message_urgent(message) + if isinstance(src, BaseOutputTransport): + message = RTVIBotTTSTextMessage(data=RTVITextMessageData(text=frame.text)) + await self.push_transport_message_urgent(message) + else: + mark_as_seen = False elif isinstance(frame, MetricsFrame): await self._handle_metrics(frame) + elif isinstance(frame, RTVIServerMessageFrame): + message = RTVIServerMessage(data=frame.data) + await self.push_transport_message_urgent(message) - async def _push_transport_message_urgent(self, model: BaseModel, exclude_none: bool = True): + if mark_as_seen: + self._frames_seen.add(frame.id) + + async def push_transport_message_urgent(self, model: BaseModel, exclude_none: bool = True): + """Push an urgent transport message to the RTVI processor. + + Args: + model: The message model to send + exclude_none: Whether to exclude None values from the model dump + """ frame = TransportMessageUrgentFrame(message=model.model_dump(exclude_none=exclude_none)) await self._rtvi.push_frame(frame) @@ -644,7 +490,7 @@ class RTVIObserver(BaseObserver): message = RTVIBotTranscriptionMessage( data=RTVITextMessageData(text=self._bot_transcription) ) - await self._push_transport_message_urgent(message) + await self.push_transport_message_urgent(message) self._bot_transcription = "" async def _handle_interruptions(self, frame: Frame): @@ -655,7 +501,7 @@ class RTVIObserver(BaseObserver): message = RTVIUserStoppedSpeakingMessage() if message: - await self._push_transport_message_urgent(message) + await self.push_transport_message_urgent(message) async def _handle_bot_speaking(self, frame: Frame): message = None @@ -665,26 +511,16 @@ class RTVIObserver(BaseObserver): message = RTVIBotStoppedSpeakingMessage() if message: - await self._push_transport_message_urgent(message) + await self.push_transport_message_urgent(message) async def _handle_llm_text_frame(self, frame: LLMTextFrame): message = RTVIBotLLMTextMessage(data=RTVITextMessageData(text=frame.text)) - await self._push_transport_message_urgent(message) + await self.push_transport_message_urgent(message) self._bot_transcription += frame.text if match_endofsentence(self._bot_transcription): await self._push_bot_transcription() - async def _handle_llm_search_response_frame(self, frame: LLMSearchResponseFrame): - message = RTVIBotLLMSearchResponseMessage( - data=RTVISearchResponseMessageData( - search_result=frame.search_result, - origins=frame.origins, - rendered_content=frame.rendered_content, - ) - ) - await self._push_transport_message_urgent(message) - async def _handle_user_transcriptions(self, frame: Frame): message = None if isinstance(frame, TranscriptionFrame): @@ -701,13 +537,26 @@ class RTVIObserver(BaseObserver): ) if message: - await self._push_transport_message_urgent(message) + await self.push_transport_message_urgent(message) async def _handle_context(self, frame: OpenAILLMContextFrame): + """Process LLM context frames to extract user messages for the RTVI client.""" try: messages = frame.context.messages - if len(messages) > 0: - message = messages[-1] + if not messages: + return + + message = messages[-1] + + # Handle Google LLM format (protobuf objects with attributes) + if hasattr(message, "role") and message.role == "user" and hasattr(message, "parts"): + text = "".join(part.text for part in message.parts if hasattr(part, "text")) + if text: + rtvi_message = RTVIUserLLMTextMessage(data=RTVITextMessageData(text=text)) + await self.push_transport_message_urgent(rtvi_message) + + # Handle OpenAI format (original implementation) + elif isinstance(message, dict): if message["role"] == "user": content = message["content"] if isinstance(content, list): @@ -715,8 +564,9 @@ class RTVIObserver(BaseObserver): else: text = content rtvi_message = RTVIUserLLMTextMessage(data=RTVITextMessageData(text=text)) - await self._push_transport_message_urgent(rtvi_message) - except TypeError as e: + await self.push_transport_message_urgent(rtvi_message) + + except Exception as e: logger.warning(f"Caught an error while trying to handle context: {e}") async def _handle_metrics(self, frame: MetricsFrame): @@ -740,7 +590,7 @@ class RTVIObserver(BaseObserver): metrics["characters"].append(d.model_dump(exclude_none=True)) message = RTVIMetricsMessage(data=metrics) - await self._push_transport_message_urgent(message) + await self.push_transport_message_urgent(message) class RTVIProcessor(FrameProcessor): @@ -748,12 +598,13 @@ class RTVIProcessor(FrameProcessor): self, *, config: RTVIConfig = RTVIConfig(config=[]), + transport: Optional[BaseTransport] = None, **kwargs, ): super().__init__(**kwargs) self._config = config - self._pipeline: FrameProcessor | None = None + self._pipeline: Optional[FrameProcessor] = None self._bot_ready = False self._client_ready = False @@ -773,8 +624,13 @@ class RTVIProcessor(FrameProcessor): self._register_event_handler("on_bot_started") self._register_event_handler("on_client_ready") - def observer(self) -> RTVIObserver: - return RTVIObserver(self) + self._input_transport = None + self._transport = transport + if self._transport: + input_transport = self._transport.input() + if isinstance(input_transport, BaseInputTransport): + self._input_transport = input_transport + self._input_transport.enable_audio_in_stream_on_start(False) def register_action(self, action: RTVIAction): id = self._action_id(action.service, action.action) @@ -863,8 +719,10 @@ class RTVIProcessor(FrameProcessor): await self._pipeline.cleanup() async def _start(self, frame: StartFrame): - self._action_task = self.create_task(self._action_task_handler()) - self._message_task = self.create_task(self._message_task_handler()) + if not self._action_task: + self._action_task = self.create_task(self._action_task_handler()) + if not self._message_task: + self._message_task = self.create_task(self._message_task_handler()) await self._call_event_handler("on_bot_started") async def _stop(self, frame: EndFrame): @@ -925,7 +783,7 @@ class RTVIProcessor(FrameProcessor): update_config = RTVIUpdateConfig.model_validate(message.data) await self._handle_update_config(message.id, update_config) case "disconnect-bot": - await self.push_frame(EndFrame()) + await self.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM) case "action": action = RTVIActionRun.model_validate(message.data) action_frame = RTVIActionFrame(message_id=message.id, rtvi_action_run=action) @@ -933,6 +791,8 @@ class RTVIProcessor(FrameProcessor): case "llm-function-call-result": data = RTVILLMFunctionCallResultData.model_validate(message.data) await self._handle_function_call_result(data) + case "raw-audio" | "raw-audio-batch": + await self._handle_audio_buffer(message.data) case _: await self._send_error_response(message.id, f"Unsupported type {message.type}") @@ -945,9 +805,34 @@ class RTVIProcessor(FrameProcessor): logger.warning(f"Exception processing message: {e}") async def _handle_client_ready(self, request_id: str): + logger.debug("Received client-ready") + if self._input_transport: + self._input_transport.start_audio_in_streaming() + self._client_ready_id = request_id await self.set_client_ready() + async def _handle_audio_buffer(self, data): + if not self._input_transport: + return + + # Extract audio batch ensuring it's a list + audio_list = data.get("base64AudioBatch") or [data.get("base64Audio")] + + try: + for base64_audio in filter(None, audio_list): # Filter out None values + pcm_bytes = base64.b64decode(base64_audio) + frame = InputAudioRawFrame( + audio=pcm_bytes, + sample_rate=data["sampleRate"], + num_channels=data["numChannels"], + ) + await self._input_transport.push_audio_frame(frame) + + except (KeyError, TypeError, ValueError) as e: + # Handle missing keys, decoding errors, and invalid types + logger.error(f"Error processing audio buffer: {e}") + async def _handle_describe_config(self, request_id: str): services = list(self._registered_services.values()) message = RTVIDescribeConfig(id=request_id, data=RTVIDescribeConfigData(config=services)) @@ -999,7 +884,7 @@ class RTVIProcessor(FrameProcessor): ) await self.push_frame(frame) - async def _handle_action(self, request_id: str | None, data: RTVIActionRun): + async def _handle_action(self, request_id: Optional[str], data: RTVIActionRun): action_id = self._action_id(data.service, data.action) if action_id not in self._registered_actions: await self._send_error_response(request_id, f"Action {action_id} not registered") diff --git a/src/pipecat/processors/gstreamer/pipeline_source.py b/src/pipecat/processors/gstreamer/pipeline_source.py index 125f46d76..12b9cef67 100644 --- a/src/pipecat/processors/gstreamer/pipeline_source.py +++ b/src/pipecat/processors/gstreamer/pipeline_source.py @@ -5,6 +5,7 @@ # import asyncio +from typing import Optional from loguru import logger from pydantic import BaseModel @@ -38,7 +39,7 @@ class GStreamerPipelineSource(FrameProcessor): class OutputParams(BaseModel): video_width: int = 1280 video_height: int = 720 - audio_sample_rate: int = 24000 + audio_sample_rate: Optional[int] = None audio_channels: int = 1 clock_sync: bool = True @@ -46,6 +47,7 @@ class GStreamerPipelineSource(FrameProcessor): super().__init__(**kwargs) self._out_params = out_params + self._sample_rate = 0 Gst.init() @@ -90,6 +92,7 @@ class GStreamerPipelineSource(FrameProcessor): await self.push_frame(frame, direction) async def _start(self, frame: StartFrame): + self._sample_rate = self._out_params.audio_sample_rate or frame.audio_out_sample_rate self._player.set_state(Gst.State.PLAYING) async def _stop(self, frame: EndFrame): @@ -122,7 +125,7 @@ class GStreamerPipelineSource(FrameProcessor): audioresample = Gst.ElementFactory.make("audioresample", None) audiocapsfilter = Gst.ElementFactory.make("capsfilter", None) audiocaps = Gst.Caps.from_string( - f"audio/x-raw,format=S16LE,rate={self._out_params.audio_sample_rate},channels={self._out_params.audio_channels},layout=interleaved" + f"audio/x-raw,format=S16LE,rate={self._sample_rate},channels={self._out_params.audio_channels},layout=interleaved" ) audiocapsfilter.set_property("caps", audiocaps) appsink_audio = Gst.ElementFactory.make("appsink", None) @@ -188,7 +191,7 @@ class GStreamerPipelineSource(FrameProcessor): (_, info) = buffer.map(Gst.MapFlags.READ) frame = OutputAudioRawFrame( audio=info.data, - sample_rate=self._out_params.audio_sample_rate, + sample_rate=self._sample_rate, num_channels=self._out_params.audio_channels, ) asyncio.run_coroutine_threadsafe(self.push_frame(frame), self.get_event_loop()) diff --git a/src/pipecat/processors/idle_frame_processor.py b/src/pipecat/processors/idle_frame_processor.py index 85ea215cf..ab98df771 100644 --- a/src/pipecat/processors/idle_frame_processor.py +++ b/src/pipecat/processors/idle_frame_processor.py @@ -30,6 +30,7 @@ class IdleFrameProcessor(FrameProcessor): self._callback = callback self._timeout = timeout self._types = types + self._idle_task = None async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) @@ -49,11 +50,13 @@ class IdleFrameProcessor(FrameProcessor): self._idle_event.set() async def cleanup(self): - await self.cancel_task(self._idle_task) + if self._idle_task: + await self.cancel_task(self._idle_task) def _create_idle_task(self): - self._idle_event = asyncio.Event() - self._idle_task = self.create_task(self._idle_task_handler()) + if not self._idle_task: + self._idle_event = asyncio.Event() + self._idle_task = self.create_task(self._idle_task_handler()) async def _idle_task_handler(self): while True: diff --git a/src/pipecat/processors/metrics/sentry.py b/src/pipecat/processors/metrics/sentry.py index 9c8adcac8..f3ee40ae0 100644 --- a/src/pipecat/processors/metrics/sentry.py +++ b/src/pipecat/processors/metrics/sentry.py @@ -4,19 +4,14 @@ # SPDX-License-Identifier: BSD 2-Clause License # -import time - from loguru import logger try: import sentry_sdk - - sentry_available = sentry_sdk.is_initialized() - if not sentry_available: - logger.warning("Sentry SDK not initialized. Sentry features will be disabled.") -except ImportError: - sentry_available = False - logger.warning("Sentry SDK not installed. Sentry features will be disabled.") +except ModuleNotFoundError as e: + logger.error(f"Exception: {e}") + logger.error("In order to use Sentry, you need to `pip install pipecat-ai[sentry]`.") + raise Exception(f"Missing module: {e}") from pipecat.processors.metrics.frame_processor_metrics import FrameProcessorMetrics @@ -24,41 +19,44 @@ from pipecat.processors.metrics.frame_processor_metrics import FrameProcessorMet class SentryMetrics(FrameProcessorMetrics): def __init__(self): super().__init__() - self._ttfb_metrics_span = None - self._processing_metrics_span = None + self._ttfb_metrics_tx = None + self._processing_metrics_tx = None + self._sentry_available = sentry_sdk.is_initialized() + if not self._sentry_available: + logger.warning("Sentry SDK not initialized. Sentry features will be disabled.") async def start_ttfb_metrics(self, report_only_initial_ttfb): - if self._should_report_ttfb: - self._start_ttfb_time = time.time() - if sentry_available: - self._ttfb_metrics_span = sentry_sdk.start_span( - op="ttfb", - description=f"TTFB for {self._processor_name()}", - start_timestamp=self._start_ttfb_time, - ) - logger.debug( - f"Sentry Span ID: {self._ttfb_metrics_span.span_id} Description: {self._ttfb_metrics_span.description} started." - ) - self._should_report_ttfb = not report_only_initial_ttfb + await super().start_ttfb_metrics(report_only_initial_ttfb) - async def stop_ttfb_metrics(self): - stop_time = time.time() - if sentry_available: - self._ttfb_metrics_span.finish(end_timestamp=stop_time) - - async def start_processing_metrics(self): - self._start_processing_time = time.time() - if sentry_available: - self._processing_metrics_span = sentry_sdk.start_span( - op="processing", - description=f"Processing for {self._processor_name()}", - start_timestamp=self._start_processing_time, + if self._should_report_ttfb and self._sentry_available: + self._ttfb_metrics_tx = sentry_sdk.start_transaction( + op="ttfb", + name=f"TTFB for {self._processor_name()}", ) logger.debug( - f"Sentry Span ID: {self._processing_metrics_span.span_id} Description: {self._processing_metrics_span.description} started." + f"Sentry transaction started (ID: {self._ttfb_metrics_tx.span_id} Name: {self._ttfb_metrics_tx.name})" + ) + + async def stop_ttfb_metrics(self): + await super().stop_ttfb_metrics() + + if self._sentry_available and self._ttfb_metrics_tx: + self._ttfb_metrics_tx.finish() + + async def start_processing_metrics(self): + await super().start_processing_metrics() + + if self._sentry_available: + self._processing_metrics_tx = sentry_sdk.start_transaction( + op="processing", + name=f"Processing for {self._processor_name()}", + ) + logger.debug( + f"Sentry transaction started (ID: {self._processing_metrics_tx.span_id} Name: {self._processing_metrics_tx.name})" ) async def stop_processing_metrics(self): - stop_time = time.time() - if sentry_available: - self._processing_metrics_span.finish(end_timestamp=stop_time) + await super().stop_processing_metrics() + + if self._sentry_available and self._processing_metrics_tx: + self._processing_metrics_tx.finish() diff --git a/src/pipecat/processors/transcript_processor.py b/src/pipecat/processors/transcript_processor.py index d31310f84..3eaff66ca 100644 --- a/src/pipecat/processors/transcript_processor.py +++ b/src/pipecat/processors/transcript_processor.py @@ -87,14 +87,65 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor): """Initialize processor with aggregation state.""" super().__init__(**kwargs) self._current_text_parts: List[str] = [] - self._aggregation_start_time: Optional[str] | None = None + self._aggregation_start_time: Optional[str] = None async def _emit_aggregated_text(self): - """Emit aggregated text as a transcript message.""" + """Aggregates and emits text fragments as a transcript message. + + This method uses a heuristic to automatically detect whether text fragments + use pre-spacing (spaces at the beginning of fragments) or not, and applies + the appropriate joining strategy. It handles fragments from different TTS + services with different formatting patterns. + + Examples: + Pre-spaced fragments (concatenated): + ``` + TTSTextFrame: ["Hello"] + TTSTextFrame: [" there"] + TTSTextFrame: ["!"] + TTSTextFrame: [" How"] + TTSTextFrame: ["'s"] + TTSTextFrame: [" it"] + TTSTextFrame: [" going"] + TTSTextFrame: ["?"] + ``` + Result: "Hello there! How's it going?" + + Word-by-word fragments (joined with spaces): + ``` + TTSTextFrame: ["Hello"] + TTSTextFrame: ["there!"] + TTSTextFrame: ["How"] + TTSTextFrame: ["is"] + TTSTextFrame: ["it"] + TTSTextFrame: ["going?"] + ``` + Result: "Hello there! How is it going?" + """ if self._current_text_parts and self._aggregation_start_time: - content = " ".join(self._current_text_parts).strip() + # Heuristic to detect pre-spaced fragments + uses_prespacing = False + if len(self._current_text_parts) > 1: + # Check if any fragment after the first one starts with whitespace + has_spaced_parts = any( + part and part[0].isspace() for part in self._current_text_parts[1:] + ) + if has_spaced_parts: + uses_prespacing = True + + # Apply appropriate joining method + if uses_prespacing: + # Pre-spaced fragments - just concatenate + content = "".join(self._current_text_parts) + else: + # Word-by-word fragments - join with spaces + content = " ".join(self._current_text_parts) + + # Clean up any excessive whitespace + content = content.strip() + if content: - logger.debug(f"Emitting aggregated assistant message: {content}") + logger.trace(f"Emitting aggregated assistant message: {content}") message = TranscriptionMessage( role="assistant", content=content, @@ -102,7 +153,7 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor): ) await self._emit_update([message]) else: - logger.debug("No content to emit after stripping whitespace") + logger.trace("No content to emit after stripping whitespace") # Reset aggregation state self._current_text_parts = [] diff --git a/src/pipecat/processors/user_idle_processor.py b/src/pipecat/processors/user_idle_processor.py index e65bd437f..e88df3540 100644 --- a/src/pipecat/processors/user_idle_processor.py +++ b/src/pipecat/processors/user_idle_processor.py @@ -102,7 +102,7 @@ class UserIdleProcessor(FrameProcessor): def _create_idle_task(self) -> None: """Creates the idle task if it hasn't been created yet.""" - if self._idle_task is None: + if not self._idle_task: self._idle_task = self.create_task(self._idle_task_handler()) @property @@ -112,7 +112,7 @@ class UserIdleProcessor(FrameProcessor): async def _stop(self) -> None: """Stops and cleans up the idle monitoring task.""" - if self._idle_task is not None: + if self._idle_task: await self.cancel_task(self._idle_task) self._idle_task = None diff --git a/src/pipecat/serializers/base_serializer.py b/src/pipecat/serializers/base_serializer.py index 8a0efc49b..c70b1408e 100644 --- a/src/pipecat/serializers/base_serializer.py +++ b/src/pipecat/serializers/base_serializer.py @@ -7,7 +7,7 @@ from abc import ABC, abstractmethod from enum import Enum -from pipecat.frames.frames import Frame +from pipecat.frames.frames import Frame, StartFrame class FrameSerializerType(Enum): @@ -21,6 +21,9 @@ class FrameSerializer(ABC): def type(self) -> FrameSerializerType: pass + async def setup(self, frame: StartFrame): + pass + @abstractmethod async def serialize(self, frame: Frame) -> str | bytes | None: pass diff --git a/src/pipecat/serializers/telnyx.py b/src/pipecat/serializers/telnyx.py index 79c6f42ff..cbf16a156 100644 --- a/src/pipecat/serializers/telnyx.py +++ b/src/pipecat/serializers/telnyx.py @@ -6,6 +6,7 @@ import base64 import json +from typing import Optional from pydantic import BaseModel @@ -22,6 +23,7 @@ from pipecat.frames.frames import ( InputAudioRawFrame, InputDTMFFrame, KeypadEntry, + StartFrame, StartInterruptionFrame, ) from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType @@ -29,8 +31,8 @@ from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializer class TelnyxFrameSerializer(FrameSerializer): class InputParams(BaseModel): - telnyx_sample_rate: int = 8000 - sample_rate: int = 16000 + telnyx_sample_rate: int = 8000 # Default Telnyx rate (8kHz) + sample_rate: Optional[int] = None # Pipeline input rate inbound_encoding: str = "PCMU" outbound_encoding: str = "PCMU" @@ -46,23 +48,30 @@ class TelnyxFrameSerializer(FrameSerializer): params.inbound_encoding = inbound_encoding self._params = params + self._telnyx_sample_rate = self._params.telnyx_sample_rate + self._sample_rate = 0 # Pipeline input rate + self._resampler = create_default_resampler() @property def type(self) -> FrameSerializerType: return FrameSerializerType.TEXT + async def setup(self, frame: StartFrame): + self._sample_rate = self._params.sample_rate or frame.audio_in_sample_rate + async def serialize(self, frame: Frame) -> str | bytes | None: if isinstance(frame, AudioRawFrame): data = frame.audio + # Output: Convert PCM at frame's rate to 8kHz encoded for Telnyx if self._params.inbound_encoding == "PCMU": serialized_data = await pcm_to_ulaw( - data, frame.sample_rate, self._params.telnyx_sample_rate, self._resampler + data, frame.sample_rate, self._telnyx_sample_rate, self._resampler ) elif self._params.inbound_encoding == "PCMA": serialized_data = await pcm_to_alaw( - data, frame.sample_rate, self._params.telnyx_sample_rate, self._resampler + data, frame.sample_rate, self._telnyx_sample_rate, self._resampler ) else: raise ValueError(f"Unsupported encoding: {self._params.inbound_encoding}") @@ -86,25 +95,26 @@ class TelnyxFrameSerializer(FrameSerializer): payload_base64 = message["media"]["payload"] payload = base64.b64decode(payload_base64) + # Input: Convert Telnyx's 8kHz encoded audio to PCM at pipeline input rate if self._params.outbound_encoding == "PCMU": deserialized_data = await ulaw_to_pcm( payload, - self._params.telnyx_sample_rate, - self._params.sample_rate, + self._telnyx_sample_rate, + self._sample_rate, self._resampler, ) elif self._params.outbound_encoding == "PCMA": deserialized_data = await alaw_to_pcm( payload, - self._params.telnyx_sample_rate, - self._params.sample_rate, + self._telnyx_sample_rate, + self._sample_rate, self._resampler, ) else: raise ValueError(f"Unsupported encoding: {self._params.outbound_encoding}") audio_frame = InputAudioRawFrame( - audio=deserialized_data, num_channels=1, sample_rate=self._params.sample_rate + audio=deserialized_data, num_channels=1, sample_rate=self._sample_rate ) return audio_frame elif message["event"] == "dtmf": diff --git a/src/pipecat/serializers/twilio.py b/src/pipecat/serializers/twilio.py index 7862fa0db..e08920132 100644 --- a/src/pipecat/serializers/twilio.py +++ b/src/pipecat/serializers/twilio.py @@ -6,6 +6,7 @@ import base64 import json +from typing import Optional from pydantic import BaseModel @@ -16,6 +17,7 @@ from pipecat.frames.frames import ( InputAudioRawFrame, InputDTMFFrame, KeypadEntry, + StartFrame, StartInterruptionFrame, TransportMessageFrame, TransportMessageUrgentFrame, @@ -25,19 +27,25 @@ from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializer class TwilioFrameSerializer(FrameSerializer): class InputParams(BaseModel): - twilio_sample_rate: int = 8000 - sample_rate: int = 16000 + twilio_sample_rate: int = 8000 # Default Twilio rate (8kHz) + sample_rate: Optional[int] = None # Pipeline input rate def __init__(self, stream_sid: str, params: InputParams = InputParams()): self._stream_sid = stream_sid self._params = params + self._twilio_sample_rate = self._params.twilio_sample_rate + self._sample_rate = 0 # Pipeline input rate + self._resampler = create_default_resampler() @property def type(self) -> FrameSerializerType: return FrameSerializerType.TEXT + async def setup(self, frame: StartFrame): + self._sample_rate = self._params.sample_rate or frame.audio_in_sample_rate + async def serialize(self, frame: Frame) -> str | bytes | None: if isinstance(frame, StartInterruptionFrame): answer = {"event": "clear", "streamSid": self._stream_sid} @@ -45,8 +53,9 @@ class TwilioFrameSerializer(FrameSerializer): elif isinstance(frame, AudioRawFrame): data = frame.audio + # Output: Convert PCM at frame's rate to 8kHz μ-law for Twilio serialized_data = await pcm_to_ulaw( - data, frame.sample_rate, self._params.twilio_sample_rate, self._resampler + data, frame.sample_rate, self._twilio_sample_rate, self._resampler ) payload = base64.b64encode(serialized_data).decode("utf-8") answer = { @@ -66,11 +75,12 @@ class TwilioFrameSerializer(FrameSerializer): payload_base64 = message["media"]["payload"] payload = base64.b64decode(payload_base64) + # Input: Convert Twilio's 8kHz μ-law to PCM at pipeline input rate deserialized_data = await ulaw_to_pcm( - payload, self._params.twilio_sample_rate, self._params.sample_rate, self._resampler + payload, self._twilio_sample_rate, self._sample_rate, self._resampler ) audio_frame = InputAudioRawFrame( - audio=deserialized_data, num_channels=1, sample_rate=self._params.sample_rate + audio=deserialized_data, num_channels=1, sample_rate=self._sample_rate ) return audio_frame elif message["event"] == "dtmf": diff --git a/src/pipecat/services/ai_services.py b/src/pipecat/services/ai_services.py index 5044c7d92..a78c268dd 100644 --- a/src/pipecat/services/ai_services.py +++ b/src/pipecat/services/ai_services.py @@ -8,17 +8,24 @@ import asyncio import io import wave from abc import abstractmethod -from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional, Tuple +from dataclasses import dataclass +from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional, Sequence, Set, Tuple, Type from loguru import logger -from pipecat.audio.utils import calculate_audio_volume, exp_smoothing +from pipecat.adapters.base_llm_adapter import BaseLLMAdapter +from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter from pipecat.frames.frames import ( AudioRawFrame, + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, CancelFrame, EndFrame, ErrorFrame, Frame, + FunctionCallCancelFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, InterimTranscriptionFrame, LLMFullResponseEndFrame, StartFrame, @@ -34,14 +41,18 @@ from pipecat.frames.frames import ( TTSTextFrame, TTSUpdateSettingsFrame, UserImageRequestFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, VisionImageRawFrame, ) from pipecat.metrics.metrics import MetricsData from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.services.websocket_service import WebsocketService from pipecat.transcriptions.language import Language -from pipecat.utils.string import match_endofsentence +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator from pipecat.utils.text.base_text_filter import BaseTextFilter +from pipecat.utils.text.simple_text_aggregator import SimpleTextAggregator from pipecat.utils.time import seconds_to_nanoseconds @@ -75,13 +86,13 @@ class AIService(FrameProcessor): ) for key, value in settings.items(): - print("Update request for:", key, value) + logger.debug("Update request for:", key, value) if key in self._settings: logger.info(f"Updating LLM setting {key} to: [{value}]") self._settings[key] = value elif key in SessionProperties.model_fields: - print("Attempting to update", key, value) + logger.debug("Attempting to update", key, value) try: from pipecat.services.openai_realtime_beta.events import ( @@ -131,32 +142,90 @@ class AIService(FrameProcessor): await self.push_frame(f) +@dataclass +class FunctionEntry: + function_name: Optional[str] + callback: Any # TODO(aleix): add proper typing. + cancel_on_interruption: bool + + class LLMService(AIService): """This class is a no-op but serves as a base class for LLM services.""" + # OpenAILLMAdapter is used as the default adapter since it aligns with most LLM implementations. + # However, subclasses should override this with a more specific adapter when necessary. + adapter_class: Type[BaseLLMAdapter] = OpenAILLMAdapter + def __init__(self, **kwargs): super().__init__(**kwargs) - self._callbacks = {} + self._functions = {} self._start_callbacks = {} + self._adapter = self.adapter_class() + self._function_call_tasks: Set[Tuple[asyncio.Task, str, str]] = set() - # TODO-CB: callback function type - def register_function(self, function_name: str | None, callback, start_callback=None): + self._register_event_handler("on_completion_timeout") + + def get_llm_adapter(self) -> BaseLLMAdapter: + return self._adapter + + def create_context_aggregator( + self, + context: OpenAILLMContext, + *, + user_kwargs: Mapping[str, Any] = {}, + assistant_kwargs: Mapping[str, Any] = {}, + ) -> Any: + pass + + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + if isinstance(frame, StartInterruptionFrame): + await self._handle_interruptions(frame) + + async def _handle_interruptions(self, frame: StartInterruptionFrame): + for function_name, entry in self._functions.items(): + if entry.cancel_on_interruption: + await self._cancel_function_call(function_name) + + def register_function( + self, + function_name: Optional[str], + callback: Any, + start_callback=None, + *, + cancel_on_interruption: bool = False, + ): # Registering a function with the function_name set to None will run that callback # for all functions - self._callbacks[function_name] = callback - # QUESTION FOR CB: maybe this isn't needed anymore? + self._functions[function_name] = FunctionEntry( + function_name=function_name, + callback=callback, + cancel_on_interruption=cancel_on_interruption, + ) + + # Start callbacks are now deprecated. if start_callback: + import warnings + + with warnings.catch_warnings(): + warnings.simplefilter("always") + warnings.warn( + "Parameter 'start_callback' is deprecated, just put your code on top of the actual function call instead.", + DeprecationWarning, + ) + self._start_callbacks[function_name] = start_callback - def unregister_function(self, function_name: str | None): - del self._callbacks[function_name] + def unregister_function(self, function_name: Optional[str]): + del self._functions[function_name] if self._start_callbacks[function_name]: del self._start_callbacks[function_name] def has_function(self, function_name: str): - if None in self._callbacks.keys(): + if None in self._functions.keys(): return True - return function_name in self._callbacks.keys() + return function_name in self._functions.keys() async def call_function( self, @@ -166,35 +235,144 @@ class LLMService(AIService): function_name: str, arguments: str, run_llm: bool = True, - ) -> None: - f = None - if function_name in self._callbacks.keys(): - f = self._callbacks[function_name] - elif None in self._callbacks.keys(): - f = self._callbacks[None] - else: - return None - await context.call_function( - f, - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - llm=self, - run_llm=run_llm, + ): + if not function_name in self._functions.keys() and not None in self._functions.keys(): + return + + task = self.create_task( + self._run_function_call(context, tool_call_id, function_name, arguments, run_llm) ) - # QUESTION FOR CB: maybe this isn't needed anymore? + self._function_call_tasks.add((task, tool_call_id, function_name)) + + task.add_done_callback(self._function_call_task_finished) + async def call_start_function(self, context: OpenAILLMContext, function_name: str): if function_name in self._start_callbacks.keys(): await self._start_callbacks[function_name](function_name, self, context) elif None in self._start_callbacks.keys(): return await self._start_callbacks[None](function_name, self, context) - async def request_image_frame(self, user_id: str, *, text_content: str | None = None): + async def request_image_frame( + self, + user_id: str, + *, + function_name: Optional[str] = None, + tool_call_id: Optional[str] = None, + text_content: Optional[str] = None, + ): await self.push_frame( - UserImageRequestFrame(user_id=user_id, context=text_content), FrameDirection.UPSTREAM + UserImageRequestFrame( + user_id=user_id, + function_name=function_name, + tool_call_id=tool_call_id, + context=text_content, + ), + FrameDirection.UPSTREAM, ) + async def _run_function_call( + self, + context: OpenAILLMContext, + tool_call_id: str, + function_name: str, + arguments: str, + run_llm: bool = True, + ): + if function_name in self._functions.keys(): + entry = self._functions[function_name] + elif None in self._functions.keys(): + entry = self._functions[None] + else: + return + + logger.debug( + f"{self} Calling function [{function_name}:{tool_call_id}] with arguments {arguments}" + ) + + # NOTE(aleix): This needs to be removed after we remove the deprecation. + await self.call_start_function(context, function_name) + + # Push a SystemFrame downstream. This frame will let our assistant context aggregator + # know that we are in the middle of a function call. Some contexts/aggregators may + # not need this. But some definitely do (Anthropic, for example). + # Also push a SystemFrame upstream for use by other processors, like STTMuteFilter. + progress_frame_downstream = FunctionCallInProgressFrame( + function_name=function_name, + tool_call_id=tool_call_id, + arguments=arguments, + cancel_on_interruption=entry.cancel_on_interruption, + ) + progress_frame_upstream = FunctionCallInProgressFrame( + function_name=function_name, + tool_call_id=tool_call_id, + arguments=arguments, + cancel_on_interruption=entry.cancel_on_interruption, + ) + + # Push frame both downstream and upstream + await self.push_frame(progress_frame_downstream, FrameDirection.DOWNSTREAM) + await self.push_frame(progress_frame_upstream, FrameDirection.UPSTREAM) + + # Define a callback function that pushes a FunctionCallResultFrame upstream & downstream. + async def function_call_result_callback(result, *, properties=None): + result_frame_downstream = FunctionCallResultFrame( + function_name=function_name, + tool_call_id=tool_call_id, + arguments=arguments, + result=result, + properties=properties, + ) + result_frame_upstream = FunctionCallResultFrame( + function_name=function_name, + tool_call_id=tool_call_id, + arguments=arguments, + result=result, + properties=properties, + ) + + await self.push_frame(result_frame_downstream, FrameDirection.DOWNSTREAM) + await self.push_frame(result_frame_upstream, FrameDirection.UPSTREAM) + + await entry.callback( + function_name, tool_call_id, arguments, self, context, function_call_result_callback + ) + + async def _cancel_function_call(self, function_name: str): + cancelled_tasks = set() + for task, tool_call_id, name in self._function_call_tasks: + if name == function_name: + # We remove the callback because we are going to cancel the task + # now, otherwise we will be removing it from the set while we + # are iterating. + task.remove_done_callback(self._function_call_task_finished) + + logger.debug(f"{self} Cancelling function call [{name}:{tool_call_id}]...") + + await self.cancel_task(task) + + frame = FunctionCallCancelFrame( + function_name=function_name, tool_call_id=tool_call_id + ) + await self.push_frame(frame) + + logger.debug(f"{self} Function call [{name}:{tool_call_id}] has been cancelled") + + cancelled_tasks.add(task) + + # Remove all cancelled tasks from our set. + for task in cancelled_tasks: + self._function_call_task_finished(task) + + def _function_call_task_finished(self, task: asyncio.Task): + tuple_to_remove = next((t for t in self._function_call_tasks if t[0] == task), None) + if tuple_to_remove: + self._function_call_tasks.discard(tuple_to_remove) + # The task is finished so this should exit immediately. We need to + # do this because otherwise the task manager would report a dangling + # task if we don't remove it. + asyncio.run_coroutine_threadsafe(self.wait_for_task(task), self.get_event_loop()) + class TTSService(AIService): def __init__( @@ -207,13 +385,19 @@ class TTSService(AIService): # if True, TTSService will push TTSStoppedFrames, otherwise subclass must do it push_stop_frames: bool = False, # if push_stop_frames is True, wait for this idle period before pushing TTSStoppedFrame - stop_frame_timeout_s: float = 1.0, + stop_frame_timeout_s: float = 2.0, # if True, TTSService will push silence audio frames after TTSStoppedFrame push_silence_after_stop: bool = False, # if push_silence_after_stop is True, send this amount of audio silence silence_time_s: float = 2.0, + # if True, we will pause processing frames while we are receiving audio + pause_frame_processing: bool = False, # TTS output sample rate - sample_rate: int = 24000, + sample_rate: Optional[int] = None, + # Text aggregator to aggregate incoming tokens and decide when to push to the TTS. + text_aggregator: Optional[BaseTextAggregator] = None, + # Text filter executed after text has been aggregated. + text_filters: Sequence[BaseTextFilter] = [], text_filter: Optional[BaseTextFilter] = None, **kwargs, ): @@ -224,15 +408,28 @@ class TTSService(AIService): self._stop_frame_timeout_s: float = stop_frame_timeout_s self._push_silence_after_stop: bool = push_silence_after_stop self._silence_time_s: float = silence_time_s - self._sample_rate: int = sample_rate + self._pause_frame_processing: bool = pause_frame_processing + self._init_sample_rate = sample_rate + self._sample_rate = 0 self._voice_id: str = "" self._settings: Dict[str, Any] = {} - self._text_filter: Optional[BaseTextFilter] = text_filter + self._text_aggregator: BaseTextAggregator = text_aggregator or SimpleTextAggregator() + self._text_filters: Sequence[BaseTextFilter] = text_filters + if text_filter: + import warnings + + with warnings.catch_warnings(): + warnings.simplefilter("always") + warnings.warn( + "Parameter 'text_filter' is deprecated, use 'text_filters' instead.", + DeprecationWarning, + ) + self._text_filters = [text_filter] self._stop_frame_task: Optional[asyncio.Task] = None self._stop_frame_queue: asyncio.Queue = asyncio.Queue() - self._current_sentence: str = "" + self._processing_text: bool = False @property def sample_rate(self) -> int: @@ -244,21 +441,24 @@ class TTSService(AIService): def set_voice(self, voice: str): self._voice_id = voice - @abstractmethod - async def flush_audio(self): - pass - - def language_to_service_language(self, language: Language) -> str | None: - return Language(language) - # Converts the text to audio. @abstractmethod async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: pass + def language_to_service_language(self, language: Language) -> Optional[str]: + return Language(language) + + async def update_setting(self, key: str, value: Any): + pass + + async def flush_audio(self): + pass + async def start(self, frame: StartFrame): await super().start(frame) - if self._push_stop_frames: + self._sample_rate = self._init_sample_rate or frame.audio_out_sample_rate + if self._push_stop_frames and not self._stop_frame_task: self._stop_frame_task = self.create_task(self._stop_frame_handler()) async def stop(self, frame: EndFrame): @@ -284,8 +484,9 @@ class TTSService(AIService): self.set_model_name(value) elif key == "voice": self.set_voice(value) - elif key == "text_filter" and self._text_filter: - self._text_filter.update_settings(value) + elif key == "text_filter": + for filter in self._text_filters: + filter.update_settings(value) else: logger.warning(f"Unknown setting for TTS service: {key}") @@ -294,6 +495,7 @@ class TTSService(AIService): async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) + if ( isinstance(frame, TextFrame) and not isinstance(frame, InterimTranscriptionFrame) @@ -302,9 +504,16 @@ class TTSService(AIService): await self._process_text_frame(frame) elif isinstance(frame, StartInterruptionFrame): await self._handle_interruption(frame, direction) + await self.push_frame(frame, direction) elif isinstance(frame, (LLMFullResponseEndFrame, EndFrame)): - sentence = self._current_sentence - self._current_sentence = "" + # We pause processing incoming frames if the LLM response included + # text (it might be that it's only a function calling response). We + # pause to avoid audio overlapping. + await self._maybe_pause_frame_processing() + + sentence = self._text_aggregator.text + self._text_aggregator.reset() + self._processing_text = False await self._push_tts_frames(sentence) if isinstance(frame, LLMFullResponseEndFrame): if self._push_text_frames: @@ -312,10 +521,19 @@ class TTSService(AIService): else: await self.push_frame(frame, direction) elif isinstance(frame, TTSSpeakFrame): + # Store if we were processing text or not so we can set it back. + processing_text = self._processing_text await self._push_tts_frames(frame.text) + # We pause processing incoming frames because we are sending data to + # the TTS. We pause to avoid audio overlapping. + await self._maybe_pause_frame_processing() await self.flush_audio() + self._processing_text = processing_text elif isinstance(frame, TTSUpdateSettingsFrame): await self._update_settings(frame.settings) + elif isinstance(frame, BotStoppedSpeakingFrame): + await self._maybe_resume_frame_processing() + await self.push_frame(frame, direction) else: await self.push_frame(frame, direction) @@ -341,37 +559,54 @@ class TTSService(AIService): await self._stop_frame_queue.put(frame) async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection): - self._current_sentence = "" - if self._text_filter: - self._text_filter.handle_interruption() - await self.push_frame(frame, direction) + self._processing_text = False + self._text_aggregator.handle_interruption() + for filter in self._text_filters: + filter.handle_interruption() + + async def _maybe_pause_frame_processing(self): + if self._processing_text and self._pause_frame_processing: + await self.pause_processing_frames() + + async def _maybe_resume_frame_processing(self): + if self._pause_frame_processing: + await self.resume_processing_frames() async def _process_text_frame(self, frame: TextFrame): - text: str | None = None + text: Optional[str] = None if not self._aggregate_sentences: text = frame.text else: - self._current_sentence += frame.text - eos_end_marker = match_endofsentence(self._current_sentence) - if eos_end_marker: - text = self._current_sentence[:eos_end_marker] - self._current_sentence = self._current_sentence[eos_end_marker:] + text = self._text_aggregator.aggregate(frame.text) if text: await self._push_tts_frames(text) async def _push_tts_frames(self, text: str): + # Remove leading newlines only + text = text.lstrip("\n") + # Don't send only whitespace. This causes problems for some TTS models. But also don't # strip all whitespace, as whitespace can influence prosody. if not text.strip(): return + # This is just a flag that indicates if we sent something to the TTS + # service. It will be cleared if we sent text because of a TTSSpeakFrame + # or when we received an LLMFullResponseEndFrame + self._processing_text = True + await self.start_processing_metrics() - if self._text_filter: - self._text_filter.reset_interruption() - text = self._text_filter.filter(text) + + # Process all filter. + for filter in self._text_filters: + filter.reset_interruption() + text = filter.filter(text) + await self.process_generator(self.run_tts(text)) + await self.stop_processing_metrics() + if self._push_text_frames: # We send the original text after the audio. This way, if we are # interrupted, the text is not added to the assistant context. @@ -395,6 +630,12 @@ class TTSService(AIService): class WordTTSService(TTSService): + """This is a base class for TTS services that support word timestamps. Word + timestamps are useful to synchronize audio with text of the spoken + words. This way only the spoken words are added to the conversation context. + + """ + def __init__(self, **kwargs): super().__init__(**kwargs) self._initial_word_timestamp = -1 @@ -414,7 +655,7 @@ class WordTTSService(TTSService): async def start(self, frame: StartFrame): await super().start(frame) - await self._create_words_task() + self._create_words_task() async def stop(self, frame: EndFrame): await super().stop(frame) @@ -434,8 +675,9 @@ class WordTTSService(TTSService): await super()._handle_interruption(frame, direction) self.reset_word_timestamps() - async def _create_words_task(self): - self._words_task = self.create_task(self._words_task_handler()) + def _create_words_task(self): + if not self._words_task: + self._words_task = self.create_task(self._words_task_handler()) async def _stop_words_task(self): if self._words_task: @@ -464,12 +706,250 @@ class WordTTSService(TTSService): self._words_queue.task_done() +class WebsocketTTSService(TTSService, WebsocketService): + """This is a base class for websocket-based TTS services. + + If an error occurs with the websocket, an "on_connection_error" event will + be triggered: + + @tts.event_handler("on_connection_error") + async def on_connection_error(tts: TTSService, error: str): + ... + + """ + + def __init__(self, *, reconnect_on_error: bool = True, **kwargs): + TTSService.__init__(self, **kwargs) + WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs) + self._register_event_handler("on_connection_error") + + async def _report_error(self, error: ErrorFrame): + await self._call_event_handler("on_connection_error", error.error) + await self.push_error(error) + + +class InterruptibleTTSService(WebsocketTTSService): + """This is a base class for websocket-based TTS services that don't support + word timestamps and that don't offer a way to correlate the generated audio + to the requested text. + + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + # Indicates if the bot is speaking. If the bot is not speaking we don't + # need to reconnect when the user speaks. If the bot is speaking and the + # user interrupts we need to reconnect. + self._bot_speaking = False + + async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection): + await super()._handle_interruption(frame, direction) + if self._bot_speaking: + await self._disconnect() + await self._connect() + + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + if isinstance(frame, BotStartedSpeakingFrame): + self._bot_speaking = True + elif isinstance(frame, BotStoppedSpeakingFrame): + self._bot_speaking = False + + +class WebsocketWordTTSService(WordTTSService, WebsocketService): + """This is a base class for websocket-based TTS services that support word + timestamps. + + If an error occurs with the websocket a "on_connection_error" event will be + triggered: + + @tts.event_handler("on_connection_error") + async def on_connection_error(tts: TTSService, error: str): + ... + + """ + + def __init__(self, *, reconnect_on_error: bool = True, **kwargs): + WordTTSService.__init__(self, **kwargs) + WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs) + self._register_event_handler("on_connection_error") + + async def _report_error(self, error: ErrorFrame): + await self._call_event_handler("on_connection_error", error.error) + await self.push_error(error) + + +class InterruptibleWordTTSService(WebsocketWordTTSService): + """This is a base class for websocket-based TTS services that support word + timestamps but don't offer a way to correlate the generated audio to the + requested text. + + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + # Indicates if the bot is speaking. If the bot is not speaking we don't + # need to reconnect when the user speaks. If the bot is speaking and the + # user interrupts we need to reconnect. + self._bot_speaking = False + + async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection): + await super()._handle_interruption(frame, direction) + if self._bot_speaking: + await self._disconnect() + await self._connect() + + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + if isinstance(frame, BotStartedSpeakingFrame): + self._bot_speaking = True + elif isinstance(frame, BotStoppedSpeakingFrame): + self._bot_speaking = False + + +class AudioContextWordTTSService(WebsocketWordTTSService): + """This is a base class for websocket-based TTS services that support word + timestamps and also allow correlating the generated audio with the requested + text. + + Each request could be multiple sentences long which are grouped by + context. For this to work, the TTS service needs to support handling + multiple requests at once (i.e. multiple simultaneous contexts). + + The audio received from the TTS will be played in context order. That is, if + we requested audio for a context "A" and then audio for context "B", the + audio from context ID "A" will be played first. + + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._contexts_queue = asyncio.Queue() + self._contexts: Dict[str, asyncio.Queue] = {} + self._audio_context_task = None + + async def create_audio_context(self, context_id: str): + """Create a new audio context.""" + await self._contexts_queue.put(context_id) + self._contexts[context_id] = asyncio.Queue() + logger.trace(f"{self} created audio context {context_id}") + + async def append_to_audio_context(self, context_id: str, frame: TTSAudioRawFrame): + """Append audio to an existing context.""" + if self.audio_context_available(context_id): + logger.trace(f"{self} appending audio {frame} to audio context {context_id}") + await self._contexts[context_id].put(frame) + else: + logger.warning(f"{self} unable to append audio to context {context_id}") + + async def remove_audio_context(self, context_id: str): + """Remove an existing audio context.""" + if self.audio_context_available(context_id): + # We just mark the audio context for deletion by appending + # None. Once we reach None while handling audio we know we can + # safely remove the context. + logger.trace(f"{self} marking audio context {context_id} for deletion") + await self._contexts[context_id].put(None) + else: + logger.warning(f"{self} unable to remove context {context_id}") + + def audio_context_available(self, context_id: str) -> bool: + """Checks whether the given audio context is registered.""" + return context_id in self._contexts + + async def start(self, frame: StartFrame): + await super().start(frame) + self._create_audio_context_task() + + async def stop(self, frame: EndFrame): + await super().stop(frame) + if self._audio_context_task: + # Indicate no more audio contexts are available. this will end the + # task cleanly after all contexts have been processed. + await self._contexts_queue.put(None) + await self.wait_for_task(self._audio_context_task) + self._audio_context_task = None + + async def cancel(self, frame: CancelFrame): + await super().cancel(frame) + await self._stop_audio_context_task() + + async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection): + await super()._handle_interruption(frame, direction) + await self._stop_audio_context_task() + self._create_audio_context_task() + + def _create_audio_context_task(self): + if not self._audio_context_task: + self._contexts_queue = asyncio.Queue() + self._contexts: Dict[str, asyncio.Queue] = {} + self._audio_context_task = self.create_task(self._audio_context_task_handler()) + + async def _stop_audio_context_task(self): + if self._audio_context_task: + await self.cancel_task(self._audio_context_task) + self._audio_context_task = None + + async def _audio_context_task_handler(self): + """In this task we process audio contexts in order.""" + running = True + while running: + context_id = await self._contexts_queue.get() + + if context_id: + # Process the audio context until the context doesn't have more + # audio available (i.e. we find None). + await self._handle_audio_context(context_id) + + # We just finished processing the context, so we can safely remove it. + del self._contexts[context_id] + + # Append some silence between sentences. + silence = b"\x00" * self.sample_rate + frame = TTSAudioRawFrame( + audio=silence, sample_rate=self.sample_rate, num_channels=1 + ) + await self.push_frame(frame) + else: + running = False + + self._contexts_queue.task_done() + + async def _handle_audio_context(self, context_id: str): + # If we don't receive any audio during this time, we consider the context finished. + AUDIO_CONTEXT_TIMEOUT = 3.0 + queue = self._contexts[context_id] + running = True + while running: + try: + frame = await asyncio.wait_for(queue.get(), timeout=AUDIO_CONTEXT_TIMEOUT) + if frame: + await self.push_frame(frame) + running = frame is not None + except asyncio.TimeoutError: + # We didn't get audio, so let's consider this context finished. + logger.trace(f"{self} time out on audio context {context_id}") + break + + class STTService(AIService): """STTService is a base class for speech-to-text services.""" - def __init__(self, audio_passthrough=False, **kwargs): + def __init__( + self, + audio_passthrough=False, + # STT input sample rate + sample_rate: Optional[int] = None, + **kwargs, + ): super().__init__(**kwargs) self._audio_passthrough = audio_passthrough + self._init_sample_rate = sample_rate + self._sample_rate = 0 self._settings: Dict[str, Any] = {} self._muted: bool = False @@ -478,11 +958,13 @@ class STTService(AIService): """Returns whether the STT service is currently muted.""" return self._muted - @abstractmethod + @property + def sample_rate(self) -> int: + return self._sample_rate + async def set_model(self, model: str): self.set_model_name(model) - @abstractmethod async def set_language(self, language: Language): pass @@ -491,6 +973,10 @@ class STTService(AIService): """Returns transcript as a string""" pass + async def start(self, frame: StartFrame): + await super().start(frame) + self._sample_rate = self._init_sample_rate or frame.audio_in_sample_rate + async def _update_settings(self, settings: Mapping[str, Any]): logger.info(f"Updating STT settings: {self._settings}") for key, value in settings.items(): @@ -504,9 +990,11 @@ class STTService(AIService): else: logger.warning(f"Unknown setting for STT service: {key}") - async def process_audio_frame(self, frame: AudioRawFrame): - if not self._muted: - await self.process_generator(self.run_stt(frame.audio)) + async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection): + if self._muted: + return + + await self.process_generator(self.run_stt(frame.audio)) async def process_frame(self, frame: Frame, direction: FrameDirection): """Processes a frame of audio data, either buffering or transcribing it.""" @@ -516,7 +1004,7 @@ class STTService(AIService): # In this service we accumulate audio internally and at the end we # push a TextFrame. We also push audio downstream in case someone # else needs it. - await self.process_audio_frame(frame) + await self.process_audio_frame(frame, direction) if self._audio_passthrough: await self.push_frame(frame, direction) elif isinstance(frame, STTUpdateSettingsFrame): @@ -529,74 +1017,64 @@ class STTService(AIService): class SegmentedSTTService(STTService): - """SegmentedSTTService is an STTService that will detect speech and will run - speech-to-text on speech segments only, instead of a continous stream. + """SegmentedSTTService is an STTService that uses VAD events to detect + speech and will run speech-to-text on speech segments only, instead of a + continous stream. Since it uses VAD it means that VAD needs to be enabled in + the pipeline. + + This service always keeps a small audio buffer to take into account that VAD + events are delayed from when the user speech really starts. """ - def __init__( - self, - *, - min_volume: float = 0.6, - max_silence_secs: float = 0.3, - max_buffer_secs: float = 1.5, - sample_rate: int = 24000, - num_channels: int = 1, - **kwargs, - ): - super().__init__(**kwargs) - self._min_volume = min_volume - self._max_silence_secs = max_silence_secs - self._max_buffer_secs = max_buffer_secs - self._sample_rate = sample_rate - self._num_channels = num_channels - (self._content, self._wave) = self._new_wave() - self._silence_num_frames = 0 - # Volume exponential smoothing - self._smoothing_factor = 0.2 - self._prev_volume = 0 + def __init__(self, *, sample_rate: Optional[int] = None, **kwargs): + super().__init__(sample_rate=sample_rate, **kwargs) + self._content = None + self._wave = None + self._audio_buffer = bytearray() + self._audio_buffer_size_1s = 0 + self._user_speaking = False - async def process_audio_frame(self, frame: AudioRawFrame): - # Try to filter out empty background noise - volume = self._get_smoothed_volume(frame) - if volume >= self._min_volume: - # If volume is high enough, write new data to wave file - self._wave.writeframes(frame.audio) - self._silence_num_frames = 0 - else: - self._silence_num_frames += frame.num_frames - self._prev_volume = volume + async def start(self, frame: StartFrame): + await super().start(frame) + self._audio_buffer_size_1s = self.sample_rate * 2 - # If buffer is not empty and we have enough data or there's been a long - # silence, transcribe the audio gathered so far. - silence_secs = self._silence_num_frames / self._sample_rate - buffer_secs = self._wave.getnframes() / self._sample_rate - if self._content.tell() > 0 and ( - buffer_secs > self._max_buffer_secs or silence_secs > self._max_silence_secs - ): - self._silence_num_frames = 0 - self._wave.close() - self._content.seek(0) - await self.process_generator(self.run_stt(self._content.read())) - (self._content, self._wave) = self._new_wave() + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) - async def stop(self, frame: EndFrame): - self._wave.close() + if isinstance(frame, UserStartedSpeakingFrame): + await self._handle_user_started_speaking(frame) + elif isinstance(frame, UserStoppedSpeakingFrame): + await self._handle_user_stopped_speaking(frame) - async def cancel(self, frame: CancelFrame): - self._wave.close() + async def _handle_user_started_speaking(self, frame: UserStartedSpeakingFrame): + self._user_speaking = True + + async def _handle_user_stopped_speaking(self, frame: UserStoppedSpeakingFrame): + self._user_speaking = False - def _new_wave(self): content = io.BytesIO() - ww = wave.open(content, "wb") - ww.setsampwidth(2) - ww.setnchannels(self._num_channels) - ww.setframerate(self._sample_rate) - return (content, ww) + wav = wave.open(content, "wb") + wav.setsampwidth(2) + wav.setnchannels(1) + wav.setframerate(self.sample_rate) + wav.writeframes(self._audio_buffer) + wav.close() + content.seek(0) - def _get_smoothed_volume(self, frame: AudioRawFrame) -> float: - volume = calculate_audio_volume(frame.audio, frame.sample_rate) - return exp_smoothing(volume, self._prev_volume, self._smoothing_factor) + await self.process_generator(self.run_stt(content.read())) + + # Start clean. + self._audio_buffer.clear() + + async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection): + # If the user is speaking the audio buffer will keep growin. + self._audio_buffer += frame.audio + + # If the user is not speaking we keep just a little bit of audio. + if not self._user_speaking and len(self._audio_buffer) > self._audio_buffer_size_1s: + discarded = len(self._audio_buffer) - self._audio_buffer_size_1s + self._audio_buffer = self._audio_buffer[discarded:] class ImageGenService(AIService): diff --git a/src/pipecat/services/anthropic.py b/src/pipecat/services/anthropic.py index 1590296af..3e369075a 100644 --- a/src/pipecat/services/anthropic.py +++ b/src/pipecat/services/anthropic.py @@ -4,34 +4,33 @@ # SPDX-License-Identifier: BSD 2-Clause License # +import asyncio import base64 import copy import io import json import re -from asyncio import CancelledError from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Mapping, Optional, Union +import httpx from loguru import logger from PIL import Image from pydantic import BaseModel, Field +from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter from pipecat.frames.frames import ( Frame, + FunctionCallCancelFrame, FunctionCallInProgressFrame, FunctionCallResultFrame, - FunctionCallResultProperties, LLMEnablePromptCachingFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesFrame, LLMTextFrame, LLMUpdateSettingsFrame, - OpenAILLMContextAssistantTimestampFrame, - StartInterruptionFrame, UserImageRawFrame, - UserImageRequestFrame, VisionImageRawFrame, ) from pipecat.metrics.metrics import LLMTokenUsage @@ -45,7 +44,6 @@ from pipecat.processors.aggregators.openai_llm_context import ( ) from pipecat.processors.frame_processor import FrameDirection from pipecat.services.ai_services import LLMService -from pipecat.utils.time import time_now_iso8601 try: from anthropic import NOT_GIVEN, AsyncAnthropic, NotGiven @@ -58,13 +56,6 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -# internal use only -- todo: refactor -@dataclass -class AnthropicImageMessageFrame(Frame): - user_image_raw_frame: UserImageRawFrame - text: Optional[str] = None - - @dataclass class AnthropicContextAggregatorPair: _user: "AnthropicUserContextAggregator" @@ -84,6 +75,9 @@ class AnthropicLLMService(LLMService): use `AsyncAnthropicBedrock` and `AsyncAnthropicVertex` clients """ + # Overriding the default adapter to use the Anthropic one. + adapter_class = AnthropicLLMAdapter + class InputParams(BaseModel): enable_prompt_caching_beta: Optional[bool] = False max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1) @@ -96,7 +90,7 @@ class AnthropicLLMService(LLMService): self, *, api_key: str, - model: str = "claude-3-5-sonnet-20241022", + model: str = "claude-3-7-sonnet-20250219", params: InputParams = InputParams(), client=None, **kwargs, @@ -122,14 +116,38 @@ class AnthropicLLMService(LLMService): def enable_prompt_caching_beta(self) -> bool: return self._enable_prompt_caching_beta - @staticmethod def create_context_aggregator( - context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True + self, + context: OpenAILLMContext, + *, + user_kwargs: Mapping[str, Any] = {}, + assistant_kwargs: Mapping[str, Any] = {}, ) -> AnthropicContextAggregatorPair: - user = AnthropicUserContextAggregator(context) - assistant = AnthropicAssistantContextAggregator( - user, expect_stripped_words=assistant_expect_stripped_words - ) + """Create an instance of AnthropicContextAggregatorPair from an + OpenAILLMContext. Constructor keyword arguments for both the user and + assistant aggregators can be provided. + + Args: + context (OpenAILLMContext): The LLM context. + user_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the user context aggregator constructor. Defaults + to an empty mapping. + assistant_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the assistant context aggregator + constructor. Defaults to an empty mapping. + + Returns: + AnthropicContextAggregatorPair: A pair of context aggregators, one + for the user and one for the assistant, encapsulated in an + AnthropicContextAggregatorPair. + + """ + context.set_llm_adapter(self.get_llm_adapter()) + + if isinstance(context, OpenAILLMContext): + context = AnthropicLLMContext.from_openai_context(context) + user = AnthropicUserContextAggregator(context, **user_kwargs) + assistant = AnthropicAssistantContextAggregator(context, **assistant_kwargs) return AnthropicContextAggregatorPair(_user=user, _assistant=assistant) async def _process_context(self, context: OpenAILLMContext): @@ -149,7 +167,7 @@ class AnthropicLLMService(LLMService): await self.start_processing_metrics() logger.debug( - f"Generating chat: {context.system} | {context.get_messages_for_logging()}" + f"{self}: Generating chat [{context.system}] | [{context.get_messages_for_logging()}]" ) messages = context.messages @@ -249,12 +267,14 @@ class AnthropicLLMService(LLMService): if total_input_tokens >= 1024: context.turns_above_cache_threshold += 1 - except CancelledError: + except asyncio.CancelledError: # If we're interrupted, we won't get a complete usage report. So set our flag to use the # token estimate. The reraise the exception so all the processors running in this task # also get cancelled. use_completion_tokens_estimate = True raise + except httpx.TimeoutException: + await self._call_event_handler("on_completion_timeout") except Exception as e: logger.exception(f"{self} exception: {e}") finally: @@ -326,9 +346,9 @@ class AnthropicLLMService(LLMService): class AnthropicLLMContext(OpenAILLMContext): def __init__( self, - messages: list[dict] | None = None, - tools: list[dict] | None = None, - tool_choice: dict | None = None, + messages: Optional[List[dict]] = None, + tools: Optional[List[dict]] = None, + tool_choice: Optional[dict] = None, *, system: Union[str, NotGiven] = NOT_GIVEN, ): @@ -357,6 +377,7 @@ class AnthropicLLMContext(OpenAILLMContext): tools=openai_context.tools, tool_choice=openai_context.tool_choice, ) + self.set_llm_adapter(openai_context.get_llm_adapter()) self._restructure_from_openai_messages() return self @@ -651,45 +672,7 @@ class AnthropicLLMContext(OpenAILLMContext): class AnthropicUserContextAggregator(LLMUserContextAggregator): - def __init__(self, context: OpenAILLMContext | AnthropicLLMContext): - super().__init__(context=context) - - if isinstance(context, OpenAILLMContext): - self._context = AnthropicLLMContext.from_openai_context(context) - - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - # Our parent method has already called push_frame(). So we can't interrupt the - # flow here and we don't need to call push_frame() ourselves. Possibly something - # to talk through (tagging @aleix). At some point we might need to refactor these - # context aggregators. - try: - if isinstance(frame, UserImageRequestFrame): - # The LLM sends a UserImageRequestFrame upstream. Cache any context provided with - # that frame so we can use it when we assemble the image message in the assistant - # context aggregator. - if frame.context: - if isinstance(frame.context, str): - self._context._user_image_request_context[frame.user_id] = frame.context - else: - logger.error( - f"Unexpected UserImageRequestFrame context type: {type(frame.context)}" - ) - del self._context._user_image_request_context[frame.user_id] - else: - if frame.user_id in self._context._user_image_request_context: - del self._context._user_image_request_context[frame.user_id] - elif isinstance(frame, UserImageRawFrame): - # Push a new AnthropicImageMessageFrame with the text context we cached - # downstream to be handled by our assistant context aggregator. This is - # necessary so that we add the message to the context in the right order. - text = self._context._user_image_request_context.get(frame.user_id) or "" - if text: - del self._context._user_image_request_context[frame.user_id] - frame = AnthropicImageMessageFrame(user_image_raw_frame=frame, text=text) - await self.push_frame(frame) - except Exception as e: - logger.error(f"Error processing frame: {e}") + pass # @@ -703,115 +686,64 @@ class AnthropicUserContextAggregator(LLMUserContextAggregator): class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator): - def __init__(self, user_context_aggregator: AnthropicUserContextAggregator, **kwargs): - super().__init__(context=user_context_aggregator._context, **kwargs) - self._user_context_aggregator = user_context_aggregator - self._function_call_in_progress = None - self._function_call_result = None - self._pending_image_frame_message = None + async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + assistant_message = {"role": "assistant", "content": []} + assistant_message["content"].append( + { + "type": "tool_use", + "id": frame.tool_call_id, + "name": frame.function_name, + "input": frame.arguments, + } + ) + self._context.add_message(assistant_message) + self._context.add_message( + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": frame.tool_call_id, + "content": "IN_PROGRESS", + } + ], + } + ) - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - # See note above about not calling push_frame() here. - if isinstance(frame, StartInterruptionFrame): - self._function_call_in_progress = None - self._function_call_finished = None - elif isinstance(frame, FunctionCallInProgressFrame): - self._function_call_in_progress = frame - elif isinstance(frame, FunctionCallResultFrame): - if ( - self._function_call_in_progress - and self._function_call_in_progress.tool_call_id == frame.tool_call_id - ): - self._function_call_in_progress = None - self._function_call_result = frame - await self._push_aggregation() - else: - logger.warning( - "FunctionCallResultFrame tool_call_id != InProgressFrame tool_call_id" - ) - self._function_call_in_progress = None - self._function_call_result = None - elif isinstance(frame, AnthropicImageMessageFrame): - self._pending_image_frame_message = frame - await self._push_aggregation() + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + if frame.result: + result = json.dumps(frame.result) + await self._update_function_call_result(frame.function_name, frame.tool_call_id, result) + else: + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "COMPLETED" + ) - async def _push_aggregation(self): - if not ( - self._aggregation or self._function_call_result or self._pending_image_frame_message - ): - return + async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "CANCELLED" + ) - run_llm = False - properties: Optional[FunctionCallResultProperties] = None + async def _update_function_call_result( + self, function_name: str, tool_call_id: str, result: Any + ): + for message in self._context.messages: + if message["role"] == "user": + for content in message["content"]: + if ( + isinstance(content, dict) + and content["type"] == "tool_result" + and content["tool_use_id"] == tool_call_id + ): + content["content"] = result - aggregation = self._aggregation - self._reset() - - try: - if self._function_call_result: - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - assistant_message = {"role": "assistant", "content": []} - if aggregation: - assistant_message["content"].append({"type": "text", "text": aggregation}) - assistant_message["content"].append( - { - "type": "tool_use", - "id": frame.tool_call_id, - "name": frame.function_name, - "input": frame.arguments, - } - ) - self._context.add_message(assistant_message) - self._context.add_message( - { - "role": "user", - "content": [ - { - "type": "tool_result", - "tool_use_id": frame.tool_call_id, - "content": json.dumps(frame.result), - } - ], - } - ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior - run_llm = True - elif aggregation: - self._context.add_message({"role": "assistant", "content": aggregation}) - - if self._pending_image_frame_message: - frame = self._pending_image_frame_message - self._pending_image_frame_message = None - self._context.add_image_frame_message( - format=frame.user_image_raw_frame.format, - size=frame.user_image_raw_frame.size, - image=frame.user_image_raw_frame.image, - text=frame.text, - ) - run_llm = True - - if run_llm: - await self._user_context_aggregator.push_context_frame() - - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() - - # Push context frame - frame = OpenAILLMContextFrame(self._context) - await self.push_frame(frame) - - # Push timestamp frame with current time - timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601()) - await self.push_frame(timestamp_frame) - - except Exception as e: - logger.error(f"Error processing frame: {e}") + async def handle_user_image_frame(self, frame: UserImageRawFrame): + await self._update_function_call_result( + frame.request.function_name, frame.request.tool_call_id, "COMPLETED" + ) + self._context.add_image_frame_message( + format=frame.format, + size=frame.size, + image=frame.image, + text=frame.request.context, + ) diff --git a/src/pipecat/services/assemblyai.py b/src/pipecat/services/assemblyai.py index 78783f8b4..94f081c5b 100644 --- a/src/pipecat/services/assemblyai.py +++ b/src/pipecat/services/assemblyai.py @@ -5,7 +5,7 @@ # import asyncio -from typing import AsyncGenerator +from typing import AsyncGenerator, Optional from loguru import logger @@ -38,20 +38,17 @@ class AssemblyAISTTService(STTService): self, *, api_key: str, - sample_rate: int = 16000, + sample_rate: Optional[int] = None, encoding: AudioEncoding = AudioEncoding("pcm_s16le"), language=Language.EN, # Only English is supported for Realtime **kwargs, ): - super().__init__(**kwargs) + super().__init__(sample_rate=sample_rate, **kwargs) aai.settings.api_key = api_key - self._transcriber: aai.RealtimeTranscriber | None = None - # Store reference to the main event loop for use in callback functions - self._loop = asyncio.get_event_loop() + self._transcriber: Optional[aai.RealtimeTranscriber] = None self._settings = { - "sample_rate": sample_rate, "encoding": encoding, "language": language, } @@ -94,6 +91,9 @@ class AssemblyAISTTService(STTService): AssemblyAI transcriber. """ + if self._transcriber: + return + def on_open(session_opened: aai.RealtimeSessionOpened): """Callback for when the connection to AssemblyAI is opened.""" logger.info(f"{self}: Connected to AssemblyAI") @@ -121,7 +121,7 @@ class AssemblyAISTTService(STTService): # Schedule the coroutine to run in the main event loop # This is necessary because this callback runs in a different thread - asyncio.run_coroutine_threadsafe(self.push_frame(frame), self._loop) + asyncio.run_coroutine_threadsafe(self.push_frame(frame), self.get_event_loop()) def on_error(error: aai.RealtimeError): """Callback for handling errors from AssemblyAI. @@ -131,14 +131,16 @@ class AssemblyAISTTService(STTService): """ logger.error(f"{self}: An error occurred: {error}") # Schedule the coroutine to run in the main event loop - asyncio.run_coroutine_threadsafe(self.push_frame(ErrorFrame(str(error))), self._loop) + asyncio.run_coroutine_threadsafe( + self.push_frame(ErrorFrame(str(error))), self.get_event_loop() + ) def on_close(): """Callback for when the connection to AssemblyAI is closed.""" logger.info(f"{self}: Disconnected from AssemblyAI") self._transcriber = aai.RealtimeTranscriber( - sample_rate=self._settings["sample_rate"], + sample_rate=self.sample_rate, encoding=self._settings["encoding"], on_data=on_data, on_error=on_error, diff --git a/src/pipecat/services/aws.py b/src/pipecat/services/aws.py index 4444f07b7..4c0417d72 100644 --- a/src/pipecat/services/aws.py +++ b/src/pipecat/services/aws.py @@ -32,7 +32,7 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -def language_to_aws_language(language: Language) -> str | None: +def language_to_aws_language(language: Language) -> Optional[str]: language_map = { # Arabic Language.AR: "arb", @@ -124,7 +124,7 @@ class PollyTTSService(TTSService): aws_session_token: Optional[str] = None, region: Optional[str] = None, voice_id: str = "Joanna", - sample_rate: int = 24000, + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): @@ -138,7 +138,6 @@ class PollyTTSService(TTSService): region_name=region, ) self._settings = { - "sample_rate": sample_rate, "engine": params.engine, "language": self.language_to_service_language(params.language) if params.language @@ -155,7 +154,7 @@ class PollyTTSService(TTSService): def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_aws_language(language) def _construct_ssml(self, text: str) -> str: @@ -198,7 +197,7 @@ class PollyTTSService(TTSService): return audio_data return None - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: await self.start_ttfb_metrics() @@ -226,9 +225,7 @@ class PollyTTSService(TTSService): yield None return - audio_data = await self._resampler.resample( - audio_data, 16000, self._settings["sample_rate"] - ) + audio_data = await self._resampler.resample(audio_data, 16000, self.sample_rate) await self.start_tts_usage_metrics(text) @@ -239,7 +236,7 @@ class PollyTTSService(TTSService): chunk = audio_data[i : i + chunk_size] if len(chunk) > 0: await self.stop_ttfb_metrics() - frame = TTSAudioRawFrame(chunk, self._settings["sample_rate"], 1) + frame = TTSAudioRawFrame(chunk, self.sample_rate, 1) yield frame yield TTSStoppedFrame() @@ -251,16 +248,3 @@ class PollyTTSService(TTSService): finally: yield TTSStoppedFrame() - - -class AWSTTSService(PollyTTSService): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - import warnings - - with warnings.catch_warnings(): - warnings.simplefilter("always") - warnings.warn( - "'AWSTTSService' is deprecated, use 'PollyTTSService' instead.", DeprecationWarning - ) diff --git a/src/pipecat/services/azure.py b/src/pipecat/services/azure.py index c7a0ce547..9df1a8ef1 100644 --- a/src/pipecat/services/azure.py +++ b/src/pipecat/services/azure.py @@ -10,6 +10,7 @@ from typing import AsyncGenerator, Optional import aiohttp from loguru import logger +from openai import AsyncAzureOpenAI from PIL import Image from pydantic import BaseModel @@ -48,7 +49,6 @@ try: PushAudioInputStream, ) from azure.cognitiveservices.speech.dialog import AudioConfig - from openai import AsyncAzureOpenAI except ModuleNotFoundError as e: logger.error(f"Exception: {e}") logger.error( @@ -57,7 +57,7 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -def language_to_azure_language(language: Language) -> str | None: +def language_to_azure_language(language: Language) -> Optional[str]: language_map = { # Afrikaans Language.AF: "af-ZA", @@ -450,14 +450,13 @@ class AzureBaseTTSService(TTSService): api_key: str, region: str, voice="en-US-SaraNeural", - sample_rate: int = 24000, + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): super().__init__(sample_rate=sample_rate, **kwargs) self._settings = { - "sample_rate": sample_rate, "emphasis": params.emphasis, "language": self.language_to_service_language(params.language) if params.language @@ -478,7 +477,7 @@ class AzureBaseTTSService(TTSService): def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_azure_language(language) def _construct_ssml(self, text: str) -> str: @@ -530,25 +529,36 @@ class AzureBaseTTSService(TTSService): class AzureTTSService(AzureBaseTTSService): def __init__(self, **kwargs): super().__init__(**kwargs) + self._speech_config = None + self._speech_synthesizer = None + self._audio_queue = asyncio.Queue() - speech_config = SpeechConfig( + async def start(self, frame: StartFrame): + await super().start(frame) + + if self._speech_config: + return + + # Now self.sample_rate is properly initialized + self._speech_config = SpeechConfig( subscription=self._api_key, region=self._region, speech_recognition_language=self._settings["language"], ) - speech_config.set_speech_synthesis_output_format( - sample_rate_to_output_format(self._settings["sample_rate"]) + self._speech_config.set_speech_synthesis_output_format( + sample_rate_to_output_format(self.sample_rate) ) - speech_config.set_service_property( + self._speech_config.set_service_property( "synthesizer.synthesis.connection.synthesisConnectionImpl", "websocket", ServicePropertyChannel.UriQueryParameter, ) - self._speech_synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=None) + self._speech_synthesizer = SpeechSynthesizer( + speech_config=self._speech_config, audio_config=None + ) # Set up event handlers - self._audio_queue = asyncio.Queue() self._speech_synthesizer.synthesizing.connect(self._handle_synthesizing) self._speech_synthesizer.synthesis_completed.connect(self._handle_completed) self._speech_synthesizer.synthesis_canceled.connect(self._handle_canceled) @@ -567,58 +577,78 @@ class AzureTTSService(AzureBaseTTSService): logger.error(f"Speech synthesis canceled: {evt.result.cancellation_details.reason}") self._audio_queue.put_nowait(None) + async def flush_audio(self): + logger.trace(f"{self}: flushing audio") + async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: - await self.start_ttfb_metrics() - yield TTSStartedFrame() + if self._speech_synthesizer is None: + error_msg = "Speech synthesizer not initialized." + logger.error(error_msg) + yield ErrorFrame(error_msg) + return - ssml = self._construct_ssml(text) + try: + await self.start_ttfb_metrics() + yield TTSStartedFrame() - # Start synthesis - self._speech_synthesizer.speak_ssml_async(ssml) + ssml = self._construct_ssml(text) + self._speech_synthesizer.speak_ssml_async(ssml) + await self.start_tts_usage_metrics(text) - await self.start_tts_usage_metrics(text) + # Stream audio chunks as they arrive + while True: + chunk = await self._audio_queue.get() + if chunk is None: # End of stream + break - # Stream audio chunks as they arrive - while True: - chunk = await self._audio_queue.get() - if chunk is None: # End of stream - break + await self.stop_ttfb_metrics() + yield TTSAudioRawFrame( + audio=chunk, + sample_rate=self.sample_rate, + num_channels=1, + ) - await self.stop_ttfb_metrics() + yield TTSStoppedFrame() - yield TTSAudioRawFrame( - audio=chunk, - sample_rate=self._settings["sample_rate"], - num_channels=1, - ) - - yield TTSStoppedFrame() + except Exception as e: + logger.error(f"{self} error during synthesis: {e}") + yield TTSStoppedFrame() + # Could add reconnection logic here if needed + return except Exception as e: - logger.error(f"{self} error generating TTS: {e}") - yield ErrorFrame(f"{self} error: {str(e)}") + logger.error(f"{self} exception: {e}") class AzureHttpTTSService(AzureBaseTTSService): def __init__(self, **kwargs): super().__init__(**kwargs) + self._speech_config = None + self._speech_synthesizer = None - speech_config = SpeechConfig( + async def start(self, frame: StartFrame): + await super().start(frame) + + if self._speech_config: + return + + self._speech_config = SpeechConfig( subscription=self._api_key, region=self._region, speech_recognition_language=self._settings["language"], ) - speech_config.set_speech_synthesis_output_format( - sample_rate_to_output_format(self._settings["sample_rate"]) + self._speech_config.set_speech_synthesis_output_format( + sample_rate_to_output_format(self.sample_rate) + ) + self._speech_synthesizer = SpeechSynthesizer( + speech_config=self._speech_config, audio_config=None ) - self._speech_synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=None) - async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") await self.start_ttfb_metrics() @@ -633,7 +663,7 @@ class AzureHttpTTSService(AzureBaseTTSService): # Azure always sends a 44-byte header. Strip it off. yield TTSAudioRawFrame( audio=result.audio_data[44:], - sample_rate=self._settings["sample_rate"], + sample_rate=self.sample_rate, num_channels=1, ) yield TTSStoppedFrame() @@ -650,44 +680,62 @@ class AzureSTTService(STTService): *, api_key: str, region: str, - language=Language.EN_US, - sample_rate=24000, - channels=1, + language: Language = Language.EN_US, + sample_rate: Optional[int] = None, **kwargs, ): - super().__init__(**kwargs) + super().__init__(sample_rate=sample_rate, **kwargs) - speech_config = SpeechConfig(subscription=api_key, region=region) - speech_config.speech_recognition_language = language - - stream_format = AudioStreamFormat(samples_per_second=sample_rate, channels=channels) - self._audio_stream = PushAudioInputStream(stream_format) - - audio_config = AudioConfig(stream=self._audio_stream) - self._speech_recognizer = SpeechRecognizer( - speech_config=speech_config, audio_config=audio_config + self._speech_config = SpeechConfig( + subscription=api_key, + region=region, + speech_recognition_language=language_to_azure_language(language), ) - self._speech_recognizer.recognized.connect(self._on_handle_recognized) + + self._audio_stream = None + self._speech_recognizer = None async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]: await self.start_processing_metrics() - self._audio_stream.write(audio) + if self._audio_stream: + self._audio_stream.write(audio) await self.stop_processing_metrics() yield None async def start(self, frame: StartFrame): await super().start(frame) + + if self._audio_stream: + return + + stream_format = AudioStreamFormat(samples_per_second=self.sample_rate, channels=1) + self._audio_stream = PushAudioInputStream(stream_format) + + audio_config = AudioConfig(stream=self._audio_stream) + + self._speech_recognizer = SpeechRecognizer( + speech_config=self._speech_config, audio_config=audio_config + ) + self._speech_recognizer.recognized.connect(self._on_handle_recognized) self._speech_recognizer.start_continuous_recognition_async() async def stop(self, frame: EndFrame): await super().stop(frame) - self._speech_recognizer.stop_continuous_recognition_async() - self._audio_stream.close() + + if self._speech_recognizer: + self._speech_recognizer.stop_continuous_recognition_async() + + if self._audio_stream: + self._audio_stream.close() async def cancel(self, frame: CancelFrame): await super().cancel(frame) - self._speech_recognizer.stop_continuous_recognition_async() - self._audio_stream.close() + + if self._speech_recognizer: + self._speech_recognizer.stop_continuous_recognition_async() + + if self._audio_stream: + self._audio_stream.close() def _on_handle_recognized(self, event): if event.result.reason == ResultReason.RecognizedSpeech and len(event.result.text) > 0: diff --git a/src/pipecat/services/base_whisper.py b/src/pipecat/services/base_whisper.py new file mode 100644 index 000000000..146fd4f39 --- /dev/null +++ b/src/pipecat/services/base_whisper.py @@ -0,0 +1,184 @@ +# +# Copyright (c) 2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import AsyncGenerator, Optional + +from loguru import logger +from openai import AsyncOpenAI +from openai.types.audio import Transcription + +from pipecat.frames.frames import ErrorFrame, Frame, TranscriptionFrame +from pipecat.services.ai_services import SegmentedSTTService +from pipecat.transcriptions.language import Language +from pipecat.utils.time import time_now_iso8601 + + +def language_to_whisper_language(language: Language) -> Optional[str]: + """Language support for Whisper API. + + Docs: https://platform.openai.com/docs/guides/speech-to-text#supported-languages + """ + BASE_LANGUAGES = { + Language.AF: "af", + Language.AR: "ar", + Language.HY: "hy", + Language.AZ: "az", + Language.BE: "be", + Language.BS: "bs", + Language.BG: "bg", + Language.CA: "ca", + Language.ZH: "zh", + Language.HR: "hr", + Language.CS: "cs", + Language.DA: "da", + Language.NL: "nl", + Language.EN: "en", + Language.ET: "et", + Language.FI: "fi", + Language.FR: "fr", + Language.GL: "gl", + Language.DE: "de", + Language.EL: "el", + Language.HE: "he", + Language.HI: "hi", + Language.HU: "hu", + Language.IS: "is", + Language.ID: "id", + Language.IT: "it", + Language.JA: "ja", + Language.KN: "kn", + Language.KK: "kk", + Language.KO: "ko", + Language.LV: "lv", + Language.LT: "lt", + Language.MK: "mk", + Language.MS: "ms", + Language.MR: "mr", + Language.MI: "mi", + Language.NE: "ne", + Language.NO: "no", + Language.FA: "fa", + Language.PL: "pl", + Language.PT: "pt", + Language.RO: "ro", + Language.RU: "ru", + Language.SR: "sr", + Language.SK: "sk", + Language.SL: "sl", + Language.ES: "es", + Language.SW: "sw", + Language.SV: "sv", + Language.TL: "tl", + Language.TA: "ta", + Language.TH: "th", + Language.TR: "tr", + Language.UK: "uk", + Language.UR: "ur", + Language.VI: "vi", + Language.CY: "cy", + } + + result = BASE_LANGUAGES.get(language) + + # If not found in base languages, try to find the base language from a variant + if not result: + lang_str = str(language.value) + base_code = lang_str.split("-")[0].lower() + result = base_code if base_code in BASE_LANGUAGES.values() else None + + return result + + +class BaseWhisperSTTService(SegmentedSTTService): + """Base class for Whisper-based speech-to-text services. + + Provides common functionality for services implementing the Whisper API interface, + including metrics generation and error handling. + + Args: + model: Name of the Whisper model to use. + api_key: Service API key. Defaults to None. + base_url: Service API base URL. Defaults to None. + language: Language of the audio input. Defaults to English. + prompt: Optional text to guide the model's style or continue a previous segment. + temperature: Sampling temperature between 0 and 1. Defaults to 0.0. + **kwargs: Additional arguments passed to SegmentedSTTService. + """ + + def __init__( + self, + *, + model: str, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + language: Optional[Language] = Language.EN, + prompt: Optional[str] = None, + temperature: Optional[float] = None, + **kwargs, + ): + super().__init__(**kwargs) + self.set_model_name(model) + self._client = self._create_client(api_key, base_url) + self._language = self.language_to_service_language(language or Language.EN) + self._prompt = prompt + self._temperature = temperature + + def _create_client(self, api_key: Optional[str], base_url: Optional[str]): + return AsyncOpenAI(api_key=api_key, base_url=base_url) + + async def set_model(self, model: str): + self.set_model_name(model) + + def can_generate_metrics(self) -> bool: + return True + + def language_to_service_language(self, language: Language) -> Optional[str]: + return language_to_whisper_language(language) + + async def set_language(self, language: Language): + """Set the language for transcription. + + Args: + language: The Language enum value to use for transcription. + """ + logger.info(f"Switching STT language to: [{language}]") + self._language = language + + async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]: + try: + await self.start_processing_metrics() + await self.start_ttfb_metrics() + + response = await self._transcribe(audio) + + await self.stop_ttfb_metrics() + await self.stop_processing_metrics() + + text = response.text.strip() + + if text: + logger.debug(f"Transcription: [{text}]") + yield TranscriptionFrame(text, "", time_now_iso8601()) + else: + logger.warning("Received empty transcription from API") + + except Exception as e: + logger.exception(f"Exception during transcription: {e}") + yield ErrorFrame(f"Error during transcription: {str(e)}") + + async def _transcribe(self, audio: bytes) -> Transcription: + """Transcribe audio data to text. + + Args: + audio: Raw audio data in WAV format. + + Returns: + Transcription: Object containing the transcribed text. + + Raises: + NotImplementedError: Must be implemented by subclasses. + """ + raise NotImplementedError diff --git a/src/pipecat/services/canonical.py b/src/pipecat/services/canonical.py index 31bd6e89a..7b62273d1 100644 --- a/src/pipecat/services/canonical.py +++ b/src/pipecat/services/canonical.py @@ -9,7 +9,7 @@ import os import uuid import wave from datetime import datetime -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple import aiohttp from loguru import logger @@ -62,17 +62,21 @@ class CanonicalMetricsService(AIService): self, *, aiohttp_session: aiohttp.ClientSession, - audio_buffer_processor: AudioBufferProcessor, call_id: str, assistant: str, api_key: str, api_url: str = "https://voiceapp.canonical.chat/api/v1", assistant_speaks_first: bool = True, output_dir: str = "recordings", - context: OpenAILLMContext | None = None, + audio_buffer_processor: Optional[AudioBufferProcessor] = None, + context: Optional[OpenAILLMContext] = None, **kwargs, ): super().__init__(**kwargs) + # Validate that at least one of audio_buffer_processor or context is provided + if audio_buffer_processor is None and context is None: + raise ValueError("At least one of audio_buffer_processor or context must be specified") + self._aiohttp_session = aiohttp_session self._audio_buffer_processor = audio_buffer_processor self._api_key = api_key @@ -85,16 +89,36 @@ class CanonicalMetricsService(AIService): async def stop(self, frame: EndFrame): await super().stop(frame) - await self._process_audio() + await self._process_completion() async def cancel(self, frame: CancelFrame): await super().cancel(frame) - await self._process_audio() + await self._process_completion() async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) await self.push_frame(frame, direction) + async def _process_completion(self): + if self._audio_buffer_processor is not None: + await self._process_audio() + elif self._context is not None: + await self._process_transcript() + + async def _process_transcript(self): + params = { + "callId": self._call_id, + "assistant": {"id": self._assistant, "speaksFirst": self._assistant_speaks_first}, + "transcript": self._context.messages, + } + response = await self._aiohttp_session.post( + f"{self._api_url}/call", + headers=self._request_headers(), + json=params, + ) + if not response.ok: + logger.error(f"Failed to process transcript: {await response.text()}") + async def _process_audio(self): audio_buffer_processor = self._audio_buffer_processor diff --git a/src/pipecat/services/cartesia.py b/src/pipecat/services/cartesia.py index 75da116d5..11796464d 100644 --- a/src/pipecat/services/cartesia.py +++ b/src/pipecat/services/cartesia.py @@ -13,23 +13,21 @@ from loguru import logger from pydantic import BaseModel from pipecat.frames.frames import ( - BotStoppedSpeakingFrame, CancelFrame, EndFrame, ErrorFrame, Frame, - LLMFullResponseEndFrame, StartFrame, StartInterruptionFrame, TTSAudioRawFrame, - TTSSpeakFrame, TTSStartedFrame, TTSStoppedFrame, ) from pipecat.processors.frame_processor import FrameDirection -from pipecat.services.ai_services import TTSService, WordTTSService -from pipecat.services.websocket_service import WebsocketService +from pipecat.services.ai_services import AudioContextWordTTSService, TTSService from pipecat.transcriptions.language import Language +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator +from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator # See .env.example for Cartesia configuration needed try: @@ -43,7 +41,7 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -def language_to_cartesia_language(language: Language) -> str | None: +def language_to_cartesia_language(language: Language) -> Optional[str]: BASE_LANGUAGES = { Language.DE: "de", Language.EN: "en", @@ -75,7 +73,7 @@ def language_to_cartesia_language(language: Language) -> str | None: return result -class CartesiaTTSService(WordTTSService, WebsocketService): +class CartesiaTTSService(AudioContextWordTTSService): class InputParams(BaseModel): language: Optional[Language] = Language.EN speed: Optional[Union[str, float]] = "" @@ -88,11 +86,12 @@ class CartesiaTTSService(WordTTSService, WebsocketService): voice_id: str, cartesia_version: str = "2024-06-10", url: str = "wss://api.cartesia.ai/tts/websocket", - model: str = "sonic", - sample_rate: int = 24000, + model: str = "sonic-2", + sample_rate: Optional[int] = None, encoding: str = "pcm_s16le", container: str = "raw", params: InputParams = InputParams(), + text_aggregator: Optional[BaseTextAggregator] = None, **kwargs, ): # Aggregating sentences still gives cleaner-sounding results and fewer @@ -105,14 +104,14 @@ class CartesiaTTSService(WordTTSService, WebsocketService): # if we're interrupted. Cartesia gives us word-by-word timestamps. We # can use those to generate text frames ourselves aligned with the # playout timing of the audio! - WordTTSService.__init__( - self, + super().__init__( aggregate_sentences=True, push_text_frames=False, + pause_frame_processing=True, sample_rate=sample_rate, + text_aggregator=text_aggregator or SkipTagsAggregator([("", "")]), **kwargs, ) - WebsocketService.__init__(self) self._api_key = api_key self._cartesia_version = cartesia_version @@ -121,7 +120,7 @@ class CartesiaTTSService(WordTTSService, WebsocketService): "output_format": { "container": container, "encoding": encoding, - "sample_rate": sample_rate, + "sample_rate": 0, }, "language": self.language_to_service_language(params.language) if params.language @@ -143,7 +142,7 @@ class CartesiaTTSService(WordTTSService, WebsocketService): await super().set_model(model) logger.info(f"Switching TTS model to: [{model}]") - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_cartesia_language(language) def _build_msg( @@ -174,6 +173,7 @@ class CartesiaTTSService(WordTTSService, WebsocketService): async def start(self, frame: StartFrame): await super().start(frame) + self._settings["output_format"]["sample_rate"] = self.sample_rate await self._connect() async def stop(self, frame: EndFrame): @@ -186,18 +186,20 @@ class CartesiaTTSService(WordTTSService, WebsocketService): async def _connect(self): await self._connect_websocket() - - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + if not self._receive_task: + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): - await self._disconnect_websocket() - if self._receive_task: await self.cancel_task(self._receive_task) self._receive_task = None + await self._disconnect_websocket() + async def _connect_websocket(self): try: + if self._websocket: + return logger.debug("Connecting to Cartesia") self._websocket = await websockets.connect( f"{self._url}?api_key={self._api_key}&cartesia_version={self._cartesia_version}" @@ -205,6 +207,7 @@ class CartesiaTTSService(WordTTSService, WebsocketService): except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: @@ -238,21 +241,19 @@ class CartesiaTTSService(WordTTSService, WebsocketService): logger.trace(f"{self}: flushing audio") msg = self._build_msg(text="", continue_transcript=False) await self._websocket.send(msg) + self._context_id = None async def _receive_messages(self): async for message in self._get_websocket(): msg = json.loads(message) - if not msg or msg["context_id"] != self._context_id: + if not msg or not self.audio_context_available(msg["context_id"]): continue if msg["type"] == "done": await self.stop_ttfb_metrics() - # Unset _context_id but not the _context_id_start_timestamp - # because we are likely still playing out audio and need the - # timestamp to set send context frames. - self._context_id = None await self.add_word_timestamps( [("TTSStoppedFrame", 0), ("LLMFullResponseEndFrame", 0), ("Reset", 0)] ) + await self.remove_audio_context(msg["context_id"]) elif msg["type"] == "timestamps": await self.add_word_timestamps( list(zip(msg["word_timestamps"]["words"], msg["word_timestamps"]["start"])) @@ -262,33 +263,21 @@ class CartesiaTTSService(WordTTSService, WebsocketService): self.start_word_timestamps() frame = TTSAudioRawFrame( audio=base64.b64decode(msg["data"]), - sample_rate=self._settings["output_format"]["sample_rate"], + sample_rate=self.sample_rate, num_channels=1, ) - await self.push_frame(frame) + await self.append_to_audio_context(msg["context_id"], frame) elif msg["type"] == "error": logger.error(f"{self} error: {msg}") await self.push_frame(TTSStoppedFrame()) await self.stop_all_metrics() await self.push_error(ErrorFrame(f"{self} error: {msg['error']}")) + self._context_id = None else: logger.error(f"{self} error, unknown message type: {msg}") - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - # If we received a TTSSpeakFrame and the LLM response included text (it - # might be that it's only a function calling response) we pause - # processing more frames until we receive a BotStoppedSpeakingFrame. - if isinstance(frame, TTSSpeakFrame): - await self.pause_processing_frames() - elif isinstance(frame, LLMFullResponseEndFrame) and self._context_id: - await self.pause_processing_frames() - elif isinstance(frame, BotStoppedSpeakingFrame): - await self.resume_processing_frames() - async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: if not self._websocket: @@ -298,6 +287,7 @@ class CartesiaTTSService(WordTTSService, WebsocketService): await self.start_ttfb_metrics() yield TTSStartedFrame() self._context_id = str(uuid.uuid4()) + await self.create_audio_context(self._context_id) msg = self._build_msg(text=text or " ") # Text must contain at least one character @@ -326,9 +316,9 @@ class CartesiaHttpTTSService(TTSService): *, api_key: str, voice_id: str, - model: str = "sonic", + model: str = "sonic-2", base_url: str = "https://api.cartesia.ai", - sample_rate: int = 24000, + sample_rate: Optional[int] = None, encoding: str = "pcm_s16le", container: str = "raw", params: InputParams = InputParams(), @@ -341,7 +331,7 @@ class CartesiaHttpTTSService(TTSService): "output_format": { "container": container, "encoding": encoding, - "sample_rate": sample_rate, + "sample_rate": 0, }, "language": self.language_to_service_language(params.language) if params.language @@ -357,9 +347,13 @@ class CartesiaHttpTTSService(TTSService): def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_cartesia_language(language) + async def start(self, frame: StartFrame): + await super().start(frame) + self._settings["output_format"]["sample_rate"] = self.sample_rate + async def stop(self, frame: EndFrame): await super().stop(frame) await self._client.close() @@ -369,10 +363,7 @@ class CartesiaHttpTTSService(TTSService): await self._client.close() async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") - - await self.start_ttfb_metrics() - yield TTSStartedFrame() + logger.debug(f"{self}: Generating TTS [{text}]") try: voice_controls = None @@ -383,6 +374,8 @@ class CartesiaHttpTTSService(TTSService): if self._settings["emotion"]: voice_controls["emotion"] = self._settings["emotion"] + await self.start_ttfb_metrics() + output = await self._client.tts.sse( model_id=self._model_name, transcript=text, @@ -393,16 +386,17 @@ class CartesiaHttpTTSService(TTSService): _experimental_voice_controls=voice_controls, ) + await self.start_tts_usage_metrics(text) + + yield TTSStartedFrame() + frame = TTSAudioRawFrame( - audio=output["audio"], - sample_rate=self._settings["output_format"]["sample_rate"], - num_channels=1, + audio=output["audio"], sample_rate=self.sample_rate, num_channels=1 ) + yield frame except Exception as e: logger.error(f"{self} exception: {e}") - - await self.start_tts_usage_metrics(text) - - await self.stop_ttfb_metrics() - yield TTSStoppedFrame() + finally: + await self.stop_ttfb_metrics() + yield TTSStoppedFrame() diff --git a/src/pipecat/services/cerebras.py b/src/pipecat/services/cerebras.py index fce3c22bf..b5e34afe5 100644 --- a/src/pipecat/services/cerebras.py +++ b/src/pipecat/services/cerebras.py @@ -7,22 +7,12 @@ from typing import List from loguru import logger +from openai import AsyncStream +from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.openai import OpenAILLMService -try: - from openai import ( - AsyncStream, - ) - from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam -except ModuleNotFoundError as e: - logger.error(f"Exception: {e}") - logger.error( - "In order to use Cerebras, you need to `pip install pipecat-ai[cerebras]`. Also, set `CEREBRAS_API_KEY` environment variable." - ) - raise Exception(f"Missing module: {e}") - class CerebrasLLMService(OpenAILLMService): """A service for interacting with Cerebras's API using the OpenAI-compatible interface. diff --git a/src/pipecat/services/deepgram.py b/src/pipecat/services/deepgram.py index a5d36370f..3750164cf 100644 --- a/src/pipecat/services/deepgram.py +++ b/src/pipecat/services/deepgram.py @@ -5,7 +5,7 @@ # import asyncio -from typing import AsyncGenerator +from typing import AsyncGenerator, Dict, Optional from loguru import logger @@ -34,6 +34,7 @@ try: AsyncListenWebSocketClient, DeepgramClient, DeepgramClientOptions, + ErrorResponse, LiveOptions, LiveResultResponse, LiveTranscriptionEvents, @@ -53,14 +54,13 @@ class DeepgramTTSService(TTSService): *, api_key: str, voice: str = "aura-helios-en", - sample_rate: int = 24000, + sample_rate: Optional[int] = None, encoding: str = "linear16", **kwargs, ): super().__init__(sample_rate=sample_rate, **kwargs) self._settings = { - "sample_rate": sample_rate, "encoding": encoding, } self.set_voice(voice) @@ -70,12 +70,12 @@ class DeepgramTTSService(TTSService): return True async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") options = SpeakOptions( model=self._voice_id, encoding=self._settings["encoding"], - sample_rate=self._settings["sample_rate"], + sample_rate=self.sample_rate, container="none", ) @@ -103,9 +103,7 @@ class DeepgramTTSService(TTSService): chunk = audio_buffer.read(chunk_size) if not chunk: break - frame = TTSAudioRawFrame( - audio=chunk, sample_rate=self._settings["sample_rate"], num_channels=1 - ) + frame = TTSAudioRawFrame(audio=chunk, sample_rate=self.sample_rate, num_channels=1) yield frame yield TTSStoppedFrame() @@ -121,15 +119,18 @@ class DeepgramSTTService(STTService): *, api_key: str, url: str = "", - live_options: LiveOptions = None, + sample_rate: Optional[int] = None, + live_options: Optional[LiveOptions] = None, + addons: Optional[Dict] = None, **kwargs, ): - super().__init__(**kwargs) + sample_rate = sample_rate or (live_options.sample_rate if live_options else None) + super().__init__(sample_rate=sample_rate, **kwargs) + default_options = LiveOptions( encoding="linear16", language=Language.EN, - model="nova-2-general", - sample_rate=16000, + model="nova-3-general", channels=1, interim_results=True, smart_format=True, @@ -149,6 +150,7 @@ class DeepgramSTTService(STTService): merged_options.language = merged_options.language.value self._settings = merged_options.to_dict() + self._addons = addons self._client = DeepgramClient( api_key, @@ -157,13 +159,10 @@ class DeepgramSTTService(STTService): options={"keepalive": "true"}, # verbose=logging.DEBUG ), ) - self._connection: AsyncListenWebSocketClient = self._client.listen.asyncwebsocket.v("1") - self._connection.on(LiveTranscriptionEvents.Transcript, self._on_message) + if self.vad_enabled: self._register_event_handler("on_speech_started") self._register_event_handler("on_utterance_end") - self._connection.on(LiveTranscriptionEvents.SpeechStarted, self._on_speech_started) - self._connection.on(LiveTranscriptionEvents.UtteranceEnd, self._on_utterance_end) @property def vad_enabled(self): @@ -187,6 +186,7 @@ class DeepgramSTTService(STTService): async def start(self, frame: StartFrame): await super().start(frame) + self._settings["sample_rate"] = self.sample_rate await self._connect() async def stop(self, frame: EndFrame): @@ -203,7 +203,25 @@ class DeepgramSTTService(STTService): async def _connect(self): logger.debug("Connecting to Deepgram") - if not await self._connection.start(self._settings): + + self._connection: AsyncListenWebSocketClient = self._client.listen.asyncwebsocket.v("1") + + self._connection.on( + LiveTranscriptionEvents(LiveTranscriptionEvents.Transcript), self._on_message + ) + self._connection.on(LiveTranscriptionEvents(LiveTranscriptionEvents.Error), self._on_error) + + if self.vad_enabled: + self._connection.on( + LiveTranscriptionEvents(LiveTranscriptionEvents.SpeechStarted), + self._on_speech_started, + ) + self._connection.on( + LiveTranscriptionEvents(LiveTranscriptionEvents.UtteranceEnd), + self._on_utterance_end, + ) + + if not await self._connection.start(options=self._settings, addons=self._addons): logger.error(f"{self}: unable to connect to Deepgram") async def _disconnect(self): @@ -215,6 +233,15 @@ class DeepgramSTTService(STTService): await self.start_ttfb_metrics() await self.start_processing_metrics() + async def _on_error(self, *args, **kwargs): + error: ErrorResponse = kwargs["error"] + logger.warning(f"{self} connection error, will retry: {error}") + await self.stop_all_metrics() + # NOTE(aleix): we don't disconnect (i.e. call finish on the connection) + # because this triggers more errors internally in the Deepgram SDK. So, + # we just forget about the previous connection and create a new one. + await self._connect() + async def _on_speech_started(self, *args, **kwargs): await self.start_metrics() await self._call_event_handler("on_speech_started", *args, **kwargs) diff --git a/src/pipecat/services/deepseek.py b/src/pipecat/services/deepseek.py index 67433dfaa..2537f2f7c 100644 --- a/src/pipecat/services/deepseek.py +++ b/src/pipecat/services/deepseek.py @@ -8,22 +8,12 @@ from typing import List from loguru import logger +from openai import AsyncStream +from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.openai import OpenAILLMService -try: - from openai import ( - AsyncStream, - ) - from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam -except ModuleNotFoundError as e: - logger.error(f"Exception: {e}") - logger.error( - "In order to use DeepSeek, you need to `pip install pipecat-ai[deepseek]`. Also, set `DEEPSEEK_API_KEY` environment variable." - ) - raise Exception(f"Missing module: {e}") - class DeepSeekLLMService(OpenAILLMService): """A service for interacting with DeepSeek's API using the OpenAI-compatible interface. diff --git a/src/pipecat/services/elevenlabs.py b/src/pipecat/services/elevenlabs.py index d37fb499b..68c71a144 100644 --- a/src/pipecat/services/elevenlabs.py +++ b/src/pipecat/services/elevenlabs.py @@ -14,22 +14,18 @@ from loguru import logger from pydantic import BaseModel, model_validator from pipecat.frames.frames import ( - BotStoppedSpeakingFrame, CancelFrame, EndFrame, ErrorFrame, Frame, - LLMFullResponseEndFrame, StartFrame, StartInterruptionFrame, TTSAudioRawFrame, - TTSSpeakFrame, TTSStartedFrame, TTSStoppedFrame, ) from pipecat.processors.frame_processor import FrameDirection -from pipecat.services.ai_services import TTSService, WordTTSService -from pipecat.services.websocket_service import WebsocketService +from pipecat.services.ai_services import InterruptibleWordTTSService, TTSService from pipecat.transcriptions.language import Language # See .env.example for ElevenLabs configuration needed @@ -55,7 +51,7 @@ ELEVENLABS_MULTILINGUAL_MODELS = { } -def language_to_elevenlabs_language(language: Language) -> str | None: +def language_to_elevenlabs_language(language: Language) -> Optional[str]: BASE_LANGUAGES = { Language.AR: "ar", Language.BG: "bg", @@ -104,17 +100,60 @@ def language_to_elevenlabs_language(language: Language) -> str | None: return result -def sample_rate_from_output_format(output_format: str) -> int: - match output_format: - case "pcm_16000": - return 16000 - case "pcm_22050": - return 22050 - case "pcm_24000": - return 24000 - case "pcm_44100": - return 44100 - return 16000 +def output_format_from_sample_rate(sample_rate: int) -> str: + match sample_rate: + case 8000: + return "pcm_8000" + case 16000: + return "pcm_16000" + case 22050: + return "pcm_22050" + case 24000: + return "pcm_24000" + case 44100: + return "pcm_44100" + logger.warning( + f"ElevenLabsTTSService: No output format available for {sample_rate} sample rate" + ) + return "pcm_24000" + + +def build_elevenlabs_voice_settings( + settings: Dict[str, Any], +) -> Optional[Dict[str, Union[float, bool]]]: + """Build voice settings dictionary for ElevenLabs based on provided settings. + + Args: + settings: Dictionary containing voice settings parameters + + Returns: + Dictionary of voice settings or None if required parameters are missing + """ + voice_settings = {} + if settings["stability"] is not None and settings["similarity_boost"] is not None: + voice_settings["stability"] = settings["stability"] + voice_settings["similarity_boost"] = settings["similarity_boost"] + if settings["style"] is not None: + voice_settings["style"] = settings["style"] + if settings["use_speaker_boost"] is not None: + voice_settings["use_speaker_boost"] = settings["use_speaker_boost"] + if settings["speed"] is not None: + voice_settings["speed"] = settings["speed"] + else: + if settings["style"] is not None: + logger.warning( + "'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set." + ) + if settings["use_speaker_boost"] is not None: + logger.warning( + "'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set." + ) + if settings["speed"] is not None: + logger.warning( + "'speed' is set but will not be applied because 'stability' and 'similarity_boost' are not both set." + ) + + return voice_settings or None def calculate_word_times( @@ -138,7 +177,7 @@ def calculate_word_times( return word_times -class ElevenLabsTTSService(WordTTSService, WebsocketService): +class ElevenLabsTTSService(InterruptibleWordTTSService): class InputParams(BaseModel): language: Optional[Language] = None optimize_streaming_latency: Optional[str] = None @@ -146,6 +185,7 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): similarity_boost: Optional[float] = None style: Optional[float] = None use_speaker_boost: Optional[bool] = None + speed: Optional[float] = None auto_mode: Optional[bool] = True @model_validator(mode="after") @@ -165,7 +205,7 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): voice_id: str, model: str = "eleven_flash_v2_5", url: str = "wss://api.elevenlabs.io", - output_format: ElevenLabsOutputFormat = "pcm_24000", + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): @@ -183,34 +223,32 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): # Finally, ElevenLabs doesn't provide information on when the bot stops # speaking for a while, so we want the parent class to send TTSStopFrame # after a short period not receiving any audio. - WordTTSService.__init__( - self, + super().__init__( aggregate_sentences=True, push_text_frames=False, push_stop_frames=True, - stop_frame_timeout_s=2.0, - sample_rate=sample_rate_from_output_format(output_format), + pause_frame_processing=True, + sample_rate=sample_rate, **kwargs, ) - WebsocketService.__init__(self) self._api_key = api_key self._url = url self._settings = { - "sample_rate": sample_rate_from_output_format(output_format), "language": self.language_to_service_language(params.language) if params.language else None, - "output_format": output_format, "optimize_streaming_latency": params.optimize_streaming_latency, "stability": params.stability, "similarity_boost": params.similarity_boost, "style": params.style, "use_speaker_boost": params.use_speaker_boost, + "speed": params.speed, "auto_mode": str(params.auto_mode).lower(), } self.set_model_name(model) self.set_voice(voice_id) + self._output_format = "" # initialized in start() self._voice_settings = self._set_voice_settings() # Indicates if we have sent TTSStartedFrame. It will reset to False when @@ -218,35 +256,17 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): self._started = False self._cumulative_time = 0 + self._receive_task = None + self._keepalive_task = None + def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_elevenlabs_language(language) def _set_voice_settings(self): - voice_settings = {} - if ( - self._settings["stability"] is not None - and self._settings["similarity_boost"] is not None - ): - voice_settings["stability"] = self._settings["stability"] - voice_settings["similarity_boost"] = self._settings["similarity_boost"] - if self._settings["style"] is not None: - voice_settings["style"] = self._settings["style"] - if self._settings["use_speaker_boost"] is not None: - voice_settings["use_speaker_boost"] = self._settings["use_speaker_boost"] - else: - if self._settings["style"] is not None: - logger.warning( - "'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set." - ) - if self._settings["use_speaker_boost"] is not None: - logger.warning( - "'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set." - ) - - return voice_settings or None + return build_elevenlabs_voice_settings(self._settings) async def set_model(self, model: str): await super().set_model(model) @@ -254,7 +274,7 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): await self._disconnect() await self._connect() - async def _update_settings(self, settings: Dict[str, Any]): + async def _update_settings(self, settings: Mapping[str, Any]): prev_voice = self._voice_id await super()._update_settings(settings) if not prev_voice == self._voice_id: @@ -264,6 +284,7 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): async def start(self, frame: StartFrame): await super().start(frame) + self._output_format = output_format_from_sample_rate(self.sample_rate) await self._connect() async def stop(self, frame: EndFrame): @@ -286,24 +307,14 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): if isinstance(frame, TTSStoppedFrame): await self.add_word_timestamps([("LLMFullResponseEndFrame", 0), ("Reset", 0)]) - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - # If we received a TTSSpeakFrame and the LLM response included text (it - # might be that it's only a function calling response) we pause - # processing more frames until we receive a BotStoppedSpeakingFrame. - if isinstance(frame, TTSSpeakFrame): - await self.pause_processing_frames() - elif isinstance(frame, LLMFullResponseEndFrame) and self._started: - await self.pause_processing_frames() - elif isinstance(frame, BotStoppedSpeakingFrame): - await self.resume_processing_frames() - async def _connect(self): await self._connect_websocket() - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) - self._keepalive_task = self.create_task(self._keepalive_task_handler()) + if not self._receive_task: + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) + + if not self._keepalive_task: + self._keepalive_task = self.create_task(self._keepalive_task_handler()) async def _disconnect(self): if self._receive_task: @@ -318,11 +329,14 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): async def _connect_websocket(self): try: + if self._websocket: + return + logger.debug("Connecting to ElevenLabs") voice_id = self._voice_id model = self.model_name - output_format = self._settings["output_format"] + output_format = self._output_format url = f"{self._url}/v1/text-to-speech/{voice_id}/stream-input?model_id={model}&output_format={output_format}&auto_mode={self._settings['auto_mode']}" if self._settings["optimize_streaming_latency"]: @@ -352,6 +366,7 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: @@ -367,15 +382,20 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): except Exception as e: logger.error(f"{self} error closing websocket: {e}") + def _get_websocket(self): + if self._websocket: + return self._websocket + raise Exception("Websocket not connected") + async def _receive_messages(self): - async for message in self._websocket: + async for message in self._get_websocket(): msg = json.loads(message) if msg.get("audio"): await self.stop_ttfb_metrics() self.start_word_timestamps() audio = base64.b64decode(msg["audio"]) - frame = TTSAudioRawFrame(audio, self._settings["sample_rate"], 1) + frame = TTSAudioRawFrame(audio, self.sample_rate, 1) await self.push_frame(frame) if msg.get("alignment"): word_times = calculate_word_times(msg["alignment"], self._cumulative_time) @@ -385,7 +405,11 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): async def _keepalive_task_handler(self): while True: await asyncio.sleep(10) - await self._send_text("") + try: + await self._send_text("") + except websockets.ConnectionClosed as e: + logger.warning(f"{self} keepalive error: {e}") + break async def _send_text(self, text: str): if self._websocket: @@ -393,7 +417,7 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService): await self._websocket.send(json.dumps(msg)) async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: if not self._websocket: @@ -428,7 +452,7 @@ class ElevenLabsHttpTTSService(TTSService): aiohttp_session: aiohttp ClientSession model: Model ID (default: "eleven_flash_v2_5" for low latency) base_url: API base URL - output_format: Audio output format (PCM) + sample_rate: Output sample rate params: Additional parameters for voice configuration """ @@ -439,6 +463,7 @@ class ElevenLabsHttpTTSService(TTSService): similarity_boost: Optional[float] = None style: Optional[float] = None use_speaker_boost: Optional[bool] = None + speed: Optional[float] = None def __init__( self, @@ -448,65 +473,42 @@ class ElevenLabsHttpTTSService(TTSService): aiohttp_session: aiohttp.ClientSession, model: str = "eleven_flash_v2_5", base_url: str = "https://api.elevenlabs.io", - output_format: ElevenLabsOutputFormat = "pcm_24000", + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): - super().__init__(sample_rate=sample_rate_from_output_format(output_format), **kwargs) + super().__init__(sample_rate=sample_rate, **kwargs) self._api_key = api_key self._base_url = base_url - self._output_format = output_format self._params = params self._session = aiohttp_session self._settings = { - "sample_rate": sample_rate_from_output_format(output_format), "language": self.language_to_service_language(params.language) if params.language else None, - "output_format": output_format, "optimize_streaming_latency": params.optimize_streaming_latency, "stability": params.stability, "similarity_boost": params.similarity_boost, "style": params.style, "use_speaker_boost": params.use_speaker_boost, + "speed": params.speed, } self.set_model_name(model) self.set_voice(voice_id) + self._output_format = "" # initialized in start() self._voice_settings = self._set_voice_settings() def can_generate_metrics(self) -> bool: return True - def _set_voice_settings(self) -> Optional[Dict[str, Union[float, bool]]]: - """Configure voice settings if stability and similarity_boost are provided. + def _set_voice_settings(self): + return build_elevenlabs_voice_settings(self._settings) - Returns: - Dictionary of voice settings or None if required parameters are missing. - """ - voice_settings: Dict[str, Union[float, bool]] = {} - if ( - self._settings["stability"] is not None - and self._settings["similarity_boost"] is not None - ): - voice_settings["stability"] = float(self._settings["stability"]) - voice_settings["similarity_boost"] = float(self._settings["similarity_boost"]) - if self._settings["style"] is not None: - voice_settings["style"] = float(self._settings["style"]) - if self._settings["use_speaker_boost"] is not None: - voice_settings["use_speaker_boost"] = bool(self._settings["use_speaker_boost"]) - else: - if self._settings["style"] is not None: - logger.warning( - "'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set." - ) - if self._settings["use_speaker_boost"] is not None: - logger.warning( - "'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set." - ) - - return voice_settings or None + async def start(self, frame: StartFrame): + await super().start(frame) + self._output_format = output_format_from_sample_rate(self.sample_rate) async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: """Generate speech from text using ElevenLabs streaming API. @@ -517,7 +519,7 @@ class ElevenLabsHttpTTSService(TTSService): Yields: Frames containing audio data and status information """ - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") url = f"{self._base_url}/v1/text-to-speech/{self._voice_id}/stream" @@ -565,18 +567,18 @@ class ElevenLabsHttpTTSService(TTSService): return await self.start_tts_usage_metrics(text) + + # Process the streaming response + CHUNK_SIZE = 1024 + yield TTSStartedFrame() - - async for chunk in response.content: - if chunk: + async for chunk in response.content.iter_chunked(CHUNK_SIZE): + if len(chunk) > 0: await self.stop_ttfb_metrics() - yield TTSAudioRawFrame(chunk, self._settings["sample_rate"], 1) - - yield TTSStoppedFrame() - + yield TTSAudioRawFrame(chunk, self.sample_rate, 1) except Exception as e: logger.error(f"Error in run_tts: {e}") yield ErrorFrame(error=str(e)) - finally: + await self.stop_ttfb_metrics() yield TTSStoppedFrame() diff --git a/src/pipecat/services/fal.py b/src/pipecat/services/fal.py index 3eeead4bb..cb39da75f 100644 --- a/src/pipecat/services/fal.py +++ b/src/pipecat/services/fal.py @@ -4,8 +4,10 @@ # SPDX-License-Identifier: BSD 2-Clause License # +import asyncio import io import os +import wave from typing import AsyncGenerator, Dict, Optional, Union import aiohttp @@ -13,8 +15,10 @@ from loguru import logger from PIL import Image from pydantic import BaseModel -from pipecat.frames.frames import ErrorFrame, Frame, URLImageRawFrame -from pipecat.services.ai_services import ImageGenService +from pipecat.frames.frames import ErrorFrame, Frame, TranscriptionFrame, URLImageRawFrame +from pipecat.services.ai_services import ImageGenService, SegmentedSTTService +from pipecat.transcriptions.language import Language +from pipecat.utils.time import time_now_iso8601 try: import fal_client @@ -26,6 +30,120 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") +def language_to_fal_language(language: Language) -> Optional[str]: + """Language support for Fal's Wizper API.""" + BASE_LANGUAGES = { + Language.AF: "af", + Language.AM: "am", + Language.AR: "ar", + Language.AS: "as", + Language.AZ: "az", + Language.BA: "ba", + Language.BE: "be", + Language.BG: "bg", + Language.BN: "bn", + Language.BO: "bo", + Language.BR: "br", + Language.BS: "bs", + Language.CA: "ca", + Language.CS: "cs", + Language.CY: "cy", + Language.DA: "da", + Language.DE: "de", + Language.EL: "el", + Language.EN: "en", + Language.ES: "es", + Language.ET: "et", + Language.EU: "eu", + Language.FA: "fa", + Language.FI: "fi", + Language.FO: "fo", + Language.FR: "fr", + Language.GL: "gl", + Language.GU: "gu", + Language.HA: "ha", + Language.HE: "he", + Language.HI: "hi", + Language.HR: "hr", + Language.HT: "ht", + Language.HU: "hu", + Language.HY: "hy", + Language.ID: "id", + Language.IS: "is", + Language.IT: "it", + Language.JA: "ja", + Language.JW: "jw", + Language.KA: "ka", + Language.KK: "kk", + Language.KM: "km", + Language.KN: "kn", + Language.KO: "ko", + Language.LA: "la", + Language.LB: "lb", + Language.LN: "ln", + Language.LO: "lo", + Language.LT: "lt", + Language.LV: "lv", + Language.MG: "mg", + Language.MI: "mi", + Language.MK: "mk", + Language.ML: "ml", + Language.MN: "mn", + Language.MR: "mr", + Language.MS: "ms", + Language.MT: "mt", + Language.MY: "my", + Language.NE: "ne", + Language.NL: "nl", + Language.NN: "nn", + Language.NO: "no", + Language.OC: "oc", + Language.PA: "pa", + Language.PL: "pl", + Language.PS: "ps", + Language.PT: "pt", + Language.RO: "ro", + Language.RU: "ru", + Language.SA: "sa", + Language.SD: "sd", + Language.SI: "si", + Language.SK: "sk", + Language.SL: "sl", + Language.SN: "sn", + Language.SO: "so", + Language.SQ: "sq", + Language.SR: "sr", + Language.SU: "su", + Language.SV: "sv", + Language.SW: "sw", + Language.TA: "ta", + Language.TE: "te", + Language.TG: "tg", + Language.TH: "th", + Language.TK: "tk", + Language.TL: "tl", + Language.TR: "tr", + Language.TT: "tt", + Language.UK: "uk", + Language.UR: "ur", + Language.UZ: "uz", + Language.VI: "vi", + Language.YI: "yi", + Language.YO: "yo", + Language.ZH: "zh", + } + + result = BASE_LANGUAGES.get(language) + + # If not found in base languages, try to find the base language from a variant + if not result: + lang_str = str(language.value) + base_code = lang_str.split("-")[0].lower() + result = base_code if base_code in BASE_LANGUAGES.values() else None + + return result + + class FalImageGenService(ImageGenService): class InputParams(BaseModel): seed: Optional[int] = None @@ -42,7 +160,7 @@ class FalImageGenService(ImageGenService): params: InputParams, aiohttp_session: aiohttp.ClientSession, model: str = "fal-ai/fast-sdxl", - key: str | None = None, + key: Optional[str] = None, **kwargs, ): super().__init__(**kwargs) @@ -53,6 +171,11 @@ class FalImageGenService(ImageGenService): os.environ["FAL_KEY"] = key async def run_image_gen(self, prompt: str) -> AsyncGenerator[Frame, None]: + def load_image_bytes(encoded_image: bytes): + buffer = io.BytesIO(encoded_image) + image = Image.open(buffer) + return (image.tobytes(), image.size, image.format) + logger.debug(f"Generating image from prompt: {prompt}") response = await fal_client.run_async( @@ -73,10 +196,114 @@ class FalImageGenService(ImageGenService): logger.debug(f"Downloading image {image_url} ...") async with self._aiohttp_session.get(image_url) as response: logger.debug(f"Downloaded image {image_url}") - image_stream = io.BytesIO(await response.content.read()) - image = Image.open(image_stream) + encoded_image = await response.content.read() + (image_bytes, size, format) = await asyncio.to_thread(load_image_bytes, encoded_image) - frame = URLImageRawFrame( - url=image_url, image=image.tobytes(), size=image.size, format=image.format - ) + frame = URLImageRawFrame(url=image_url, image=image_bytes, size=size, format=format) yield frame + + +class FalSTTService(SegmentedSTTService): + """Speech-to-text service using Fal's Wizper API. + + This service uses Fal's Wizper API to perform speech-to-text transcription on audio + segments. It inherits from SegmentedSTTService to handle audio buffering and speech detection. + + Args: + api_key: Fal API key. If not provided, will check FAL_KEY environment variable. + sample_rate: Audio sample rate in Hz. If not provided, uses the pipeline's rate. + params: Configuration parameters for the Wizper API. + **kwargs: Additional arguments passed to SegmentedSTTService. + """ + + class InputParams(BaseModel): + """Configuration parameters for Fal's Wizper API. + + Attributes: + language: Language of the audio input. Defaults to English. + task: Task to perform ('transcribe' or 'translate'). Defaults to 'transcribe'. + chunk_level: Level of chunking ('segment'). Defaults to 'segment'. + version: Version of Wizper model to use. Defaults to '3'. + """ + + language: Optional[Language] = Language.EN + task: str = "transcribe" + chunk_level: str = "segment" + version: str = "3" + + def __init__( + self, + *, + api_key: Optional[str] = None, + sample_rate: Optional[int] = None, + params: InputParams = InputParams(), + **kwargs, + ): + super().__init__( + sample_rate=sample_rate, + **kwargs, + ) + + if api_key: + os.environ["FAL_KEY"] = api_key + elif "FAL_KEY" not in os.environ: + raise ValueError( + "FAL_KEY must be provided either through api_key parameter or environment variable" + ) + + self._fal_client = fal_client.AsyncClient(key=api_key or os.getenv("FAL_KEY")) + self._settings = { + "task": params.task, + "language": self.language_to_service_language(params.language) + if params.language + else "en", + "chunk_level": params.chunk_level, + "version": params.version, + } + + def can_generate_metrics(self) -> bool: + return True + + def language_to_service_language(self, language: Language) -> Optional[str]: + return language_to_fal_language(language) + + async def set_language(self, language: Language): + logger.info(f"Switching STT language to: [{language}]") + self._settings["language"] = self.language_to_service_language(language) + + async def set_model(self, model: str): + await super().set_model(model) + logger.info(f"Switching STT model to: [{model}]") + + async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]: + """Transcribes an audio segment using Fal's Wizper API. + + Args: + audio: Raw audio bytes in WAV format (already converted by base class). + + Yields: + Frame: TranscriptionFrame containing the transcribed text. + + Note: + The audio is already in WAV format from the SegmentedSTTService. + Only non-empty transcriptions are yielded. + """ + try: + # Send to Fal directly (audio is already in WAV format from base class) + data_uri = fal_client.encode(audio, "audio/x-wav") + response = await self._fal_client.run( + "fal-ai/wizper", + arguments={"audio_url": data_uri, **self._settings}, + ) + + if response and "text" in response: + text = response["text"].strip() + if text: # Only yield non-empty text + logger.debug(f"Transcription: [{text}]") + yield TranscriptionFrame( + text, "", time_now_iso8601(), Language(self._settings["language"]) + ) + + except Exception as e: + logger.error(f"Fal Wizper error: {e}") + yield ErrorFrame(f"Fal Wizper error: {str(e)}") diff --git a/src/pipecat/services/fireworks.py b/src/pipecat/services/fireworks.py index 0e50fc980..8e40e1a7b 100644 --- a/src/pipecat/services/fireworks.py +++ b/src/pipecat/services/fireworks.py @@ -8,19 +8,11 @@ from typing import List from loguru import logger +from openai.types.chat import ChatCompletionMessageParam from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.openai import OpenAILLMService -try: - from openai.types.chat import ChatCompletionMessageParam -except ModuleNotFoundError as e: - logger.error(f"Exception: {e}") - logger.error( - "In order to use Fireworks, you need to `pip install pipecat-ai[fireworks]`. Also, set `FIREWORKS_API_KEY` environment variable." - ) - raise Exception(f"Missing module: {e}") - class FireworksLLMService(OpenAILLMService): """A service for interacting with Fireworks AI using the OpenAI-compatible interface. diff --git a/src/pipecat/services/fish.py b/src/pipecat/services/fish.py index 6fadab683..9e6a8b91e 100644 --- a/src/pipecat/services/fish.py +++ b/src/pipecat/services/fish.py @@ -11,22 +11,18 @@ from loguru import logger from pydantic import BaseModel from pipecat.frames.frames import ( - BotStoppedSpeakingFrame, CancelFrame, EndFrame, ErrorFrame, Frame, - LLMFullResponseEndFrame, StartFrame, StartInterruptionFrame, TTSAudioRawFrame, - TTSSpeakFrame, TTSStartedFrame, TTSStoppedFrame, ) from pipecat.processors.frame_processor import FrameDirection -from pipecat.services.ai_services import TTSService -from pipecat.services.websocket_service import WebsocketService +from pipecat.services.ai_services import InterruptibleTTSService from pipecat.transcriptions.language import Language try: @@ -43,7 +39,7 @@ except ModuleNotFoundError as e: FishAudioOutputFormat = Literal["opus", "mp3", "pcm", "wav"] -class FishAudioTTSService(TTSService, WebsocketService): +class FishAudioTTSService(InterruptibleTTSService): class InputParams(BaseModel): language: Optional[Language] = Language.EN latency: Optional[str] = "normal" # "normal" or "balanced" @@ -56,11 +52,16 @@ class FishAudioTTSService(TTSService, WebsocketService): api_key: str, model: str, # This is the reference_id output_format: FishAudioOutputFormat = "pcm", - sample_rate: int = 24000, + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): - super().__init__(sample_rate=sample_rate, **kwargs) + super().__init__( + push_stop_frames=True, + pause_frame_processing=True, + sample_rate=sample_rate, + **kwargs, + ) self._api_key = api_key self._base_url = "wss://api.fish.audio/v1/tts/live" @@ -70,7 +71,7 @@ class FishAudioTTSService(TTSService, WebsocketService): self._started = False self._settings = { - "sample_rate": sample_rate, + "sample_rate": 0, "latency": params.latency, "format": output_format, "prosody": { @@ -92,6 +93,7 @@ class FishAudioTTSService(TTSService, WebsocketService): async def start(self, frame: StartFrame): await super().start(frame) + self._settings["sample_rate"] = self.sample_rate await self._connect() async def stop(self, frame: EndFrame): @@ -104,16 +106,21 @@ class FishAudioTTSService(TTSService, WebsocketService): async def _connect(self): await self._connect_websocket() - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + if not self._receive_task: + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): - await self._disconnect_websocket() if self._receive_task: await self.cancel_task(self._receive_task) self._receive_task = None + await self._disconnect_websocket() + async def _connect_websocket(self): try: + if self._websocket: + return + logger.debug("Connecting to Fish Audio") headers = {"Authorization": f"Bearer {self._api_key}"} self._websocket = await websockets.connect(self._base_url, extra_headers=headers) @@ -125,6 +132,7 @@ class FishAudioTTSService(TTSService, WebsocketService): except Exception as e: logger.error(f"Fish Audio initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: @@ -141,11 +149,24 @@ class FishAudioTTSService(TTSService, WebsocketService): except Exception as e: logger.error(f"Error closing websocket: {e}") + async def flush_audio(self): + """Flush any buffered audio by sending a flush event to Fish Audio.""" + logger.trace(f"{self}: Flushing audio buffers") + if not self._websocket: + return + flush_message = {"event": "flush"} + await self._get_websocket().send(ormsgpack.packb(flush_message)) + def _get_websocket(self): if self._websocket: return self._websocket raise Exception("Websocket not connected") + async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection): + await super()._handle_interruption(frame, direction) + await self.stop_all_metrics() + self._request_id = None + async def _receive_messages(self): async for message in self._get_websocket(): try: @@ -157,9 +178,7 @@ class FishAudioTTSService(TTSService, WebsocketService): audio_data = msg.get("audio") # Only process larger chunks to remove msgpack overhead if audio_data and len(audio_data) > 1024: - frame = TTSAudioRawFrame( - audio_data, self._settings["sample_rate"], 1 - ) + frame = TTSAudioRawFrame(audio_data, self.sample_rate, 1) await self.push_frame(frame) await self.stop_ttfb_metrics() continue @@ -167,23 +186,8 @@ class FishAudioTTSService(TTSService, WebsocketService): except Exception as e: logger.error(f"Error processing message: {e}") - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - if isinstance(frame, TTSSpeakFrame): - await self.pause_processing_frames() - elif isinstance(frame, LLMFullResponseEndFrame) and self._request_id: - await self.pause_processing_frames() - elif isinstance(frame, BotStoppedSpeakingFrame): - await self.resume_processing_frames() - - async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection): - await super()._handle_interruption(frame, direction) - await self.stop_all_metrics() - self._request_id = None - async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating Fish TTS: [{text}]") + logger.debug(f"{self}: Generating Fish TTS: [{text}]") try: if not self._websocket or self._websocket.closed: await self._connect() diff --git a/src/pipecat/services/gemini_multimodal_live/events.py b/src/pipecat/services/gemini_multimodal_live/events.py index b2107e7f8..60f688997 100644 --- a/src/pipecat/services/gemini_multimodal_live/events.py +++ b/src/pipecat/services/gemini_multimodal_live/events.py @@ -48,7 +48,7 @@ class AudioInputMessage(BaseModel): realtimeInput: RealtimeInput @classmethod - def from_raw_audio(cls, raw_audio: bytes, sample_rate=16000) -> "AudioInputMessage": + def from_raw_audio(cls, raw_audio: bytes, sample_rate: int) -> "AudioInputMessage": data = base64.b64encode(raw_audio).decode("utf-8") return cls( realtimeInput=RealtimeInput( diff --git a/src/pipecat/services/gemini_multimodal_live/gemini.py b/src/pipecat/services/gemini_multimodal_live/gemini.py index 9f355d1ba..801af46b4 100644 --- a/src/pipecat/services/gemini_multimodal_live/gemini.py +++ b/src/pipecat/services/gemini_multimodal_live/gemini.py @@ -9,12 +9,14 @@ import base64 import json from dataclasses import dataclass from enum import Enum -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Mapping, Optional, Union import websockets from loguru import logger from pydantic import BaseModel, Field +from pipecat.adapters.schemas.tools_schema import ToolsSchema +from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter from pipecat.frames.frames import ( BotStartedSpeakingFrame, BotStoppedSpeakingFrame, @@ -36,6 +38,8 @@ from pipecat.frames.frames import ( TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, + TTSTextFrame, + UserImageRawFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) @@ -115,10 +119,10 @@ class GeminiMultimodalLiveUserContextAggregator(OpenAIUserContextAggregator): class GeminiMultimodalLiveAssistantContextAggregator(OpenAIAssistantContextAggregator): - async def _push_aggregation(self): - # We don't want to store any images in the context. Revisit this later when the API evolves. - self._pending_image_frame_message = None - await super()._push_aggregation() + async def handle_user_image_frame(self, frame: UserImageRawFrame): + # We don't want to store any images in the context. Revisit this later + # when the API evolves. + pass @dataclass @@ -152,6 +156,9 @@ class InputParams(BaseModel): class GeminiMultimodalLiveLLMService(LLMService): + # Overriding the default adapter to use the Gemini one. + adapter_class = GeminiLLMAdapter + def __init__( self, *, @@ -162,7 +169,7 @@ class GeminiMultimodalLiveLLMService(LLMService): start_audio_paused: bool = False, start_video_paused: bool = False, system_instruction: Optional[str] = None, - tools: Optional[List[dict]] = None, + tools: Optional[Union[List[dict], ToolsSchema]] = None, transcribe_user_audio: bool = False, transcribe_model_audio: bool = False, params: InputParams = InputParams(), @@ -203,6 +210,8 @@ class GeminiMultimodalLiveLLMService(LLMService): self._bot_audio_buffer = bytearray() self._bot_text_buffer = "" + self._sample_rate = 24000 + self._settings = { "frequency_penalty": params.frequency_penalty, "max_tokens": params.max_tokens, @@ -305,6 +314,7 @@ class GeminiMultimodalLiveLLMService(LLMService): # context.add_message({"role": "assistant", "content": [{"type": "text", "text": text}]}) await self.push_frame(LLMFullResponseStartFrame()) await self.push_frame(LLMTextFrame(text=text)) + await self.push_frame(TTSTextFrame(text=text)) await self.push_frame(LLMFullResponseEndFrame()) async def _transcribe_audio(self, audio, context): @@ -332,10 +342,8 @@ class GeminiMultimodalLiveLLMService(LLMService): async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) - # logger.debug(f"Processing frame: {frame}") - if isinstance(frame, TranscriptionFrame): - pass + await self.push_frame(frame, direction) elif isinstance(frame, OpenAILLMContextFrame): context: GeminiMultimodalLiveContext = GeminiMultimodalLiveContext.upgrade( frame.context @@ -352,31 +360,35 @@ class GeminiMultimodalLiveLLMService(LLMService): # Support just one tool call per context frame for now tool_result_message = context.messages[-1] await self._tool_result(tool_result_message) - elif isinstance(frame, InputAudioRawFrame): await self._send_user_audio(frame) + await self.push_frame(frame, direction) elif isinstance(frame, InputImageRawFrame): await self._send_user_video(frame) + await self.push_frame(frame, direction) elif isinstance(frame, StartInterruptionFrame): await self._handle_interruption() + await self.push_frame(frame, direction) elif isinstance(frame, UserStartedSpeakingFrame): await self._handle_user_started_speaking(frame) + await self.push_frame(frame, direction) elif isinstance(frame, UserStoppedSpeakingFrame): await self._handle_user_stopped_speaking(frame) + await self.push_frame(frame, direction) elif isinstance(frame, BotStartedSpeakingFrame): # Ignore this frame. Use the serverContent API message instead - pass + await self.push_frame(frame, direction) elif isinstance(frame, BotStoppedSpeakingFrame): # ignore this frame. Use the serverContent.turnComplete API message - pass + await self.push_frame(frame, direction) elif isinstance(frame, LLMMessagesAppendFrame): await self._create_single_response(frame.messages) elif isinstance(frame, LLMUpdateSettingsFrame): await self._update_settings(frame.settings) elif isinstance(frame, LLMSetToolsFrame): await self._update_settings() - - await self.push_frame(frame, direction) + else: + await self.push_frame(frame, direction) # # websocket communication @@ -433,7 +445,7 @@ class GeminiMultimodalLiveLLMService(LLMService): ) if self._tools: logger.debug(f"Gemini is configuring to use tools{self._tools}") - config.setup.tools = self._tools + config.setup.tools = self.get_llm_adapter().from_standard_tools(self._tools) await self.send_client_event(config) except Exception as e: @@ -521,7 +533,7 @@ class GeminiMultimodalLiveLLMService(LLMService): if self._audio_input_paused: return # Send all audio to Gemini - evt = events.AudioInputMessage.from_raw_audio(frame.audio) + evt = events.AudioInputMessage.from_raw_audio(frame.audio, frame.sample_rate) await self.send_client_event(evt) # Manage a buffer of audio to use for transcription audio = frame.audio @@ -650,7 +662,7 @@ class GeminiMultimodalLiveLLMService(LLMService): inline_data = part.inlineData if not inline_data: return - if inline_data.mimeType != "audio/pcm;rate=24000": + if inline_data.mimeType != f"audio/pcm;rate={self._sample_rate}": logger.warning(f"Unrecognized server_content format {inline_data.mimeType}") return @@ -665,7 +677,7 @@ class GeminiMultimodalLiveLLMService(LLMService): self._bot_audio_buffer.extend(audio) frame = TTSAudioRawFrame( audio=audio, - sample_rate=24000, + sample_rate=self._sample_rate, num_channels=1, ) await self.push_frame(frame) @@ -699,11 +711,39 @@ class GeminiMultimodalLiveLLMService(LLMService): await self.push_frame(TTSStoppedFrame()) def create_context_aggregator( - self, context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = False + self, + context: OpenAILLMContext, + *, + user_kwargs: Mapping[str, Any] = {}, + assistant_kwargs: Mapping[str, Any] = {}, ) -> GeminiMultimodalLiveContextAggregatorPair: + """Create an instance of GeminiMultimodalLiveContextAggregatorPair from + an OpenAILLMContext. Constructor keyword arguments for both the user and + assistant aggregators can be provided. + + Args: + context (OpenAILLMContext): The LLM context. + user_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the user context aggregator constructor. Defaults + to an empty mapping. + assistant_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the assistant context aggregator + constructor. Defaults to an empty mapping. + + Returns: + GeminiMultimodalLiveContextAggregatorPair: A pair of context + aggregators, one for the user and one for the assistant, + encapsulated in an GeminiMultimodalLiveContextAggregatorPair. + + """ + context.set_llm_adapter(self.get_llm_adapter()) + GeminiMultimodalLiveContext.upgrade(context) - user = GeminiMultimodalLiveUserContextAggregator(context) + user = GeminiMultimodalLiveUserContextAggregator(context, **user_kwargs) + + default_assistant_kwargs = {"expect_stripped_words": False} + default_assistant_kwargs.update(assistant_kwargs) assistant = GeminiMultimodalLiveAssistantContextAggregator( - user, expect_stripped_words=assistant_expect_stripped_words + context, **default_assistant_kwargs ) return GeminiMultimodalLiveContextAggregatorPair(_user=user, _assistant=assistant) diff --git a/src/pipecat/services/gladia.py b/src/pipecat/services/gladia.py index 3cdeebd3c..09bcd7aa3 100644 --- a/src/pipecat/services/gladia.py +++ b/src/pipecat/services/gladia.py @@ -34,7 +34,7 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -def language_to_gladia_language(language: Language) -> str | None: +def language_to_gladia_language(language: Language) -> Optional[str]: BASE_LANGUAGES = { Language.AF: "af", Language.AM: "am", @@ -131,12 +131,12 @@ def language_to_gladia_language(language: Language) -> str | None: class GladiaSTTService(STTService): class InputParams(BaseModel): - sample_rate: Optional[int] = 16000 language: Optional[Language] = Language.EN endpointing: Optional[float] = 0.2 maximum_duration_without_endpointing: Optional[int] = 10 audio_enhancer: Optional[bool] = None words_accurate_timestamps: Optional[bool] = None + speech_threshold: Optional[float] = 0.99 def __init__( self, @@ -144,17 +144,17 @@ class GladiaSTTService(STTService): api_key: str, url: str = "https://api.gladia.io/v2/live", confidence: float = 0.5, + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): - super().__init__(**kwargs) - + super().__init__(sample_rate=sample_rate, **kwargs) self._api_key = api_key self._url = url self._settings = { "encoding": "wav/pcm", "bit_depth": 16, - "sample_rate": params.sample_rate, + "sample_rate": 0, "channels": 1, "language_config": { "languages": [self.language_to_service_language(params.language)] @@ -166,32 +166,45 @@ class GladiaSTTService(STTService): "maximum_duration_without_endpointing": params.maximum_duration_without_endpointing, "pre_processing": { "audio_enhancer": params.audio_enhancer, + "speech_threshold": params.speech_threshold, }, "realtime_processing": { "words_accurate_timestamps": params.words_accurate_timestamps, }, } self._confidence = confidence + self._websocket = None + self._receive_task = None - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_gladia_language(language) async def start(self, frame: StartFrame): await super().start(frame) + if self._websocket: + return + self._settings["sample_rate"] = self.sample_rate response = await self._setup_gladia() self._websocket = await websockets.connect(response["url"]) - self._receive_task = self.create_task(self._receive_task_handler()) + if not self._receive_task: + self._receive_task = self.create_task(self._receive_task_handler()) async def stop(self, frame: EndFrame): await super().stop(frame) await self._send_stop_recording() - await self._websocket.close() - await self.wait_for_task(self._receive_task) + if self._websocket: + await self._websocket.close() + self._websocket = None + if self._receive_task: + await self.wait_for_task(self._receive_task) + self._receive_task = None async def cancel(self, frame: CancelFrame): await super().cancel(frame) await self._websocket.close() - await self.cancel_task(self._receive_task) + if self._receive_task: + await self.cancel_task(self._receive_task) + self._receive_task = None async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]: await self.start_processing_metrics() diff --git a/src/pipecat/services/google/__init__.py b/src/pipecat/services/google/__init__.py index 1b81980e6..3e63a3ba9 100644 --- a/src/pipecat/services/google/__init__.py +++ b/src/pipecat/services/google/__init__.py @@ -1,2 +1,3 @@ from .frames import LLMSearchResponseFrame from .google import * +from .rtvi import * diff --git a/src/pipecat/services/google/google.py b/src/pipecat/services/google/google.py index d705bec47..3d207a254 100644 --- a/src/pipecat/services/google/google.py +++ b/src/pipecat/services/google/google.py @@ -8,28 +8,48 @@ import asyncio import base64 import io import json +import os +import time +import uuid + +from google.api_core.exceptions import DeadlineExceeded +from openai import AsyncStream +from openai.types.chat import ChatCompletionChunk + +from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter + +# Suppress gRPC fork warnings +os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false" + from dataclasses import dataclass -from typing import Any, AsyncGenerator, Dict, List, Literal, Optional +from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional, Union from loguru import logger from PIL import Image -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator from pipecat.frames.frames import ( AudioRawFrame, + CancelFrame, + EndFrame, ErrorFrame, Frame, - FunctionCallResultProperties, + FunctionCallCancelFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, + InterimTranscriptionFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesFrame, LLMTextFrame, LLMUpdateSettingsFrame, - OpenAILLMContextAssistantTimestampFrame, + StartFrame, + TranscriptionFrame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, URLImageRawFrame, + UserImageRawFrame, VisionImageRawFrame, ) from pipecat.metrics.metrics import LLMTokenUsage @@ -38,10 +58,12 @@ from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContextFrame, ) from pipecat.processors.frame_processor import FrameDirection -from pipecat.services.ai_services import ImageGenService, LLMService, TTSService +from pipecat.services.ai_services import ImageGenService, LLMService, STTService, TTSService from pipecat.services.google.frames import LLMSearchResponseFrame from pipecat.services.openai import ( OpenAIAssistantContextAggregator, + OpenAILLMService, + OpenAIUnhandledFunctionException, OpenAIUserContextAggregator, ) from pipecat.transcriptions.language import Language @@ -51,10 +73,14 @@ try: import google.ai.generativelanguage as glm import google.generativeai as gai from google import genai - from google.cloud import texttospeech_v1 + from google.api_core.client_options import ClientOptions + from google.auth.transport.requests import Request + from google.cloud import speech_v2, texttospeech_v1 + from google.cloud.speech_v2.types import cloud_speech from google.genai import types from google.generativeai.types import GenerationConfig from google.oauth2 import service_account + except ModuleNotFoundError as e: logger.error(f"Exception: {e}") logger.error( @@ -63,7 +89,7 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -def language_to_google_language(language: Language) -> str | None: +def language_to_google_tts_language(language: Language) -> Optional[str]: language_map = { # Afrikaans Language.AF: "af-ZA", @@ -223,8 +249,307 @@ def language_to_google_language(language: Language) -> str | None: return language_map.get(language) +def language_to_google_stt_language(language: Language) -> Optional[str]: + """Maps Language enum to Google Speech-to-Text V2 language codes. + + Args: + language: Language enum value. + + Returns: + Optional[str]: Google STT language code or None if not supported. + """ + language_map = { + # Afrikaans + Language.AF: "af-ZA", + Language.AF_ZA: "af-ZA", + # Albanian + Language.SQ: "sq-AL", + Language.SQ_AL: "sq-AL", + # Amharic + Language.AM: "am-ET", + Language.AM_ET: "am-ET", + # Arabic + Language.AR: "ar-EG", # Default to Egypt + Language.AR_AE: "ar-AE", + Language.AR_BH: "ar-BH", + Language.AR_DZ: "ar-DZ", + Language.AR_EG: "ar-EG", + Language.AR_IQ: "ar-IQ", + Language.AR_JO: "ar-JO", + Language.AR_KW: "ar-KW", + Language.AR_LB: "ar-LB", + Language.AR_MA: "ar-MA", + Language.AR_OM: "ar-OM", + Language.AR_QA: "ar-QA", + Language.AR_SA: "ar-SA", + Language.AR_SY: "ar-SY", + Language.AR_TN: "ar-TN", + Language.AR_YE: "ar-YE", + # Armenian + Language.HY: "hy-AM", + Language.HY_AM: "hy-AM", + # Azerbaijani + Language.AZ: "az-AZ", + Language.AZ_AZ: "az-AZ", + # Basque + Language.EU: "eu-ES", + Language.EU_ES: "eu-ES", + # Bengali + Language.BN: "bn-IN", # Default to India + Language.BN_BD: "bn-BD", + Language.BN_IN: "bn-IN", + # Bosnian + Language.BS: "bs-BA", + Language.BS_BA: "bs-BA", + # Bulgarian + Language.BG: "bg-BG", + Language.BG_BG: "bg-BG", + # Burmese + Language.MY: "my-MM", + Language.MY_MM: "my-MM", + # Catalan + Language.CA: "ca-ES", + Language.CA_ES: "ca-ES", + # Chinese + Language.ZH: "cmn-Hans-CN", # Default to Simplified Chinese + Language.ZH_CN: "cmn-Hans-CN", + Language.ZH_HK: "cmn-Hans-HK", + Language.ZH_TW: "cmn-Hant-TW", + Language.YUE: "yue-Hant-HK", # Cantonese + Language.YUE_CN: "yue-Hant-HK", + # Croatian + Language.HR: "hr-HR", + Language.HR_HR: "hr-HR", + # Czech + Language.CS: "cs-CZ", + Language.CS_CZ: "cs-CZ", + # Danish + Language.DA: "da-DK", + Language.DA_DK: "da-DK", + # Dutch + Language.NL: "nl-NL", # Default to Netherlands + Language.NL_BE: "nl-BE", + Language.NL_NL: "nl-NL", + # English + Language.EN: "en-US", # Default to US + Language.EN_AU: "en-AU", + Language.EN_CA: "en-CA", + Language.EN_GB: "en-GB", + Language.EN_GH: "en-GH", + Language.EN_HK: "en-HK", + Language.EN_IN: "en-IN", + Language.EN_IE: "en-IE", + Language.EN_KE: "en-KE", + Language.EN_NG: "en-NG", + Language.EN_NZ: "en-NZ", + Language.EN_PH: "en-PH", + Language.EN_SG: "en-SG", + Language.EN_TZ: "en-TZ", + Language.EN_US: "en-US", + Language.EN_ZA: "en-ZA", + # Estonian + Language.ET: "et-EE", + Language.ET_EE: "et-EE", + # Filipino + Language.FIL: "fil-PH", + Language.FIL_PH: "fil-PH", + # Finnish + Language.FI: "fi-FI", + Language.FI_FI: "fi-FI", + # French + Language.FR: "fr-FR", # Default to France + Language.FR_BE: "fr-BE", + Language.FR_CA: "fr-CA", + Language.FR_CH: "fr-CH", + Language.FR_FR: "fr-FR", + # Galician + Language.GL: "gl-ES", + Language.GL_ES: "gl-ES", + # Georgian + Language.KA: "ka-GE", + Language.KA_GE: "ka-GE", + # German + Language.DE: "de-DE", # Default to Germany + Language.DE_AT: "de-AT", + Language.DE_CH: "de-CH", + Language.DE_DE: "de-DE", + # Greek + Language.EL: "el-GR", + Language.EL_GR: "el-GR", + # Gujarati + Language.GU: "gu-IN", + Language.GU_IN: "gu-IN", + # Hebrew + Language.HE: "iw-IL", + Language.HE_IL: "iw-IL", + # Hindi + Language.HI: "hi-IN", + Language.HI_IN: "hi-IN", + # Hungarian + Language.HU: "hu-HU", + Language.HU_HU: "hu-HU", + # Icelandic + Language.IS: "is-IS", + Language.IS_IS: "is-IS", + # Indonesian + Language.ID: "id-ID", + Language.ID_ID: "id-ID", + # Italian + Language.IT: "it-IT", + Language.IT_IT: "it-IT", + Language.IT_CH: "it-CH", + # Japanese + Language.JA: "ja-JP", + Language.JA_JP: "ja-JP", + # Javanese + Language.JV: "jv-ID", + Language.JV_ID: "jv-ID", + # Kannada + Language.KN: "kn-IN", + Language.KN_IN: "kn-IN", + # Kazakh + Language.KK: "kk-KZ", + Language.KK_KZ: "kk-KZ", + # Khmer + Language.KM: "km-KH", + Language.KM_KH: "km-KH", + # Korean + Language.KO: "ko-KR", + Language.KO_KR: "ko-KR", + # Lao + Language.LO: "lo-LA", + Language.LO_LA: "lo-LA", + # Latvian + Language.LV: "lv-LV", + Language.LV_LV: "lv-LV", + # Lithuanian + Language.LT: "lt-LT", + Language.LT_LT: "lt-LT", + # Macedonian + Language.MK: "mk-MK", + Language.MK_MK: "mk-MK", + # Malay + Language.MS: "ms-MY", + Language.MS_MY: "ms-MY", + # Malayalam + Language.ML: "ml-IN", + Language.ML_IN: "ml-IN", + # Marathi + Language.MR: "mr-IN", + Language.MR_IN: "mr-IN", + # Mongolian + Language.MN: "mn-MN", + Language.MN_MN: "mn-MN", + # Nepali + Language.NE: "ne-NP", + Language.NE_NP: "ne-NP", + # Norwegian + Language.NO: "no-NO", + Language.NB: "no-NO", + Language.NB_NO: "no-NO", + # Persian + Language.FA: "fa-IR", + Language.FA_IR: "fa-IR", + # Polish + Language.PL: "pl-PL", + Language.PL_PL: "pl-PL", + # Portuguese + Language.PT: "pt-PT", # Default to Portugal + Language.PT_BR: "pt-BR", + Language.PT_PT: "pt-PT", + # Punjabi + Language.PA: "pa-Guru-IN", + Language.PA_IN: "pa-Guru-IN", + # Romanian + Language.RO: "ro-RO", + Language.RO_RO: "ro-RO", + # Russian + Language.RU: "ru-RU", + Language.RU_RU: "ru-RU", + # Serbian + Language.SR: "sr-RS", + Language.SR_RS: "sr-RS", + # Sinhala + Language.SI: "si-LK", + Language.SI_LK: "si-LK", + # Slovak + Language.SK: "sk-SK", + Language.SK_SK: "sk-SK", + # Slovenian + Language.SL: "sl-SI", + Language.SL_SI: "sl-SI", + # Spanish + Language.ES: "es-ES", # Default to Spain + Language.ES_AR: "es-AR", + Language.ES_BO: "es-BO", + Language.ES_CL: "es-CL", + Language.ES_CO: "es-CO", + Language.ES_CR: "es-CR", + Language.ES_DO: "es-DO", + Language.ES_EC: "es-EC", + Language.ES_ES: "es-ES", + Language.ES_GT: "es-GT", + Language.ES_HN: "es-HN", + Language.ES_MX: "es-MX", + Language.ES_NI: "es-NI", + Language.ES_PA: "es-PA", + Language.ES_PE: "es-PE", + Language.ES_PR: "es-PR", + Language.ES_PY: "es-PY", + Language.ES_SV: "es-SV", + Language.ES_US: "es-US", + Language.ES_UY: "es-UY", + Language.ES_VE: "es-VE", + # Sundanese + Language.SU: "su-ID", + Language.SU_ID: "su-ID", + # Swahili + Language.SW: "sw-TZ", # Default to Tanzania + Language.SW_KE: "sw-KE", + Language.SW_TZ: "sw-TZ", + # Swedish + Language.SV: "sv-SE", + Language.SV_SE: "sv-SE", + # Tamil + Language.TA: "ta-IN", # Default to India + Language.TA_IN: "ta-IN", + Language.TA_MY: "ta-MY", + Language.TA_SG: "ta-SG", + Language.TA_LK: "ta-LK", + # Telugu + Language.TE: "te-IN", + Language.TE_IN: "te-IN", + # Thai + Language.TH: "th-TH", + Language.TH_TH: "th-TH", + # Turkish + Language.TR: "tr-TR", + Language.TR_TR: "tr-TR", + # Ukrainian + Language.UK: "uk-UA", + Language.UK_UA: "uk-UA", + # Urdu + Language.UR: "ur-IN", # Default to India + Language.UR_IN: "ur-IN", + Language.UR_PK: "ur-PK", + # Uzbek + Language.UZ: "uz-UZ", + Language.UZ_UZ: "uz-UZ", + # Vietnamese + Language.VI: "vi-VN", + Language.VI_VN: "vi-VN", + # Xhosa + Language.XH: "xh-ZA", + # Zulu + Language.ZU: "zu-ZA", + Language.ZU_ZA: "zu-ZA", + } + + return language_map.get(language) + + class GoogleUserContextAggregator(OpenAIUserContextAggregator): - async def _push_aggregation(self): + async def push_aggregation(self): if len(self._aggregation) > 0: self._context.add_message( glm.Content(role="user", parts=[glm.Part(text=self._aggregation)]) @@ -239,96 +564,75 @@ class GoogleUserContextAggregator(OpenAIUserContextAggregator): await self.push_frame(frame) # Reset our accumulator state. - self._reset() + self.reset() class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator): - async def _push_aggregation(self): - if not ( - self._aggregation or self._function_call_result or self._pending_image_frame_message - ): - return + async def handle_aggregation(self, aggregation: str): + self._context.add_message(glm.Content(role="model", parts=[glm.Part(text=aggregation)])) - run_llm = False - properties: Optional[FunctionCallResultProperties] = None - - aggregation = self._aggregation - self._reset() - - try: - if self._function_call_result: - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - logger.debug(f"FunctionCallResultFrame result: {frame.arguments}") - self._context.add_message( - glm.Content( - role="model", - parts=[ - glm.Part( - function_call=glm.FunctionCall( - name=frame.function_name, args=frame.arguments - ) - ) - ], + async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + self._context.add_message( + glm.Content( + role="model", + parts=[ + glm.Part( + function_call=glm.FunctionCall( + id=frame.tool_call_id, name=frame.function_name, args=frame.arguments ) ) - response = frame.result - if isinstance(response, str): - response = {"response": response} - self._context.add_message( - glm.Content( - role="user", - parts=[ - glm.Part( - function_response=glm.FunctionResponse( - name=frame.function_name, response=response - ) - ) - ], + ], + ) + ) + self._context.add_message( + glm.Content( + role="user", + parts=[ + glm.Part( + function_response=glm.FunctionResponse( + id=frame.tool_call_id, + name=frame.function_name, + response={"response": "IN_PROGRESS"}, ) ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior is to run the LLM if there are no function calls in progress - run_llm = not bool(self._function_calls_in_progress) - else: - if aggregation.strip(): - self._context.add_message( - glm.Content(role="model", parts=[glm.Part(text=aggregation)]) - ) + ], + ) + ) - if self._pending_image_frame_message: - frame = self._pending_image_frame_message - self._pending_image_frame_message = None - self._context.add_image_frame_message( - format=frame.user_image_raw_frame.format, - size=frame.user_image_raw_frame.size, - image=frame.user_image_raw_frame.image, - text=frame.text, - ) - run_llm = True + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + if frame.result: + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, frame.result + ) + else: + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "COMPLETED" + ) - if run_llm: - await self._user_context_aggregator.push_context_frame() + async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "CANCELLED" + ) - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() + async def _update_function_call_result( + self, function_name: str, tool_call_id: str, result: Any + ): + for message in self._context.messages: + if message.role == "user": + for part in message.parts: + if part.function_response and part.function_response.id == tool_call_id: + part.function_response.response = {"value": json.dumps(result)} - # Push context frame - frame = OpenAILLMContextFrame(self._context) - await self.push_frame(frame) - - # Push timestamp frame with current time - timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601()) - await self.push_frame(timestamp_frame) - - except Exception as e: - logger.exception(f"Error processing frame: {e}") + async def handle_user_image_frame(self, frame: UserImageRawFrame): + await self._update_function_call_result( + frame.request.function_name, frame.request.tool_call_id, "COMPLETED" + ) + self._context.add_image_frame_message( + format=frame.format, + size=frame.size, + image=frame.image, + text=frame.request.context, + ) @dataclass @@ -346,9 +650,9 @@ class GoogleContextAggregatorPair: class GoogleLLMContext(OpenAILLMContext): def __init__( self, - messages: list[dict] | None = None, - tools: list[dict] | None = None, - tool_choice: dict | None = None, + messages: Optional[List[dict]] = None, + tools: Optional[List[dict]] = None, + tool_choice: Optional[dict] = None, ): super().__init__(messages=messages, tools=tools, tool_choice=tool_choice) self.system_message = None @@ -408,7 +712,9 @@ class GoogleLLMContext(OpenAILLMContext): self.add_message(glm.Content(role="user", parts=parts)) - def add_audio_frames_message(self, *, audio_frames: list[AudioRawFrame], text: str = None): + def add_audio_frames_message( + self, *, audio_frames: list[AudioRawFrame], text: str = "Audio follows" + ): if not audio_frames: return @@ -417,8 +723,9 @@ class GoogleLLMContext(OpenAILLMContext): parts = [] data = b"".join(frame.audio for frame in audio_frames) - if text: - parts.append(glm.Part(text=text)) + # NOTE(aleix): According to the docs only text or inline_data should be needed. + # (see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference) + parts.append(glm.Part(text=text)) parts.append( glm.Part( inline_data=glm.Blob( @@ -604,9 +911,9 @@ class GoogleLLMContext(OpenAILLMContext): # Check if we only have function-related messages (no regular text) has_regular_messages = any( len(msg.parts) == 1 - and hasattr(msg.parts[0], "text") - and not hasattr(msg.parts[0], "function_call") - and not hasattr(msg.parts[0], "function_response") + and not getattr(msg.parts[0], "text", None) + and getattr(msg.parts[0], "function_call", None) + and getattr(msg.parts[0], "function_response", None) for msg in self._messages ) @@ -621,13 +928,16 @@ class GoogleLLMContext(OpenAILLMContext): class GoogleLLMService(LLMService): - """This class implements inference with Google's AI models + """This class implements inference with Google's AI models. This service translates internally from OpenAILLMContext to the messages format expected by the Google AI model. We are using the OpenAILLMContext as a lingua franca for all LLM services, so that it is easy to switch between different LLMs. """ + # Overriding the default adapter to use the Gemini one. + adapter_class = GeminiLLMAdapter + class InputParams(BaseModel): max_tokens: Optional[int] = Field(default=4096, ge=1) temperature: Optional[float] = Field(default=None, ge=0.0, le=2.0) @@ -639,7 +949,7 @@ class GoogleLLMService(LLMService): self, *, api_key: str, - model: str = "gemini-1.5-flash-latest", + model: str = "gemini-2.0-flash-001", params: InputParams = InputParams(), system_instruction: Optional[str] = None, tools: Optional[List[Dict[str, Any]]] = None, @@ -681,8 +991,8 @@ class GoogleLLMService(LLMService): try: logger.debug( - # f"Generating chat: {self._system_instruction} | {context.get_messages_for_logging()}" - f"Generating chat: {context.get_messages_for_logging()}" + # f"{self}: Generating chat [{self._system_instruction}] | [{context.get_messages_for_logging()}]" + f"{self}: Generating chat [{context.get_messages_for_logging()}]" ) messages = context.messages @@ -744,7 +1054,7 @@ class GoogleLLMService(LLMService): args = type(c.function_call).to_dict(c.function_call).get("args", {}) await self.call_function( context=context, - tool_call_id="what_should_this_be", + tool_call_id=str(uuid.uuid4()), function_name=c.function_call.name, arguments=args, ) @@ -814,6 +1124,8 @@ class GoogleLLMService(LLMService): else: logger.exception(f"{self} error: {e}") + except DeadlineExceeded: + await self._call_event_handler("on_completion_timeout") except Exception as e: logger.exception(f"{self} exception: {e}") finally: @@ -856,17 +1168,231 @@ class GoogleLLMService(LLMService): if context: await self._process_context(context) - @staticmethod def create_context_aggregator( - context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True + self, + context: OpenAILLMContext, + *, + user_kwargs: Mapping[str, Any] = {}, + assistant_kwargs: Mapping[str, Any] = {}, ) -> GoogleContextAggregatorPair: - user = GoogleUserContextAggregator(context) - assistant = GoogleAssistantContextAggregator( - user, expect_stripped_words=assistant_expect_stripped_words - ) + """Create an instance of GoogleContextAggregatorPair from an + OpenAILLMContext. Constructor keyword arguments for both the user and + assistant aggregators can be provided. + + Args: + context (OpenAILLMContext): The LLM context. + user_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the user context aggregator constructor. Defaults + to an empty mapping. + assistant_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the assistant context aggregator + constructor. Defaults to an empty mapping. + + Returns: + GoogleContextAggregatorPair: A pair of context aggregators, one for + the user and one for the assistant, encapsulated in an + GoogleContextAggregatorPair. + + """ + context.set_llm_adapter(self.get_llm_adapter()) + + if isinstance(context, OpenAILLMContext): + context = GoogleLLMContext.upgrade_to_google(context) + user = GoogleUserContextAggregator(context, **user_kwargs) + assistant = GoogleAssistantContextAggregator(context, **assistant_kwargs) return GoogleContextAggregatorPair(_user=user, _assistant=assistant) +class GoogleLLMOpenAIBetaService(OpenAILLMService): + """This class implements inference with Google's AI LLM models using the OpenAI format. + Ref - https://ai.google.dev/gemini-api/docs/openai + """ + + def __init__( + self, + *, + api_key: str, + base_url: str = "https://generativelanguage.googleapis.com/v1beta/openai/", + model: str = "gemini-2.0-flash", + **kwargs, + ): + super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs) + + async def _process_context(self, context: OpenAILLMContext): + functions_list = [] + arguments_list = [] + tool_id_list = [] + func_idx = 0 + function_name = "" + arguments = "" + tool_call_id = "" + + await self.start_ttfb_metrics() + + chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions( + context + ) + + async for chunk in chunk_stream: + if chunk.usage: + tokens = LLMTokenUsage( + prompt_tokens=chunk.usage.prompt_tokens, + completion_tokens=chunk.usage.completion_tokens, + total_tokens=chunk.usage.total_tokens, + ) + await self.start_llm_usage_metrics(tokens) + + if chunk.choices is None or len(chunk.choices) == 0: + continue + + await self.stop_ttfb_metrics() + + if not chunk.choices[0].delta: + continue + + if chunk.choices[0].delta.tool_calls: + # We're streaming the LLM response to enable the fastest response times. + # For text, we just yield each chunk as we receive it and count on consumers + # to do whatever coalescing they need (eg. to pass full sentences to TTS) + # + # If the LLM is a function call, we'll do some coalescing here. + # If the response contains a function name, we'll yield a frame to tell consumers + # that they can start preparing to call the function with that name. + # We accumulate all the arguments for the rest of the streamed response, then when + # the response is done, we package up all the arguments and the function name and + # yield a frame containing the function name and the arguments. + logger.debug(f"Tool call: {chunk.choices[0].delta.tool_calls}") + tool_call = chunk.choices[0].delta.tool_calls[0] + if tool_call.index != func_idx: + functions_list.append(function_name) + arguments_list.append(arguments) + tool_id_list.append(tool_call_id) + function_name = "" + arguments = "" + tool_call_id = "" + func_idx += 1 + if tool_call.function and tool_call.function.name: + function_name += tool_call.function.name + tool_call_id = tool_call.id + if tool_call.function and tool_call.function.arguments: + # Keep iterating through the response to collect all the argument fragments + arguments += tool_call.function.arguments + elif chunk.choices[0].delta.content: + await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content)) + + # if we got a function name and arguments, check to see if it's a function with + # a registered handler. If so, run the registered callback, save the result to + # the context, and re-prompt to get a chat answer. If we don't have a registered + # handler, raise an exception. + if function_name and arguments: + # added to the list as last function name and arguments not added to the list + functions_list.append(function_name) + arguments_list.append(arguments) + tool_id_list.append(tool_call_id) + + logger.debug( + f"Function list: {functions_list}, Arguments list: {arguments_list}, Tool ID list: {tool_id_list}" + ) + for index, (function_name, arguments, tool_id) in enumerate( + zip(functions_list, arguments_list, tool_id_list), start=1 + ): + if function_name == "": + # TODO: Remove the _process_context method once Google resolves the bug + # where the index is incorrectly set to None instead of returning the actual index, + # which currently results in an empty function name(''). + continue + if self.has_function(function_name): + run_llm = False + arguments = json.loads(arguments) + await self.call_function( + context=context, + function_name=function_name, + arguments=arguments, + tool_call_id=tool_id, + run_llm=run_llm, + ) + else: + raise OpenAIUnhandledFunctionException( + f"The LLM tried to call a function named '{function_name}', but there isn't a callback registered for that function." + ) + + +class GoogleVertexLLMService(OpenAILLMService): + """Implements inference with Google's AI models via Vertex AI while + maintaining OpenAI API compatibility. + + Reference: + https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/call-vertex-using-openai-library + + """ + + class InputParams(OpenAILLMService.InputParams): + """Input parameters specific to Vertex AI.""" + + # https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations + location: str = "us-east4" + project_id: str + + def __init__( + self, + *, + credentials: Optional[str] = None, + credentials_path: Optional[str] = None, + model: str = "google/gemini-2.0-flash-001", + params: InputParams = OpenAILLMService.InputParams(), + **kwargs, + ): + """Initializes the VertexLLMService. + Args: + credentials (Optional[str]): JSON string of service account credentials. + credentials_path (Optional[str]): Path to the service account JSON file. + model (str): Model identifier. Defaults to "google/gemini-2.0-flash-001". + params (InputParams): Vertex AI input parameters. + **kwargs: Additional arguments for OpenAILLMService. + """ + base_url = self._get_base_url(params) + self._api_key = self._get_api_token(credentials, credentials_path) + + super().__init__(api_key=self._api_key, base_url=base_url, model=model, **kwargs) + + @staticmethod + def _get_base_url(params: InputParams) -> str: + """Constructs the base URL for Vertex AI API.""" + return ( + f"https://{params.location}-aiplatform.googleapis.com/v1/" + f"projects/{params.project_id}/locations/{params.location}/endpoints/openapi" + ) + + @staticmethod + def _get_api_token(credentials: Optional[str], credentials_path: Optional[str]) -> str: + """Retrieves an authentication token using Google service account credentials. + Args: + credentials (Optional[str]): JSON string of service account credentials. + credentials_path (Optional[str]): Path to the service account JSON file. + Returns: + str: OAuth token for API authentication. + """ + creds: Optional[service_account.Credentials] = None + + if credentials: + # Parse and load credentials from JSON string + creds = service_account.Credentials.from_service_account_info( + json.loads(credentials), scopes=["https://www.googleapis.com/auth/cloud-platform"] + ) + elif credentials_path: + # Load credentials from JSON file + creds = service_account.Credentials.from_service_account_file( + credentials_path, scopes=["https://www.googleapis.com/auth/cloud-platform"] + ) + + if not creds: + raise ValueError("No valid credentials provided.") + + creds.refresh(Request()) # Ensure token is up-to-date, lifetime is 1 hour. + + return creds.token + + class GoogleTTSService(TTSService): class InputParams(BaseModel): pitch: Optional[str] = None @@ -883,14 +1409,13 @@ class GoogleTTSService(TTSService): credentials: Optional[str] = None, credentials_path: Optional[str] = None, voice_id: str = "en-US-Neural2-A", - sample_rate: int = 24000, + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): super().__init__(sample_rate=sample_rate, **kwargs) self._settings = { - "sample_rate": sample_rate, "pitch": params.pitch, "rate": params.rate, "volume": params.volume, @@ -927,8 +1452,8 @@ class GoogleTTSService(TTSService): def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: - return language_to_google_language(language) + def language_to_service_language(self, language: Language) -> Optional[str]: + return language_to_google_tts_language(language) def _construct_ssml(self, text: str) -> str: ssml = "" @@ -977,15 +1502,18 @@ class GoogleTTSService(TTSService): return ssml async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: await self.start_ttfb_metrics() + # Check if the voice is a Chirp voice (including Chirp 3) or Journey voice + is_chirp_voice = "chirp" in self._voice_id.lower() is_journey_voice = "journey" in self._voice_id.lower() # Create synthesis input based on voice_id - if is_journey_voice: + if is_chirp_voice or is_journey_voice: + # Chirp and Journey voices don't support SSML, use plain text synthesis_input = texttospeech_v1.SynthesisInput(text=text) else: ssml = self._construct_ssml(text) @@ -996,7 +1524,7 @@ class GoogleTTSService(TTSService): ) audio_config = texttospeech_v1.AudioConfig( audio_encoding=texttospeech_v1.AudioEncoding.LINEAR16, - sample_rate_hertz=self._settings["sample_rate"], + sample_rate_hertz=self.sample_rate, ) request = texttospeech_v1.SynthesizeSpeechRequest( @@ -1019,7 +1547,7 @@ class GoogleTTSService(TTSService): if not chunk: break await self.stop_ttfb_metrics() - frame = TTSAudioRawFrame(chunk, self._settings["sample_rate"], 1) + frame = TTSAudioRawFrame(chunk, self.sample_rate, 1) yield frame await asyncio.sleep(0) # Allow other tasks to run @@ -1098,3 +1626,466 @@ class GoogleImageGenService(ImageGenService): except Exception as e: logger.error(f"{self} error generating image: {e}") yield ErrorFrame(f"Image generation error: {str(e)}") + + +class GoogleSTTService(STTService): + """Google Cloud Speech-to-Text V2 service implementation. + + Provides real-time speech recognition using Google Cloud's Speech-to-Text V2 API + with streaming support. Handles audio transcription and optional voice activity detection. + + Attributes: + InputParams: Configuration parameters for the STT service. + """ + + # Google Cloud's STT service has a connection time limit of 5 minutes per stream. + # They've shared an "endless streaming" example that guided this implementation: + # https://cloud.google.com/speech-to-text/docs/transcribe-streaming-audio#endless-streaming + + STREAMING_LIMIT = 240000 # 4 minutes in milliseconds + + class InputParams(BaseModel): + """Configuration parameters for Google Speech-to-Text. + + Attributes: + languages: Single language or list of recognition languages. First language is primary. + model: Speech recognition model to use. + use_separate_recognition_per_channel: Process each audio channel separately. + enable_automatic_punctuation: Add punctuation to transcripts. + enable_spoken_punctuation: Include spoken punctuation in transcript. + enable_spoken_emojis: Include spoken emojis in transcript. + profanity_filter: Filter profanity from transcript. + enable_word_time_offsets: Include timing information for each word. + enable_word_confidence: Include confidence scores for each word. + enable_interim_results: Stream partial recognition results. + enable_voice_activity_events: Detect voice activity in audio. + """ + + languages: Union[Language, List[Language]] = Field(default_factory=lambda: [Language.EN_US]) + model: Optional[str] = "latest_long" + use_separate_recognition_per_channel: Optional[bool] = False + enable_automatic_punctuation: Optional[bool] = True + enable_spoken_punctuation: Optional[bool] = False + enable_spoken_emojis: Optional[bool] = False + profanity_filter: Optional[bool] = False + enable_word_time_offsets: Optional[bool] = False + enable_word_confidence: Optional[bool] = False + enable_interim_results: Optional[bool] = True + enable_voice_activity_events: Optional[bool] = False + + @field_validator("languages", mode="before") + @classmethod + def validate_languages(cls, v) -> List[Language]: + if isinstance(v, Language): + return [v] + return v + + @property + def language_list(self) -> List[Language]: + """Get languages as a guaranteed list.""" + assert isinstance(self.languages, list) + return self.languages + + def __init__( + self, + *, + credentials: Optional[str] = None, + credentials_path: Optional[str] = None, + location: str = "global", + sample_rate: Optional[int] = None, + params: InputParams = InputParams(), + **kwargs, + ): + """Initialize the Google STT service. + + Args: + credentials: JSON string containing Google Cloud service account credentials. + credentials_path: Path to service account credentials JSON file. + location: Google Cloud location (e.g., "global", "us-central1"). + sample_rate: Audio sample rate in Hertz. + params: Configuration parameters for the service. + **kwargs: Additional arguments passed to STTService. + + Raises: + ValueError: If neither credentials nor credentials_path is provided. + ValueError: If project ID is not found in credentials. + """ + super().__init__(sample_rate=sample_rate, **kwargs) + + self._location = location + self._stream = None + self._config = None + self._request_queue = asyncio.Queue() + self._streaming_task = None + + # Used for keep-alive logic + self._stream_start_time = 0 + self._last_audio_input = [] + self._audio_input = [] + self._result_end_time = 0 + self._is_final_end_time = 0 + self._final_request_end_time = 0 + self._bridging_offset = 0 + self._last_transcript_was_final = False + self._new_stream = True + self._restart_counter = 0 + + # Configure client options based on location + client_options = None + if self._location != "global": + client_options = ClientOptions(api_endpoint=f"{self._location}-speech.googleapis.com") + + # Extract project ID and create client + if credentials: + json_account_info = json.loads(credentials) + self._project_id = json_account_info.get("project_id") + creds = service_account.Credentials.from_service_account_info(json_account_info) + elif credentials_path: + with open(credentials_path) as f: + json_account_info = json.load(f) + self._project_id = json_account_info.get("project_id") + creds = service_account.Credentials.from_service_account_file(credentials_path) + else: + raise ValueError("Either credentials or credentials_path must be provided") + + if not self._project_id: + raise ValueError("Project ID not found in credentials") + + self._client = speech_v2.SpeechAsyncClient(credentials=creds, client_options=client_options) + + self._settings = { + "language_codes": [ + self.language_to_service_language(lang) for lang in params.language_list + ], + "model": params.model, + "use_separate_recognition_per_channel": params.use_separate_recognition_per_channel, + "enable_automatic_punctuation": params.enable_automatic_punctuation, + "enable_spoken_punctuation": params.enable_spoken_punctuation, + "enable_spoken_emojis": params.enable_spoken_emojis, + "profanity_filter": params.profanity_filter, + "enable_word_time_offsets": params.enable_word_time_offsets, + "enable_word_confidence": params.enable_word_confidence, + "enable_interim_results": params.enable_interim_results, + "enable_voice_activity_events": params.enable_voice_activity_events, + } + + def language_to_service_language(self, language: Language | List[Language]) -> str | List[str]: + """Convert Language enum(s) to Google STT language code(s). + + Args: + language: Single Language enum or list of Language enums. + + Returns: + str | List[str]: Google STT language code(s). + """ + if isinstance(language, list): + return [language_to_google_stt_language(lang) or "en-US" for lang in language] + return language_to_google_stt_language(language) or "en-US" + + async def _reconnect_if_needed(self): + """Reconnect the stream if it's currently active.""" + if self._streaming_task: + logger.debug("Reconnecting stream due to configuration changes") + await self._disconnect() + await self._connect() + + async def set_language(self, language: Language): + """Update the service's recognition language. + + A convenience method for setting a single language. + + Args: + language: New language for recognition. + """ + logger.debug(f"Switching STT language to: {language}") + await self.set_languages([language]) + + async def set_languages(self, languages: List[Language]): + """Update the service's recognition languages. + + Args: + languages: List of languages for recognition. First language is primary. + """ + logger.debug(f"Switching STT languages to: {languages}") + self._settings["language_codes"] = [ + self.language_to_service_language(lang) for lang in languages + ] + # Recreate stream with new languages + await self._reconnect_if_needed() + + async def set_model(self, model: str): + """Update the service's recognition model.""" + logger.debug(f"Switching STT model to: {model}") + await super().set_model(model) + self._settings["model"] = model + # Recreate stream with new model + await self._reconnect_if_needed() + + async def start(self, frame: StartFrame): + await super().start(frame) + await self._connect() + + async def stop(self, frame: EndFrame): + await super().stop(frame) + await self._disconnect() + + async def cancel(self, frame: CancelFrame): + await super().cancel(frame) + await self._disconnect() + + async def update_options( + self, + *, + languages: Optional[List[Language]] = None, + model: Optional[str] = None, + enable_automatic_punctuation: Optional[bool] = None, + enable_spoken_punctuation: Optional[bool] = None, + enable_spoken_emojis: Optional[bool] = None, + profanity_filter: Optional[bool] = None, + enable_word_time_offsets: Optional[bool] = None, + enable_word_confidence: Optional[bool] = None, + enable_interim_results: Optional[bool] = None, + enable_voice_activity_events: Optional[bool] = None, + location: Optional[str] = None, + ) -> None: + """Update service options dynamically. + + Args: + languages: New list of recongition languages. + model: New recognition model. + enable_automatic_punctuation: Enable/disable automatic punctuation. + enable_spoken_punctuation: Enable/disable spoken punctuation. + enable_spoken_emojis: Enable/disable spoken emojis. + profanity_filter: Enable/disable profanity filter. + enable_word_time_offsets: Enable/disable word timing info. + enable_word_confidence: Enable/disable word confidence scores. + enable_interim_results: Enable/disable interim results. + enable_voice_activity_events: Enable/disable voice activity detection. + location: New Google Cloud location. + + Note: + Changes that affect the streaming configuration will cause + the stream to be reconnected. + """ + # Update settings with new values + if languages is not None: + logger.debug(f"Updating language to: {languages}") + self._settings["language_codes"] = [ + self.language_to_service_language(lang) for lang in languages + ] + + if model is not None: + logger.debug(f"Updating model to: {model}") + self._settings["model"] = model + + if enable_automatic_punctuation is not None: + logger.debug(f"Updating automatic punctuation to: {enable_automatic_punctuation}") + self._settings["enable_automatic_punctuation"] = enable_automatic_punctuation + + if enable_spoken_punctuation is not None: + logger.debug(f"Updating spoken punctuation to: {enable_spoken_punctuation}") + self._settings["enable_spoken_punctuation"] = enable_spoken_punctuation + + if enable_spoken_emojis is not None: + logger.debug(f"Updating spoken emojis to: {enable_spoken_emojis}") + self._settings["enable_spoken_emojis"] = enable_spoken_emojis + + if profanity_filter is not None: + logger.debug(f"Updating profanity filter to: {profanity_filter}") + self._settings["profanity_filter"] = profanity_filter + + if enable_word_time_offsets is not None: + logger.debug(f"Updating word time offsets to: {enable_word_time_offsets}") + self._settings["enable_word_time_offsets"] = enable_word_time_offsets + + if enable_word_confidence is not None: + logger.debug(f"Updating word confidence to: {enable_word_confidence}") + self._settings["enable_word_confidence"] = enable_word_confidence + + if enable_interim_results is not None: + logger.debug(f"Updating interim results to: {enable_interim_results}") + self._settings["enable_interim_results"] = enable_interim_results + + if enable_voice_activity_events is not None: + logger.debug(f"Updating voice activity events to: {enable_voice_activity_events}") + self._settings["enable_voice_activity_events"] = enable_voice_activity_events + + if location is not None: + logger.debug(f"Updating location to: {location}") + self._location = location + + # Reconnect the stream for updates + await self._reconnect_if_needed() + + async def _connect(self): + """Initialize streaming recognition config and stream.""" + logger.debug("Connecting to Google Speech-to-Text") + + # Set stream start time + self._stream_start_time = int(time.time() * 1000) + self._new_stream = True + + self._config = cloud_speech.StreamingRecognitionConfig( + config=cloud_speech.RecognitionConfig( + explicit_decoding_config=cloud_speech.ExplicitDecodingConfig( + encoding=cloud_speech.ExplicitDecodingConfig.AudioEncoding.LINEAR16, + sample_rate_hertz=self.sample_rate, + audio_channel_count=1, + ), + language_codes=self._settings["language_codes"], + model=self._settings["model"], + features=cloud_speech.RecognitionFeatures( + enable_automatic_punctuation=self._settings["enable_automatic_punctuation"], + enable_spoken_punctuation=self._settings["enable_spoken_punctuation"], + enable_spoken_emojis=self._settings["enable_spoken_emojis"], + profanity_filter=self._settings["profanity_filter"], + enable_word_time_offsets=self._settings["enable_word_time_offsets"], + enable_word_confidence=self._settings["enable_word_confidence"], + ), + ), + streaming_features=cloud_speech.StreamingRecognitionFeatures( + enable_voice_activity_events=self._settings["enable_voice_activity_events"], + interim_results=self._settings["enable_interim_results"], + ), + ) + + self._streaming_task = self.create_task(self._stream_audio()) + + async def _disconnect(self): + """Clean up streaming recognition resources.""" + if self._streaming_task: + logger.debug("Disconnecting from Google Speech-to-Text") + # Send sentinel value to stop request generator + await self._request_queue.put(None) + await self.cancel_task(self._streaming_task) + self._streaming_task = None + # Clear any remaining items in the queue + while not self._request_queue.empty(): + try: + self._request_queue.get_nowait() + self._request_queue.task_done() + except asyncio.QueueEmpty: + break + + async def _request_generator(self): + """Generates requests for the streaming recognize method.""" + recognizer_path = f"projects/{self._project_id}/locations/{self._location}/recognizers/_" + logger.trace(f"Using recognizer path: {recognizer_path}") + + try: + # Send initial config + yield cloud_speech.StreamingRecognizeRequest( + recognizer=recognizer_path, + streaming_config=self._config, + ) + + while True: + try: + audio_data = await self._request_queue.get() + if audio_data is None: # Sentinel value to stop + break + + # Check streaming limit + if (int(time.time() * 1000) - self._stream_start_time) > self.STREAMING_LIMIT: + logger.debug("Streaming limit reached, initiating graceful reconnection") + # Instead of immediate reconnection, we'll break and let the stream close naturally + self._last_audio_input = self._audio_input + self._audio_input = [] + self._restart_counter += 1 + # Put the current audio chunk back in the queue + await self._request_queue.put(audio_data) + break + + self._audio_input.append(audio_data) + yield cloud_speech.StreamingRecognizeRequest(audio=audio_data) + + except asyncio.CancelledError: + break + finally: + self._request_queue.task_done() + + except Exception as e: + logger.error(f"Error in request generator: {e}") + raise + + async def _stream_audio(self): + """Handle bi-directional streaming with Google STT.""" + try: + while True: + try: + # Start bi-directional streaming + streaming_recognize = await self._client.streaming_recognize( + requests=self._request_generator() + ) + + # Process responses + await self._process_responses(streaming_recognize) + + # If we're here, check if we need to reconnect + if (int(time.time() * 1000) - self._stream_start_time) > self.STREAMING_LIMIT: + logger.debug("Reconnecting stream after timeout") + # Reset stream start time + self._stream_start_time = int(time.time() * 1000) + continue + else: + # Normal stream end + break + + except Exception as e: + logger.warning(f"{self} Reconnecting: {e}") + + await asyncio.sleep(1) # Brief delay before reconnecting + self._stream_start_time = int(time.time() * 1000) + continue + + except Exception as e: + logger.error(f"Error in streaming task: {e}") + await self.push_frame(ErrorFrame(str(e))) + + async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]: + """Process an audio chunk for STT transcription.""" + if self._streaming_task: + # Queue the audio data + await self._request_queue.put(audio) + yield None + + async def _process_responses(self, streaming_recognize): + """Process streaming recognition responses.""" + try: + async for response in streaming_recognize: + # Check streaming limit + if (int(time.time() * 1000) - self._stream_start_time) > self.STREAMING_LIMIT: + logger.debug("Stream timeout reached in response processing") + break + + if not response.results: + continue + + for result in response.results: + if not result.alternatives: + continue + + transcript = result.alternatives[0].transcript + if not transcript: + continue + + primary_language = self._settings["language_codes"][0] + + if result.is_final: + self._last_transcript_was_final = True + await self.push_frame( + TranscriptionFrame(transcript, "", time_now_iso8601(), primary_language) + ) + else: + self._last_transcript_was_final = False + await self.push_frame( + InterimTranscriptionFrame( + transcript, "", time_now_iso8601(), primary_language + ) + ) + + except Exception as e: + logger.error(f"Error processing Google STT responses: {e}") + + # Re-raise the exception to let it propagate (e.g. in the case of a timeout, propagate to _stream_audio to reconnect) + raise diff --git a/src/pipecat/services/google/rtvi.py b/src/pipecat/services/google/rtvi.py new file mode 100644 index 000000000..88e67e6c6 --- /dev/null +++ b/src/pipecat/services/google/rtvi.py @@ -0,0 +1,54 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import List, Literal, Optional + +from pydantic import BaseModel + +from pipecat.frames.frames import Frame +from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.processors.frameworks.rtvi import RTVIObserver +from pipecat.services.google.frames import LLMSearchOrigin, LLMSearchResponseFrame + + +class RTVISearchResponseMessageData(BaseModel): + search_result: Optional[str] + rendered_content: Optional[str] + origins: List[LLMSearchOrigin] + + +class RTVIBotLLMSearchResponseMessage(BaseModel): + label: Literal["rtvi-ai"] = "rtvi-ai" + type: Literal["bot-llm-search-response"] = "bot-llm-search-response" + data: RTVISearchResponseMessageData + + +class GoogleRTVIObserver(RTVIObserver): + def __init__(self, rtvi: FrameProcessor): + super().__init__(rtvi) + + async def on_push_frame( + self, + src: FrameProcessor, + dst: FrameProcessor, + frame: Frame, + direction: FrameDirection, + timestamp: int, + ): + await super().on_push_frame(src, dst, frame, direction, timestamp) + + if isinstance(frame, LLMSearchResponseFrame): + await self._handle_llm_search_response_frame(frame) + + async def _handle_llm_search_response_frame(self, frame: LLMSearchResponseFrame): + message = RTVIBotLLMSearchResponseMessage( + data=RTVISearchResponseMessageData( + search_result=frame.search_result, + origins=frame.origins, + rendered_content=frame.rendered_content, + ) + ) + await self.push_transport_message_urgent(message) diff --git a/src/pipecat/services/grok.py b/src/pipecat/services/grok.py index 7221cc09e..faed13050 100644 --- a/src/pipecat/services/grok.py +++ b/src/pipecat/services/grok.py @@ -7,7 +7,7 @@ import json from dataclasses import dataclass -from typing import Optional +from typing import Any, Mapping, Optional from loguru import logger @@ -17,6 +17,7 @@ from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContext, OpenAILLMContextFrame, ) +from pipecat.processors.frame_processor import FrameDirection from pipecat.services.openai import ( OpenAIAssistantContextAggregator, OpenAILLMService, @@ -24,95 +25,15 @@ from pipecat.services.openai import ( ) -class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator): - """Custom assistant context aggregator for Grok that handles empty content requirement.""" - - async def _push_aggregation(self): - if not ( - self._aggregation or self._function_call_result or self._pending_image_frame_message - ): - return - - run_llm = False - properties: Optional[FunctionCallResultProperties] = None - - aggregation = self._aggregation - self._reset() - - try: - if self._function_call_result: - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - # Grok requires an empty content field for function calls - self._context.add_message( - { - "role": "assistant", - "content": "", # Required by Grok - "tool_calls": [ - { - "id": frame.tool_call_id, - "function": { - "name": frame.function_name, - "arguments": json.dumps(frame.arguments), - }, - "type": "function", - } - ], - } - ) - self._context.add_message( - { - "role": "tool", - "content": json.dumps(frame.result), - "tool_call_id": frame.tool_call_id, - } - ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior is to run the LLM if there are no function calls in progress - run_llm = not bool(self._function_calls_in_progress) - - else: - self._context.add_message({"role": "assistant", "content": aggregation}) - - if self._pending_image_frame_message: - frame = self._pending_image_frame_message - self._pending_image_frame_message = None - self._context.add_image_frame_message( - format=frame.user_image_raw_frame.format, - size=frame.user_image_raw_frame.size, - image=frame.user_image_raw_frame.image, - text=frame.text, - ) - run_llm = True - - if run_llm: - await self._user_context_aggregator.push_context_frame() - - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() - - frame = OpenAILLMContextFrame(self._context) - await self.push_frame(frame) - - except Exception as e: - logger.error(f"Error processing frame: {e}") - - @dataclass class GrokContextAggregatorPair: _user: "OpenAIUserContextAggregator" - _assistant: "GrokAssistantContextAggregator" + _assistant: "OpenAIAssistantContextAggregator" def user(self) -> "OpenAIUserContextAggregator": return self._user - def assistant(self) -> "GrokAssistantContextAggregator": + def assistant(self) -> "OpenAIAssistantContextAggregator": return self._assistant @@ -125,7 +46,7 @@ class GrokLLMService(OpenAILLMService): Args: api_key (str): The API key for accessing Grok's API base_url (str, optional): The base URL for Grok API. Defaults to "https://api.x.ai/v1" - model (str, optional): The model identifier to use. Defaults to "grok-beta" + model (str, optional): The model identifier to use. Defaults to "grok-2" **kwargs: Additional keyword arguments passed to OpenAILLMService """ @@ -134,7 +55,7 @@ class GrokLLMService(OpenAILLMService): *, api_key: str, base_url: str = "https://api.x.ai/v1", - model: str = "grok-beta", + model: str = "grok-2", **kwargs, ): super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs) @@ -206,12 +127,34 @@ class GrokLLMService(OpenAILLMService): if tokens.completion_tokens > self._completion_tokens: self._completion_tokens = tokens.completion_tokens - @staticmethod def create_context_aggregator( - context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True + self, + context: OpenAILLMContext, + *, + user_kwargs: Mapping[str, Any] = {}, + assistant_kwargs: Mapping[str, Any] = {}, ) -> GrokContextAggregatorPair: - user = OpenAIUserContextAggregator(context) - assistant = GrokAssistantContextAggregator( - user, expect_stripped_words=assistant_expect_stripped_words - ) + """Create an instance of GrokContextAggregatorPair from an + OpenAILLMContext. Constructor keyword arguments for both the user and + assistant aggregators can be provided. + + Args: + context (OpenAILLMContext): The LLM context. + user_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the user context aggregator constructor. Defaults + to an empty mapping. + assistant_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the assistant context aggregator + constructor. Defaults to an empty mapping. + + Returns: + GrokContextAggregatorPair: A pair of context aggregators, one for + the user and one for the assistant, encapsulated in an + GrokContextAggregatorPair. + + """ + context.set_llm_adapter(self.get_llm_adapter()) + + user = OpenAIUserContextAggregator(context, **user_kwargs) + assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs) return GrokContextAggregatorPair(_user=user, _assistant=assistant) diff --git a/src/pipecat/services/groq.py b/src/pipecat/services/groq.py index dc30247d9..bf0304df2 100644 --- a/src/pipecat/services/groq.py +++ b/src/pipecat/services/groq.py @@ -5,9 +5,25 @@ # -from loguru import logger +from typing import AsyncGenerator, Optional +from loguru import logger +from pydantic import BaseModel + +from pipecat.frames.frames import Frame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame +from pipecat.services.ai_services import TTSService +from pipecat.services.base_whisper import BaseWhisperSTTService, Transcription from pipecat.services.openai import OpenAILLMService +from pipecat.transcriptions.language import Language + +try: + from groq import AsyncGroq +except ModuleNotFoundError as e: + logger.error(f"Exception: {e}") + logger.error( + "In order to use Groq, you need to `pip install pipecat-ai[groq]`. Also, set a `GROQ_API_KEY` environment variable." + ) + raise Exception(f"Missing module: {e}") class GroqLLMService(OpenAILLMService): @@ -19,7 +35,7 @@ class GroqLLMService(OpenAILLMService): Args: api_key (str): The API key for accessing Groq's API base_url (str, optional): The base URL for Groq API. Defaults to "https://api.groq.com/openai/v1" - model (str, optional): The model identifier to use. Defaults to "llama-3.1-70b-versatile" + model (str, optional): The model identifier to use. Defaults to "llama-3.3-70b-versatile" **kwargs: Additional keyword arguments passed to OpenAILLMService """ @@ -28,7 +44,7 @@ class GroqLLMService(OpenAILLMService): *, api_key: str, base_url: str = "https://api.groq.com/openai/v1", - model: str = "llama-3.1-70b-versatile", + model: str = "llama-3.3-70b-versatile", **kwargs, ): super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs) @@ -37,3 +53,125 @@ class GroqLLMService(OpenAILLMService): """Create OpenAI-compatible client for Groq API endpoint.""" logger.debug(f"Creating Groq client with api {base_url}") return super().create_client(api_key, base_url, **kwargs) + + +class GroqSTTService(BaseWhisperSTTService): + """Groq Whisper speech-to-text service. + + Uses Groq's Whisper API to convert audio to text. Requires a Groq API key + set via the api_key parameter or GROQ_API_KEY environment variable. + + Args: + model: Whisper model to use. Defaults to "whisper-large-v3-turbo". + api_key: Groq API key. Defaults to None. + base_url: API base URL. Defaults to "https://api.groq.com/openai/v1". + language: Language of the audio input. Defaults to English. + prompt: Optional text to guide the model's style or continue a previous segment. + temperature: Optional sampling temperature between 0 and 1. Defaults to 0.0. + **kwargs: Additional arguments passed to BaseWhisperSTTService. + """ + + def __init__( + self, + *, + model: str = "whisper-large-v3-turbo", + api_key: Optional[str] = None, + base_url: str = "https://api.groq.com/openai/v1", + language: Optional[Language] = Language.EN, + prompt: Optional[str] = None, + temperature: Optional[float] = None, + **kwargs, + ): + super().__init__( + model=model, + api_key=api_key, + base_url=base_url, + language=language, + prompt=prompt, + temperature=temperature, + **kwargs, + ) + + async def _transcribe(self, audio: bytes) -> Transcription: + assert self._language is not None # Assigned in the BaseWhisperSTTService class + + # Build kwargs dict with only set parameters + kwargs = { + "file": ("audio.wav", audio, "audio/wav"), + "model": self.model_name, + "response_format": "json", + "language": self._language, + } + + if self._prompt is not None: + kwargs["prompt"] = self._prompt + + if self._temperature is not None: + kwargs["temperature"] = self._temperature + + return await self._client.audio.transcriptions.create(**kwargs) + + +class GroqTTSService(TTSService): + class InputParams(BaseModel): + language: Optional[Language] = Language.EN + speed: Optional[float] = 1.0 + seed: Optional[int] = None + + GROQ_SAMPLE_RATE = 48000 # Groq TTS only supports 48kHz sample rate + + def __init__( + self, + *, + api_key: str, + output_format: str = "wav", + params: InputParams = InputParams(), + model_name: str = "playai-tts", + voice_id: str = "Celeste-PlayAI", + sample_rate: Optional[int] = GROQ_SAMPLE_RATE, + **kwargs, + ): + if sample_rate != self.GROQ_SAMPLE_RATE: + logger.warning(f"Groq TTS only supports {self.GROQ_SAMPLE_RATE}Hz sample rate. ") + super().__init__( + pause_frame_processing=True, + sample_rate=sample_rate, + **kwargs, + ) + + self._api_key = api_key + self._model_name = model_name + self._output_format = output_format + self._voice_id = voice_id + self._params = params + + self._client = AsyncGroq(api_key=self._api_key) + + def can_generate_metrics(self) -> bool: + return True + + async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: + logger.debug(f"{self}: Generating TTS [{text}]") + measuring_ttfb = True + await self.start_ttfb_metrics() + yield TTSStartedFrame() + + response = await self._client.audio.speech.create( + model=self._model_name, + voice=self._voice_id, + response_format=self._output_format, + input=text, + ) + + async for data in response.iter_bytes(): + if measuring_ttfb: + await self.stop_ttfb_metrics() + measuring_ttfb = False + # remove wav header if present + if data.startswith(b"RIFF"): + data = data[44:] + if len(data) == 0: + continue + yield TTSAudioRawFrame(data, self.sample_rate, 1) + + yield TTSStoppedFrame() diff --git a/src/pipecat/services/lmnt.py b/src/pipecat/services/lmnt.py index 327901423..d3cc92603 100644 --- a/src/pipecat/services/lmnt.py +++ b/src/pipecat/services/lmnt.py @@ -5,7 +5,7 @@ # import json -from typing import AsyncGenerator +from typing import AsyncGenerator, Optional from loguru import logger @@ -21,8 +21,7 @@ from pipecat.frames.frames import ( TTSStoppedFrame, ) from pipecat.processors.frame_processor import FrameDirection -from pipecat.services.ai_services import TTSService -from pipecat.services.websocket_service import WebsocketService +from pipecat.services.ai_services import InterruptibleTTSService from pipecat.transcriptions.language import Language # See .env.example for LMNT configuration needed @@ -36,7 +35,7 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -def language_to_lmnt_language(language: Language) -> str | None: +def language_to_lmnt_language(language: Language) -> Optional[str]: BASE_LANGUAGES = { Language.DE: "de", Language.EN: "en", @@ -60,37 +59,36 @@ def language_to_lmnt_language(language: Language) -> str | None: return result -class LmntTTSService(TTSService, WebsocketService): +class LmntTTSService(InterruptibleTTSService): def __init__( self, *, api_key: str, voice_id: str, - sample_rate: int = 24000, + sample_rate: Optional[int] = None, language: Language = Language.EN, **kwargs, ): - TTSService.__init__( - self, + super().__init__( push_stop_frames=True, + pause_frame_processing=True, sample_rate=sample_rate, **kwargs, ) - WebsocketService.__init__(self) self._api_key = api_key self._voice_id = voice_id self._settings = { - "sample_rate": sample_rate, "language": self.language_to_service_language(language), "format": "raw", # Use raw format for direct PCM data } self._started = False + self._receive_task = None def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_lmnt_language(language) async def start(self, frame: StartFrame): @@ -113,18 +111,22 @@ class LmntTTSService(TTSService, WebsocketService): async def _connect(self): await self._connect_websocket() - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + if not self._receive_task: + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): - await self._disconnect_websocket() - if self._receive_task: await self.cancel_task(self._receive_task) self._receive_task = None + await self._disconnect_websocket() + async def _connect_websocket(self): """Connect to LMNT websocket.""" try: + if self._websocket: + return + logger.debug("Connecting to LMNT") # Build initial connection message @@ -132,7 +134,7 @@ class LmntTTSService(TTSService, WebsocketService): "X-API-Key": self._api_key, "voice": self._voice_id, "format": self._settings["format"], - "sample_rate": self._settings["sample_rate"], + "sample_rate": self.sample_rate, "language": self._settings["language"], } @@ -145,6 +147,7 @@ class LmntTTSService(TTSService, WebsocketService): except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): """Disconnect from LMNT websocket.""" @@ -153,8 +156,9 @@ class LmntTTSService(TTSService, WebsocketService): if self._websocket: logger.debug("Disconnecting from LMNT") - # Send EOF message before closing - await self._websocket.send(json.dumps({"eof": True})) + # NOTE(aleix): sending EOF message before closing is causing + # errors on the websocket, so we just skip it for now. + # await self._websocket.send(json.dumps({"eof": True})) await self._websocket.close() self._websocket = None @@ -167,6 +171,11 @@ class LmntTTSService(TTSService, WebsocketService): return self._websocket raise Exception("Websocket not connected") + async def flush_audio(self): + if not self._websocket: + return + await self._get_websocket().send(json.dumps({"flush": True})) + async def _receive_messages(self): """Receive messages from LMNT websocket.""" async for message in self._get_websocket(): @@ -175,7 +184,7 @@ class LmntTTSService(TTSService, WebsocketService): await self.stop_ttfb_metrics() frame = TTSAudioRawFrame( audio=message, - sample_rate=self._settings["sample_rate"], + sample_rate=self.sample_rate, num_channels=1, ) await self.push_frame(frame) @@ -193,7 +202,7 @@ class LmntTTSService(TTSService, WebsocketService): async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: """Generate TTS audio from text.""" - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: if not self._websocket: diff --git a/src/pipecat/services/neuphonic.py b/src/pipecat/services/neuphonic.py new file mode 100644 index 000000000..407e54a83 --- /dev/null +++ b/src/pipecat/services/neuphonic.py @@ -0,0 +1,345 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import base64 +import json +from typing import Any, AsyncGenerator, Mapping, Optional + +from loguru import logger +from pydantic import BaseModel + +from pipecat.frames.frames import ( + BotStoppedSpeakingFrame, + CancelFrame, + EndFrame, + ErrorFrame, + Frame, + LLMFullResponseEndFrame, + StartFrame, + StartInterruptionFrame, + TTSAudioRawFrame, + TTSSpeakFrame, + TTSStartedFrame, + TTSStoppedFrame, +) +from pipecat.processors.frame_processor import FrameDirection +from pipecat.services.ai_services import InterruptibleTTSService, TTSService +from pipecat.transcriptions.language import Language + +# See .env.example for Neuphonic configuration needed +try: + import websockets + from pyneuphonic import Neuphonic, TTSConfig +except ModuleNotFoundError as e: + logger.error(f"Exception: {e}") + logger.error( + "In order to use Neuphonic, you need to `pip install pipecat-ai[neuphonic]`. Also, set `NEUPHONIC_API_KEY` environment variable." + ) + raise Exception(f"Missing module: {e}") + + +def language_to_neuphonic_lang_code(language: Language) -> Optional[str]: + BASE_LANGUAGES = { + Language.DE: "de", + Language.EN: "en", + Language.ES: "es", + Language.NL: "nl", + Language.AR: "ar", + Language.FR: "fr", + Language.PT: "pt", + Language.RU: "ru", + Language.HI: "HI", + Language.ZH: "zh", + } + + result = BASE_LANGUAGES.get(language) + + # If not found in base languages, try to find the base language from a variant + if not result: + # Convert enum value to string and get the base language part (e.g. es-ES -> es) + lang_str = str(language.value) + base_code = lang_str.split("-")[0].lower() + # Look up the base code in our supported languages + result = base_code if base_code in BASE_LANGUAGES.values() else None + + return result + + +class NeuphonicTTSService(InterruptibleTTSService): + class InputParams(BaseModel): + language: Optional[Language] = Language.EN + speed: Optional[float] = 1.0 + + def __init__( + self, + *, + api_key: str, + voice_id: Optional[str] = None, + url: str = "wss://api.neuphonic.com", + sample_rate: Optional[int] = 22050, + encoding: str = "pcm_linear", + params: InputParams = InputParams(), + **kwargs, + ): + super().__init__( + aggregate_sentences=True, + push_text_frames=False, + push_stop_frames=True, + stop_frame_timeout_s=2.0, + sample_rate=sample_rate, + **kwargs, + ) + + self._api_key = api_key + self._url = url + self._settings = { + "lang_code": self.language_to_service_language(params.language), + "speed": params.speed, + "encoding": encoding, + "sampling_rate": sample_rate, + } + self.set_voice(voice_id) + + # Indicates if we have sent TTSStartedFrame. It will reset to False when + # there's an interruption or TTSStoppedFrame. + self._started = False + self._cumulative_time = 0 + + def can_generate_metrics(self) -> bool: + return True + + def language_to_service_language(self, language: Language) -> Optional[str]: + return language_to_neuphonic_lang_code(language) + + async def _update_settings(self, settings: Mapping[str, Any]): + if "voice_id" in settings: + self.set_voice(settings["voice_id"]) + + await super()._update_settings(settings) + await self._disconnect() + await self._connect() + logger.info(f"Switching TTS to settings: [{self._settings}]") + + async def start(self, frame: StartFrame): + await super().start(frame) + await self._connect() + + async def stop(self, frame: EndFrame): + await super().stop(frame) + await self._disconnect() + + async def cancel(self, frame: CancelFrame): + await super().cancel(frame) + await self._disconnect() + + async def flush_audio(self): + if self._websocket: + msg = {"text": ""} + await self._websocket.send(json.dumps(msg)) + + async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM): + await super().push_frame(frame, direction) + if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)): + self._started = False + + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + # If we received a TTSSpeakFrame and the LLM response included text (it + # might be that it's only a function calling response) we pause + # processing more frames until we receive a BotStoppedSpeakingFrame. + if isinstance(frame, TTSSpeakFrame): + await self.pause_processing_frames() + elif isinstance(frame, LLMFullResponseEndFrame) and self._started: + await self.pause_processing_frames() + elif isinstance(frame, BotStoppedSpeakingFrame): + await self.resume_processing_frames() + + async def _connect(self): + await self._connect_websocket() + + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) + self._keepalive_task = self.create_task(self._keepalive_task_handler()) + + async def _disconnect(self): + if self._receive_task: + await self.cancel_task(self._receive_task) + self._receive_task = None + + if self._keepalive_task: + await self.cancel_task(self._keepalive_task) + self._keepalive_task = None + + await self._disconnect_websocket() + + async def _connect_websocket(self): + try: + logger.debug("Connecting to Neuphonic") + + tts_config = { + **self._settings, + "voice_id": self._voice_id, + } + + query_params = [f"api_key={self._api_key}"] + for key, value in tts_config.items(): + if value is not None: + query_params.append(f"{key}={value}") + + url = f"{self._url}/speak/{self._settings['lang_code']}?{'&'.join(query_params)}" + + self._websocket = await websockets.connect(url) + + except Exception as e: + logger.error(f"{self} initialization error: {e}") + self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") + + async def _disconnect_websocket(self): + try: + await self.stop_all_metrics() + + if self._websocket: + logger.debug("Disconnecting from Neuphonic") + await self._websocket.close() + self._websocket = None + + self._started = False + except Exception as e: + logger.error(f"{self} error closing websocket: {e}") + + async def _receive_messages(self): + async for message in self._websocket: + if isinstance(message, str): + msg = json.loads(message) + if msg.get("data", {}).get("audio") is not None: + await self.stop_ttfb_metrics() + + audio = base64.b64decode(msg["data"]["audio"]) + frame = TTSAudioRawFrame(audio, self.sample_rate, 1) + await self.push_frame(frame) + + async def _keepalive_task_handler(self): + while True: + await asyncio.sleep(10) + await self._send_text("") + + async def _send_text(self, text: str): + if self._websocket: + msg = {"text": text} + logger.debug(f"Sending text to websocket: {msg}") + await self._websocket.send(json.dumps(msg)) + + async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: + logger.debug(f"Generating TTS: [{text}]") + + try: + if not self._websocket: + await self._connect() + + try: + if not self._started: + await self.start_ttfb_metrics() + yield TTSStartedFrame() + self._started = True + self._cumulative_time = 0 + + await self._send_text(text) + await self.start_tts_usage_metrics(text) + except Exception as e: + logger.error(f"{self} error sending message: {e}") + yield TTSStoppedFrame() + await self._disconnect() + await self._connect() + return + yield None + except Exception as e: + logger.error(f"{self} exception: {e}") + + +class NeuphonicHttpTTSService(TTSService): + """Neuphonic Text-to-Speech service using HTTP streaming. + + Args: + api_key: Neuphonic API key + voice_id: ID of the voice to use + url: Base URL for the Neuphonic API (default: "https://api.neuphonic.com") + sample_rate: Sample rate for audio output (default: 22050Hz) + encoding: Audio encoding format (default: "pcm_linear") + params: Additional parameters for TTS generation including language and speed + **kwargs: Additional keyword arguments passed to the parent class + """ + + class InputParams(BaseModel): + language: Optional[Language] = Language.EN + speed: Optional[float] = 1.0 + + def __init__( + self, + *, + api_key: str, + voice_id: Optional[str] = None, + url: str = "https://api.neuphonic.com", + sample_rate: Optional[int] = 22050, + encoding: str = "pcm_linear", + params: InputParams = InputParams(), + **kwargs, + ): + super().__init__(sample_rate=sample_rate, **kwargs) + + self._api_key = api_key + self._url = url + self._settings = { + "lang_code": self.language_to_service_language(params.language), + "speed": params.speed, + "encoding": encoding, + "sampling_rate": sample_rate, + } + self.set_voice(voice_id) + + def can_generate_metrics(self) -> bool: + return True + + async def start(self, frame: StartFrame): + await super().start(frame) + + async def flush_audio(self): + pass + + async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: + """Generate speech from text using Neuphonic streaming API. + + Args: + text: The text to convert to speech + Yields: + Frames containing audio data and status information + """ + logger.debug(f"Generating TTS: [{text}]") + + client = Neuphonic(api_key=self._api_key, base_url=self._url.replace("https://", "")) + + sse = client.tts.AsyncSSEClient() + + try: + await self.start_ttfb_metrics() + response = sse.send(text, TTSConfig(**self._settings, voice_id=self._voice_id)) + + await self.start_tts_usage_metrics(text) + yield TTSStartedFrame() + + async for message in response: + if message.status_code != 200: + logger.error(f"{self} error: {message.errors}") + yield ErrorFrame(error=f"Neuphonic API error: {message.errors}") + + await self.stop_ttfb_metrics() + yield TTSAudioRawFrame(message.data.audio, self.sample_rate, 1) + except Exception as e: + logger.error(f"Error in run_tts: {e}") + yield ErrorFrame(error=str(e)) + finally: + yield TTSStoppedFrame() diff --git a/src/pipecat/services/openai.py b/src/pipecat/services/openai.py index ab81a1abb..3f85d917c 100644 --- a/src/pipecat/services/openai.py +++ b/src/pipecat/services/openai.py @@ -8,33 +8,39 @@ import base64 import io import json from dataclasses import dataclass -from typing import Any, AsyncGenerator, Dict, List, Literal, Optional +from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional import aiohttp import httpx from loguru import logger +from openai import ( + NOT_GIVEN, + AsyncOpenAI, + AsyncStream, + BadRequestError, + DefaultAsyncHttpxClient, +) +from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam from PIL import Image from pydantic import BaseModel, Field from pipecat.frames.frames import ( ErrorFrame, Frame, + FunctionCallCancelFrame, FunctionCallInProgressFrame, FunctionCallResultFrame, - FunctionCallResultProperties, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesFrame, LLMTextFrame, LLMUpdateSettingsFrame, - OpenAILLMContextAssistantTimestampFrame, - StartInterruptionFrame, + StartFrame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, URLImageRawFrame, UserImageRawFrame, - UserImageRequestFrame, VisionImageRawFrame, ) from pipecat.metrics.metrics import LLMTokenUsage @@ -47,25 +53,13 @@ from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContextFrame, ) from pipecat.processors.frame_processor import FrameDirection -from pipecat.services.ai_services import ImageGenService, LLMService, TTSService -from pipecat.utils.time import time_now_iso8601 - -try: - from openai import ( - NOT_GIVEN, - AsyncOpenAI, - AsyncStream, - BadRequestError, - DefaultAsyncHttpxClient, - ) - from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam -except ModuleNotFoundError as e: - logger.error(f"Exception: {e}") - logger.error( - "In order to use OpenAI, you need to `pip install pipecat-ai[openai]`. Also, set `OPENAI_API_KEY` environment variable." - ) - raise Exception(f"Missing module: {e}") - +from pipecat.services.ai_services import ( + ImageGenService, + LLMService, + TTSService, +) +from pipecat.services.base_whisper import BaseWhisperSTTService, Transcription +from pipecat.transcriptions.language import Language ValidVoice = Literal["alloy", "echo", "fable", "onyx", "nova", "shimmer"] @@ -103,7 +97,7 @@ class BaseOpenAILLMService(LLMService): seed: Optional[int] = Field(default_factory=lambda: NOT_GIVEN, ge=0) temperature: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=2.0) # Note: top_k is currently not supported by the OpenAI client library, - # so top_k is ignore right now. + # so top_k is ignored right now. top_k: Optional[int] = Field(default=None, ge=0) top_p: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0) max_tokens: Optional[int] = Field(default_factory=lambda: NOT_GIVEN, ge=1) @@ -118,6 +112,7 @@ class BaseOpenAILLMService(LLMService): base_url=None, organization=None, project=None, + default_headers: Mapping[str, str] | None = None, params: InputParams = InputParams(), **kwargs, ): @@ -134,10 +129,23 @@ class BaseOpenAILLMService(LLMService): } self.set_model_name(model) self._client = self.create_client( - api_key=api_key, base_url=base_url, organization=organization, project=project, **kwargs + api_key=api_key, + base_url=base_url, + organization=organization, + project=project, + default_headers=default_headers, + **kwargs, ) - def create_client(self, api_key=None, base_url=None, organization=None, project=None, **kwargs): + def create_client( + self, + api_key=None, + base_url=None, + organization=None, + project=None, + default_headers=None, + **kwargs, + ): return AsyncOpenAI( api_key=api_key, base_url=base_url, @@ -148,6 +156,7 @@ class BaseOpenAILLMService(LLMService): max_keepalive_connections=100, max_connections=1000, keepalive_expiry=None ) ), + default_headers=default_headers, ) def can_generate_metrics(self) -> bool: @@ -180,7 +189,7 @@ class BaseOpenAILLMService(LLMService): async def _stream_chat_completions( self, context: OpenAILLMContext ) -> AsyncStream[ChatCompletionChunk]: - logger.debug(f"Generating chat: {context.get_messages_for_logging()}") + logger.debug(f"{self}: Generating chat [{context.get_messages_for_logging()}]") messages: List[ChatCompletionMessageParam] = context.get_messages() @@ -259,7 +268,6 @@ class BaseOpenAILLMService(LLMService): if tool_call.function and tool_call.function.name: function_name += tool_call.function.name tool_call_id = tool_call.id - await self.call_start_function(context, function_name) if tool_call.function and tool_call.function.arguments: # Keep iterating through the response to collect all the argument fragments arguments += tool_call.function.arguments @@ -313,11 +321,15 @@ class BaseOpenAILLMService(LLMService): await self.push_frame(frame, direction) if context: - await self.push_frame(LLMFullResponseStartFrame()) - await self.start_processing_metrics() - await self._process_context(context) - await self.stop_processing_metrics() - await self.push_frame(LLMFullResponseEndFrame()) + try: + await self.push_frame(LLMFullResponseStartFrame()) + await self.start_processing_metrics() + await self._process_context(context) + except httpx.TimeoutException: + await self._call_event_handler("on_completion_timeout") + finally: + await self.stop_processing_metrics() + await self.push_frame(LLMFullResponseEndFrame()) @dataclass @@ -342,14 +354,35 @@ class OpenAILLMService(BaseOpenAILLMService): ): super().__init__(model=model, params=params, **kwargs) - @staticmethod def create_context_aggregator( - context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True + self, + context: OpenAILLMContext, + *, + user_kwargs: Mapping[str, Any] = {}, + assistant_kwargs: Mapping[str, Any] = {}, ) -> OpenAIContextAggregatorPair: - user = OpenAIUserContextAggregator(context) - assistant = OpenAIAssistantContextAggregator( - user, expect_stripped_words=assistant_expect_stripped_words - ) + """Create an instance of OpenAIContextAggregatorPair from an + OpenAILLMContext. Constructor keyword arguments for both the user and + assistant aggregators can be provided. + + Args: + context (OpenAILLMContext): The LLM context. + user_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the user context aggregator constructor. Defaults + to an empty mapping. + assistant_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the assistant context aggregator + constructor. Defaults to an empty mapping. + + Returns: + OpenAIContextAggregatorPair: A pair of context aggregators, one for + the user and one for the assistant, encapsulated in an + OpenAIContextAggregatorPair. + + """ + context.set_llm_adapter(self.get_llm_adapter()) + user = OpenAIUserContextAggregator(context, **user_kwargs) + assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs) return OpenAIContextAggregatorPair(_user=user, _assistant=assistant) @@ -358,6 +391,7 @@ class OpenAIImageGenService(ImageGenService): self, *, api_key: str, + base_url: Optional[str] = None, aiohttp_session: aiohttp.ClientSession, image_size: Literal["256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"], model: str = "dall-e-3", @@ -365,7 +399,7 @@ class OpenAIImageGenService(ImageGenService): super().__init__() self.set_model_name(model) self._image_size = image_size - self._client = AsyncOpenAI(api_key=api_key) + self._client = AsyncOpenAI(api_key=api_key, base_url=base_url) self._aiohttp_session = aiohttp_session async def run_image_gen(self, prompt: str) -> AsyncGenerator[Frame, None]: @@ -390,46 +424,102 @@ class OpenAIImageGenService(ImageGenService): yield frame -class OpenAITTSService(TTSService): - """OpenAI Text-to-Speech service that generates audio from text. +class OpenAISTTService(BaseWhisperSTTService): + """OpenAI Speech-to-Text service that generates text from audio. - This service uses the OpenAI TTS API to generate PCM-encoded audio at 24kHz. - When using with DailyTransport, configure the sample rate in DailyParams - as shown below: - - DailyParams( - audio_out_enabled=True, - audio_out_sample_rate=24_000, - ) + Uses OpenAI's transcription API to convert audio to text. Requires an OpenAI API key + set via the api_key parameter or OPENAI_API_KEY environment variable. Args: + model: Model to use — either gpt-4o or Whisper. Defaults to "gpt-4o-transcribe". api_key: OpenAI API key. Defaults to None. - voice: Voice ID to use. Defaults to "alloy". - model: TTS model to use ("tts-1" or "tts-1-hd"). Defaults to "tts-1". - sample_rate: Output audio sample rate in Hz. Defaults to 24000. - **kwargs: Additional keyword arguments passed to TTSService. - - The service returns PCM-encoded audio at the specified sample rate. + base_url: API base URL. Defaults to None. + language: Language of the audio input. Defaults to English. + prompt: Optional text to guide the model's style or continue a previous segment. + temperature: Optional sampling temperature between 0 and 1. Defaults to 0.0. + **kwargs: Additional arguments passed to BaseWhisperSTTService. """ def __init__( self, *, - api_key: str | None = None, - voice: str = "alloy", - model: Literal["tts-1", "tts-1-hd"] = "tts-1", - sample_rate: int = 24000, + model: str = "gpt-4o-transcribe", + api_key: Optional[str] = None, + base_url: Optional[str] = None, + language: Optional[Language] = Language.EN, + prompt: Optional[str] = None, + temperature: Optional[float] = None, **kwargs, ): + super().__init__( + model=model, + api_key=api_key, + base_url=base_url, + language=language, + prompt=prompt, + temperature=temperature, + **kwargs, + ) + + async def _transcribe(self, audio: bytes) -> Transcription: + assert self._language is not None # Assigned in the BaseWhisperSTTService class + + # Build kwargs dict with only set parameters + kwargs = { + "file": ("audio.wav", audio, "audio/wav"), + "model": self.model_name, + "language": self._language, + } + + if self._prompt is not None: + kwargs["prompt"] = self._prompt + + if self._temperature is not None: + kwargs["temperature"] = self._temperature + + return await self._client.audio.transcriptions.create(**kwargs) + + +class OpenAITTSService(TTSService): + """OpenAI Text-to-Speech service that generates audio from text. + + This service uses the OpenAI TTS API to generate PCM-encoded audio at 24kHz. + + Args: + api_key: OpenAI API key. Defaults to None. + voice: Voice ID to use. Defaults to "alloy". + model: TTS model to use. Defaults to "gpt-4o-mini-tts". + sample_rate: Output audio sample rate in Hz. Defaults to None. + **kwargs: Additional keyword arguments passed to TTSService. + + The service returns PCM-encoded audio at the specified sample rate. + + """ + + OPENAI_SAMPLE_RATE = 24000 # OpenAI TTS always outputs at 24kHz + + def __init__( + self, + *, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + voice: str = "alloy", + model: str = "gpt-4o-mini-tts", + sample_rate: Optional[int] = None, + instructions: Optional[str] = None, + **kwargs, + ): + if sample_rate and sample_rate != self.OPENAI_SAMPLE_RATE: + logger.warning( + f"OpenAI TTS only supports {self.OPENAI_SAMPLE_RATE}Hz sample rate. " + f"Current rate of {self.sample_rate}Hz may cause issues." + ) super().__init__(sample_rate=sample_rate, **kwargs) - self._settings = { - "sample_rate": sample_rate, - } self.set_model_name(model) self.set_voice(voice) - - self._client = AsyncOpenAI(api_key=api_key) + self._instructions = instructions + self._client = AsyncOpenAI(api_key=api_key, base_url=base_url) def can_generate_metrics(self) -> bool: return True @@ -438,16 +528,30 @@ class OpenAITTSService(TTSService): logger.info(f"Switching TTS model to: [{model}]") self.set_model_name(model) + async def start(self, frame: StartFrame): + await super().start(frame) + if self.sample_rate != self.OPENAI_SAMPLE_RATE: + logger.warning( + f"OpenAI TTS requires {self.OPENAI_SAMPLE_RATE}Hz sample rate. " + f"Current rate of {self.sample_rate}Hz may cause issues." + ) + async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: await self.start_ttfb_metrics() + # Setup extra body parameters + extra_body = {} + if self._instructions: + extra_body["instructions"] = self._instructions + async with self._client.audio.speech.with_streaming_response.create( input=text or " ", # Text must contain at least one character model=self.model_name, voice=VALID_VOICES[self._voice_id], response_format="pcm", + extra_body=extra_body, ) as r: if r.status_code != 200: error = await r.text() @@ -461,169 +565,80 @@ class OpenAITTSService(TTSService): await self.start_tts_usage_metrics(text) + CHUNK_SIZE = 1024 + yield TTSStartedFrame() - async for chunk in r.iter_bytes(8192): + async for chunk in r.iter_bytes(CHUNK_SIZE): if len(chunk) > 0: await self.stop_ttfb_metrics() - frame = TTSAudioRawFrame(chunk, self._settings["sample_rate"], 1) + frame = TTSAudioRawFrame(chunk, self.sample_rate, 1) yield frame yield TTSStoppedFrame() except BadRequestError as e: logger.exception(f"{self} error generating TTS: {e}") -# internal use only -- todo: refactor -@dataclass -class OpenAIImageMessageFrame(Frame): - user_image_raw_frame: UserImageRawFrame - text: Optional[str] = None - - class OpenAIUserContextAggregator(LLMUserContextAggregator): - def __init__(self, context: OpenAILLMContext): - super().__init__(context=context) - - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - # Our parent method has already called push_frame(). So we can't interrupt the - # flow here and we don't need to call push_frame() ourselves. - try: - if isinstance(frame, UserImageRequestFrame): - # The LLM sends a UserImageRequestFrame upstream. Cache any context provided with - # that frame so we can use it when we assemble the image message in the assistant - # context aggregator. - if frame.context: - if isinstance(frame.context, str): - self._context._user_image_request_context[frame.user_id] = frame.context - else: - logger.error( - f"Unexpected UserImageRequestFrame context type: {type(frame.context)}" - ) - del self._context._user_image_request_context[frame.user_id] - else: - if frame.user_id in self._context._user_image_request_context: - del self._context._user_image_request_context[frame.user_id] - elif isinstance(frame, UserImageRawFrame): - # Push a new OpenAIImageMessageFrame with the text context we cached - # downstream to be handled by our assistant context aggregator. This is - # necessary so that we add the message to the context in the right order. - text = self._context._user_image_request_context.get(frame.user_id) or "" - if text: - del self._context._user_image_request_context[frame.user_id] - frame = OpenAIImageMessageFrame(user_image_raw_frame=frame, text=text) - await self.push_frame(frame) - except Exception as e: - logger.error(f"Error processing frame: {e}") + pass class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator): - def __init__(self, user_context_aggregator: OpenAIUserContextAggregator, **kwargs): - super().__init__(context=user_context_aggregator._context, **kwargs) - self._user_context_aggregator = user_context_aggregator - self._function_calls_in_progress = {} - self._function_call_result = None - self._pending_image_frame_message = None + async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + self._context.add_message( + { + "role": "assistant", + "tool_calls": [ + { + "id": frame.tool_call_id, + "function": { + "name": frame.function_name, + "arguments": json.dumps(frame.arguments), + }, + "type": "function", + } + ], + } + ) + self._context.add_message( + { + "role": "tool", + "content": "IN_PROGRESS", + "tool_call_id": frame.tool_call_id, + } + ) - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - # See note above about not calling push_frame() here. - if isinstance(frame, StartInterruptionFrame): - self._function_calls_in_progress.clear() - self._function_call_finished = None - elif isinstance(frame, FunctionCallInProgressFrame): - logger.debug(f"FunctionCallInProgressFrame: {frame}") - self._function_calls_in_progress[frame.tool_call_id] = frame - elif isinstance(frame, FunctionCallResultFrame): - logger.debug(f"FunctionCallResultFrame: {frame}") - if frame.tool_call_id in self._function_calls_in_progress: - del self._function_calls_in_progress[frame.tool_call_id] - self._function_call_result = frame - # TODO-CB: Kwin wants us to refactor this out of here but I REFUSE - await self._push_aggregation() - else: - logger.warning( - "FunctionCallResultFrame tool_call_id does not match any function call in progress" - ) - self._function_call_result = None - elif isinstance(frame, OpenAIImageMessageFrame): - self._pending_image_frame_message = frame - await self._push_aggregation() + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + if frame.result: + result = json.dumps(frame.result) + await self._update_function_call_result(frame.function_name, frame.tool_call_id, result) + else: + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "COMPLETED" + ) - async def _push_aggregation(self): - if not ( - self._aggregation or self._function_call_result or self._pending_image_frame_message - ): - return + async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "CANCELLED" + ) - run_llm = False - properties: Optional[FunctionCallResultProperties] = None + async def _update_function_call_result( + self, function_name: str, tool_call_id: str, result: Any + ): + for message in self._context.messages: + if ( + message["role"] == "tool" + and message["tool_call_id"] + and message["tool_call_id"] == tool_call_id + ): + message["content"] = result - aggregation = self._aggregation - self._reset() - - try: - if self._function_call_result: - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - self._context.add_message( - { - "role": "assistant", - "tool_calls": [ - { - "id": frame.tool_call_id, - "function": { - "name": frame.function_name, - "arguments": json.dumps(frame.arguments), - }, - "type": "function", - } - ], - } - ) - self._context.add_message( - { - "role": "tool", - "content": json.dumps(frame.result), - "tool_call_id": frame.tool_call_id, - } - ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior is to run the LLM if there are no function calls in progress - run_llm = not bool(self._function_calls_in_progress) - - else: - self._context.add_message({"role": "assistant", "content": aggregation}) - - if self._pending_image_frame_message: - frame = self._pending_image_frame_message - self._pending_image_frame_message = None - self._context.add_image_frame_message( - format=frame.user_image_raw_frame.format, - size=frame.user_image_raw_frame.size, - image=frame.user_image_raw_frame.image, - text=frame.text, - ) - run_llm = True - - if run_llm: - await self._user_context_aggregator.push_context_frame() - - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() - - # Push context frame - frame = OpenAILLMContextFrame(self._context) - await self.push_frame(frame) - - # Push timestamp frame with current time - timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601()) - await self.push_frame(timestamp_frame) - - except Exception as e: - logger.error(f"Error processing frame: {e}") + async def handle_user_image_frame(self, frame: UserImageRawFrame): + await self._update_function_call_result( + frame.request.function_name, frame.request.tool_call_id, "COMPLETED" + ) + self._context.add_image_frame_message( + format=frame.format, + size=frame.size, + image=frame.image, + text=frame.request.context, + ) diff --git a/src/pipecat/services/openai_realtime_beta/__init__.py b/src/pipecat/services/openai_realtime_beta/__init__.py index a1cbafecd..595105d7f 100644 --- a/src/pipecat/services/openai_realtime_beta/__init__.py +++ b/src/pipecat/services/openai_realtime_beta/__init__.py @@ -1,2 +1,9 @@ -from .events import InputAudioTranscription, SessionProperties, TurnDetection +from .azure import AzureRealtimeBetaLLMService +from .events import ( + InputAudioNoiseReduction, + InputAudioTranscription, + SemanticTurnDetection, + SessionProperties, + TurnDetection, +) from .openai import OpenAIRealtimeBetaLLMService diff --git a/src/pipecat/services/openai_realtime_beta/azure.py b/src/pipecat/services/openai_realtime_beta/azure.py new file mode 100644 index 000000000..5f046b8b0 --- /dev/null +++ b/src/pipecat/services/openai_realtime_beta/azure.py @@ -0,0 +1,64 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import os + +from loguru import logger + +from .openai import OpenAIRealtimeBetaLLMService + +try: + import websockets +except ModuleNotFoundError as e: + logger.error(f"Exception: {e}") + logger.error( + "In order to use OpenAI, you need to `pip install pipecat-ai[openai]`. Also, set `OPENAI_API_KEY` environment variable." + ) + raise Exception(f"Missing module: {e}") + + +class AzureRealtimeBetaLLMService(OpenAIRealtimeBetaLLMService): + """Subclass of OpenAI Realtime API Service with adjustments for Azure's wss connection.""" + + def __init__( + self, + *, + api_key: str, + base_url: str, + **kwargs, + ): + """Constructor takes the same arguments as the parent class, OpenAIRealtimeBetaLLMService. + + Note that the following are required arguments: + api_key: The API key for the Azure OpenAI service. + base_url: The base URL for the Azure OpenAI service. + + base_url should be set to the full Azure endpoint URL including the api-version and the deployment name. For example, + + wss://my-project.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=my-realtime-deployment + """ + super().__init__(base_url=base_url, api_key=api_key, **kwargs) + self.api_key = api_key + self.base_url = base_url + + async def _connect(self): + try: + if self._websocket: + # Here we assume that if we have a websocket, we are connected. We + # handle disconnections in the send/recv code paths. + return + + logger.info(f"Connecting to {self.base_url}, api key: {self.api_key}") + self._websocket = await websockets.connect( + uri=self.base_url, + extra_headers={ + "api-key": self.api_key, + }, + ) + self._receive_task = self.create_task(self._receive_task_handler()) + except Exception as e: + logger.error(f"{self} initialization error: {e}") + self._websocket = None diff --git a/src/pipecat/services/openai_realtime_beta/context.py b/src/pipecat/services/openai_realtime_beta/context.py index da287194a..c8381976f 100644 --- a/src/pipecat/services/openai_realtime_beta/context.py +++ b/src/pipecat/services/openai_realtime_beta/context.py @@ -12,6 +12,7 @@ from loguru import logger from pipecat.frames.frames import ( Frame, + FunctionCallResultFrame, FunctionCallResultProperties, LLMMessagesUpdateFrame, LLMSetToolsFrame, @@ -166,7 +167,7 @@ class OpenAIRealtimeUserContextAggregator(OpenAIUserContextAggregator): if isinstance(frame, LLMSetToolsFrame): await self.push_frame(frame, direction) - async def _push_aggregation(self): + async def push_aggregation(self): # for the moment, ignore all user input coming into the pipeline. # todo: think about whether/how to fix this to allow for text input from # upstream (transport/transcription, or other sources) @@ -174,68 +175,12 @@ class OpenAIRealtimeUserContextAggregator(OpenAIUserContextAggregator): class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator): - async def _push_aggregation(self): - # the only thing we implement here is function calling. in all other cases, messages - # are added to the context when we receive openai realtime api events - if not self._function_call_result: - return + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + await super().handle_function_call_result(frame) - properties: Optional[FunctionCallResultProperties] = None - - self._reset() - try: - run_llm = True - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - # The "tool_call" message from the LLM that triggered the function call - self._context.add_message( - { - "role": "assistant", - "tool_calls": [ - { - "id": frame.tool_call_id, - "function": { - "name": frame.function_name, - "arguments": json.dumps(frame.arguments), - }, - "type": "function", - } - ], - } - ) - # The result of the function call. Need to add this both to our context here and to - # the openai realtime api context. - result_message = { - "role": "tool", - "content": json.dumps(frame.result), - "tool_call_id": frame.tool_call_id, - } - - self._context.add_message(result_message) - # The standard function callback code path pushes the FunctionCallResultFrame from the llm itself, - # so we didn't have a chance to add the result to the openai realtime api context. Let's push a - # special frame to do that. - await self._user_context_aggregator.push_frame( - RealtimeFunctionCallResultFrame(result_frame=frame) - ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior is to run the LLM if there are no function calls in progress - run_llm = not bool(self._function_calls_in_progress) - - if run_llm: - await self._user_context_aggregator.push_context_frame() - - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() - - frame = OpenAILLMContextFrame(self._context) - await self.push_frame(frame) - - except Exception as e: - logger.error(f"Error processing frame: {e}") + # The standard function callback code path pushes the FunctionCallResultFrame from the llm itself, + # so we didn't have a chance to add the result to the openai realtime api context. Let's push a + # special frame to do that. + await self.push_frame( + RealtimeFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM + ) diff --git a/src/pipecat/services/openai_realtime_beta/events.py b/src/pipecat/services/openai_realtime_beta/events.py index 17ce0a6d4..c2dcb7f09 100644 --- a/src/pipecat/services/openai_realtime_beta/events.py +++ b/src/pipecat/services/openai_realtime_beta/events.py @@ -17,7 +17,29 @@ from pydantic import BaseModel, Field class InputAudioTranscription(BaseModel): - model: Optional[str] = "whisper-1" + """Configuration for audio transcription settings. + + Attributes: + model: Transcription model to use (e.g., "gpt-4o-transcribe", "whisper-1"). + language: Optional language code for transcription. + prompt: Optional transcription hint text. + """ + + model: str = "gpt-4o-transcribe" + language: Optional[str] + prompt: Optional[str] + + def __init__( + self, + model: Optional[str] = "gpt-4o-transcribe", + language: Optional[str] = None, + prompt: Optional[str] = None, + ): + super().__init__(model=model, language=language, prompt=prompt) + if self.model != "gpt-4o-transcribe" and (self.language or self.prompt): + raise ValueError( + "Fields 'language' and 'prompt' are only supported when model is 'gpt-4o-transcribe'" + ) class TurnDetection(BaseModel): @@ -27,6 +49,17 @@ class TurnDetection(BaseModel): silence_duration_ms: Optional[int] = 800 +class SemanticTurnDetection(BaseModel): + type: Optional[Literal["semantic_vad"]] = "semantic_vad" + eagerness: Optional[Literal["low", "medium", "high", "auto"]] = None + create_response: Optional[bool] = None + interrupt_response: Optional[bool] = None + + +class InputAudioNoiseReduction(BaseModel): + type: Optional[Literal["near_field", "far_field"]] + + class SessionProperties(BaseModel): modalities: Optional[List[Literal["text", "audio"]]] = None instructions: Optional[str] = None @@ -34,8 +67,11 @@ class SessionProperties(BaseModel): input_audio_format: Optional[Literal["pcm16", "g711_ulaw", "g711_alaw"]] = None output_audio_format: Optional[Literal["pcm16", "g711_ulaw", "g711_alaw"]] = None input_audio_transcription: Optional[InputAudioTranscription] = None + input_audio_noise_reduction: Optional[InputAudioNoiseReduction] = None # set turn_detection to False to disable turn detection - turn_detection: Optional[Union[TurnDetection, bool]] = Field(default=None) + turn_detection: Optional[Union[TurnDetection, SemanticTurnDetection, bool]] = Field( + default=None + ) tools: Optional[List[Dict]] = None tool_choice: Optional[Literal["auto", "none", "required"]] = None temperature: Optional[float] = None @@ -93,6 +129,7 @@ class RealtimeError(BaseModel): code: Optional[str] = "" message: str param: Optional[str] = None + event_id: Optional[str] = None # @@ -150,6 +187,11 @@ class ConversationItemDeleteEvent(ClientEvent): item_id: str +class ConversationItemRetrieveEvent(ClientEvent): + type: Literal["conversation.item.retrieve"] = "conversation.item.retrieve" + item_id: str + + class ResponseCreateEvent(ClientEvent): type: Literal["response.create"] = "response.create" response: Optional[ResponseProperties] = None @@ -193,6 +235,13 @@ class ConversationItemCreated(ServerEvent): item: ConversationItem +class ConversationItemInputAudioTranscriptionDelta(ServerEvent): + type: Literal["conversation.item.input_audio_transcription.delta"] + item_id: str + content_index: int + delta: str + + class ConversationItemInputAudioTranscriptionCompleted(ServerEvent): type: Literal["conversation.item.input_audio_transcription.completed"] item_id: str @@ -219,6 +268,11 @@ class ConversationItemDeleted(ServerEvent): item_id: str +class ConversationItemRetrieved(ServerEvent): + type: Literal["conversation.item.retrieved"] + item: ConversationItem + + class ResponseCreated(ServerEvent): type: Literal["response.created"] response: "Response" @@ -400,10 +454,12 @@ _server_event_types = { "input_audio_buffer.speech_started": InputAudioBufferSpeechStarted, "input_audio_buffer.speech_stopped": InputAudioBufferSpeechStopped, "conversation.item.created": ConversationItemCreated, + "conversation.item.input_audio_transcription.delta": ConversationItemInputAudioTranscriptionDelta, "conversation.item.input_audio_transcription.completed": ConversationItemInputAudioTranscriptionCompleted, "conversation.item.input_audio_transcription.failed": ConversationItemInputAudioTranscriptionFailed, "conversation.item.truncated": ConversationItemTruncated, "conversation.item.deleted": ConversationItemDeleted, + "conversation.item.retrieved": ConversationItemRetrieved, "response.created": ResponseCreated, "response.done": ResponseDone, "response.output_item.added": ResponseOutputItemAdded, diff --git a/src/pipecat/services/openai_realtime_beta/openai.py b/src/pipecat/services/openai_realtime_beta/openai.py index a42ed7b9a..a1f4bc731 100644 --- a/src/pipecat/services/openai_realtime_beta/openai.py +++ b/src/pipecat/services/openai_realtime_beta/openai.py @@ -4,15 +4,25 @@ # SPDX-License-Identifier: BSD 2-Clause License # -import asyncio import base64 import json import time from dataclasses import dataclass +from typing import Any, Mapping -import websockets from loguru import logger +from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter + +try: + import websockets +except ModuleNotFoundError as e: + logger.error(f"Exception: {e}") + logger.error( + "In order to use OpenAI, you need to `pip install pipecat-ai[openai]`. Also, set `OPENAI_API_KEY` environment variable." + ) + raise Exception(f"Missing module: {e}") + from pipecat.frames.frames import ( BotStoppedSpeakingFrame, CancelFrame, @@ -20,6 +30,7 @@ from pipecat.frames.frames import ( ErrorFrame, Frame, InputAudioRawFrame, + InterimTranscriptionFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesAppendFrame, @@ -33,6 +44,7 @@ from pipecat.frames.frames import ( TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, + TTSTextFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) @@ -68,6 +80,9 @@ class OpenAIUnhandledFunctionException(Exception): class OpenAIRealtimeBetaLLMService(LLMService): + # Overriding the default adapter to use the OpenAIRealtimeLLMAdapter one. + adapter_class = OpenAIRealtimeLLMAdapter + def __init__( self, *, @@ -101,12 +116,35 @@ class OpenAIRealtimeBetaLLMService(LLMService): self._messages_added_manually = {} self._user_and_response_message_tuple = None + self._register_event_handler("on_conversation_item_created") + self._register_event_handler("on_conversation_item_updated") + self._retrieve_conversation_item_futures = {} + def can_generate_metrics(self) -> bool: return True def set_audio_input_paused(self, paused: bool): self._audio_input_paused = paused + async def retrieve_conversation_item(self, item_id: str): + future = self.get_event_loop().create_future() + retrieval_in_flight = False + if not self._retrieve_conversation_item_futures.get(item_id): + self._retrieve_conversation_item_futures[item_id] = [] + else: + retrieval_in_flight = True + self._retrieve_conversation_item_futures[item_id].append(future) + if not retrieval_in_flight: + await self.send_client_event( + # Set event_id to "rci_{item_id}" so that we can identify an + # error later if the retrieval fails. We don't need a UUID + # suffix to the event_id because we're ensuring only one + # in-flight retrieval per item_id. (Note: "rci" = "retrieve + # conversation item") + events.ConversationItemRetrieveEvent(item_id=item_id, event_id=f"rci_{item_id}") + ) + return await future + # # standard AIService frame handling # @@ -340,8 +378,12 @@ class OpenAIRealtimeBetaLLMService(LLMService): await self._handle_evt_audio_done(evt) elif evt.type == "conversation.item.created": await self._handle_evt_conversation_item_created(evt) + elif evt.type == "conversation.item.input_audio_transcription.delta": + await self._handle_evt_input_audio_transcription_delta(evt) elif evt.type == "conversation.item.input_audio_transcription.completed": await self.handle_evt_input_audio_transcription_completed(evt) + elif evt.type == "conversation.item.retrieved": + await self._handle_conversation_item_retrieved(evt) elif evt.type == "response.done": await self._handle_evt_response_done(evt) elif evt.type == "input_audio_buffer.speech_started": @@ -351,9 +393,10 @@ class OpenAIRealtimeBetaLLMService(LLMService): elif evt.type == "response.audio_transcript.delta": await self._handle_evt_audio_transcript_delta(evt) elif evt.type == "error": - await self._handle_evt_error(evt) - # errors are fatal, so exit the receive loop - return + if not await self._maybe_handle_evt_retrieve_conversation_item_error(evt): + await self._handle_evt_error(evt) + # errors are fatal, so exit the receive loop + return async def _handle_evt_session_created(self, evt): # session.created is received right after connecting. Send a message @@ -395,6 +438,8 @@ class OpenAIRealtimeBetaLLMService(LLMService): # receive a BotStoppedSpeakingFrame from the output transport. async def _handle_evt_conversation_item_created(self, evt): + await self._call_event_handler("on_conversation_item_created", evt.item.id, evt.item) + # This will get sent from the server every time a new "message" is added # to the server's conversation state, whether we create it via the API # or the server creates it from LLM output. @@ -411,7 +456,16 @@ class OpenAIRealtimeBetaLLMService(LLMService): self._current_assistant_response = evt.item await self.push_frame(LLMFullResponseStartFrame()) + async def _handle_evt_input_audio_transcription_delta(self, evt): + if self._send_transcription_frames: + await self.push_frame( + # no way to get a language code? + InterimTranscriptionFrame(evt.delta, "", time_now_iso8601()) + ) + async def handle_evt_input_audio_transcription_completed(self, evt): + await self._call_event_handler("on_conversation_item_updated", evt.item_id, None) + if self._send_transcription_frames: await self.push_frame( # no way to get a language code? @@ -429,6 +483,12 @@ class OpenAIRealtimeBetaLLMService(LLMService): # User message without preceding conversation.item.created. Bug? logger.warning(f"Transcript for unknown user message: {evt}") + async def _handle_conversation_item_retrieved(self, evt: events.ConversationItemRetrieved): + futures = self._retrieve_conversation_item_futures.pop(evt.item.id, None) + if futures: + for future in futures: + future.set_result(evt.item) + async def _handle_evt_response_done(self, evt): # todo: figure out whether there's anything we need to do for "cancelled" events # usage metrics @@ -441,7 +501,15 @@ class OpenAIRealtimeBetaLLMService(LLMService): await self.stop_processing_metrics() await self.push_frame(LLMFullResponseEndFrame()) self._current_assistant_response = None + # error handling + if evt.response.status == "failed": + await self.push_error( + ErrorFrame(error=evt.response.status_details["error"]["message"], fatal=True) + ) + return # response content + for item in evt.response.output: + await self._call_event_handler("on_conversation_item_updated", item.id, item) pair = self._user_and_response_message_tuple if pair: user, assistant = pair @@ -458,6 +526,7 @@ class OpenAIRealtimeBetaLLMService(LLMService): async def _handle_evt_audio_transcript_delta(self, evt): if evt.delta: await self.push_frame(LLMTextFrame(evt.delta)) + await self.push_frame(TTSTextFrame(evt.delta)) async def _handle_evt_speech_started(self, evt): await self._truncate_current_audio_response() @@ -472,6 +541,22 @@ class OpenAIRealtimeBetaLLMService(LLMService): await self.push_frame(StopInterruptionFrame()) await self.push_frame(UserStoppedSpeakingFrame()) + async def _maybe_handle_evt_retrieve_conversation_item_error(self, evt: events.ErrorEvent): + """If the given error event is an error retrieving a conversation item: + - set an exception on the future that retrieve_conversation_item() is waiting on + - return true + Otherwise: + - return false + """ + if evt.error.code == "item_retrieve_invalid_item_id": + item_id = evt.error.event_id.split("_", 1)[1] # event_id is of the form "rci_{item_id}" + futures = self._retrieve_conversation_item_futures.pop(item_id, None) + if futures: + for future in futures: + future.set_exception(Exception(evt.error.message)) + return True + return False + async def _handle_evt_error(self, evt): # Errors are fatal to this connection. Send an ErrorFrame. await self.push_error(ErrorFrame(error=f"Error: {evt}", fatal=True)) @@ -494,7 +579,7 @@ class OpenAIRealtimeBetaLLMService(LLMService): arguments = json.loads(item.arguments) if self.has_function(function_name): run_llm = index == total_items - 1 - if function_name in self._callbacks.keys(): + if function_name in self._functions.keys(): await self.call_function( context=self._context, tool_call_id=tool_id, @@ -502,7 +587,7 @@ class OpenAIRealtimeBetaLLMService(LLMService): arguments=arguments, run_llm=run_llm, ) - elif None in self._callbacks.keys(): + elif None in self._functions.keys(): await self.call_function( context=self._context, tool_call_id=tool_id, @@ -563,11 +648,37 @@ class OpenAIRealtimeBetaLLMService(LLMService): await self.send_client_event(events.InputAudioBufferAppendEvent(audio=payload)) def create_context_aggregator( - self, context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = False + self, + context: OpenAILLMContext, + *, + user_kwargs: Mapping[str, Any] = {}, + assistant_kwargs: Mapping[str, Any] = {}, ) -> OpenAIContextAggregatorPair: + """Create an instance of OpenAIContextAggregatorPair from an + OpenAILLMContext. Constructor keyword arguments for both the user and + assistant aggregators can be provided. + + Args: + context (OpenAILLMContext): The LLM context. + user_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the user context aggregator constructor. Defaults + to an empty mapping. + assistant_kwargs (Mapping[str, Any], optional): Additional keyword + arguments for the assistant context aggregator + constructor. Defaults to an empty mapping. + + Returns: + OpenAIContextAggregatorPair: A pair of context aggregators, one for + the user and one for the assistant, encapsulated in an + OpenAIContextAggregatorPair. + + """ + context.set_llm_adapter(self.get_llm_adapter()) + OpenAIRealtimeLLMContext.upgrade_to_realtime(context) - user = OpenAIRealtimeUserContextAggregator(context) - assistant = OpenAIRealtimeAssistantContextAggregator( - user, expect_stripped_words=assistant_expect_stripped_words - ) + user = OpenAIRealtimeUserContextAggregator(context, **user_kwargs) + + default_assistant_kwargs = {"expect_stripped_words": False} + default_assistant_kwargs.update(assistant_kwargs) + assistant = OpenAIRealtimeAssistantContextAggregator(context, **default_assistant_kwargs) return OpenAIContextAggregatorPair(_user=user, _assistant=assistant) diff --git a/src/pipecat/services/openpipe.py b/src/pipecat/services/openpipe.py index b72201923..c89c9d6a3 100644 --- a/src/pipecat/services/openpipe.py +++ b/src/pipecat/services/openpipe.py @@ -4,15 +4,15 @@ # SPDX-License-Identifier: BSD 2-Clause License # -from typing import Dict, List +from typing import Dict, List, Optional from loguru import logger +from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.openai import OpenAILLMService try: - from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam from openpipe import AsyncOpenAI as OpenPipeAI from openpipe import AsyncStream except ModuleNotFoundError as e: @@ -28,11 +28,11 @@ class OpenPipeLLMService(OpenAILLMService): self, *, model: str = "gpt-4o", - api_key: str | None = None, - base_url: str | None = None, - openpipe_api_key: str | None = None, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + openpipe_api_key: Optional[str] = None, openpipe_base_url: str = "https://app.openpipe.ai/api/v1", - tags: Dict[str, str] | None = None, + tags: Optional[Dict[str, str]] = None, **kwargs, ): super().__init__( diff --git a/src/pipecat/services/openrouter.py b/src/pipecat/services/openrouter.py index 4c99fb767..d25990d2e 100644 --- a/src/pipecat/services/openrouter.py +++ b/src/pipecat/services/openrouter.py @@ -4,23 +4,12 @@ # SPDX-License-Identifier: BSD 2-Clause License # -from typing import Dict, List +from typing import Optional from loguru import logger -from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.openai import OpenAILLMService -try: - from openai import AsyncStream, OpenAI - from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam -except ModuleNotFoundError as e: - logger.error(f"Exception: {e}") - logger.error( - "In order to use OpenRouter, you need to `pip install pipecat-ai[openrouter]`. Also, set `OPENROUTER_API_KEY` environment variable." - ) - raise Exception(f"Missing module: {e}") - class OpenRouterLLMService(OpenAILLMService): """A service for interacting with OpenRouter's API using the OpenAI-compatible interface. @@ -38,7 +27,7 @@ class OpenRouterLLMService(OpenAILLMService): def __init__( self, *, - api_key: str | None = None, + api_key: Optional[str] = None, model: str = "openai/gpt-4o-2024-11-20", base_url: str = "https://openrouter.ai/api/v1", **kwargs, diff --git a/src/pipecat/services/perplexity.py b/src/pipecat/services/perplexity.py new file mode 100644 index 000000000..b0c560ce4 --- /dev/null +++ b/src/pipecat/services/perplexity.py @@ -0,0 +1,130 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import List + +from loguru import logger +from openai import NOT_GIVEN, AsyncStream +from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam + +from pipecat.metrics.metrics import LLMTokenUsage +from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext +from pipecat.services.openai import OpenAILLMService + + +class PerplexityLLMService(OpenAILLMService): + """A service for interacting with Perplexity's API. + + This service extends OpenAILLMService to work with Perplexity's API while maintaining + compatibility with the OpenAI-style interface. It specifically handles the difference + in token usage reporting between Perplexity (incremental) and OpenAI (final summary). + + Args: + api_key (str): The API key for accessing Perplexity's API + base_url (str, optional): The base URL for Perplexity's API. Defaults to "https://api.perplexity.ai" + model (str, optional): The model identifier to use. Defaults to "sonar" + **kwargs: Additional keyword arguments passed to OpenAILLMService + """ + + def __init__( + self, + *, + api_key: str, + base_url: str = "https://api.perplexity.ai", + model: str = "sonar", + **kwargs, + ): + super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs) + # Counters for accumulating token usage metrics + self._prompt_tokens = 0 + self._completion_tokens = 0 + self._total_tokens = 0 + self._has_reported_prompt_tokens = False + self._is_processing = False + + async def get_chat_completions( + self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam] + ) -> AsyncStream[ChatCompletionChunk]: + """Get chat completions from Perplexity API using OpenAI-compatible parameters. + + Args: + context: The context containing conversation history and settings + messages: The messages to send to the API + + Returns: + A stream of chat completion chunks + """ + params = { + "model": self.model_name, + "stream": True, + "messages": messages, + } + + # Add OpenAI-compatible parameters if they're set + if self._settings["frequency_penalty"] is not NOT_GIVEN: + params["frequency_penalty"] = self._settings["frequency_penalty"] + if self._settings["presence_penalty"] is not NOT_GIVEN: + params["presence_penalty"] = self._settings["presence_penalty"] + if self._settings["temperature"] is not NOT_GIVEN: + params["temperature"] = self._settings["temperature"] + if self._settings["top_p"] is not NOT_GIVEN: + params["top_p"] = self._settings["top_p"] + if self._settings["max_tokens"] is not NOT_GIVEN: + params["max_tokens"] = self._settings["max_tokens"] + + chunks = await self._client.chat.completions.create(**params) + return chunks + + async def _process_context(self, context: OpenAILLMContext): + """Process a context through the LLM and accumulate token usage metrics. + + This method overrides the parent class implementation to handle + Perplexity's incremental token reporting style, accumulating the counts + and reporting them once at the end of processing. + + Args: + context (OpenAILLMContext): The context to process, containing messages + and other information needed for the LLM interaction. + """ + # Reset all counters and flags at the start of processing + self._prompt_tokens = 0 + self._completion_tokens = 0 + self._total_tokens = 0 + self._has_reported_prompt_tokens = False + self._is_processing = True + + try: + await super()._process_context(context) + finally: + self._is_processing = False + # Report final accumulated token usage at the end of processing + if self._prompt_tokens > 0 or self._completion_tokens > 0: + self._total_tokens = self._prompt_tokens + self._completion_tokens + tokens = LLMTokenUsage( + prompt_tokens=self._prompt_tokens, + completion_tokens=self._completion_tokens, + total_tokens=self._total_tokens, + ) + await super().start_llm_usage_metrics(tokens) + + async def start_llm_usage_metrics(self, tokens: LLMTokenUsage): + """Accumulate token usage metrics during processing. + + Perplexity reports token usage incrementally during streaming, + unlike OpenAI which provides a final summary. We accumulate the + counts and report the total at the end of processing. + """ + if not self._is_processing: + return + + # Record prompt tokens the first time we see them + if not self._has_reported_prompt_tokens and tokens.prompt_tokens > 0: + self._prompt_tokens = tokens.prompt_tokens + self._has_reported_prompt_tokens = True + + # Update completion tokens count if it has increased + if tokens.completion_tokens > self._completion_tokens: + self._completion_tokens = tokens.completion_tokens diff --git a/src/pipecat/services/playht.py b/src/pipecat/services/playht.py index 07e78ae52..75677876f 100644 --- a/src/pipecat/services/playht.py +++ b/src/pipecat/services/playht.py @@ -16,22 +16,18 @@ from loguru import logger from pydantic import BaseModel from pipecat.frames.frames import ( - BotStoppedSpeakingFrame, CancelFrame, EndFrame, ErrorFrame, Frame, - LLMFullResponseEndFrame, StartFrame, StartInterruptionFrame, TTSAudioRawFrame, - TTSSpeakFrame, TTSStartedFrame, TTSStoppedFrame, ) from pipecat.processors.frame_processor import FrameDirection -from pipecat.services.ai_services import TTSService -from pipecat.services.websocket_service import WebsocketService +from pipecat.services.ai_services import InterruptibleTTSService, TTSService from pipecat.transcriptions.language import Language try: @@ -46,7 +42,7 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -def language_to_playht_language(language: Language) -> str | None: +def language_to_playht_language(language: Language) -> Optional[str]: BASE_LANGUAGES = { Language.AF: "afrikans", Language.AM: "amharic", @@ -100,7 +96,7 @@ def language_to_playht_language(language: Language) -> str | None: return result -class PlayHTTTSService(TTSService, WebsocketService): +class PlayHTTTSService(InterruptibleTTSService): class InputParams(BaseModel): language: Optional[Language] = Language.EN speed: Optional[float] = 1.0 @@ -113,17 +109,16 @@ class PlayHTTTSService(TTSService, WebsocketService): user_id: str, voice_url: str, voice_engine: str = "Play3.0-mini", - sample_rate: int = 24000, + sample_rate: Optional[int] = None, output_format: str = "wav", params: InputParams = InputParams(), **kwargs, ): - TTSService.__init__( - self, + super().__init__( + pause_frame_processing=True, sample_rate=sample_rate, **kwargs, ) - WebsocketService.__init__(self) self._api_key = api_key self._user_id = user_id @@ -132,7 +127,6 @@ class PlayHTTTSService(TTSService, WebsocketService): self._request_id = None self._settings = { - "sample_rate": sample_rate, "language": self.language_to_service_language(params.language) if params.language else "english", @@ -147,7 +141,7 @@ class PlayHTTTSService(TTSService, WebsocketService): def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_playht_language(language) async def start(self, frame: StartFrame): @@ -165,17 +159,21 @@ class PlayHTTTSService(TTSService, WebsocketService): async def _connect(self): await self._connect_websocket() - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + if not self._receive_task: + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): - await self._disconnect_websocket() - if self._receive_task: await self.cancel_task(self._receive_task) self._receive_task = None + await self._disconnect_websocket() + async def _connect_websocket(self): try: + if self._websocket: + return + logger.debug("Connecting to PlayHT") if not self._websocket_url: @@ -185,12 +183,14 @@ class PlayHTTTSService(TTSService, WebsocketService): raise ValueError("WebSocket URL is not a string") self._websocket = await websockets.connect(self._websocket_url) - except ValueError as ve: - logger.error(f"{self} initialization error: {ve}") + except ValueError as e: + logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: @@ -250,7 +250,7 @@ class PlayHTTTSService(TTSService, WebsocketService): if message.startswith(b"RIFF"): continue await self.stop_ttfb_metrics() - frame = TTSAudioRawFrame(message, self._settings["sample_rate"], 1) + frame = TTSAudioRawFrame(message, self.sample_rate, 1) await self.push_frame(frame) else: logger.debug(f"Received text message: {message}") @@ -270,21 +270,8 @@ class PlayHTTTSService(TTSService, WebsocketService): except json.JSONDecodeError: logger.error(f"Invalid JSON message: {message}") - async def process_frame(self, frame: Frame, direction: FrameDirection): - await super().process_frame(frame, direction) - - # If we received a TTSSpeakFrame and the LLM response included text (it - # might be that it's only a function calling response) we pause - # processing more frames until we receive a BotStoppedSpeakingFrame. - if isinstance(frame, TTSSpeakFrame): - await self.pause_processing_frames() - elif isinstance(frame, LLMFullResponseEndFrame) and self._request_id: - await self.pause_processing_frames() - elif isinstance(frame, BotStoppedSpeakingFrame): - await self.resume_processing_frames() - async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: # Reconnect if the websocket is closed @@ -301,7 +288,7 @@ class PlayHTTTSService(TTSService, WebsocketService): "voice": self._voice_id, "voice_engine": self._settings["voice_engine"], "output_format": self._settings["output_format"], - "sample_rate": self._settings["sample_rate"], + "sample_rate": self.sample_rate, "language": self._settings["language"], "speed": self._settings["speed"], "seed": self._settings["seed"], @@ -338,8 +325,9 @@ class PlayHTHttpTTSService(TTSService): api_key: str, user_id: str, voice_url: str, - voice_engine: str = "Play3.0-mini-http", # Options: Play3.0-mini-http, Play3.0-mini-ws - sample_rate: int = 24000, + voice_engine: str = "Play3.0-mini", + protocol: str = "http", # Options: http, ws + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): @@ -352,19 +340,35 @@ class PlayHTHttpTTSService(TTSService): user_id=self._user_id, api_key=self._api_key, ) + + # Check if voice_engine contains protocol information (backward compatibility) + if "-http" in voice_engine: + # Extract the base engine name + voice_engine = voice_engine.replace("-http", "") + protocol = "http" + elif "-ws" in voice_engine: + # Extract the base engine name + voice_engine = voice_engine.replace("-ws", "") + protocol = "ws" + self._settings = { - "sample_rate": sample_rate, "language": self.language_to_service_language(params.language) if params.language else "english", "format": Format.FORMAT_WAV, "voice_engine": voice_engine, + "protocol": protocol, "speed": params.speed, "seed": params.seed, } self.set_model_name(voice_engine) self.set_voice(voice_url) + async def start(self, frame: StartFrame): + await super().start(frame) + self._settings["sample_rate"] = self.sample_rate + + def _create_options(self) -> TTSOptions: language_str = self._settings["language"] playht_language = None if language_str: @@ -374,10 +378,10 @@ class PlayHTHttpTTSService(TTSService): playht_language = lang break - self._options = TTSOptions( + return TTSOptions( voice=self._voice_id, language=playht_language, - sample_rate=self._settings["sample_rate"], + sample_rate=self.sample_rate, format=self._settings["format"], speed=self._settings["speed"], seed=self._settings["seed"], @@ -386,25 +390,30 @@ class PlayHTHttpTTSService(TTSService): def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_playht_language(language) async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: - b = bytearray() - in_header = True + options = self._create_options() await self.start_ttfb_metrics() playht_gen = self._client.tts( - text, voice_engine=self._settings["voice_engine"], options=self._options + text, + voice_engine=self._settings["voice_engine"], + protocol=self._settings["protocol"], + options=options, ) await self.start_tts_usage_metrics(text) yield TTSStartedFrame() + + b = bytearray() + in_header = True async for chunk in playht_gen: # skip the RIFF header. if in_header: @@ -419,11 +428,12 @@ class PlayHTHttpTTSService(TTSService): fh.read(size) (data, size) = struct.unpack("<4sI", fh.read(8)) in_header = False - else: - if len(chunk): - await self.stop_ttfb_metrics() - frame = TTSAudioRawFrame(chunk, self._settings["sample_rate"], 1) - yield frame - yield TTSStoppedFrame() + elif len(chunk) > 0: + await self.stop_ttfb_metrics() + frame = TTSAudioRawFrame(chunk, self.sample_rate, 1) + yield frame except Exception as e: logger.error(f"{self} error generating TTS: {e}") + finally: + await self.stop_ttfb_metrics() + yield TTSStoppedFrame() diff --git a/src/pipecat/services/rime.py b/src/pipecat/services/rime.py index 444461f6b..a1a455eb5 100644 --- a/src/pipecat/services/rime.py +++ b/src/pipecat/services/rime.py @@ -4,6 +4,9 @@ # SPDX-License-Identifier: BSD 2-Clause License # +import base64 +import json +import uuid from typing import AsyncGenerator, Optional import aiohttp @@ -11,13 +14,333 @@ from loguru import logger from pydantic import BaseModel from pipecat.frames.frames import ( + CancelFrame, + EndFrame, ErrorFrame, Frame, + StartFrame, + StartInterruptionFrame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, ) -from pipecat.services.ai_services import TTSService +from pipecat.processors.frame_processor import FrameDirection +from pipecat.services.ai_services import AudioContextWordTTSService, TTSService +from pipecat.transcriptions.language import Language +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator +from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator + +try: + import websockets +except ModuleNotFoundError as e: + logger.error(f"Exception: {e}") + logger.error( + "In order to use Rime, you need to `pip install pipecat-ai[rime]`. Also, set `RIME_API_KEY` environment variable." + ) + raise Exception(f"Missing module: {e}") + + +def language_to_rime_language(language: Language) -> str: + """Convert pipecat Language to Rime language code. + + Args: + language: The pipecat Language enum value. + + Returns: + str: Three-letter language code used by Rime (e.g., 'eng' for English). + """ + LANGUAGE_MAP = { + Language.EN: "eng", + Language.ES: "spa", + } + return LANGUAGE_MAP.get(language, "eng") + + +class RimeTTSService(AudioContextWordTTSService): + """Text-to-Speech service using Rime's websocket API. + + Uses Rime's websocket JSON API to convert text to speech with word-level timing + information. Supports interruptions and maintains context across multiple messages + within a turn. + """ + + class InputParams(BaseModel): + """Configuration parameters for Rime TTS service.""" + + language: Optional[Language] = Language.EN + speed_alpha: Optional[float] = 1.0 + reduce_latency: Optional[bool] = False + + def __init__( + self, + *, + api_key: str, + voice_id: str, + url: str = "wss://users-ws.rime.ai/ws2", + model: str = "mistv2", + sample_rate: Optional[int] = None, + params: InputParams = InputParams(), + text_aggregator: Optional[BaseTextAggregator] = None, + **kwargs, + ): + """Initialize Rime TTS service. + + Args: + api_key: Rime API key for authentication. + voice_id: ID of the voice to use. + url: Rime websocket API endpoint. + model: Model ID to use for synthesis. + sample_rate: Audio sample rate in Hz. + params: Additional configuration parameters. + """ + # Initialize with parent class settings for proper frame handling + super().__init__( + aggregate_sentences=True, + push_text_frames=False, + push_stop_frames=True, + pause_frame_processing=True, + sample_rate=sample_rate, + text_aggregator=text_aggregator or SkipTagsAggregator([("spell(", ")")]), + **kwargs, + ) + + # Store service configuration + self._api_key = api_key + self._url = url + self._voice_id = voice_id + self._model = model + self._settings = { + "speaker": voice_id, + "modelId": model, + "audioFormat": "pcm", + "samplingRate": 0, + "lang": self.language_to_service_language(params.language) + if params.language + else "eng", + "speedAlpha": params.speed_alpha, + "reduceLatency": params.reduce_latency, + } + + # State tracking + self._context_id = None # Tracks current turn + self._receive_task = None + self._cumulative_time = 0 # Accumulates time across messages + + def can_generate_metrics(self) -> bool: + return True + + def language_to_service_language(self, language: Language) -> str | None: + """Convert pipecat language to Rime language code.""" + return language_to_rime_language(language) + + async def set_model(self, model: str): + """Update the TTS model.""" + self._model = model + await super().set_model(model) + + def _build_msg(self, text: str = "") -> dict: + """Build JSON message for Rime API.""" + return {"text": text, "contextId": self._context_id} + + def _build_clear_msg(self) -> dict: + """Build clear operation message.""" + return {"operation": "clear"} + + def _build_eos_msg(self) -> dict: + """Build end-of-stream operation message.""" + return {"operation": "eos"} + + async def start(self, frame: StartFrame): + """Start the service and establish websocket connection.""" + await super().start(frame) + self._settings["samplingRate"] = self.sample_rate + await self._connect() + + async def stop(self, frame: EndFrame): + """Stop the service and close connection.""" + await super().stop(frame) + await self._disconnect() + + async def cancel(self, frame: CancelFrame): + """Cancel current operation and clean up.""" + await super().cancel(frame) + await self._disconnect() + + async def _connect(self): + """Establish websocket connection and start receive task.""" + await self._connect_websocket() + + if not self._receive_task: + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) + + async def _disconnect(self): + """Close websocket connection and clean up tasks.""" + if self._receive_task: + await self.cancel_task(self._receive_task) + self._receive_task = None + + await self._disconnect_websocket() + + async def _connect_websocket(self): + """Connect to Rime websocket API with configured settings.""" + try: + if self._websocket: + return + + params = "&".join(f"{k}={v}" for k, v in self._settings.items()) + url = f"{self._url}?{params}" + headers = {"Authorization": f"Bearer {self._api_key}"} + self._websocket = await websockets.connect(url, extra_headers=headers) + except Exception as e: + logger.error(f"{self} initialization error: {e}") + self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") + + async def _disconnect_websocket(self): + """Close websocket connection and reset state.""" + try: + await self.stop_all_metrics() + if self._websocket: + await self._websocket.send(json.dumps(self._build_eos_msg())) + await self._websocket.close() + self._websocket = None + self._context_id = None + except Exception as e: + logger.error(f"{self} error closing websocket: {e}") + + def _get_websocket(self): + """Get active websocket connection or raise exception.""" + if self._websocket: + return self._websocket + raise Exception("Websocket not connected") + + async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection): + """Handle interruption by clearing current context.""" + await super()._handle_interruption(frame, direction) + await self.stop_all_metrics() + if self._context_id: + await self._get_websocket().send(json.dumps(self._build_clear_msg())) + self._context_id = None + + def _calculate_word_times(self, words: list, starts: list, ends: list) -> list: + """Calculate word timing pairs with proper spacing and punctuation. + + Args: + words: List of words from Rime. + starts: List of start times for each word. + ends: List of end times for each word. + + Returns: + List of (word, timestamp) pairs with proper timing. + """ + word_pairs = [] + for i, (word, start_time, _) in enumerate(zip(words, starts, ends)): + if not word.strip(): + continue + + # Adjust timing by adding cumulative time + adjusted_start = start_time + self._cumulative_time + + # Handle punctuation by appending to previous word + is_punctuation = bool(word.strip(",.!?") == "") + if is_punctuation and word_pairs: + prev_word, prev_time = word_pairs[-1] + word_pairs[-1] = (prev_word + word, prev_time) + else: + word_pairs.append((word, adjusted_start)) + + return word_pairs + + async def flush_audio(self): + if not self._context_id or not self._websocket: + return + + logger.trace(f"{self}: flushing audio") + await self._get_websocket().send(json.dumps({"text": " "})) + self._context_id = None + + async def _receive_messages(self): + """Process incoming websocket messages.""" + async for message in self._get_websocket(): + msg = json.loads(message) + + if not msg or not self.audio_context_available(msg["contextId"]): + continue + + if msg["type"] == "chunk": + # Process audio chunk + await self.stop_ttfb_metrics() + self.start_word_timestamps() + frame = TTSAudioRawFrame( + audio=base64.b64decode(msg["data"]), + sample_rate=self.sample_rate, + num_channels=1, + ) + await self.append_to_audio_context(msg["contextId"], frame) + + elif msg["type"] == "timestamps": + # Process word timing information + timestamps = msg.get("word_timestamps", {}) + words = timestamps.get("words", []) + starts = timestamps.get("start", []) + ends = timestamps.get("end", []) + + if words and starts: + # Calculate word timing pairs + word_pairs = self._calculate_word_times(words, starts, ends) + if word_pairs: + await self.add_word_timestamps(word_pairs) + self._cumulative_time = ends[-1] + self._cumulative_time + logger.debug(f"Updated cumulative time to: {self._cumulative_time}") + + elif msg["type"] == "error": + logger.error(f"{self} error: {msg}") + await self.push_frame(TTSStoppedFrame()) + await self.stop_all_metrics() + await self.push_error(ErrorFrame(f"{self} error: {msg['message']}")) + self._context_id = None + + async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM): + """Push frame and handle end-of-turn conditions.""" + await super().push_frame(frame, direction) + if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)): + if isinstance(frame, TTSStoppedFrame): + await self.add_word_timestamps([("LLMFullResponseEndFrame", 0), ("Reset", 0)]) + + async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: + """Generate speech from text. + + Args: + text: The text to convert to speech. + + Yields: + Frames containing audio data and timing information. + """ + logger.debug(f"{self}: Generating TTS [{text}]") + try: + if not self._websocket: + await self._connect() + + try: + if not self._context_id: + await self.start_ttfb_metrics() + yield TTSStartedFrame() + self._cumulative_time = 0 + self._context_id = str(uuid.uuid4()) + await self.create_audio_context(self._context_id) + + msg = self._build_msg(text=text) + await self._get_websocket().send(json.dumps(msg)) + await self.start_tts_usage_metrics(text) + except Exception as e: + logger.error(f"{self} error sending message: {e}") + yield TTSStoppedFrame() + await self._disconnect() + await self._connect() + return + yield None + except Exception as e: + logger.error(f"{self} exception: {e}") class RimeHttpTTSService(TTSService): @@ -32,18 +355,19 @@ class RimeHttpTTSService(TTSService): self, *, api_key: str, - voice_id: str = "eva", - model: str = "mist", - sample_rate: int = 24000, + voice_id: str, + aiohttp_session: aiohttp.ClientSession, + model: str = "mistv2", + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): super().__init__(sample_rate=sample_rate, **kwargs) self._api_key = api_key + self._session = aiohttp_session self._base_url = "https://users.rime.ai/v1/rime-tts" self._settings = { - "samplingRate": sample_rate, "speedAlpha": params.speed_alpha, "reduceLatency": params.reduce_latency, "pauseBetweenBrackets": params.pause_between_brackets, @@ -59,7 +383,7 @@ class RimeHttpTTSService(TTSService): return True async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") headers = { "Accept": "audio/pcm", @@ -71,39 +395,35 @@ class RimeHttpTTSService(TTSService): payload["text"] = text payload["speaker"] = self._voice_id payload["modelId"] = self._model_name + payload["samplingRate"] = self.sample_rate try: await self.start_ttfb_metrics() - await self.start_tts_usage_metrics(text) - yield TTSStartedFrame() + async with self._session.post( + self._base_url, json=payload, headers=headers + ) as response: + if response.status != 200: + error_message = f"Rime TTS error: HTTP {response.status}" + logger.error(error_message) + yield ErrorFrame(error=error_message) + return - async with aiohttp.ClientSession() as session: - async with session.post(self._base_url, json=payload, headers=headers) as response: - if response.status != 200: - error_message = f"Rime TTS error: HTTP {response.status}" - logger.error(error_message) - yield ErrorFrame(error=error_message) - return + await self.start_tts_usage_metrics(text) - # Process the streaming response - chunk_size = 8192 - first_chunk = True + yield TTSStartedFrame() - async for chunk in response.content.iter_chunked(chunk_size): - if first_chunk: - await self.stop_ttfb_metrics() - first_chunk = False - - if chunk: - frame = TTSAudioRawFrame(chunk, self._settings["samplingRate"], 1) - yield frame - - yield TTSStoppedFrame() + # Process the streaming response + CHUNK_SIZE = 1024 + async for chunk in response.content.iter_chunked(CHUNK_SIZE): + if len(chunk) > 0: + await self.stop_ttfb_metrics() + frame = TTSAudioRawFrame(chunk, self.sample_rate, 1) + yield frame except Exception as e: logger.exception(f"Error generating TTS: {e}") yield ErrorFrame(error=f"Rime TTS error: {str(e)}") - finally: + await self.stop_ttfb_metrics() yield TTSStoppedFrame() diff --git a/src/pipecat/services/riva.py b/src/pipecat/services/riva.py index cac1946d5..de065aef4 100644 --- a/src/pipecat/services/riva.py +++ b/src/pipecat/services/riva.py @@ -49,7 +49,7 @@ class FastPitchTTSService(TTSService): api_key: str, server: str = "grpc.nvcf.nvidia.com:443", voice_id: str = "English-US.Female-1", - sample_rate: int = 24000, + sample_rate: Optional[int] = None, function_id: str = "0149dedb-2be8-4195-b9a0-e57e0e14f972", params: InputParams = InputParams(), **kwargs, @@ -57,7 +57,6 @@ class FastPitchTTSService(TTSService): super().__init__(sample_rate=sample_rate, **kwargs) self._api_key = api_key self._voice_id = voice_id - self._sample_rate = sample_rate self._language_code = params.language self._quality = params.quality @@ -87,7 +86,7 @@ class FastPitchTTSService(TTSService): text, self._voice_id, self._language_code, - sample_rate_hz=self._sample_rate, + sample_rate_hz=self.sample_rate, audio_prompt_file=None, quality=self._quality, custom_dictionary={}, @@ -102,7 +101,7 @@ class FastPitchTTSService(TTSService): await self.start_ttfb_metrics() yield TTSStartedFrame() - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") try: queue = asyncio.Queue() @@ -114,7 +113,7 @@ class FastPitchTTSService(TTSService): await self.stop_ttfb_metrics() frame = TTSAudioRawFrame( audio=resp.audio, - sample_rate=self._sample_rate, + sample_rate=self.sample_rate, num_channels=1, ) yield frame @@ -136,10 +135,11 @@ class ParakeetSTTService(STTService): api_key: str, server: str = "grpc.nvcf.nvidia.com:443", function_id: str = "1598d209-5e27-4d3c-8079-4751568b1081", + sample_rate: Optional[int] = None, params: InputParams = InputParams(), **kwargs, ): - super().__init__(**kwargs) + super().__init__(sample_rate=sample_rate, **kwargs) self._api_key = api_key self._profanity_filter = False self._automatic_punctuation = False @@ -154,7 +154,6 @@ class ParakeetSTTService(STTService): self._stop_history_eou = -1 self._stop_threshold_eou = -1.0 self._custom_configuration = "" - self._sample_rate: int = 16000 self.set_model_name("parakeet-ctc-1.1b-asr") @@ -166,6 +165,20 @@ class ParakeetSTTService(STTService): self._asr_service = riva.client.ASRService(auth) + self._queue = asyncio.Queue() + self._config = None + self._thread_task = None + self._response_task = None + + def can_generate_metrics(self) -> bool: + return False + + async def start(self, frame: StartFrame): + await super().start(frame) + + if self._config: + return + config = riva.client.StreamingRecognitionConfig( config=riva.client.RecognitionConfig( encoding=riva.client.AudioEncoding.LINEAR_PCM, @@ -175,14 +188,16 @@ class ParakeetSTTService(STTService): profanity_filter=self._profanity_filter, enable_automatic_punctuation=self._automatic_punctuation, verbatim_transcripts=not self._no_verbatim_transcripts, - sample_rate_hertz=self._sample_rate, + sample_rate_hertz=self.sample_rate, audio_channel_count=1, ), interim_results=True, ) + riva.client.add_word_boosting_to_config( config, self._boosted_lm_words, self._boosted_lm_score ) + riva.client.add_endpoint_parameters_to_config( config, self._start_history, @@ -193,18 +208,15 @@ class ParakeetSTTService(STTService): self._stop_threshold_eou, ) riva.client.add_custom_configuration_to_config(config, self._custom_configuration) + self._config = config - self._queue = asyncio.Queue() + if not self._thread_task: + self._thread_task = self.create_task(self._thread_task_handler()) - def can_generate_metrics(self) -> bool: - return False - - async def start(self, frame: StartFrame): - await super().start(frame) - self._thread_task = self.create_task(self._thread_task_handler()) - self._response_task = self.create_task(self._response_task_handler()) - self._response_queue = asyncio.Queue() + if not self._response_task: + self._response_queue = asyncio.Queue() + self._response_task = self.create_task(self._response_task_handler()) async def stop(self, frame: EndFrame): await super().stop(frame) @@ -215,8 +227,13 @@ class ParakeetSTTService(STTService): await self._stop_tasks() async def _stop_tasks(self): - await self.cancel_task(self._thread_task) - await self.cancel_task(self._response_task) + if self._thread_task: + await self.cancel_task(self._thread_task) + self._thread_task = None + + if self._response_task: + await self.cancel_task(self._response_task) + self._response_task = None def _response_handler(self): responses = self._asr_service.streaming_response_generator( diff --git a/src/pipecat/services/simli.py b/src/pipecat/services/simli.py index 5a6aecbe4..0fb490914 100644 --- a/src/pipecat/services/simli.py +++ b/src/pipecat/services/simli.py @@ -41,16 +41,23 @@ class SimliVideoService(FrameProcessor): self._pipecat_resampler_event = asyncio.Event() self._pipecat_resampler: AudioResampler = None - self._simli_resampler = AudioResampler("s16", 1, 16000) + self._simli_resampler = AudioResampler("s16", "mono", 16000) + self._initialized = False self._audio_task: asyncio.Task = None self._video_task: asyncio.Task = None async def _start_connection(self): - await self._simli_client.Initialize() + if not self._initialized: + await self._simli_client.Initialize() + self._initialized = True + # Create task to consume and process audio and video - self._audio_task = self.create_task(self._consume_and_process_audio()) - self._video_task = self.create_task(self._consume_and_process_video()) + if not self._audio_task: + self._audio_task = self.create_task(self._consume_and_process_audio()) + + if not self._video_task: + self._video_task = self.create_task(self._consume_and_process_video()) async def _consume_and_process_audio(self): await self._pipecat_resampler_event.wait() @@ -117,5 +124,7 @@ class SimliVideoService(FrameProcessor): await self._simli_client.stop() if self._audio_task: await self.cancel_task(self._audio_task) + self._audio_task = None if self._video_task: await self.cancel_task(self._video_task) + self._video_task = None diff --git a/src/pipecat/services/tavus.py b/src/pipecat/services/tavus.py index b0ca699cb..9083dd53d 100644 --- a/src/pipecat/services/tavus.py +++ b/src/pipecat/services/tavus.py @@ -37,6 +37,7 @@ class TavusVideoService(AIService): replica_id: str, persona_id: str = "pipecat0", # Use `pipecat0` so that your TTS voice is used in place of the Tavus persona session: aiohttp.ClientSession, + sample_rate: int = 16000, **kwargs, ) -> None: super().__init__(**kwargs) @@ -44,6 +45,7 @@ class TavusVideoService(AIService): self._replica_id = replica_id self._persona_id = persona_id self._session = session + self._sample_rate = sample_rate self._conversation_id: str @@ -94,7 +96,7 @@ class TavusVideoService(AIService): async def _encode_audio_and_send(self, audio: bytes, in_rate: int, done: bool) -> None: """Encodes audio to base64 and sends it to Tavus""" if not done: - audio = await self._resampler.resample(audio, in_rate, 16000) + audio = await self._resampler.resample(audio, in_rate, self._sample_rate) audio_base64 = base64.b64encode(audio).decode("utf-8") logger.trace(f"{self}: sending {len(audio)} bytes") await self._send_audio_message(audio_base64, done=done) @@ -108,7 +110,7 @@ class TavusVideoService(AIService): elif isinstance(frame, TTSAudioRawFrame): await self._encode_audio_and_send(frame.audio, frame.sample_rate, done=False) elif isinstance(frame, TTSStoppedFrame): - await self._encode_audio_and_send(b"\x00", 16000, done=True) + await self._encode_audio_and_send(b"\x00", self._sample_rate, done=True) await self.stop_ttfb_metrics() await self.stop_processing_metrics() elif isinstance(frame, StartInterruptionFrame): @@ -137,6 +139,7 @@ class TavusVideoService(AIService): "inference_id": self._current_idx_str, "audio": audio_base64, "done": done, + "sample_rate": self._sample_rate, }, } ) diff --git a/src/pipecat/services/ultravox.py b/src/pipecat/services/ultravox.py new file mode 100644 index 000000000..52a5d05eb --- /dev/null +++ b/src/pipecat/services/ultravox.py @@ -0,0 +1,403 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +"""This module implements Ultravox speech-to-text with a locally-loaded model.""" + +import json +import os +import time +from typing import AsyncGenerator, List, Optional + +import numpy as np +from huggingface_hub import login +from loguru import logger + +from pipecat.frames.frames import ( + AudioRawFrame, + CancelFrame, + EndFrame, + ErrorFrame, + Frame, + LLMFullResponseEndFrame, + LLMFullResponseStartFrame, + LLMTextFrame, + StartFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, +) +from pipecat.processors.frame_processor import FrameDirection +from pipecat.services.ai_services import AIService + +try: + from transformers import AutoTokenizer + from vllm import AsyncLLMEngine, SamplingParams + from vllm.engine.arg_utils import AsyncEngineArgs +except ModuleNotFoundError as e: + logger.error(f"Exception: {e}") + logger.error("In order to use Ultravox, you need to `pip install pipecat-ai[ultravox]`.") + raise Exception(f"Missing module: {e}") + + +class AudioBuffer: + """Buffer to collect audio frames before processing. + + Attributes: + frames: List of AudioRawFrames to process + started_at: Timestamp when speech started + is_processing: Flag to prevent concurrent processing + """ + + def __init__(self): + self.frames: List[AudioRawFrame] = [] + self.started_at: Optional[float] = None + self.is_processing: bool = False + + +class UltravoxModel: + """Model wrapper for the Ultravox multimodal model. + + This class handles loading and running the Ultravox model for speech-to-text. + + Args: + model_name: The name or path of the Ultravox model to load + + Attributes: + model_name: The name of the loaded model + engine: The vLLM engine for model inference + tokenizer: The tokenizer for the model + stop_token_ids: Optional token IDs to stop generation + """ + + def __init__(self, model_name: str = "fixie-ai/ultravox-v0_4_1-llama-3_1-8b"): + self.model_name = model_name + self._initialize_engine() + self._initialize_tokenizer() + self.stop_token_ids = None + + def _initialize_engine(self): + """Initialize the vLLM engine for inference.""" + engine_args = AsyncEngineArgs( + model=self.model_name, + gpu_memory_utilization=0.9, + max_model_len=8192, + trust_remote_code=True, + ) + self.engine = AsyncLLMEngine.from_engine_args(engine_args) + + def _initialize_tokenizer(self): + """Initialize the tokenizer for the model.""" + self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) + + def format_prompt(self, messages: list): + """Format chat messages into a prompt for the model. + + Args: + messages: List of message dictionaries with 'role' and 'content' + + Returns: + str: Formatted prompt string + """ + return self.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + + async def generate( + self, + messages: list, + temperature: float = 0.7, + max_tokens: int = 100, + audio: np.ndarray = None, + ): + """Generate text from audio input using the model. + + Args: + messages: List of message dictionaries + temperature: Sampling temperature + max_tokens: Maximum tokens to generate + audio: Audio data as numpy array + + Yields: + str: JSON chunks of the generated response + """ + sampling_params = SamplingParams( + temperature=temperature, max_tokens=max_tokens, stop_token_ids=self.stop_token_ids + ) + + mm_data = {"audio": audio} + inputs = {"prompt": self.format_prompt(messages), "multi_modal_data": mm_data} + results_generator = self.engine.generate(inputs, sampling_params, str(time.time())) + + previous_text = "" + first_chunk = True + + async for output in results_generator: + prompt_output = output.outputs + new_text = prompt_output[0].text[len(previous_text) :] + previous_text = prompt_output[0].text + + # Construct OpenAI-compatible chunk + chunk = { + "id": str(int(time.time() * 1000)), + "object": "chat.completion.chunk", + "created": int(time.time()), + "model": self.model_name, + "choices": [ + { + "index": 0, + "delta": {}, + "finish_reason": None, + } + ], + } + + # Include the role in the first chunk + if first_chunk: + chunk["choices"][0]["delta"]["role"] = "assistant" + first_chunk = False + + # Add new text to the delta if any + if new_text: + chunk["choices"][0]["delta"]["content"] = new_text + + # Capture a finish reason if it's provided + finish_reason = prompt_output[0].finish_reason or None + if finish_reason and finish_reason != "none": + chunk["choices"][0]["finish_reason"] = finish_reason + + yield json.dumps(chunk) + + +class UltravoxSTTService(AIService): + """Service to transcribe audio using the Ultravox multimodal model. + + This service collects audio frames and processes them with Ultravox + to generate text transcriptions. + + Args: + model_size: The Ultravox model to use (ModelSize enum or string) + hf_token: Hugging Face token for model access + temperature: Sampling temperature for generation + max_tokens: Maximum tokens to generate + **kwargs: Additional arguments passed to AIService + + Attributes: + model: The UltravoxModel instance + buffer: Buffer to collect audio frames + temperature: Temperature for text generation + max_tokens: Maximum tokens to generate + _connection_active: Flag indicating if service is active + """ + + def __init__( + self, + *, + model_size: str = "fixie-ai/ultravox-v0_4_1-llama-3_1-8b", + hf_token: Optional[str] = None, + temperature: float = 0.7, + max_tokens: int = 100, + **kwargs, + ): + super().__init__(**kwargs) + + # Authenticate with Hugging Face if token provided + if hf_token: + login(token=hf_token) + elif os.environ.get("HF_TOKEN"): + login(token=os.environ.get("HF_TOKEN")) + else: + logger.warning("No Hugging Face token provided. Model may not load correctly.") + + # Initialize model + model_name = model_size if isinstance(model_size, str) else model_size.value + self._model = UltravoxModel(model_name=model_name) + + # Initialize service state + self._buffer = AudioBuffer() + self._temperature = temperature + self._max_tokens = max_tokens + self._connection_active = False + + logger.info(f"Initialized UltravoxSTTService with model: {model_name}") + + def can_generate_metrics(self) -> bool: + """Indicates whether this service can generate metrics. + + Returns: + bool: True, as this service supports metric generation. + """ + return True + + async def start(self, frame: StartFrame): + """Handle service start. + + Args: + frame: StartFrame that triggered this method + """ + await super().start(frame) + self._connection_active = True + logger.info("UltravoxSTTService started") + + async def stop(self, frame: EndFrame): + """Handle service stop. + + Args: + frame: EndFrame that triggered this method + """ + await super().stop(frame) + self._connection_active = False + logger.info("UltravoxSTTService stopped") + + async def cancel(self, frame: CancelFrame): + """Handle service cancellation. + + Args: + frame: CancelFrame that triggered this method + """ + await super().cancel(frame) + self._connection_active = False + self._buffer = AudioBuffer() + logger.info("UltravoxSTTService cancelled") + + async def process_frame(self, frame: Frame, direction: FrameDirection): + """Process incoming frames. + + This method collects audio frames and processes them when speech ends. + + Args: + frame: The frame to process + direction: Direction of the frame (input/output) + """ + await super().process_frame(frame, direction) + + if isinstance(frame, UserStartedSpeakingFrame): + logger.info("Speech started") + self._buffer = AudioBuffer() + self._buffer.started_at = time.time() + + elif isinstance(frame, AudioRawFrame) and self._buffer.started_at is not None: + self._buffer.frames.append(frame) + + elif isinstance(frame, UserStoppedSpeakingFrame): + if self._buffer.frames and not self._buffer.is_processing: + logger.info("Speech ended, processing buffer...") + await self.process_generator(self._process_audio_buffer()) + return # Return early to avoid pushing None frame + + # Only push the original frame if we haven't processed audio + if frame is not None: + await self.push_frame(frame, direction) + + async def _process_audio_buffer(self) -> AsyncGenerator[Frame, None]: + """Process collected audio frames with Ultravox. + + This method concatenates audio frames, processes them with the model, + and yields the resulting text frames. + + Yields: + Frame: TextFrame containing the transcribed text + """ + try: + self._buffer.is_processing = True + + # Check if we have valid frames before processing + if not self._buffer.frames: + logger.warning("No audio frames to process") + yield ErrorFrame("No audio frames to process") + return + + # Process audio frames + audio_arrays = [] + for f in self._buffer.frames: + if hasattr(f, "audio") and f.audio: + # Handle bytes data - these are int16 PCM samples + if isinstance(f.audio, bytes): + try: + # Convert bytes to int16 array + arr = np.frombuffer(f.audio, dtype=np.int16) + if arr.size > 0: # Check if array is not empty + audio_arrays.append(arr) + except Exception as e: + logger.error(f"Error processing bytes audio frame: {e}") + # Handle numpy array data + elif isinstance(f.audio, np.ndarray): + if f.audio.size > 0: # Check if array is not empty + # Ensure it's int16 data + if f.audio.dtype != np.int16: + logger.info(f"Converting array from {f.audio.dtype} to int16") + audio_arrays.append(f.audio.astype(np.int16)) + else: + audio_arrays.append(f.audio) + + # Only proceed if we have valid audio arrays + if not audio_arrays: + logger.warning("No valid audio data found in frames") + yield ErrorFrame("No valid audio data found in frames") + return + + # Concatenate audio frames - all should be int16 now + audio_data = np.concatenate(audio_arrays) + + audio_int16 = audio_data # Already in int16 format + # Save int16 audio + + # Convert int16 to float32 and normalize for model input + audio_float32 = audio_int16.astype(np.float32) / 32768.0 + + # Generate text using the model + if self._model: + try: + logger.info("Generating text from audio using model...") + full_response = "" + + # Start metrics tracking + await self.start_ttfb_metrics() + await self.start_processing_metrics() + + async for response in self.model.generate( + messages=[{"role": "user", "content": "<|audio|>\n"}], + temperature=self.temperature, + max_tokens=self.max_tokens, + audio=audio_float32, + ): + # Stop TTFB metrics after first response + await self.stop_ttfb_metrics() + + chunk = json.loads(response) + if "choices" in chunk and len(chunk["choices"]) > 0: + delta = chunk["choices"][0]["delta"] + if "content" in delta: + new_text = delta["content"] + full_response += new_text + + # Stop processing metrics after completion + await self.stop_processing_metrics() + + logger.info(f"Generated text: {full_response}") + # Create a transcription frame with the generated text + yield LLMFullResponseStartFrame() + + text_frame = LLMTextFrame(text=full_response.strip()) + yield text_frame + + yield LLMFullResponseEndFrame() + + except Exception as e: + logger.error(f"Error generating text from model: {e}") + yield ErrorFrame(f"Error generating text: {str(e)}") + else: + logger.warning("No model available for text generation") + yield ErrorFrame("No model available for text generation") + + except Exception as e: + logger.error(f"Error processing audio buffer: {e}") + import traceback + + logger.error(traceback.format_exc()) + yield ErrorFrame(f"Error processing audio: {str(e)}") + finally: + self._buffer.is_processing = False + self._buffer.frames = [] + self._buffer.started_at = None diff --git a/src/pipecat/services/websocket_service.py b/src/pipecat/services/websocket_service.py index 7e480f2b7..2e82ddaba 100644 --- a/src/pipecat/services/websocket_service.py +++ b/src/pipecat/services/websocket_service.py @@ -10,16 +10,19 @@ from typing import Awaitable, Callable, Optional import websockets from loguru import logger +from websockets.protocol import State from pipecat.frames.frames import ErrorFrame +from pipecat.utils.network import exponential_backoff_time class WebsocketService(ABC): """Base class for websocket-based services with reconnection logic.""" - def __init__(self): + def __init__(self, *, reconnect_on_error: bool = True, **kwargs): """Initialize websocket attributes.""" self._websocket: Optional[websockets.WebSocketClientProtocol] = None + self._reconnect_on_error = reconnect_on_error async def _verify_connection(self) -> bool: """Verify websocket connection is working. @@ -50,27 +53,6 @@ class WebsocketService(ABC): await self._connect_websocket() return await self._verify_connection() - def _calculate_wait_time( - self, attempt: int, min_wait: float = 4, max_wait: float = 10, multiplier: float = 1 - ) -> float: - """Calculate exponential backoff wait time. - - Args: - attempt: Current attempt number (1-based) - min_wait: Minimum wait time in seconds - max_wait: Maximum wait time in seconds - multiplier: Base multiplier for exponential calculation - - Returns: - Wait time in seconds - """ - try: - exp = 2 ** (attempt - 1) * multiplier - result = max(0, min(exp, max_wait)) - return max(min_wait, result) - except (ValueError, ArithmeticError): - return max_wait - async def _receive_task_handler(self, report_error: Callable[[ErrorFrame], Awaitable[None]]): """Handles WebSocket message receiving with automatic retry logic. @@ -83,35 +65,63 @@ class WebsocketService(ABC): while True: try: await self._receive_messages() - logger.debug(f"{self} connection established successfully") retry_count = 0 # Reset counter on successful message receive + if self._websocket and self._websocket.state == State.CLOSED: + raise websockets.ConnectionClosedOK( + self._websocket.close_rcvd, + self._websocket.close_sent, + self._websocket.close_rcvd_then_sent, + ) except Exception as e: - retry_count += 1 - if retry_count >= MAX_RETRIES: - message = f"{self} error receiving messages: {e}" - logger.error(message) - await report_error(ErrorFrame(message, fatal=True)) + message = f"{self} error receiving messages: {e}" + logger.error(message) + + if self._reconnect_on_error: + retry_count += 1 + if retry_count >= MAX_RETRIES: + await report_error(ErrorFrame(message, fatal=True)) + break + + logger.warning(f"{self} connection error, will retry: {e}") + await report_error(ErrorFrame(message)) + + try: + if await self._reconnect_websocket(retry_count): + retry_count = 0 # Reset counter on successful reconnection + wait_time = exponential_backoff_time(retry_count) + await asyncio.sleep(wait_time) + except Exception as reconnect_error: + logger.error(f"{self} reconnection failed: {reconnect_error}") + else: + await report_error(ErrorFrame(message)) break - logger.warning(f"{self} connection error, will retry: {e}") + @abstractmethod + async def _connect(self): + """Implement service-specific connection logic. This function will + connect to the websocket via _connect_websocket() among other connection + logic.""" + pass - try: - if await self._reconnect_websocket(retry_count): - retry_count = 0 # Reset counter on successful reconnection - wait_time = self._calculate_wait_time(retry_count) - await asyncio.sleep(wait_time) - except Exception as reconnect_error: - logger.error(f"{self} reconnection failed: {reconnect_error}") - continue + @abstractmethod + async def _disconnect(self): + """Implement service-specific disconnection logic. This function will + disconnect to the websocket via _connect_websocket() among other + connection logic. + + """ + pass @abstractmethod async def _connect_websocket(self): - """Implement service-specific websocket connection logic.""" + """Implement service-specific websocket connection logic. This function + should only connect to the websocket.""" pass @abstractmethod async def _disconnect_websocket(self): - """Implement service-specific websocket disconnection logic.""" + """Implement service-specific websocket disconnection logic. This + function should only disconnect from the websocket.""" pass @abstractmethod diff --git a/src/pipecat/services/whisper.py b/src/pipecat/services/whisper.py index 18c25b6b5..40d714291 100644 --- a/src/pipecat/services/whisper.py +++ b/src/pipecat/services/whisper.py @@ -8,13 +8,14 @@ import asyncio from enum import Enum -from typing import AsyncGenerator +from typing import AsyncGenerator, Optional import numpy as np from loguru import logger from pipecat.frames.frames import ErrorFrame, Frame, TranscriptionFrame from pipecat.services.ai_services import SegmentedSTTService +from pipecat.transcriptions.language import Language from pipecat.utils.time import time_now_iso8601 try: @@ -26,18 +27,219 @@ except ModuleNotFoundError as e: class Model(Enum): - """Class of basic Whisper model selection options""" + """Class of basic Whisper model selection options. + Available models: + Multilingual models: + TINY: Smallest multilingual model + BASE: Basic multilingual model + MEDIUM: Good balance for multilingual + LARGE: Best quality multilingual + DISTIL_LARGE_V2: Fast multilingual + + English-only models: + DISTIL_MEDIUM_EN: Fast English-only + """ + + # Multilingual models TINY = "tiny" BASE = "base" MEDIUM = "medium" LARGE = "large-v3" DISTIL_LARGE_V2 = "Systran/faster-distil-whisper-large-v2" + + # English-only models DISTIL_MEDIUM_EN = "Systran/faster-distil-whisper-medium.en" +def language_to_whisper_language(language: Language) -> Optional[str]: + """Maps pipecat Language enum to Whisper language codes. + + Args: + language: A Language enum value representing the input language. + + Returns: + str or None: The corresponding Whisper language code, or None if not supported. + + Note: + Only includes languages officially supported by Whisper. + """ + language_map = { + # Arabic + Language.AR: "ar", + Language.AR_AE: "ar", + Language.AR_BH: "ar", + Language.AR_DZ: "ar", + Language.AR_EG: "ar", + Language.AR_IQ: "ar", + Language.AR_JO: "ar", + Language.AR_KW: "ar", + Language.AR_LB: "ar", + Language.AR_LY: "ar", + Language.AR_MA: "ar", + Language.AR_OM: "ar", + Language.AR_QA: "ar", + Language.AR_SA: "ar", + Language.AR_SY: "ar", + Language.AR_TN: "ar", + Language.AR_YE: "ar", + # Bengali + Language.BN: "bn", + Language.BN_BD: "bn", + Language.BN_IN: "bn", + # Czech + Language.CS: "cs", + Language.CS_CZ: "cs", + # Danish + Language.DA: "da", + Language.DA_DK: "da", + # German + Language.DE: "de", + Language.DE_AT: "de", + Language.DE_CH: "de", + Language.DE_DE: "de", + # Greek + Language.EL: "el", + Language.EL_GR: "el", + # English + Language.EN: "en", + Language.EN_AU: "en", + Language.EN_CA: "en", + Language.EN_GB: "en", + Language.EN_HK: "en", + Language.EN_IE: "en", + Language.EN_IN: "en", + Language.EN_KE: "en", + Language.EN_NG: "en", + Language.EN_NZ: "en", + Language.EN_PH: "en", + Language.EN_SG: "en", + Language.EN_TZ: "en", + Language.EN_US: "en", + Language.EN_ZA: "en", + # Spanish + Language.ES: "es", + Language.ES_AR: "es", + Language.ES_BO: "es", + Language.ES_CL: "es", + Language.ES_CO: "es", + Language.ES_CR: "es", + Language.ES_CU: "es", + Language.ES_DO: "es", + Language.ES_EC: "es", + Language.ES_ES: "es", + Language.ES_GQ: "es", + Language.ES_GT: "es", + Language.ES_HN: "es", + Language.ES_MX: "es", + Language.ES_NI: "es", + Language.ES_PA: "es", + Language.ES_PE: "es", + Language.ES_PR: "es", + Language.ES_PY: "es", + Language.ES_SV: "es", + Language.ES_US: "es", + Language.ES_UY: "es", + Language.ES_VE: "es", + # Persian + Language.FA: "fa", + Language.FA_IR: "fa", + # Finnish + Language.FI: "fi", + Language.FI_FI: "fi", + # French + Language.FR: "fr", + Language.FR_BE: "fr", + Language.FR_CA: "fr", + Language.FR_CH: "fr", + Language.FR_FR: "fr", + # Hindi + Language.HI: "hi", + Language.HI_IN: "hi", + # Hungarian + Language.HU: "hu", + Language.HU_HU: "hu", + # Indonesian + Language.ID: "id", + Language.ID_ID: "id", + # Italian + Language.IT: "it", + Language.IT_IT: "it", + # Japanese + Language.JA: "ja", + Language.JA_JP: "ja", + # Korean + Language.KO: "ko", + Language.KO_KR: "ko", + # Dutch + Language.NL: "nl", + Language.NL_BE: "nl", + Language.NL_NL: "nl", + # Polish + Language.PL: "pl", + Language.PL_PL: "pl", + # Portuguese + Language.PT: "pt", + Language.PT_BR: "pt", + Language.PT_PT: "pt", + # Romanian + Language.RO: "ro", + Language.RO_RO: "ro", + # Russian + Language.RU: "ru", + Language.RU_RU: "ru", + # Slovak + Language.SK: "sk", + Language.SK_SK: "sk", + # Swedish + Language.SV: "sv", + Language.SV_SE: "sv", + # Thai + Language.TH: "th", + Language.TH_TH: "th", + # Turkish + Language.TR: "tr", + Language.TR_TR: "tr", + # Ukrainian + Language.UK: "uk", + Language.UK_UA: "uk", + # Urdu + Language.UR: "ur", + Language.UR_IN: "ur", + Language.UR_PK: "ur", + # Vietnamese + Language.VI: "vi", + Language.VI_VN: "vi", + # Chinese + Language.ZH: "zh", + Language.ZH_CN: "zh", + Language.ZH_HK: "zh", + Language.ZH_TW: "zh", + } + return language_map.get(language) + + class WhisperSTTService(SegmentedSTTService): - """Class to transcribe audio with a locally-downloaded Whisper model""" + """Class to transcribe audio with a locally-downloaded Whisper model. + + This service uses Faster Whisper to perform speech-to-text transcription on audio + segments. It supports multiple languages and various model sizes. + + Args: + model: The Whisper model to use for transcription. Can be a Model enum or string. + device: The device to run inference on ('cpu', 'cuda', or 'auto'). + compute_type: The compute type for inference ('default', 'int8', 'int8_float16', etc.). + no_speech_prob: Probability threshold for filtering out non-speech segments. + language: The default language for transcription. + **kwargs: Additional arguments passed to SegmentedSTTService. + + Attributes: + _device: The device used for inference. + _compute_type: The compute type for inference. + _no_speech_prob: Threshold for non-speech filtering. + _model: The loaded Whisper model instance. + _settings: Dictionary containing service settings. + """ def __init__( self, @@ -46,6 +248,7 @@ class WhisperSTTService(SegmentedSTTService): device: str = "auto", compute_type: str = "default", no_speech_prob: float = 0.4, + language: Language = Language.EN, **kwargs, ): super().__init__(**kwargs) @@ -53,15 +256,48 @@ class WhisperSTTService(SegmentedSTTService): self._compute_type = compute_type self.set_model_name(model if isinstance(model, str) else model.value) self._no_speech_prob = no_speech_prob - self._model: WhisperModel | None = None + self._model: Optional[WhisperModel] = None + + self._settings = { + "language": language, + } + self._load() def can_generate_metrics(self) -> bool: + """Indicates whether this service can generate metrics. + + Returns: + bool: True, as this service supports metric generation. + """ return True + def language_to_service_language(self, language: Language) -> Optional[str]: + """Convert from pipecat Language to Whisper language code. + + Args: + language: The Language enum value to convert. + + Returns: + str or None: The corresponding Whisper language code, or None if not supported. + """ + return language_to_whisper_language(language) + + async def set_language(self, language: Language): + """Set the language for transcription. + + Args: + language: The Language enum value to use for transcription. + """ + logger.info(f"Switching STT language to: [{language}]") + self._settings["language"] = language + def _load(self): - """Loads the Whisper model. Note that if this is the first time - this model is being run, it will take time to download. + """Loads the Whisper model. + + Note: + If this is the first time this model is being run, + it will take time to download from the Hugging Face model hub. """ logger.debug("Loading Whisper model...") self._model = WhisperModel( @@ -70,7 +306,19 @@ class WhisperSTTService(SegmentedSTTService): logger.debug("Loaded Whisper model") async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]: - """Transcribes given audio using Whisper""" + """Transcribes given audio using Whisper. + + Args: + audio: Raw audio bytes in 16-bit PCM format. + + Yields: + Frame: Either a TranscriptionFrame containing the transcribed text + or an ErrorFrame if transcription fails. + + Note: + The audio is expected to be 16-bit signed PCM data. + The service will normalize it to float32 in the range [-1, 1]. + """ if not self._model: logger.error(f"{self} error: Whisper model not available") yield ErrorFrame("Whisper model not available") @@ -82,7 +330,10 @@ class WhisperSTTService(SegmentedSTTService): # Divide by 32768 because we have signed 16-bit data. audio_float = np.frombuffer(audio, dtype=np.int16).astype(np.float32) / 32768.0 - segments, _ = await asyncio.to_thread(self._model.transcribe, audio_float) + whisper_lang = self.language_to_service_language(self._settings["language"]) + segments, _ = await asyncio.to_thread( + self._model.transcribe, audio_float, language=whisper_lang + ) text: str = "" for segment in segments: if segment.no_speech_prob < self._no_speech_prob: @@ -93,4 +344,4 @@ class WhisperSTTService(SegmentedSTTService): if text: logger.debug(f"Transcription: [{text}]") - yield TranscriptionFrame(text, "", time_now_iso8601()) + yield TranscriptionFrame(text, "", time_now_iso8601(), self._settings["language"]) diff --git a/src/pipecat/services/xtts.py b/src/pipecat/services/xtts.py index a10247acd..d2702d520 100644 --- a/src/pipecat/services/xtts.py +++ b/src/pipecat/services/xtts.py @@ -4,7 +4,7 @@ # SPDX-License-Identifier: BSD 2-Clause License # -from typing import Any, AsyncGenerator, Dict +from typing import Any, AsyncGenerator, Dict, Optional import aiohttp from loguru import logger @@ -29,7 +29,7 @@ from pipecat.transcriptions.language import Language # https://github.com/coqui-ai/xtts-streaming-server -def language_to_xtts_language(language: Language) -> str | None: +def language_to_xtts_language(language: Language) -> Optional[str]: BASE_LANGUAGES = { Language.CS: "cs", Language.DE: "de", @@ -76,7 +76,7 @@ class XTTSService(TTSService): base_url: str, aiohttp_session: aiohttp.ClientSession, language: Language = Language.EN, - sample_rate: int = 24000, + sample_rate: Optional[int] = None, **kwargs, ): super().__init__(sample_rate=sample_rate, **kwargs) @@ -86,7 +86,7 @@ class XTTSService(TTSService): "base_url": base_url, } self.set_voice(voice_id) - self._studio_speakers: Dict[str, Any] | None = None + self._studio_speakers: Optional[Dict[str, Any]] = None self._aiohttp_session = aiohttp_session self._resampler = create_default_resampler() @@ -94,11 +94,15 @@ class XTTSService(TTSService): def can_generate_metrics(self) -> bool: return True - def language_to_service_language(self, language: Language) -> str | None: + def language_to_service_language(self, language: Language) -> Optional[str]: return language_to_xtts_language(language) async def start(self, frame: StartFrame): await super().start(frame) + + if self._studio_speakers: + return + async with self._aiohttp_session.get(self._settings["base_url"] + "/studio_speakers") as r: if r.status != 200: text = await r.text() @@ -114,7 +118,7 @@ class XTTSService(TTSService): self._studio_speakers = await r.json() async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: - logger.debug(f"Generating TTS: [{text}]") + logger.debug(f"{self}: Generating TTS [{text}]") if not self._studio_speakers: logger.error(f"{self} no studio speakers available") @@ -146,8 +150,10 @@ class XTTSService(TTSService): yield TTSStartedFrame() + CHUNK_SIZE = 1024 + buffer = bytearray() - async for chunk in r.content.iter_chunked(1024): + async for chunk in r.content.iter_chunked(CHUNK_SIZE): if len(chunk) > 0: await self.stop_ttfb_metrics() # Append new chunk to the buffer. @@ -164,18 +170,18 @@ class XTTSService(TTSService): # XTTS uses 24000 so we need to resample to our desired rate. resampled_audio = await self._resampler.resample( - bytes(process_data), 24000, self._sample_rate + bytes(process_data), 24000, self.sample_rate ) # Create the frame with the resampled audio - frame = TTSAudioRawFrame(resampled_audio, self._sample_rate, 1) + frame = TTSAudioRawFrame(resampled_audio, self.sample_rate, 1) yield frame # Process any remaining data in the buffer. if len(buffer) > 0: resampled_audio = await self._resampler.resample( - bytes(buffer), 24000, self._sample_rate + bytes(buffer), 24000, self.sample_rate ) - frame = TTSAudioRawFrame(resampled_audio, self._sample_rate, 1) + frame = TTSAudioRawFrame(resampled_audio, self.sample_rate, 1) yield frame yield TTSStoppedFrame() diff --git a/src/pipecat/tests/utils.py b/src/pipecat/tests/utils.py index 62f1c7571..e2368ba09 100644 --- a/src/pipecat/tests/utils.py +++ b/src/pipecat/tests/utils.py @@ -6,23 +6,30 @@ import asyncio from dataclasses import dataclass -from typing import Awaitable, Callable, Sequence, Tuple +from typing import Any, Awaitable, Callable, Dict, Optional, Sequence, Tuple -from pipecat.clocks.system_clock import SystemClock from pipecat.frames.frames import ( - ControlFrame, + EndFrame, Frame, HeartbeatFrame, StartFrame, + SystemFrame, ) from pipecat.observers.base_observer import BaseObserver +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -from pipecat.utils.asyncio import TaskManager @dataclass -class EndTestFrame(ControlFrame): - pass +class SleepFrame(SystemFrame): + """This frame is used by test framework to introduce some sleep time before + the next frame is pushed. This is useful to control system frames vs data or + control frames. + """ + + sleep: float = 0.1 class HeartbeatsObserver(BaseObserver): @@ -48,79 +55,106 @@ class HeartbeatsObserver(BaseObserver): class QueuedFrameProcessor(FrameProcessor): - def __init__(self, queue: asyncio.Queue, ignore_start: bool = True): + def __init__( + self, + *, + queue: asyncio.Queue, + queue_direction: FrameDirection, + ignore_start: bool = True, + ): super().__init__() self._queue = queue + self._queue_direction = queue_direction self._ignore_start = ignore_start async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) - if self._ignore_start and isinstance(frame, StartFrame): - await self.push_frame(frame, direction) - else: - await self._queue.put(frame) - await self.push_frame(frame, direction) + if direction == self._queue_direction: + if not isinstance(frame, StartFrame) or not self._ignore_start: + await self._queue.put(frame) + await self.push_frame(frame, direction) async def run_test( processor: FrameProcessor, + *, frames_to_send: Sequence[Frame], - expected_down_frames: Sequence[type], - expected_up_frames: Sequence[type] = [], + expected_down_frames: Optional[Sequence[type]] = None, + expected_up_frames: Optional[Sequence[type]] = None, + ignore_start: bool = True, + start_metadata: Dict[str, Any] = {}, + send_end_frame: bool = True, ) -> Tuple[Sequence[Frame], Sequence[Frame]]: received_up = asyncio.Queue() received_down = asyncio.Queue() - source = QueuedFrameProcessor(received_up) - sink = QueuedFrameProcessor(received_down) + source = QueuedFrameProcessor( + queue=received_up, + queue_direction=FrameDirection.UPSTREAM, + ignore_start=ignore_start, + ) + sink = QueuedFrameProcessor( + queue=received_down, + queue_direction=FrameDirection.DOWNSTREAM, + ignore_start=ignore_start, + ) - source.link(processor) - processor.link(sink) + pipeline = Pipeline([source, processor, sink]) - task_manager = TaskManager() - task_manager.set_event_loop(asyncio.get_event_loop()) - await source.queue_frame(StartFrame(clock=SystemClock(), task_manager=task_manager)) + task = PipelineTask( + pipeline, + params=PipelineParams(start_metadata=start_metadata), + cancel_on_idle_timeout=False, + ) - for frame in frames_to_send: - await processor.process_frame(frame, FrameDirection.DOWNSTREAM) + async def push_frames(): + # Just give a little head start to the runner. + await asyncio.sleep(0.01) + for frame in frames_to_send: + if isinstance(frame, SleepFrame): + await asyncio.sleep(frame.sleep) + else: + await task.queue_frame(frame) - await processor.queue_frame(EndTestFrame()) - await processor.queue_frame(EndTestFrame(), FrameDirection.UPSTREAM) + if send_end_frame: + await task.queue_frame(EndFrame()) + + runner = PipelineRunner() + await asyncio.gather(runner.run(task), push_frames()) # # Down frames # received_down_frames: Sequence[Frame] = [] - running = True - while running: - frame = await received_down.get() - running = not isinstance(frame, EndTestFrame) - if running: - received_down_frames.append(frame) + if expected_down_frames is not None: + while not received_down.empty(): + frame = await received_down.get() + if not isinstance(frame, EndFrame) or not send_end_frame: + received_down_frames.append(frame) - print("received DOWN frames =", received_down_frames) + print("received DOWN frames =", received_down_frames) + print("expected DOWN frames =", expected_down_frames) - assert len(received_down_frames) == len(expected_down_frames) + assert len(received_down_frames) == len(expected_down_frames) - for real, expected in zip(received_down_frames, expected_down_frames): - assert isinstance(real, expected) + for real, expected in zip(received_down_frames, expected_down_frames): + assert isinstance(real, expected) # # Up frames # received_up_frames: Sequence[Frame] = [] - running = True - while running: - frame = await received_up.get() - running = not isinstance(frame, EndTestFrame) - if running: + if expected_up_frames is not None: + while not received_up.empty(): + frame = await received_up.get() received_up_frames.append(frame) - print("received UP frames =", received_up_frames) + print("received UP frames =", received_up_frames) + print("expected UP frames =", expected_up_frames) - assert len(received_up_frames) == len(expected_up_frames) + assert len(received_up_frames) == len(expected_up_frames) - for real, expected in zip(received_up_frames, expected_up_frames): - assert isinstance(real, expected) + for real, expected in zip(received_up_frames, expected_up_frames): + assert isinstance(real, expected) return (received_down_frames, received_up_frames) diff --git a/src/pipecat/transcriptions/language.py b/src/pipecat/transcriptions/language.py index 5dc4a437f..75f714a72 100644 --- a/src/pipecat/transcriptions/language.py +++ b/src/pipecat/transcriptions/language.py @@ -54,6 +54,12 @@ class Language(StrEnum): AZ = "az" AZ_AZ = "az-AZ" + # Bashkir + BA = "ba" + + # Belarusian + BE = "be" + # Bulgarian BG = "bg" BG_BG = "bg-BG" @@ -63,6 +69,12 @@ class Language(StrEnum): BN_BD = "bn-BD" BN_IN = "bn-IN" + # Tibetan + BO = "bo" + + # Breton + BR = "br" + # Bosnian BS = "bs" BS_BA = "bs-BA" @@ -98,6 +110,7 @@ class Language(StrEnum): EN_AU = "en-AU" EN_CA = "en-CA" EN_GB = "en-GB" + EN_GH = "en-GH" EN_HK = "en-HK" EN_IE = "en-IE" EN_IN = "en-IN" @@ -155,6 +168,9 @@ class Language(StrEnum): FIL = "fil" FIL_PH = "fil-PH" + # Faroese + FO = "fo" + # French FR = "fr" FR_BE = "fr-BE" @@ -174,6 +190,9 @@ class Language(StrEnum): GU = "gu" GU_IN = "gu-IN" + # Hausa + HA = "ha" + # Hebrew HE = "he" HE_IL = "he-IL" @@ -186,6 +205,9 @@ class Language(StrEnum): HR = "hr" HR_HR = "hr-HR" + # Haitian Creole + HT = "ht" + # Hungarian HU = "hu" HU_HU = "hu-HU" @@ -205,6 +227,7 @@ class Language(StrEnum): # Italian IT = "it" IT_IT = "it-IT" + IT_CH = "it-CH" # Inuktitut IU_CANS = "iu-Cans" @@ -219,6 +242,7 @@ class Language(StrEnum): # Javanese JV = "jv" JV_ID = "jv-ID" + JW = "jw" # Fal requires for Javanese # Georgian KA = "ka" @@ -240,6 +264,15 @@ class Language(StrEnum): KO = "ko" KO_KR = "ko-KR" + # Latin + LA = "la" + + # Luxembourgish + LB = "lb" + + # Lingala + LN = "ln" + # Lao LO = "lo" LO_LA = "lo-LA" @@ -252,6 +285,9 @@ class Language(StrEnum): LV = "lv" LV_LV = "lv-LV" + # Malagasy + MG = "mg" + # Macedonian MK = "mk" MK_MK = "mk-MK" @@ -264,6 +300,9 @@ class Language(StrEnum): MN = "mn" MN_MN = "mn-MN" + # Maori + MI = "mi" + # Marathi MR = "mr" MR_IN = "mr-IN" @@ -281,9 +320,10 @@ class Language(StrEnum): MY_MM = "my-MM" # Norwegian - NB = "nb" + NB = "nb" # Norwegian Bokmål NB_NO = "nb-NO" NO = "no" + NN = "nn" # Norwegian Nynorsk # Nepali NE = "ne" @@ -294,6 +334,9 @@ class Language(StrEnum): NL_BE = "nl-BE" NL_NL = "nl-NL" + # Occitan + OC = "oc" + # Odia OR = "or" OR_IN = "or-IN" @@ -323,6 +366,12 @@ class Language(StrEnum): RU = "ru" RU_RU = "ru-RU" + # Sanskrit + SA = "sa" + + # Sindhi + SD = "sd" + # Sinhala SI = "si" SI_LK = "si-LK" @@ -335,6 +384,9 @@ class Language(StrEnum): SL = "sl" SL_SI = "sl-SI" + # Shona + SN = "sn" + # Somali SO = "so" SO_SO = "so-SO" @@ -376,14 +428,23 @@ class Language(StrEnum): TE = "te" TE_IN = "te-IN" + # Tajik + TG = "tg" + # Thai TH = "th" TH_TH = "th-TH" + # Turkmen + TK = "tk" + # Turkish TR = "tr" TR_TR = "tr-TR" + # Tatar + TT = "tt" + # Ukrainian UK = "uk" UK_UA = "uk-UA" @@ -405,6 +466,12 @@ class Language(StrEnum): WUU = "wuu" WUU_CN = "wuu-CN" + # Yiddish + YI = "yi" + + # Yoruba + YO = "yo" + # Yue Chinese YUE = "yue" YUE_CN = "yue-CN" diff --git a/src/pipecat/transports/base_input.py b/src/pipecat/transports/base_input.py index 192101e62..971dfe066 100644 --- a/src/pipecat/transports/base_input.py +++ b/src/pipecat/transports/base_input.py @@ -6,6 +6,7 @@ import asyncio from concurrent.futures import ThreadPoolExecutor +from typing import Optional from loguru import logger @@ -13,6 +14,8 @@ from pipecat.audio.vad.vad_analyzer import VADAnalyzer, VADState from pipecat.frames.frames import ( BotInterruptionFrame, CancelFrame, + EmulateUserStartedSpeakingFrame, + EmulateUserStoppedSpeakingFrame, EndFrame, FilterUpdateSettingsFrame, Frame, @@ -35,6 +38,9 @@ class BaseInputTransport(FrameProcessor): self._params = params + # Input sample rate. It will be initialized on StartFrame. + self._sample_rate = 0 + # We read audio from a single queue one at a time and we then run VAD in # a thread. Therefore, only one thread should be necessary. self._executor = ThreadPoolExecutor(max_workers=1) @@ -43,12 +49,32 @@ class BaseInputTransport(FrameProcessor): # if passthrough is enabled. self._audio_task = None + def enable_audio_in_stream_on_start(self, enabled: bool) -> None: + logger.debug(f"Enabling audio on start. {enabled}") + self._params.audio_in_stream_on_start = enabled + + def start_audio_in_streaming(self): + pass + + @property + def sample_rate(self) -> int: + return self._sample_rate + + @property + def vad_analyzer(self) -> Optional[VADAnalyzer]: + return self._params.vad_analyzer + async def start(self, frame: StartFrame): + self._sample_rate = self._params.audio_in_sample_rate or frame.audio_in_sample_rate + + # Configure VAD analyzer. + if self._params.vad_enabled and self._params.vad_analyzer: + self._params.vad_analyzer.set_sample_rate(self._sample_rate) # Start audio filter. if self._params.audio_in_filter: - await self._params.audio_in_filter.start(self._params.audio_in_sample_rate) + await self._params.audio_in_filter.start(self._sample_rate) # Create audio input queue and task if needed. - if self._params.audio_in_enabled or self._params.vad_enabled: + if not self._audio_task and (self._params.audio_in_enabled or self._params.vad_enabled): self._audio_in_queue = asyncio.Queue() self._audio_task = self.create_task(self._audio_task_handler()) @@ -67,9 +93,6 @@ class BaseInputTransport(FrameProcessor): await self.cancel_task(self._audio_task) self._audio_task = None - def vad_analyzer(self) -> VADAnalyzer | None: - return self._params.vad_analyzer - async def push_audio_frame(self, frame: InputAudioRawFrame): if self._params.audio_in_enabled or self._params.vad_enabled: await self._audio_in_queue.put(frame) @@ -91,9 +114,13 @@ class BaseInputTransport(FrameProcessor): await self.cancel(frame) await self.push_frame(frame, direction) elif isinstance(frame, BotInterruptionFrame): - logger.debug("Bot interruption") - await self._start_interruption() - await self.push_frame(StartInterruptionFrame()) + await self._handle_bot_interruption(frame) + elif isinstance(frame, EmulateUserStartedSpeakingFrame): + logger.debug("Emulating user started speaking") + await self._handle_user_interruption(UserStartedSpeakingFrame()) + elif isinstance(frame, EmulateUserStoppedSpeakingFrame): + logger.debug("Emulating user stopped speaking") + await self._handle_user_interruption(UserStoppedSpeakingFrame()) # All other system frames elif isinstance(frame, SystemFrame): await self.push_frame(frame, direction) @@ -104,9 +131,8 @@ class BaseInputTransport(FrameProcessor): await self.push_frame(frame, direction) await self.stop(frame) elif isinstance(frame, VADParamsUpdateFrame): - vad_analyzer = self.vad_analyzer() - if vad_analyzer: - vad_analyzer.set_params(frame.params) + if self.vad_analyzer: + self.vad_analyzer.set_params(frame.params) elif isinstance(frame, FilterUpdateSettingsFrame) and self._params.audio_in_filter: await self._params.audio_in_filter.process_frame(frame) # Other frames @@ -117,36 +143,40 @@ class BaseInputTransport(FrameProcessor): # Handle interruptions # - async def _handle_interruptions(self, frame: Frame): + async def _handle_bot_interruption(self, frame: BotInterruptionFrame): + logger.debug("Bot interruption") if self.interruptions_allowed: + await self._start_interruption() + await self.push_frame(StartInterruptionFrame()) + + async def _handle_user_interruption(self, frame: Frame): + if isinstance(frame, UserStartedSpeakingFrame): + logger.debug("User started speaking") + await self.push_frame(frame) # Make sure we notify about interruptions quickly out-of-band. - if isinstance(frame, UserStartedSpeakingFrame): - logger.debug("User started speaking") + if self.interruptions_allowed: await self._start_interruption() # Push an out-of-band frame (i.e. not using the ordered push # frame task) to stop everything, specially at the output # transport. await self.push_frame(StartInterruptionFrame()) - elif isinstance(frame, UserStoppedSpeakingFrame): - logger.debug("User stopped speaking") + elif isinstance(frame, UserStoppedSpeakingFrame): + logger.debug("User stopped speaking") + await self.push_frame(frame) + if self.interruptions_allowed: await self._stop_interruption() await self.push_frame(StopInterruptionFrame()) - await self.push_frame(frame) - # # Audio input # async def _vad_analyze(self, audio_frame: InputAudioRawFrame) -> VADState: state = VADState.QUIET - vad_analyzer = self.vad_analyzer() - if vad_analyzer: - logger.trace(f"{self}: analyzing VAD on {audio_frame}") + if self.vad_analyzer: state = await self.get_event_loop().run_in_executor( - self._executor, vad_analyzer.analyze_audio, audio_frame.audio + self._executor, self.vad_analyzer.analyze_audio, audio_frame.audio ) - logger.trace(f"{self}: done analyzing VAD on {audio_frame}") return state async def _handle_vad(self, audio_frame: InputAudioRawFrame, vad_state: VADState): @@ -163,7 +193,7 @@ class BaseInputTransport(FrameProcessor): frame = UserStoppedSpeakingFrame() if frame: - await self._handle_interruptions(frame) + await self._handle_user_interruption(frame) vad_state = new_vad_state return vad_state diff --git a/src/pipecat/transports/base_output.py b/src/pipecat/transports/base_output.py index cf3e52228..a2ee5aa92 100644 --- a/src/pipecat/transports/base_output.py +++ b/src/pipecat/transports/base_output.py @@ -13,7 +13,7 @@ from typing import AsyncGenerator, List from loguru import logger from PIL import Image -from pipecat.audio.vad.vad_analyzer import VAD_STOP_SECS +from pipecat.audio.utils import create_default_resampler from pipecat.frames.frames import ( BotSpeakingFrame, BotStartedSpeakingFrame, @@ -37,6 +37,8 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor from pipecat.transports.base_transport import TransportParams from pipecat.utils.time import nanoseconds_to_seconds +BOT_VAD_STOP_SECS = 0.3 + class BaseOutputTransport(FrameProcessor): def __init__(self, params: TransportParams, **kwargs): @@ -57,12 +59,12 @@ class BaseOutputTransport(FrameProcessor): # framerate. self._camera_images = None - # We will write 20ms audio at a time. If we receive long audio frames we - # will chunk them. This will help with interruption handling. - audio_bytes_10ms = ( - int(self._params.audio_out_sample_rate / 100) * self._params.audio_out_channels * 2 - ) - self._audio_chunk_size = audio_bytes_10ms * 2 + # Output sample rate. It will be initialized on StartFrame. + self._sample_rate = 0 + self._resampler = create_default_resampler() + + # Chunk size that will be written. It will be computed on StartFrame + self._audio_chunk_size = 0 self._audio_buffer = bytearray() self._stopped_event = asyncio.Event() @@ -70,10 +72,21 @@ class BaseOutputTransport(FrameProcessor): # Indicates if the bot is currently speaking. self._bot_speaking = False + @property + def sample_rate(self) -> int: + return self._sample_rate + async def start(self, frame: StartFrame): + self._sample_rate = self._params.audio_out_sample_rate or frame.audio_out_sample_rate + + # We will write 20ms audio at a time. If we receive long audio frames we + # will chunk them. This will help with interruption handling. + audio_bytes_10ms = int(self._sample_rate / 100) * self._params.audio_out_channels * 2 + self._audio_chunk_size = audio_bytes_10ms * 2 + # Start audio mixer. if self._params.audio_out_mixer: - await self._params.audio_out_mixer.start(self._params.audio_out_sample_rate) + await self._params.audio_out_mixer.start(self._sample_rate) self._create_camera_task() self._create_sink_tasks() @@ -157,6 +170,8 @@ class BaseOutputTransport(FrameProcessor): # TODO(aleix): Images and audio should support presentation timestamps. elif frame.pts: await self._sink_clock_queue.put((frame.pts, frame.id, frame)) + elif direction == FrameDirection.UPSTREAM: + await self.push_frame(frame, direction) else: await self._sink_queue.put(frame) @@ -178,12 +193,18 @@ class BaseOutputTransport(FrameProcessor): if not self._params.audio_out_enabled: return + # We might need to resample if incoming audio doesn't match the + # transport sample rate. + resampled = await self._resampler.resample( + frame.audio, frame.sample_rate, self._sample_rate + ) + cls = type(frame) - self._audio_buffer.extend(frame.audio) + self._audio_buffer.extend(resampled) while len(self._audio_buffer) >= self._audio_chunk_size: chunk = cls( bytes(self._audio_buffer[: self._audio_chunk_size]), - sample_rate=frame.sample_rate, + sample_rate=self._sample_rate, num_channels=frame.num_channels, ) await self._sink_queue.put(chunk) @@ -211,16 +232,21 @@ class BaseOutputTransport(FrameProcessor): await self.push_frame(BotStoppedSpeakingFrame()) await self.push_frame(BotStoppedSpeakingFrame(), FrameDirection.UPSTREAM) self._bot_speaking = False + # Clean audio buffer (there could be tiny left overs if not multiple + # to our output chunk size). + self._audio_buffer = bytearray() # # Sink tasks # def _create_sink_tasks(self): - self._sink_queue = asyncio.Queue() - self._sink_clock_queue = asyncio.PriorityQueue() - self._sink_task = self.create_task(self._sink_task_handler()) - self._sink_clock_task = self.create_task(self._sink_clock_task_handler()) + if not self._sink_task: + self._sink_queue = asyncio.Queue() + self._sink_task = self.create_task(self._sink_task_handler()) + if not self._sink_clock_task: + self._sink_clock_queue = asyncio.PriorityQueue() + self._sink_clock_task = self.create_task(self._sink_clock_task_handler()) async def _cancel_sink_tasks(self): # Stop sink tasks. @@ -298,20 +324,15 @@ class BaseOutputTransport(FrameProcessor): # Generate an audio frame with only the mixer's part. frame = OutputAudioRawFrame( audio=await self._params.audio_out_mixer.mix(silence), - sample_rate=self._params.audio_out_sample_rate, + sample_rate=self._sample_rate, num_channels=self._params.audio_out_channels, ) yield frame - vad_stop_secs = ( - self._params.vad_analyzer.params.stop_secs - if self._params.vad_analyzer - else VAD_STOP_SECS - ) if self._params.audio_out_mixer: - return with_mixer(vad_stop_secs) + return with_mixer(BOT_VAD_STOP_SECS) else: - return without_mixer(vad_stop_secs) + return without_mixer(BOT_VAD_STOP_SECS) async def _sink_task_handler(self): async for frame in self._next_frame(): @@ -342,7 +363,7 @@ class BaseOutputTransport(FrameProcessor): def _create_camera_task(self): # Create camera output queue and task if needed. - if self._params.camera_out_enabled: + if not self._camera_out_task and self._params.camera_out_enabled: self._camera_out_queue = asyncio.Queue() self._camera_out_task = self.create_task(self._camera_out_task_handler()) diff --git a/src/pipecat/transports/base_transport.py b/src/pipecat/transports/base_transport.py index cf6d624b0..06f6cb920 100644 --- a/src/pipecat/transports/base_transport.py +++ b/src/pipecat/transports/base_transport.py @@ -4,19 +4,16 @@ # SPDX-License-Identifier: BSD 2-Clause License # -import asyncio -import inspect -from abc import ABC, abstractmethod +from abc import abstractmethod from typing import Optional -from loguru import logger from pydantic import BaseModel, ConfigDict from pipecat.audio.filters.base_audio_filter import BaseAudioFilter from pipecat.audio.mixers.base_audio_mixer import BaseAudioMixer from pipecat.audio.vad.vad_analyzer import VADAnalyzer from pipecat.processors.frame_processor import FrameProcessor -from pipecat.utils.utils import obj_count, obj_id +from pipecat.utils.base_object import BaseObject class TransportParams(BaseModel): @@ -30,21 +27,21 @@ class TransportParams(BaseModel): camera_out_framerate: int = 30 camera_out_color_format: str = "RGB" audio_out_enabled: bool = False - audio_out_is_live: bool = False - audio_out_sample_rate: int = 24000 + audio_out_sample_rate: Optional[int] = None audio_out_channels: int = 1 audio_out_bitrate: int = 96000 audio_out_mixer: Optional[BaseAudioMixer] = None audio_in_enabled: bool = False - audio_in_sample_rate: int = 16000 + audio_in_sample_rate: Optional[int] = None audio_in_channels: int = 1 audio_in_filter: Optional[BaseAudioFilter] = None + audio_in_stream_on_start: bool = True vad_enabled: bool = False vad_audio_passthrough: bool = False - vad_analyzer: VADAnalyzer | None = None + vad_analyzer: Optional[VADAnalyzer] = None -class BaseTransport(ABC): +class BaseTransport(BaseObject): def __init__( self, *, @@ -52,54 +49,14 @@ class BaseTransport(ABC): input_name: Optional[str] = None, output_name: Optional[str] = None, ): - self._id: int = obj_id() - self._name = name or f"{self.__class__.__name__}#{obj_count(self)}" + super().__init__(name=name) self._input_name = input_name self._output_name = output_name - self._event_handlers: dict = {} - - @property - def id(self) -> int: - return self._id - - @property - def name(self) -> str: - return self._name @abstractmethod def input(self) -> FrameProcessor: - raise NotImplementedError + pass @abstractmethod def output(self) -> FrameProcessor: - raise NotImplementedError - - def event_handler(self, event_name: str): - def decorator(handler): - self.add_event_handler(event_name, handler) - return handler - - return decorator - - def add_event_handler(self, event_name: str, handler): - if event_name not in self._event_handlers: - raise Exception(f"Event handler {event_name} not registered") - self._event_handlers[event_name].append(handler) - - def _register_event_handler(self, event_name: str): - if event_name in self._event_handlers: - raise Exception(f"Event handler {event_name} already registered") - self._event_handlers[event_name] = [] - - async def _call_event_handler(self, event_name: str, *args, **kwargs): - try: - for handler in self._event_handlers[event_name]: - if inspect.iscoroutinefunction(handler): - await handler(self, *args, **kwargs) - else: - handler(self, *args, **kwargs) - except Exception as e: - logger.exception(f"Exception in event handler {event_name}: {e}") - - def __str__(self): - return self.name + pass diff --git a/src/pipecat/transports/local/audio.py b/src/pipecat/transports/local/audio.py index 52681900d..bc8d2dd16 100644 --- a/src/pipecat/transports/local/audio.py +++ b/src/pipecat/transports/local/audio.py @@ -6,6 +6,7 @@ import asyncio from concurrent.futures import ThreadPoolExecutor +from typing import Optional from loguru import logger @@ -25,38 +26,52 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") +class LocalAudioTransportParams(TransportParams): + input_device_index: Optional[int] = None + output_device_index: Optional[int] = None + + class LocalAudioInputTransport(BaseInputTransport): - def __init__(self, py_audio: pyaudio.PyAudio, params: TransportParams): + _params: LocalAudioTransportParams + + def __init__(self, py_audio: pyaudio.PyAudio, params: LocalAudioTransportParams): super().__init__(params) + self._py_audio = py_audio - sample_rate = self._params.audio_in_sample_rate - num_frames = int(sample_rate / 100) * 2 # 20ms of audio - - self._in_stream = py_audio.open( - format=py_audio.get_format_from_width(2), - channels=params.audio_in_channels, - rate=params.audio_in_sample_rate, - frames_per_buffer=num_frames, - stream_callback=self._audio_in_callback, - input=True, - ) + self._in_stream = None + self._sample_rate = 0 async def start(self, frame: StartFrame): await super().start(frame) + + if self._in_stream: + return + + self._sample_rate = self._params.audio_in_sample_rate or frame.audio_in_sample_rate + num_frames = int(self._sample_rate / 100) * 2 # 20ms of audio + + self._in_stream = self._py_audio.open( + format=self._py_audio.get_format_from_width(2), + channels=self._params.audio_in_channels, + rate=self._sample_rate, + frames_per_buffer=num_frames, + stream_callback=self._audio_in_callback, + input=True, + input_device_index=self._params.input_device_index, + ) self._in_stream.start_stream() async def cleanup(self): await super().cleanup() - self._in_stream.stop_stream() - # This is not very pretty (taken from PyAudio docs). - while self._in_stream.is_active(): - await asyncio.sleep(0.1) - self._in_stream.close() + if self._in_stream: + self._in_stream.stop_stream() + self._in_stream.close() + self._in_stream = None def _audio_in_callback(self, in_data, frame_count, time_info, status): frame = InputAudioRawFrame( audio=in_data, - sample_rate=self._params.audio_in_sample_rate, + sample_rate=self._sample_rate, num_channels=self._params.audio_in_channels, ) @@ -66,44 +81,58 @@ class LocalAudioInputTransport(BaseInputTransport): class LocalAudioOutputTransport(BaseOutputTransport): - def __init__(self, py_audio: pyaudio.PyAudio, params: TransportParams): + _params: LocalAudioTransportParams + + def __init__(self, py_audio: pyaudio.PyAudio, params: LocalAudioTransportParams): super().__init__(params) + self._py_audio = py_audio + + self._out_stream = None + self._sample_rate = 0 # We only write audio frames from a single task, so only one thread # should be necessary. self._executor = ThreadPoolExecutor(max_workers=1) - self._out_stream = py_audio.open( - format=py_audio.get_format_from_width(2), - channels=params.audio_out_channels, - rate=params.audio_out_sample_rate, - output=True, - ) - async def start(self, frame: StartFrame): await super().start(frame) + + if self._out_stream: + return + + self._sample_rate = self._params.audio_out_sample_rate or frame.audio_out_sample_rate + + self._out_stream = self._py_audio.open( + format=self._py_audio.get_format_from_width(2), + channels=self._params.audio_out_channels, + rate=self._sample_rate, + output=True, + output_device_index=self._params.output_device_index, + ) self._out_stream.start_stream() async def cleanup(self): await super().cleanup() - self._out_stream.stop_stream() - # This is not very pretty (taken from PyAudio docs). - while self._out_stream.is_active(): - await asyncio.sleep(0.1) - self._out_stream.close() + if self._out_stream: + self._out_stream.stop_stream() + self._out_stream.close() + self._out_stream = None async def write_raw_audio_frames(self, frames: bytes): - await self.get_event_loop().run_in_executor(self._executor, self._out_stream.write, frames) + if self._out_stream: + await self.get_event_loop().run_in_executor( + self._executor, self._out_stream.write, frames + ) class LocalAudioTransport(BaseTransport): - def __init__(self, params: TransportParams): + def __init__(self, params: LocalAudioTransportParams): super().__init__() self._params = params self._pyaudio = pyaudio.PyAudio() - self._input: LocalAudioInputTransport | None = None - self._output: LocalAudioOutputTransport | None = None + self._input: Optional[LocalAudioInputTransport] = None + self._output: Optional[LocalAudioOutputTransport] = None # # BaseTransport diff --git a/src/pipecat/transports/local/tk.py b/src/pipecat/transports/local/tk.py index 44ca45b33..4338992e3 100644 --- a/src/pipecat/transports/local/tk.py +++ b/src/pipecat/transports/local/tk.py @@ -7,6 +7,7 @@ import asyncio import tkinter as tk from concurrent.futures import ThreadPoolExecutor +from typing import Optional import numpy as np from loguru import logger @@ -33,38 +34,51 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") +class TkTransportParams(TransportParams): + audio_input_device_index: Optional[int] = None + audio_output_device_index: Optional[int] = None + + class TkInputTransport(BaseInputTransport): - def __init__(self, py_audio: pyaudio.PyAudio, params: TransportParams): + _params: TkTransportParams + + def __init__(self, py_audio: pyaudio.PyAudio, params: TkTransportParams): super().__init__(params) - - sample_rate = self._params.audio_in_sample_rate - num_frames = int(sample_rate / 100) * 2 # 20ms of audio - - self._in_stream = py_audio.open( - format=py_audio.get_format_from_width(2), - channels=params.audio_in_channels, - rate=params.audio_in_sample_rate, - frames_per_buffer=num_frames, - stream_callback=self._audio_in_callback, - input=True, - ) + self._py_audio = py_audio + self._in_stream = None + self._sample_rate = 0 async def start(self, frame: StartFrame): await super().start(frame) + + if self._in_stream: + return + + self._sample_rate = self._params.audio_in_sample_rate or frame.audio_in_sample_rate + num_frames = int(self._sample_rate / 100) * 2 # 20ms of audio + + self._in_stream = self._py_audio.open( + format=self._py_audio.get_format_from_width(2), + channels=self._params.audio_in_channels, + rate=self._sample_rate, + frames_per_buffer=num_frames, + stream_callback=self._audio_in_callback, + input=True, + input_device_index=self._params.audio_input_device_index, + ) self._in_stream.start_stream() async def cleanup(self): await super().cleanup() - self._in_stream.stop_stream() - # This is not very pretty (taken from PyAudio docs). - while self._in_stream.is_active(): - await asyncio.sleep(0.1) - self._in_stream.close() + if self._in_stream: + self._in_stream.stop_stream() + self._in_stream.close() + self._in_stream = None def _audio_in_callback(self, in_data, frame_count, time_info, status): frame = InputAudioRawFrame( audio=in_data, - sample_rate=self._params.audio_in_sample_rate, + sample_rate=self._sample_rate, num_channels=self._params.audio_in_channels, ) @@ -74,20 +88,18 @@ class TkInputTransport(BaseInputTransport): class TkOutputTransport(BaseOutputTransport): - def __init__(self, tk_root: tk.Tk, py_audio: pyaudio.PyAudio, params: TransportParams): + _params: TkTransportParams + + def __init__(self, tk_root: tk.Tk, py_audio: pyaudio.PyAudio, params: TkTransportParams): super().__init__(params) + self._py_audio = py_audio + self._out_stream = None + self._sample_rate = 0 # We only write audio frames from a single task, so only one thread # should be necessary. self._executor = ThreadPoolExecutor(max_workers=1) - self._out_stream = py_audio.open( - format=py_audio.get_format_from_width(2), - channels=params.audio_out_channels, - rate=params.audio_out_sample_rate, - output=True, - ) - # Start with a neutral gray background. array = np.ones((1024, 1024, 3)) * 128 data = f"P5 {1024} {1024} 255 ".encode() + array.astype(np.uint8).tobytes() @@ -97,18 +109,33 @@ class TkOutputTransport(BaseOutputTransport): async def start(self, frame: StartFrame): await super().start(frame) + + if self._out_stream: + return + + self._sample_rate = self._params.audio_out_sample_rate or frame.audio_out_sample_rate + + self._out_stream = self._py_audio.open( + format=self._py_audio.get_format_from_width(2), + channels=self._params.audio_out_channels, + rate=self._sample_rate, + output=True, + output_device_index=self._params.audio_output_device_index, + ) self._out_stream.start_stream() async def cleanup(self): await super().cleanup() - self._out_stream.stop_stream() - # This is not very pretty (taken from PyAudio docs). - while self._out_stream.is_active(): - await asyncio.sleep(0.1) - self._out_stream.close() + if self._out_stream: + self._out_stream.stop_stream() + self._out_stream.close() + self._out_stream = None async def write_raw_audio_frames(self, frames: bytes): - await self.get_event_loop().run_in_executor(self._executor, self._out_stream.write, frames) + if self._out_stream: + await self.get_event_loop().run_in_executor( + self._executor, self._out_stream.write, frames + ) async def write_frame_to_camera(self, frame: OutputImageRawFrame): self.get_event_loop().call_soon(self._write_frame_to_tk, frame) @@ -126,14 +153,14 @@ class TkOutputTransport(BaseOutputTransport): class TkLocalTransport(BaseTransport): - def __init__(self, tk_root: tk.Tk, params: TransportParams): + def __init__(self, tk_root: tk.Tk, params: TkTransportParams): super().__init__() self._tk_root = tk_root self._params = params self._pyaudio = pyaudio.PyAudio() - self._input: TkInputTransport | None = None - self._output: TkOutputTransport | None = None + self._input: Optional[TkInputTransport] = None + self._output: Optional[TkOutputTransport] = None # # BaseTransport diff --git a/src/pipecat/transports/network/fastapi_websocket.py b/src/pipecat/transports/network/fastapi_websocket.py index b53daa3b4..937dda80c 100644 --- a/src/pipecat/transports/network/fastapi_websocket.py +++ b/src/pipecat/transports/network/fastapi_websocket.py @@ -10,7 +10,7 @@ import io import time import typing import wave -from typing import Awaitable, Callable +from typing import Awaitable, Callable, Optional from loguru import logger from pydantic import BaseModel @@ -23,6 +23,8 @@ from pipecat.frames.frames import ( OutputAudioRawFrame, StartFrame, StartInterruptionFrame, + TransportMessageFrame, + TransportMessageUrgentFrame, ) from pipecat.processors.frame_processor import FrameDirection from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType @@ -44,7 +46,7 @@ except ModuleNotFoundError as e: class FastAPIWebsocketParams(TransportParams): add_wav_header: bool = False serializer: FrameSerializer - session_timeout: int | None = None + session_timeout: Optional[int] = None class FastAPIWebsocketCallbacks(BaseModel): @@ -53,44 +55,99 @@ class FastAPIWebsocketCallbacks(BaseModel): on_session_timeout: Callable[[WebSocket], Awaitable[None]] +class FastAPIWebsocketClient: + def __init__(self, websocket: WebSocket, is_binary: bool, callbacks: FastAPIWebsocketCallbacks): + self._websocket = websocket + self._closing = False + self._is_binary = is_binary + self._callbacks = callbacks + + def receive(self) -> typing.AsyncIterator[bytes | str]: + return self._websocket.iter_bytes() if self._is_binary else self._websocket.iter_text() + + async def send(self, data: str | bytes): + if self._can_send(): + if self._is_binary: + await self._websocket.send_bytes(data) + else: + await self._websocket.send_text(data) + + async def disconnect(self): + if self.is_connected and not self.is_closing: + self._closing = True + await self._websocket.close() + await self.trigger_client_disconnected() + + async def trigger_client_disconnected(self): + await self._callbacks.on_client_disconnected(self._websocket) + + async def trigger_client_connected(self): + await self._callbacks.on_client_connected(self._websocket) + + async def trigger_client_timout(self): + await self._callbacks.on_session_timeout(self._websocket) + + def _can_send(self): + return self.is_connected and not self.is_closing + + @property + def is_connected(self) -> bool: + return self._websocket.client_state == WebSocketState.CONNECTED + + @property + def is_closing(self) -> bool: + return self._closing + + class FastAPIWebsocketInputTransport(BaseInputTransport): def __init__( self, - websocket: WebSocket, + transport: BaseTransport, + client: FastAPIWebsocketClient, params: FastAPIWebsocketParams, - callbacks: FastAPIWebsocketCallbacks, **kwargs, ): super().__init__(params, **kwargs) - - self._websocket = websocket + self._transport = transport + self._client = client self._params = params - self._callbacks = callbacks + self._receive_task = None + self._monitor_websocket_task = None async def start(self, frame: StartFrame): await super().start(frame) - if self._params.session_timeout: + await self._params.serializer.setup(frame) + if not self._monitor_websocket_task and self._params.session_timeout: self._monitor_websocket_task = self.create_task(self._monitor_websocket()) - await self._callbacks.on_client_connected(self._websocket) - self._receive_task = self.create_task(self._receive_messages()) + await self._client.trigger_client_connected() + if not self._receive_task: + self._receive_task = self.create_task(self._receive_messages()) + + async def _stop_tasks(self): + if self._monitor_websocket_task: + await self.cancel_task(self._monitor_websocket_task) + self._monitor_websocket_task = None + if self._receive_task: + await self.cancel_task(self._receive_task) + self._receive_task = None async def stop(self, frame: EndFrame): await super().stop(frame) - await self.cancel_task(self._receive_task) + await self._stop_tasks() + await self._client.disconnect() async def cancel(self, frame: CancelFrame): await super().cancel(frame) - await self.cancel_task(self._receive_task) + await self._stop_tasks() + await self._client.disconnect() - def _iter_data(self) -> typing.AsyncIterator[bytes | str]: - if self._params.serializer.type == FrameSerializerType.BINARY: - return self._websocket.iter_bytes() - else: - return self._websocket.iter_text() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() async def _receive_messages(self): try: - async for message in self._iter_data(): + async for message in self._client.receive(): frame = await self._params.serializer.deserialize(message) if not frame: @@ -101,26 +158,55 @@ class FastAPIWebsocketInputTransport(BaseInputTransport): else: await self.push_frame(frame) except Exception as e: - logger.error(f"{self} exception receiving data (class: {e.__class__.__name__})") + logger.error(f"{self} exception receiving data: {e.__class__.__name__} ({e})") - await self._callbacks.on_client_disconnected(self._websocket) + await self._client.trigger_client_disconnected() async def _monitor_websocket(self): """Wait for self._params.session_timeout seconds, if the websocket is still open, trigger timeout event.""" await asyncio.sleep(self._params.session_timeout) - await self._callbacks.on_session_timeout(self._websocket) + await self._client.trigger_client_timout() class FastAPIWebsocketOutputTransport(BaseOutputTransport): - def __init__(self, websocket: WebSocket, params: FastAPIWebsocketParams, **kwargs): + def __init__( + self, + transport: BaseTransport, + client: FastAPIWebsocketClient, + params: FastAPIWebsocketParams, + **kwargs, + ): super().__init__(params, **kwargs) - self._websocket = websocket + self._transport = transport + self._client = client self._params = params - self._send_interval = (self._audio_chunk_size / self._params.audio_out_sample_rate) / 2 + # write_raw_audio_frames() is called quickly, as soon as we get audio + # (e.g. from the TTS), and since this is just a network connection we + # would be sending it to quickly. Instead, we want to block to emulate + # an audio device, this is what the send interval is. It will be + # computed on StartFrame. + self._send_interval = 0 self._next_send_time = 0 + async def start(self, frame: StartFrame): + await super().start(frame) + await self._params.serializer.setup(frame) + self._send_interval = (self._audio_chunk_size / self.sample_rate) / 2 + + async def stop(self, frame: EndFrame): + await super().stop(frame) + await self._client.disconnect() + + async def cancel(self, frame: CancelFrame): + await super().cancel(frame) + await self._client.disconnect() + + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) @@ -128,15 +214,21 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport): await self._write_frame(frame) self._next_send_time = 0 + async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame): + await self._write_frame(frame) + async def write_raw_audio_frames(self, frames: bytes): - if self._websocket.client_state != WebSocketState.CONNECTED: + if self._client.is_closing: + return + + if not self._client.is_connected: # Simulate audio playback with a sleep. await self._write_audio_sleep() return frame = OutputAudioRawFrame( audio=frames, - sample_rate=self._params.audio_out_sample_rate, + sample_rate=self.sample_rate, num_channels=self._params.audio_out_channels, ) @@ -156,24 +248,16 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport): await self._write_frame(frame) - self._websocket_audio_buffer = bytes() - # Simulate audio playback with a sleep. await self._write_audio_sleep() async def _write_frame(self, frame: Frame): try: payload = await self._params.serializer.serialize(frame) - if payload and self._websocket.client_state == WebSocketState.CONNECTED: - await self._send_data(payload) + if payload: + await self._client.send(payload) except Exception as e: - logger.error(f"{self} exception sending data (class: {e.__class__.__name__})") - - def _send_data(self, data: str | bytes): - if self._params.serializer.type == FrameSerializerType.BINARY: - return self._websocket.send_bytes(data) - else: - return self._websocket.send_text(data) + logger.error(f"{self} exception sending data: {e.__class__.__name__} ({e})") async def _write_audio_sleep(self): # Simulate a clock. @@ -191,10 +275,11 @@ class FastAPIWebsocketTransport(BaseTransport): self, websocket: WebSocket, params: FastAPIWebsocketParams, - input_name: str | None = None, - output_name: str | None = None, + input_name: Optional[str] = None, + output_name: Optional[str] = None, ): super().__init__(input_name=input_name, output_name=output_name) + self._params = params self._callbacks = FastAPIWebsocketCallbacks( @@ -203,11 +288,14 @@ class FastAPIWebsocketTransport(BaseTransport): on_session_timeout=self._on_session_timeout, ) + is_binary = self._params.serializer.type == FrameSerializerType.BINARY + self._client = FastAPIWebsocketClient(websocket, is_binary, self._callbacks) + self._input = FastAPIWebsocketInputTransport( - websocket, self._params, self._callbacks, name=self._input_name + self, self._client, self._params, name=self._input_name ) self._output = FastAPIWebsocketOutputTransport( - websocket, self._params, name=self._output_name + self, self._client, self._params, name=self._output_name ) # Register supported handlers. The user will only be able to register diff --git a/src/pipecat/transports/network/websocket_client.py b/src/pipecat/transports/network/websocket_client.py index 56a7b57ac..11a000e69 100644 --- a/src/pipecat/transports/network/websocket_client.py +++ b/src/pipecat/transports/network/websocket_client.py @@ -30,7 +30,7 @@ from pipecat.serializers.protobuf import ProtobufFrameSerializer from pipecat.transports.base_input import BaseInputTransport from pipecat.transports.base_output import BaseOutputTransport from pipecat.transports.base_transport import BaseTransport, TransportParams -from pipecat.utils.asyncio import TaskManager +from pipecat.utils.asyncio import BaseTaskManager class WebsocketClientParams(TransportParams): @@ -57,12 +57,12 @@ class WebsocketClientSession: self._callbacks = callbacks self._transport_name = transport_name - self._task_manager: Optional[TaskManager] = None + self._task_manager: Optional[BaseTaskManager] = None - self._websocket: websockets.WebSocketClientProtocol | None = None + self._websocket: Optional[websockets.WebSocketClientProtocol] = None @property - def task_manager(self) -> TaskManager: + def task_manager(self) -> BaseTaskManager: if not self._task_manager: raise Exception( f"{self._transport_name}::WebsocketClientSession: TaskManager not initialized (pipeline not started?)" @@ -101,7 +101,7 @@ class WebsocketClientSession: if self._websocket: await self._websocket.send(message) except Exception as e: - logger.error(f"{self} exception sending data (class: {e.__class__.__name__})") + logger.error(f"{self} exception sending data: {e.__class__.__name__} ({e})") async def _client_task_handler(self): try: @@ -109,7 +109,7 @@ class WebsocketClientSession: async for message in self._websocket: await self._callbacks.on_message(self._websocket, message) except Exception as e: - logger.error(f"{self} exception receiving data (class: {e.__class__.__name__})") + logger.error(f"{self} exception receiving data: {e.__class__.__name__} ({e})") await self._callbacks.on_disconnected(self._websocket) @@ -118,14 +118,21 @@ class WebsocketClientSession: class WebsocketClientInputTransport(BaseInputTransport): - def __init__(self, session: WebsocketClientSession, params: WebsocketClientParams): + def __init__( + self, + transport: BaseTransport, + session: WebsocketClientSession, + params: WebsocketClientParams, + ): super().__init__(params) + self._transport = transport self._session = session self._params = params async def start(self, frame: StartFrame): await super().start(frame) + await self._params.serializer.setup(frame) await self._session.setup(frame) await self._session.connect() @@ -137,6 +144,10 @@ class WebsocketClientInputTransport(BaseInputTransport): await super().cancel(frame) await self._session.disconnect() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def on_message(self, websocket, message): frame = await self._params.serializer.deserialize(message) if not frame: @@ -148,17 +159,30 @@ class WebsocketClientInputTransport(BaseInputTransport): class WebsocketClientOutputTransport(BaseOutputTransport): - def __init__(self, session: WebsocketClientSession, params: WebsocketClientParams): + def __init__( + self, + transport: BaseTransport, + session: WebsocketClientSession, + params: WebsocketClientParams, + ): super().__init__(params) + self._transport = transport self._session = session self._params = params - self._send_interval = (self._audio_chunk_size / self._params.audio_out_sample_rate) / 2 + # write_raw_audio_frames() is called quickly, as soon as we get audio + # (e.g. from the TTS), and since this is just a network connection we + # would be sending it to quickly. Instead, we want to block to emulate + # an audio device, this is what the send interval is. It will be + # computed on StartFrame. + self._send_interval = 0 self._next_send_time = 0 async def start(self, frame: StartFrame): await super().start(frame) + self._send_interval = (self._audio_chunk_size / self.sample_rate) / 2 + await self._params.serializer.setup(frame) await self._session.setup(frame) await self._session.connect() @@ -170,13 +194,17 @@ class WebsocketClientOutputTransport(BaseOutputTransport): await super().cancel(frame) await self._session.disconnect() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame): await self._write_frame(frame) async def write_raw_audio_frames(self, frames: bytes): frame = OutputAudioRawFrame( audio=frames, - sample_rate=self._params.audio_out_sample_rate, + sample_rate=self.sample_rate, num_channels=self._params.audio_out_channels, ) @@ -232,8 +260,8 @@ class WebsocketClientTransport(BaseTransport): ) self._session = WebsocketClientSession(uri, params, callbacks, self.name) - self._input: WebsocketClientInputTransport | None = None - self._output: WebsocketClientOutputTransport | None = None + self._input: Optional[WebsocketClientInputTransport] = None + self._output: Optional[WebsocketClientOutputTransport] = None # Register supported handlers. The user will only be able to register # these handlers. @@ -242,12 +270,12 @@ class WebsocketClientTransport(BaseTransport): def input(self) -> WebsocketClientInputTransport: if not self._input: - self._input = WebsocketClientInputTransport(self._session, self._params) + self._input = WebsocketClientInputTransport(self, self._session, self._params) return self._input def output(self) -> WebsocketClientOutputTransport: if not self._output: - self._output = WebsocketClientOutputTransport(self._session, self._params) + self._output = WebsocketClientOutputTransport(self, self._session, self._params) return self._output async def _on_connected(self, websocket): diff --git a/src/pipecat/transports/network/websocket_server.py b/src/pipecat/transports/network/websocket_server.py index 684e030ad..e542342a2 100644 --- a/src/pipecat/transports/network/websocket_server.py +++ b/src/pipecat/transports/network/websocket_server.py @@ -6,9 +6,10 @@ import asyncio import io +import json import time import wave -from typing import Awaitable, Callable +from typing import Awaitable, Callable, Optional from loguru import logger from pydantic import BaseModel @@ -21,10 +22,11 @@ from pipecat.frames.frames import ( OutputAudioRawFrame, StartFrame, StartInterruptionFrame, + TransportMessageFrame, + TransportMessageUrgentFrame, ) from pipecat.processors.frame_processor import FrameDirection from pipecat.serializers.base_serializer import FrameSerializer -from pipecat.serializers.protobuf import ProtobufFrameSerializer from pipecat.transports.base_input import BaseInputTransport from pipecat.transports.base_output import BaseOutputTransport from pipecat.transports.base_transport import BaseTransport, TransportParams @@ -39,19 +41,21 @@ except ModuleNotFoundError as e: class WebsocketServerParams(TransportParams): add_wav_header: bool = False - serializer: FrameSerializer = ProtobufFrameSerializer() - session_timeout: int | None = None + serializer: FrameSerializer + session_timeout: Optional[int] = None class WebsocketServerCallbacks(BaseModel): on_client_connected: Callable[[websockets.WebSocketServerProtocol], Awaitable[None]] on_client_disconnected: Callable[[websockets.WebSocketServerProtocol], Awaitable[None]] on_session_timeout: Callable[[websockets.WebSocketServerProtocol], Awaitable[None]] + on_websocket_ready: Callable[[], Awaitable[None]] class WebsocketServerInputTransport(BaseInputTransport): def __init__( self, + transport: BaseTransport, host: str, port: int, params: WebsocketServerParams, @@ -60,31 +64,54 @@ class WebsocketServerInputTransport(BaseInputTransport): ): super().__init__(params, **kwargs) + self._transport = transport self._host = host self._port = port self._params = params self._callbacks = callbacks - self._websocket: websockets.WebSocketServerProtocol | None = None + self._websocket: Optional[websockets.WebSocketServerProtocol] = None + + self._server_task = None + + # This task will monitor the websocket connection periodically. + self._monitor_task = None self._stop_server_event = asyncio.Event() async def start(self, frame: StartFrame): await super().start(frame) - self._server_task = self.create_task(self._server_task_handler()) + await self._params.serializer.setup(frame) + if not self._server_task: + self._server_task = self.create_task(self._server_task_handler()) async def stop(self, frame: EndFrame): await super().stop(frame) self._stop_server_event.set() - await self.wait_for_task(self._server_task) + if self._monitor_task: + await self.cancel_task(self._monitor_task) + self._monitor_task = None + if self._server_task: + await self.wait_for_task(self._server_task) + self._server_task = None async def cancel(self, frame: CancelFrame): await super().cancel(frame) - await self.cancel_task(self._server_task) + if self._monitor_task: + await self.cancel_task(self._monitor_task) + self._monitor_task = None + if self._server_task: + await self.cancel_task(self._server_task) + self._server_task = None + + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() async def _server_task_handler(self): logger.info(f"Starting websocket server on {self._host}:{self._port}") async with websockets.serve(self._client_handler, self._host, self._port) as server: + await self._callbacks.on_websocket_ready() await self._stop_server_event.wait() async def _client_handler(self, websocket: websockets.WebSocketServerProtocol, path): @@ -99,8 +126,10 @@ class WebsocketServerInputTransport(BaseInputTransport): await self._callbacks.on_client_connected(websocket) # Create a task to monitor the websocket connection - if self._params.session_timeout: - self.create_task(self._monitor_websocket(websocket)) + if not self._monitor_task and self._params.session_timeout: + self._monitor_task = self.create_task( + self._monitor_websocket(websocket, self._params.session_timeout) + ) # Handle incoming messages try: @@ -115,7 +144,7 @@ class WebsocketServerInputTransport(BaseInputTransport): else: await self.push_frame(frame) except Exception as e: - logger.error(f"{self} exception receiving data (class: {e.__class__.__name__})") + logger.error(f"{self} exception receiving data: {e.__class__.__name__} ({e})") # Notify disconnection await self._callbacks.on_client_disconnected(websocket) @@ -125,10 +154,13 @@ class WebsocketServerInputTransport(BaseInputTransport): logger.info(f"Client {websocket.remote_address} disconnected") - async def _monitor_websocket(self, websocket: websockets.WebSocketServerProtocol): - """Wait for self._params.session_timeout seconds, if the websocket is still open, trigger timeout event.""" + async def _monitor_websocket( + self, websocket: websockets.WebSocketServerProtocol, session_timeout: int + ): + """Wait for session_timeout seconds, if the websocket is still open, + trigger timeout event.""" try: - await asyncio.sleep(self._params.session_timeout) + await asyncio.sleep(session_timeout) if not websocket.closed: await self._callbacks.on_session_timeout(websocket) except asyncio.CancelledError: @@ -137,22 +169,37 @@ class WebsocketServerInputTransport(BaseInputTransport): class WebsocketServerOutputTransport(BaseOutputTransport): - def __init__(self, params: WebsocketServerParams, **kwargs): + def __init__(self, transport: BaseTransport, params: WebsocketServerParams, **kwargs): super().__init__(params, **kwargs) + self._transport = transport self._params = params - self._websocket: websockets.WebSocketServerProtocol | None = None + self._websocket: Optional[websockets.WebSocketServerProtocol] = None - self._send_interval = (self._audio_chunk_size / self._params.audio_out_sample_rate) / 2 + # write_raw_audio_frames() is called quickly, as soon as we get audio + # (e.g. from the TTS), and since this is just a network connection we + # would be sending it to quickly. Instead, we want to block to emulate + # an audio device, this is what the send interval is. It will be + # computed on StartFrame. + self._send_interval = 0 self._next_send_time = 0 - async def set_client_connection(self, websocket: websockets.WebSocketServerProtocol | None): + async def set_client_connection(self, websocket: Optional[websockets.WebSocketServerProtocol]): if self._websocket: await self._websocket.close() logger.warning("Only one client allowed, using new connection") self._websocket = websocket + async def start(self, frame: StartFrame): + await super().start(frame) + await self._params.serializer.setup(frame) + self._send_interval = (self._audio_chunk_size / self.sample_rate) / 2 + + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) @@ -160,6 +207,9 @@ class WebsocketServerOutputTransport(BaseOutputTransport): await self._write_frame(frame) self._next_send_time = 0 + async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame): + await self._write_frame(frame) + async def write_raw_audio_frames(self, frames: bytes): if not self._websocket: # Simulate audio playback with a sleep. @@ -168,7 +218,7 @@ class WebsocketServerOutputTransport(BaseOutputTransport): frame = OutputAudioRawFrame( audio=frames, - sample_rate=self._params.audio_out_sample_rate, + sample_rate=self.sample_rate, num_channels=self._params.audio_out_channels, ) @@ -197,7 +247,7 @@ class WebsocketServerOutputTransport(BaseOutputTransport): if payload and self._websocket: await self._websocket.send(payload) except Exception as e: - logger.error(f"{self} exception sending data (class: {e.__class__.__name__})") + logger.error(f"{self} exception sending data: {e.__class__.__name__} ({e})") async def _write_audio_sleep(self): # Simulate a clock. @@ -213,14 +263,13 @@ class WebsocketServerOutputTransport(BaseOutputTransport): class WebsocketServerTransport(BaseTransport): def __init__( self, + params: WebsocketServerParams, host: str = "localhost", port: int = 8765, - params: WebsocketServerParams = WebsocketServerParams(), - input_name: str | None = None, - output_name: str | None = None, - loop: asyncio.AbstractEventLoop | None = None, + input_name: Optional[str] = None, + output_name: Optional[str] = None, ): - super().__init__(input_name=input_name, output_name=output_name, loop=loop) + super().__init__(input_name=input_name, output_name=output_name) self._host = host self._port = port self._params = params @@ -229,27 +278,31 @@ class WebsocketServerTransport(BaseTransport): on_client_connected=self._on_client_connected, on_client_disconnected=self._on_client_disconnected, on_session_timeout=self._on_session_timeout, + on_websocket_ready=self._on_websocket_ready, ) - self._input: WebsocketServerInputTransport | None = None - self._output: WebsocketServerOutputTransport | None = None - self._websocket: websockets.WebSocketServerProtocol | None = None + self._input: Optional[WebsocketServerInputTransport] = None + self._output: Optional[WebsocketServerOutputTransport] = None + self._websocket: Optional[websockets.WebSocketServerProtocol] = None # Register supported handlers. The user will only be able to register # these handlers. self._register_event_handler("on_client_connected") self._register_event_handler("on_client_disconnected") self._register_event_handler("on_session_timeout") + self._register_event_handler("on_websocket_ready") def input(self) -> WebsocketServerInputTransport: if not self._input: self._input = WebsocketServerInputTransport( - self._host, self._port, self._params, self._callbacks, name=self._input_name + self, self._host, self._port, self._params, self._callbacks, name=self._input_name ) return self._input def output(self) -> WebsocketServerOutputTransport: if not self._output: - self._output = WebsocketServerOutputTransport(self._params, name=self._output_name) + self._output = WebsocketServerOutputTransport( + self, self._params, name=self._output_name + ) return self._output async def _on_client_connected(self, websocket): @@ -268,3 +321,6 @@ class WebsocketServerTransport(BaseTransport): async def _on_session_timeout(self, websocket): await self._call_event_handler("on_session_timeout", websocket) + + async def _on_websocket_ready(self): + await self._call_event_handler("on_websocket_ready") diff --git a/src/pipecat/transports/services/daily.py b/src/pipecat/transports/services/daily.py index ebf2b830f..af7d2308c 100644 --- a/src/pipecat/transports/services/daily.py +++ b/src/pipecat/transports/services/daily.py @@ -6,22 +6,18 @@ import asyncio import time -import warnings from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from typing import Any, Awaitable, Callable, Mapping, Optional import aiohttp from daily import ( - CallClient, - Daily, - EventHandler, VirtualCameraDevice, VirtualMicrophoneDevice, VirtualSpeakerDevice, ) from loguru import logger -from pydantic import BaseModel, model_validator +from pydantic import BaseModel from pipecat.audio.vad.vad_analyzer import VADAnalyzer, VADParams from pipecat.frames.frames import ( @@ -46,7 +42,7 @@ from pipecat.transcriptions.language import Language from pipecat.transports.base_input import BaseInputTransport from pipecat.transports.base_output import BaseOutputTransport from pipecat.transports.base_transport import BaseTransport, TransportParams -from pipecat.utils.asyncio import TaskManager +from pipecat.utils.asyncio import BaseTaskManager try: from daily import CallClient, Daily, EventHandler @@ -62,20 +58,41 @@ VAD_RESET_PERIOD_MS = 2000 @dataclass class DailyTransportMessageFrame(TransportMessageFrame): - participant_id: str | None = None + """Frame for transport messages in Daily calls. + + Attributes: + participant_id: Optional ID of the participant this message is for/from. + """ + + participant_id: Optional[str] = None @dataclass class DailyTransportMessageUrgentFrame(TransportMessageUrgentFrame): - participant_id: str | None = None + """Frame for urgent transport messages in Daily calls. + + Attributes: + participant_id: Optional ID of the participant this message is for/from. + """ + + participant_id: Optional[str] = None class WebRTCVADAnalyzer(VADAnalyzer): - def __init__(self, *, sample_rate=16000, num_channels=1, params: VADParams = VADParams()): - super().__init__(sample_rate=sample_rate, num_channels=num_channels, params=params) + """Voice Activity Detection analyzer using WebRTC. + + Implements voice activity detection using Daily's native WebRTC VAD. + + Args: + sample_rate: Audio sample rate in Hz. + params: VAD configuration parameters (VADParams). + """ + + def __init__(self, *, sample_rate: Optional[int] = None, params: VADParams = VADParams()): + super().__init__(sample_rate=sample_rate, params=params) self._webrtc_vad = Daily.create_native_vad( - reset_period_ms=VAD_RESET_PERIOD_MS, sample_rate=sample_rate, channels=num_channels + reset_period_ms=VAD_RESET_PERIOD_MS, sample_rate=self.sample_rate, channels=1 ) logger.debug("Loaded native WebRTC VAD") @@ -90,13 +107,32 @@ class WebRTCVADAnalyzer(VADAnalyzer): class DailyDialinSettings(BaseModel): + """Settings for Daily's dial-in functionality. + + Attributes: + call_id: CallId is represented by UUID and represents the sessionId in the SIP Network. + call_domain: Call Domain is represented by UUID and represents your Daily Domain on the SIP Network. + """ + call_id: str = "" call_domain: str = "" class DailyTranscriptionSettings(BaseModel): + """Configuration settings for Daily's transcription service. + + Attributes: + language: ISO language code for transcription (e.g. "en"). + model: Transcription model to use (e.g. "nova-2-general"). + profanity_filter: Whether to filter profanity from transcripts. + redact: Whether to redact sensitive information. + endpointing: Whether to use endpointing to determine speech segments. + punctuate: Whether to add punctuation to transcripts. + includeRawResponse: Whether to include raw response data. + extra: Additional parameters passed to the Deepgram transcription service. + """ + language: str = "en" - tier: Optional[str] = None model: str = "nova-2-general" profanity_filter: bool = True redact: bool = False @@ -105,18 +141,18 @@ class DailyTranscriptionSettings(BaseModel): includeRawResponse: bool = True extra: Mapping[str, Any] = {"interim_results": True} - @model_validator(mode="before") - def check_deprecated_fields(cls, values): - with warnings.catch_warnings(): - warnings.simplefilter("always") - if "tier" in values: - warnings.warn( - "Field 'tier' is deprecated, use 'model' instead.", DeprecationWarning - ) - return values - class DailyParams(TransportParams): + """Configuration parameters for Daily transport. + + Args: + api_url: Daily API base URL + api_key: Daily API authentication key + dialin_settings: Optional settings for dial-in functionality + transcription_enabled: Whether to enable speech transcription + transcription_settings: Configuration for transcription service + """ + api_url: str = "https://api.daily.co/v1" api_key: str = "" dialin_settings: Optional[DailyDialinSettings] = None @@ -125,6 +161,33 @@ class DailyParams(TransportParams): class DailyCallbacks(BaseModel): + """Callback handlers for Daily events. + + Attributes: + on_joined: Called when bot successfully joined a room. + on_left: Called when bot left a room. + on_error: Called when an error occurs. + on_app_message: Called when receiving an app message. + on_call_state_updated: Called when call state changes. + on_dialin_connected: Called when dial-in is connected. + on_dialin_ready: Called when dial-in is ready. + on_dialin_stopped: Called when dial-in is stopped. + on_dialin_error: Called when dial-in encounters an error. + on_dialin_warning: Called when dial-in has a warning. + on_dialout_answered: Called when dial-out is answered. + on_dialout_connected: Called when dial-out is connected. + on_dialout_stopped: Called when dial-out is stopped. + on_dialout_error: Called when dial-out encounters an error. + on_dialout_warning: Called when dial-out has a warning. + on_participant_joined: Called when a participant joins. + on_participant_left: Called when a participant leaves. + on_participant_updated: Called when participant info is updated. + on_transcription_message: Called when receiving transcription. + on_recording_started: Called when recording starts. + on_recording_stopped: Called when recording stops. + on_recording_error: Called when recording encounters an error. + """ + on_joined: Callable[[Mapping[str, Any]], Awaitable[None]] on_left: Callable[[], Awaitable[None]] on_error: Callable[[str], Awaitable[None]] @@ -166,6 +229,19 @@ def completion_callback(future): class DailyTransportClient(EventHandler): + """Core client for interacting with Daily's API. + + Manages the connection to Daily rooms and handles all low-level API interactions. + + Args: + room_url: URL of the Daily room to connect to. + token: Optional authentication token for the room. + bot_name: Display name for the bot in the call. + params: Configuration parameters (DailyParams). + callbacks: Event callback handlers (DailyCallbacks). + transport_name: Name identifier for the transport. + """ + _daily_initialized: bool = False # This is necessary to override EventHandler's __new__ method. @@ -175,7 +251,7 @@ class DailyTransportClient(EventHandler): def __init__( self, room_url: str, - token: str | None, + token: Optional[str], bot_name: str, params: DailyParams, callbacks: DailyCallbacks, @@ -188,7 +264,7 @@ class DailyTransportClient(EventHandler): Daily.init() self._room_url: str = room_url - self._token: str | None = token + self._token: Optional[str] = token self._bot_name: str = bot_name self._params: DailyParams = params self._callbacks = callbacks @@ -199,11 +275,12 @@ class DailyTransportClient(EventHandler): self._transcription_ids = [] self._transcription_status = None + self._joining = False self._joined = False self._joined_event = asyncio.Event() self._leave_counter = 0 - self._task_manager: Optional[TaskManager] = None + self._task_manager: Optional[BaseTaskManager] = None # We use the executor to cleanup the client. We just do it from one # place, so only one thread is really needed. @@ -222,33 +299,13 @@ class DailyTransportClient(EventHandler): self._callback_queue = asyncio.Queue() self._callback_task = None - self._camera: VirtualCameraDevice | None = None - if self._params.camera_out_enabled: - self._camera = Daily.create_camera_device( - self._camera_name(), - width=self._params.camera_out_width, - height=self._params.camera_out_height, - color_format=self._params.camera_out_color_format, - ) + # Input and ouput sample rates. They will be initialize on setup(). + self._in_sample_rate = 0 + self._out_sample_rate = 0 - self._mic: VirtualMicrophoneDevice | None = None - if self._params.audio_out_enabled: - self._mic = Daily.create_microphone_device( - self._mic_name(), - sample_rate=self._params.audio_out_sample_rate, - channels=self._params.audio_out_channels, - non_blocking=True, - ) - - self._speaker: VirtualSpeakerDevice | None = None - if self._params.audio_in_enabled or self._params.vad_enabled: - self._speaker = Daily.create_speaker_device( - self._speaker_name(), - sample_rate=self._params.audio_in_sample_rate, - channels=self._params.audio_in_channels, - non_blocking=True, - ) - Daily.select_speaker_device(self._speaker_name()) + self._camera: Optional[VirtualCameraDevice] = None + self._mic: Optional[VirtualMicrophoneDevice] = None + self._speaker: Optional[VirtualSpeakerDevice] = None def _camera_name(self): return f"camera-{self}" @@ -259,6 +316,10 @@ class DailyTransportClient(EventHandler): def _speaker_name(self): return f"speaker-{self}" + @property + def room_url(self) -> str: + return self._room_url + @property def participant_id(self) -> str: return self._participant_id @@ -277,11 +338,11 @@ class DailyTransportClient(EventHandler): ) await future - async def read_next_audio_frame(self) -> InputAudioRawFrame | None: + async def read_next_audio_frame(self) -> Optional[InputAudioRawFrame]: if not self._speaker: return None - sample_rate = self._params.audio_in_sample_rate + sample_rate = self._in_sample_rate num_channels = self._params.audio_in_channels num_frames = int(sample_rate / 100) * 2 # 20ms of audio @@ -315,6 +376,34 @@ class DailyTransportClient(EventHandler): self._camera.write_frame(frame.image) async def setup(self, frame: StartFrame): + self._in_sample_rate = self._params.audio_in_sample_rate or frame.audio_in_sample_rate + self._out_sample_rate = self._params.audio_out_sample_rate or frame.audio_out_sample_rate + + if self._params.camera_out_enabled and not self._camera: + self._camera = Daily.create_camera_device( + self._camera_name(), + width=self._params.camera_out_width, + height=self._params.camera_out_height, + color_format=self._params.camera_out_color_format, + ) + + if self._params.audio_out_enabled and not self._mic: + self._mic = Daily.create_microphone_device( + self._mic_name(), + sample_rate=self._out_sample_rate, + channels=self._params.audio_out_channels, + non_blocking=True, + ) + + if (self._params.audio_in_enabled or self._params.vad_enabled) and not self._speaker: + self._speaker = Daily.create_speaker_device( + self._speaker_name(), + sample_rate=self._in_sample_rate, + channels=self._params.audio_in_channels, + non_blocking=True, + ) + Daily.select_speaker_device(self._speaker_name()) + if not self._task_manager: self._task_manager = frame.task_manager self._callback_task = self._task_manager.create_task( @@ -323,13 +412,14 @@ class DailyTransportClient(EventHandler): ) async def join(self): - # Transport already joined, ignore. - if self._joined: + # Transport already joined or joining, ignore. + if self._joined or self._joining: # Increment leave counter if we already joined. self._leave_counter += 1 return logger.info(f"Joining {self._room_url}") + self._joining = True # For performance reasons, never subscribe to video streams (unless a # video renderer is registered). @@ -344,6 +434,7 @@ class DailyTransportClient(EventHandler): if not error: self._joined = True + self._joining = False # Increment leave counter if we successfully joined. self._leave_counter += 1 @@ -362,6 +453,7 @@ class DailyTransportClient(EventHandler): except asyncio.TimeoutError: error_msg = f"Time out joining {self._room_url}" logger.error(error_msg) + self._joining = False await self._callbacks.on_error(error_msg) async def _start_transcription(self): @@ -534,7 +626,7 @@ class DailyTransportClient(EventHandler): self._client.stop_recording(stream_id, completion=completion_callback(future)) await future - async def send_prebuilt_chat_message(self, message: str, user_name: str | None = None): + async def send_prebuilt_chat_message(self, message: str, user_name: Optional[str] = None): if not self._joined: return @@ -590,6 +682,13 @@ class DailyTransportClient(EventHandler): ) await future + async def update_remote_participants(self, remote_participants: Mapping[str, Any] = None): + future = self._get_event_loop().create_future() + self._client.update_remote_participants( + remote_participants=remote_participants, completion=completion_callback(future) + ) + await future + # # # Daily (EventHandler) @@ -703,35 +802,68 @@ class DailyTransportClient(EventHandler): class DailyInputTransport(BaseInputTransport): - def __init__(self, client: DailyTransportClient, params: DailyParams, **kwargs): + """Handles incoming media streams and events from Daily calls. + + Processes incoming audio, video, transcriptions and other events from Daily. + + Args: + client: DailyTransportClient instance. + params: Configuration parameters. + """ + + def __init__( + self, + transport: BaseTransport, + client: DailyTransportClient, + params: DailyParams, + **kwargs, + ): super().__init__(params, **kwargs) + self._transport = transport self._client = client + self._params = params self._video_renderers = {} + # Whether we have seen a StartFrame already. + self._initialized = False + # Task that gets audio data from a device or the network and queues it # internally to be processed. self._audio_in_task = None - self._vad_analyzer: VADAnalyzer | None = params.vad_analyzer - if params.vad_enabled and not params.vad_analyzer: - self._vad_analyzer = WebRTCVADAnalyzer( - sample_rate=self._params.audio_in_sample_rate, - num_channels=self._params.audio_in_channels, - ) + self._vad_analyzer: Optional[VADAnalyzer] = params.vad_analyzer + + @property + def vad_analyzer(self) -> Optional[VADAnalyzer]: + return self._vad_analyzer + + def start_audio_in_streaming(self): + # Create audio task. It reads audio frames from Daily and push them + # internally for VAD processing. + if not self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled): + logger.debug(f"Start receiving audio") + self._audio_in_task = self.create_task(self._audio_in_task_handler()) async def start(self, frame: StartFrame): # Parent start. await super().start(frame) + + if self._initialized: + return + + self._initialized = True + # Setup client. await self._client.setup(frame) # Join the room. await self._client.join() - # Create audio task. It reads audio frames from Daily and push them - # internally for VAD processing. - if self._params.audio_in_enabled or self._params.vad_enabled: - self._audio_in_task = self.create_task(self._audio_in_task_handler()) + # Inialize WebRTC VAD if needed. + if self._params.vad_enabled and not self._params.vad_analyzer: + self._vad_analyzer = WebRTCVADAnalyzer(sample_rate=self.sample_rate) + if self._params.audio_in_stream_on_start: + self.start_audio_in_streaming() async def stop(self, frame: EndFrame): # Parent stop. @@ -756,9 +888,7 @@ class DailyInputTransport(BaseInputTransport): async def cleanup(self): await super().cleanup() await self._client.cleanup() - - def vad_analyzer(self) -> VADAnalyzer | None: - return self._vad_analyzer + await self._transport.cleanup() # # FrameProcessor @@ -768,7 +898,7 @@ class DailyInputTransport(BaseInputTransport): await super().process_frame(frame, direction) if isinstance(frame, UserImageRequestFrame): - await self.request_participant_image(frame.user_id) + await self.request_participant_image(frame) # # Frames @@ -805,16 +935,16 @@ class DailyInputTransport(BaseInputTransport): self._video_renderers[participant_id] = { "framerate": framerate, "timestamp": 0, - "render_next_frame": False, + "render_next_frame": [], } await self._client.capture_participant_video( participant_id, self._on_participant_video_frame, framerate, video_source, color_format ) - async def request_participant_image(self, participant_id: str): - if participant_id in self._video_renderers: - self._video_renderers[participant_id]["render_next_frame"] = True + async def request_participant_image(self, frame: UserImageRequestFrame): + if frame.user_id in self._video_renderers: + self._video_renderers[frame.user_id]["render_next_frame"].append(frame) async def _on_participant_video_frame(self, participant_id: str, buffer, size, format): render_frame = False @@ -823,31 +953,59 @@ class DailyInputTransport(BaseInputTransport): prev_time = self._video_renderers[participant_id]["timestamp"] framerate = self._video_renderers[participant_id]["framerate"] + # Some times we render frames because of a request. + request_frame = None + if framerate > 0: next_time = prev_time + 1 / framerate render_frame = (next_time - curr_time) < 0.1 elif self._video_renderers[participant_id]["render_next_frame"]: - self._video_renderers[participant_id]["render_next_frame"] = False + request_frame = self._video_renderers[participant_id]["render_next_frame"].pop(0) render_frame = True if render_frame: frame = UserImageRawFrame( - user_id=participant_id, image=buffer, size=size, format=format + user_id=participant_id, + request=request_frame, + image=buffer, + size=size, + format=format, ) await self.push_frame(frame) self._video_renderers[participant_id]["timestamp"] = curr_time class DailyOutputTransport(BaseOutputTransport): - def __init__(self, client: DailyTransportClient, params: DailyParams, **kwargs): + """Handles outgoing media streams and events to Daily calls. + + Manages sending audio, video and other data to Daily calls. + + Args: + client: DailyTransportClient instance. + params: Configuration parameters. + """ + + def __init__( + self, transport: BaseTransport, client: DailyTransportClient, params: DailyParams, **kwargs + ): super().__init__(params, **kwargs) + self._transport = transport self._client = client + # Whether we have seen a StartFrame already. + self._initialized = False + async def start(self, frame: StartFrame): # Parent start. await super().start(frame) + + if self._initialized: + return + + self._initialized = True + # Setup client. await self._client.setup(frame) # Join the room. @@ -868,6 +1026,7 @@ class DailyOutputTransport(BaseOutputTransport): async def cleanup(self): await super().cleanup() await self._client.cleanup() + await self._transport.cleanup() async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame): await self._client.send_message(frame) @@ -880,14 +1039,28 @@ class DailyOutputTransport(BaseOutputTransport): class DailyTransport(BaseTransport): + """Transport implementation for Daily audio and video calls. + + Handles audio/video streaming, transcription, recordings, dial-in, + dial-out, and call management through Daily's API. + + Args: + room_url: URL of the Daily room to connect to. + token: Optional authentication token for the room. + bot_name: Display name for the bot in the call. + params: Configuration parameters (DailyParams) for the transport. + input_name: Optional name for the input transport. + output_name: Optional name for the output transport. + """ + def __init__( self, room_url: str, - token: str | None, + token: Optional[str], bot_name: str, params: DailyParams = DailyParams(), - input_name: str | None = None, - output_name: str | None = None, + input_name: Optional[str] = None, + output_name: Optional[str] = None, ): super().__init__(input_name=input_name, output_name=output_name) @@ -918,8 +1091,8 @@ class DailyTransport(BaseTransport): self._params = params self._client = DailyTransportClient(room_url, token, bot_name, params, callbacks, self.name) - self._input: DailyInputTransport | None = None - self._output: DailyOutputTransport | None = None + self._input: Optional[DailyInputTransport] = None + self._output: Optional[DailyOutputTransport] = None self._other_participant_has_joined = False @@ -955,18 +1128,26 @@ class DailyTransport(BaseTransport): def input(self) -> DailyInputTransport: if not self._input: - self._input = DailyInputTransport(self._client, self._params, name=self._input_name) + self._input = DailyInputTransport( + self, self._client, self._params, name=self._input_name + ) return self._input def output(self) -> DailyOutputTransport: if not self._output: - self._output = DailyOutputTransport(self._client, self._params, name=self._output_name) + self._output = DailyOutputTransport( + self, self._client, self._params, name=self._output_name + ) return self._output # # DailyTransport # + @property + def room_url(self) -> str: + return self._client.room_url + @property def participant_id(self) -> str: return self._client.participant_id @@ -1006,7 +1187,7 @@ class DailyTransport(BaseTransport): async def stop_recording(self, stream_id=None): await self._client.stop_recording(stream_id) - async def send_prebuilt_chat_message(self, message: str, user_name: str | None = None): + async def send_prebuilt_chat_message(self, message: str, user_name: Optional[str] = None): """Sends a chat message to Daily's Prebuilt main room. Args: @@ -1035,6 +1216,9 @@ class DailyTransport(BaseTransport): participant_settings=participant_settings, profile_settings=profile_settings ) + async def update_remote_participants(self, remote_participants: Mapping[str, Any] = None): + await self._client.update_remote_participants(remote_participants=remote_participants) + async def _on_joined(self, data): await self._call_event_handler("on_joined", data) diff --git a/src/pipecat/transports/services/helpers/daily_rest.py b/src/pipecat/transports/services/helpers/daily_rest.py index 9becacebc..b64aba2d6 100644 --- a/src/pipecat/transports/services/helpers/daily_rest.py +++ b/src/pipecat/transports/services/helpers/daily_rest.py @@ -195,6 +195,10 @@ class DailyMeetingTokenProperties(BaseModel): default=None, description="Start cloud recording when the user joins the room. This can be used to always record and archive meetings, for example in a customer support context.", ) + permissions: Optional[dict] = Field( + default=None, + description="Specifies the initial default permissions for a non-meeting-owner participant joining a call.", + ) class DailyMeetingTokenParams(BaseModel): diff --git a/src/pipecat/transports/services/livekit.py b/src/pipecat/transports/services/livekit.py index 11926f86c..8ce5c885c 100644 --- a/src/pipecat/transports/services/livekit.py +++ b/src/pipecat/transports/services/livekit.py @@ -27,7 +27,7 @@ from pipecat.processors.frame_processor import FrameDirection from pipecat.transports.base_input import BaseInputTransport from pipecat.transports.base_output import BaseOutputTransport from pipecat.transports.base_transport import BaseTransport, TransportParams -from pipecat.utils.asyncio import TaskManager +from pipecat.utils.asyncio import BaseTaskManager try: from livekit import rtc @@ -40,12 +40,12 @@ except ModuleNotFoundError as e: @dataclass class LiveKitTransportMessageFrame(TransportMessageFrame): - participant_id: str | None = None + participant_id: Optional[str] = None @dataclass class LiveKitTransportMessageUrgentFrame(TransportMessageUrgentFrame): - participant_id: str | None = None + participant_id: Optional[str] = None class LiveKitParams(TransportParams): @@ -79,16 +79,16 @@ class LiveKitTransportClient: self._params = params self._callbacks = callbacks self._transport_name = transport_name - self._room: rtc.Room | None = None + self._room: Optional[rtc.Room] = None self._participant_id: str = "" self._connected = False self._disconnect_counter = 0 - self._audio_source: rtc.AudioSource | None = None - self._audio_track: rtc.LocalAudioTrack | None = None + self._audio_source: Optional[rtc.AudioSource] = None + self._audio_track: Optional[rtc.LocalAudioTrack] = None self._audio_tracks = {} self._audio_queue = asyncio.Queue() self._other_participant_has_joined = False - self._task_manager: Optional[TaskManager] = None + self._task_manager: Optional[BaseTaskManager] = None @property def participant_id(self) -> str: @@ -101,6 +101,7 @@ class LiveKitTransportClient: return self._room async def setup(self, frame: StartFrame): + self._out_sample_rate = self._params.audio_out_sample_rate or frame.audio_out_sample_rate if not self._task_manager: self._task_manager = frame.task_manager self._room = rtc.Room(loop=self._task_manager.get_event_loop()) @@ -138,7 +139,7 @@ class LiveKitTransportClient: # Set up audio source and track self._audio_source = rtc.AudioSource( - self._params.audio_out_sample_rate, self._params.audio_out_channels + self._out_sample_rate, self._params.audio_out_channels ) self._audio_track = rtc.LocalAudioTrack.create_audio_track( "pipecat-audio", self._audio_source @@ -171,7 +172,7 @@ class LiveKitTransportClient: logger.info(f"Disconnected from {self._room_name}") await self._callbacks.on_disconnected() - async def send_data(self, data: bytes, participant_id: str | None = None): + async def send_data(self, data: bytes, participant_id: Optional[str] = None): if not self._connected: return @@ -344,18 +345,30 @@ class LiveKitTransportClient: class LiveKitInputTransport(BaseInputTransport): - def __init__(self, client: LiveKitTransportClient, params: LiveKitParams, **kwargs): + def __init__( + self, + transport: BaseTransport, + client: LiveKitTransportClient, + params: LiveKitParams, + **kwargs, + ): super().__init__(params, **kwargs) + self._transport = transport self._client = client + self._audio_in_task = None - self._vad_analyzer: VADAnalyzer | None = params.vad_analyzer + self._vad_analyzer: Optional[VADAnalyzer] = params.vad_analyzer self._resampler = create_default_resampler() + @property + def vad_analyzer(self) -> Optional[VADAnalyzer]: + return self._vad_analyzer + async def start(self, frame: StartFrame): await super().start(frame) await self._client.setup(frame) await self._client.connect() - if self._params.audio_in_enabled or self._params.vad_enabled: + if not self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled): self._audio_in_task = self.create_task(self._audio_in_task_handler()) logger.info("LiveKitInputTransport started") @@ -372,8 +385,9 @@ class LiveKitInputTransport(BaseInputTransport): if self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled): await self.cancel_task(self._audio_in_task) - def vad_analyzer(self) -> VADAnalyzer | None: - return self._vad_analyzer + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() async def push_app_message(self, message: Any, sender: str): frame = LiveKitTransportMessageUrgentFrame(message=message, participant_id=sender) @@ -401,19 +415,26 @@ class LiveKitInputTransport(BaseInputTransport): audio_frame = audio_frame_event.frame audio_data = await self._resampler.resample( - audio_frame.data.tobytes(), audio_frame.sample_rate, self._params.audio_in_sample_rate + audio_frame.data.tobytes(), audio_frame.sample_rate, self.sample_rate ) return AudioRawFrame( audio=audio_data, - sample_rate=self._params.audio_in_sample_rate, + sample_rate=self.sample_rate, num_channels=audio_frame.num_channels, ) class LiveKitOutputTransport(BaseOutputTransport): - def __init__(self, client: LiveKitTransportClient, params: LiveKitParams, **kwargs): + def __init__( + self, + transport: BaseTransport, + client: LiveKitTransportClient, + params: LiveKitParams, + **kwargs, + ): super().__init__(params, **kwargs) + self._transport = transport self._client = client async def start(self, frame: StartFrame): @@ -431,6 +452,10 @@ class LiveKitOutputTransport(BaseOutputTransport): await super().cancel(frame) await self._client.disconnect() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame): if isinstance(frame, (LiveKitTransportMessageFrame, LiveKitTransportMessageUrgentFrame)): await self._client.send_data(frame.message.encode(), frame.participant_id) @@ -448,7 +473,7 @@ class LiveKitOutputTransport(BaseOutputTransport): return rtc.AudioFrame( data=pipecat_audio, - sample_rate=self._params.audio_out_sample_rate, + sample_rate=self.sample_rate, num_channels=self._params.audio_out_channels, samples_per_channel=samples_per_channel, ) @@ -461,8 +486,8 @@ class LiveKitTransport(BaseTransport): token: str, room_name: str, params: LiveKitParams = LiveKitParams(), - input_name: str | None = None, - output_name: str | None = None, + input_name: Optional[str] = None, + output_name: Optional[str] = None, ): super().__init__(input_name=input_name, output_name=output_name) @@ -481,8 +506,8 @@ class LiveKitTransport(BaseTransport): self._client = LiveKitTransportClient( url, token, room_name, self._params, callbacks, self.name ) - self._input: LiveKitInputTransport | None = None - self._output: LiveKitOutputTransport | None = None + self._input: Optional[LiveKitInputTransport] = None + self._output: Optional[LiveKitOutputTransport] = None self._register_event_handler("on_connected") self._register_event_handler("on_disconnected") @@ -497,13 +522,15 @@ class LiveKitTransport(BaseTransport): def input(self) -> LiveKitInputTransport: if not self._input: - self._input = LiveKitInputTransport(self._client, self._params, name=self._input_name) + self._input = LiveKitInputTransport( + self, self._client, self._params, name=self._input_name + ) return self._input def output(self) -> LiveKitOutputTransport: if not self._output: self._output = LiveKitOutputTransport( - self._client, self._params, name=self._output_name + self, self._client, self._params, name=self._output_name ) return self._output @@ -560,25 +587,18 @@ class LiveKitTransport(BaseTransport): await self._input.push_app_message(data.decode(), participant_id) await self._call_event_handler("on_data_received", data, participant_id) - async def send_message(self, message: str, participant_id: str | None = None): + async def send_message(self, message: str, participant_id: Optional[str] = None): if self._output: frame = LiveKitTransportMessageFrame(message=message, participant_id=participant_id) await self._output.send_message(frame) - async def send_message_urgent(self, message: str, participant_id: str | None = None): + async def send_message_urgent(self, message: str, participant_id: Optional[str] = None): if self._output: frame = LiveKitTransportMessageUrgentFrame( message=message, participant_id=participant_id ) await self._output.send_message(frame) - async def cleanup(self): - if self._input: - await self._input.cleanup() - if self._output: - await self._output.cleanup() - await self._client.disconnect() - async def on_room_event(self, event): # Handle room events pass diff --git a/src/pipecat/utils/asyncio.py b/src/pipecat/utils/asyncio.py index ab40a8195..acc4acec8 100644 --- a/src/pipecat/utils/asyncio.py +++ b/src/pipecat/utils/asyncio.py @@ -5,12 +5,76 @@ # import asyncio +from abc import ABC, abstractmethod from typing import Coroutine, Optional, Set from loguru import logger -class TaskManager: +class BaseTaskManager(ABC): + @abstractmethod + def set_event_loop(self, loop: asyncio.AbstractEventLoop): + pass + + @abstractmethod + def get_event_loop(self) -> asyncio.AbstractEventLoop: + pass + + @abstractmethod + def create_task(self, coroutine: Coroutine, name: str) -> asyncio.Task: + """ + Creates and schedules a new asyncio Task that runs the given coroutine. + + The task is added to a global set of created tasks. + + Args: + loop (asyncio.AbstractEventLoop): The event loop to use for creating the task. + coroutine (Coroutine): The coroutine to be executed within the task. + name (str): The name to assign to the task for identification. + + Returns: + asyncio.Task: The created task object. + """ + pass + + @abstractmethod + async def wait_for_task(self, task: asyncio.Task, timeout: Optional[float] = None): + """Wait for an asyncio.Task to complete with optional timeout handling. + + This function awaits the specified asyncio.Task and handles scenarios for + timeouts, cancellations, and other exceptions. It also ensures that the task + is removed from the set of registered tasks upon completion or failure. + + Args: + task (asyncio.Task): The asyncio Task to wait for. + timeout (Optional[float], optional): The maximum number of seconds + to wait for the task to complete. If None, waits indefinitely. + Defaults to None. + """ + pass + + @abstractmethod + async def cancel_task(self, task: asyncio.Task, timeout: Optional[float] = None): + """Cancels the given asyncio Task and awaits its completion with an + optional timeout. + + This function removes the task from the set of registered tasks upon + completion or failure. + + Args: + task (asyncio.Task): The task to be cancelled. + timeout (Optional[float]): The optional timeout in seconds to wait for the task to cancel. + + """ + pass + + @abstractmethod + def current_tasks(self) -> Set[asyncio.Task]: + """Returns the list of currently created/registered tasks.""" + pass + + +class TaskManager(BaseTaskManager): def __init__(self) -> None: self._tasks: Set[asyncio.Task] = set() self._loop: Optional[asyncio.AbstractEventLoop] = None @@ -80,6 +144,7 @@ class TaskManager: logger.warning(f"{name}: timed out waiting for task to finish") except asyncio.CancelledError: logger.trace(f"{name}: unexpected task cancellation (maybe Ctrl-C?)") + raise except Exception as e: logger.exception(f"{name}: unexpected exception while stopping task: {e}") finally: diff --git a/src/pipecat/utils/base_object.py b/src/pipecat/utils/base_object.py new file mode 100644 index 000000000..1dee24ce7 --- /dev/null +++ b/src/pipecat/utils/base_object.py @@ -0,0 +1,87 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import inspect +from abc import ABC +from typing import Optional + +from loguru import logger + +from pipecat.utils.utils import obj_count, obj_id + + +class BaseObject(ABC): + def __init__(self, *, name: Optional[str] = None): + self._id: int = obj_id() + self._name = name or f"{self.__class__.__name__}#{obj_count(self)}" + + # Registered event handlers. + self._event_handlers: dict = {} + + # Set of tasks being executed. When a task finishes running it gets + # automatically removed from the set. When we cleanup we wait for all + # event tasks still being executed. + self._event_tasks = set() + + @property + def id(self) -> int: + return self._id + + @property + def name(self) -> str: + return self._name + + async def cleanup(self): + if self._event_tasks: + event_names, tasks = zip(*self._event_tasks) + logger.debug(f"{self} wating on event handlers to finish {list(event_names)}...") + await asyncio.wait(tasks) + + def event_handler(self, event_name: str): + def decorator(handler): + self.add_event_handler(event_name, handler) + return handler + + return decorator + + def add_event_handler(self, event_name: str, handler): + if event_name not in self._event_handlers: + raise Exception(f"Event handler {event_name} not registered") + self._event_handlers[event_name].append(handler) + + def _register_event_handler(self, event_name: str): + if event_name in self._event_handlers: + raise Exception(f"Event handler {event_name} already registered") + self._event_handlers[event_name] = [] + + async def _call_event_handler(self, event_name: str, *args, **kwargs): + # Create the task. + task = asyncio.create_task(self._run_task(event_name, *args, **kwargs)) + + # Add it to our list of event tasks. + self._event_tasks.add((event_name, task)) + + # Remove the task from the event tasks list when the task completes. + task.add_done_callback(self._event_task_finished) + + async def _run_task(self, event_name: str, *args, **kwargs): + try: + for handler in self._event_handlers[event_name]: + if inspect.iscoroutinefunction(handler): + await handler(self, *args, **kwargs) + else: + handler(self, *args, **kwargs) + except Exception as e: + logger.exception(f"Exception in event handler {event_name}: {e}") + + def _event_task_finished(self, task: asyncio.Task): + tuple_to_remove = next((t for t in self._event_tasks if t[1] == task), None) + if tuple_to_remove: + self._event_tasks.discard(tuple_to_remove) + + def __str__(self): + return self.name diff --git a/src/pipecat/utils/network.py b/src/pipecat/utils/network.py new file mode 100644 index 000000000..27bec990c --- /dev/null +++ b/src/pipecat/utils/network.py @@ -0,0 +1,27 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + + +def exponential_backoff_time( + attempt: int, min_wait: float = 4, max_wait: float = 10, multiplier: float = 1 +) -> float: + """Calculate exponential backoff wait time. + + Args: + attempt: Current attempt number (1-based) + min_wait: Minimum wait time in seconds + max_wait: Maximum wait time in seconds + multiplier: Base multiplier for exponential calculation + + Returns: + Wait time in seconds + """ + try: + exp = 2 ** (attempt - 1) * multiplier + result = max(0, min(exp, max_wait)) + return max(min_wait, result) + except (ValueError, ArithmeticError): + return max_wait diff --git a/src/pipecat/utils/string.py b/src/pipecat/utils/string.py index c269c5e57..69036a665 100644 --- a/src/pipecat/utils/string.py +++ b/src/pipecat/utils/string.py @@ -5,21 +5,126 @@ # import re +from typing import Optional, Sequence, Tuple ENDOFSENTENCE_PATTERN_STR = r""" (? str: + """Replace occurrences of a substring within a matched section of a given + text. + + Args: + text (str): The input text in which replacements will be made. + match (re.Match): A regex match object representing the section of text to modify. + old (str): The substring to be replaced. + new (str): The substring to replace `old` with. + + Returns: + str: The modified text with the specified replacements made within the matched section. + + """ + start = match.start() + end = match.end() + replacement = text[start:end].replace(old, new) + text = text[:start] + replacement + text[end:] + return text + def match_endofsentence(text: str) -> int: - match = ENDOFSENTENCE_PATTERN.search(text.rstrip()) + """Finds the position of the end of a sentence in the provided text string. + + This function processes the input text by replacing periods in email + addresses and numbers with ampersands to prevent them from being + misidentified as sentence terminals. It then searches for the end of a + sentence using a specified regex pattern. + + Args: + text (str): The input text in which to find the end of the sentence. + + Returns: + int: The position of the end of the sentence if found, otherwise 0. + + """ + text = text.rstrip() + + # Replace email dots by ampersands so we can find the end of sentence. For + # example, first.last@email.com becomes first&last@email&com. + emails = list(EMAIL_PATTERN.finditer(text)) + for email_match in emails: + text = replace_match(text, email_match, ".", "&") + + # Replace number dots by ampersands so we can find the end of sentence. + numbers = list(NUMBER_PATTERN.finditer(text)) + for number_match in numbers: + text = replace_match(text, number_match, ".", "&") + + # Match against the new text. + match = ENDOFSENTENCE_PATTERN.search(text) + return match.end() if match else 0 + + +def parse_start_end_tags( + text: str, + tags: Sequence[StartEndTags], + current_tag: Optional[StartEndTags], + current_tag_index: int, +) -> Tuple[Optional[StartEndTags], int]: + """Parses the given text to identify a pair of start/end tags. + + If a start tag was previously found (i.e. current_tags is valid), wait for + the corresponding end tag. Otherwise, wait for a start tag. + + This function will return the index in the text that we should start parsing + in the next call and the current or new tags. + + Parameters: + - text (str): The text to be parsed. + - tags (Sequence[StartEndTags]): List of tuples containing start and end tags. + - current_tags (Optional[StartEndTags]): The currently active tags, if any. + - current_tags_index (int): The current index in the text. + + Returns: + Tuple[Optional[StartEndTags], int]: A tuple containing None or the current + tag and the index of the text. + + """ + # If we are already inside a tag, check if the end tag is in the text. + if current_tag: + _, end_tag = current_tag + if end_tag in text[current_tag_index:]: + return (None, len(text)) + return (current_tag, current_tag_index) + + # Check if any start tag appears in the text + for start_tag, end_tag in tags: + start_tag_count = text[current_tag_index:].count(start_tag) + end_tag_count = text[current_tag_index:].count(end_tag) + if start_tag_count == 0 and end_tag_count == 0: + return (None, current_tag_index) + elif start_tag_count > end_tag_count: + return ((start_tag, end_tag), len(text)) + elif start_tag_count == end_tag_count: + return (None, len(text)) + + return (None, current_tag_index) diff --git a/src/pipecat/utils/test_frame_processor.py b/src/pipecat/utils/test_frame_processor.py deleted file mode 100644 index fde476007..000000000 --- a/src/pipecat/utils/test_frame_processor.py +++ /dev/null @@ -1,46 +0,0 @@ -from typing import List - -from pipecat.processors.frame_processor import FrameProcessor - - -class TestException(Exception): - pass - - -class TestFrameProcessor(FrameProcessor): - def __init__(self, test_frames): - self.test_frames = test_frames - self._list_counter = 0 - super().__init__() - - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - - if not self.test_frames[ - 0 - ]: # then we've run out of required frames but the generator is still going? - raise TestException(f"Oops, got an extra frame, {frame}") - if isinstance(self.test_frames[0], List): - # We need to consume frames until we see the next frame type after this - next_frame = self.test_frames[1] - if isinstance(frame, next_frame): - # we're done iterating the list I guess - print(f"TestFrameProcessor got expected list exit frame: {frame}") - # pop twice to get rid of the list, as well as the next frame - self.test_frames.pop(0) - self.test_frames.pop(0) - self.list_counter = 0 - else: - fl = self.test_frames[0] - fl_el = fl[self._list_counter % len(fl)] - if isinstance(frame, fl_el): - print(f"TestFrameProcessor got expected list frame: {frame}") - self._list_counter += 1 - else: - raise TestException(f"Inside a list, expected {fl_el} but got {frame}") - - else: - if not isinstance(frame, self.test_frames[0]): - raise TestException(f"Expected {self.test_frames[0]}, but got {frame}") - print(f"TestFrameProcessor got expected frame: {frame}") - self.test_frames.pop(0) diff --git a/src/pipecat/utils/text/base_text_aggregator.py b/src/pipecat/utils/text/base_text_aggregator.py new file mode 100644 index 000000000..452e5598e --- /dev/null +++ b/src/pipecat/utils/text/base_text_aggregator.py @@ -0,0 +1,57 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from abc import ABC, abstractmethod +from typing import Optional + + +class BaseTextAggregator(ABC): + """This is the base class for text aggregators. Text aggregators are usually + used by the TTS service to aggregate LLM tokens and decide when the + aggregated text should be pushed to the TTS service. + + Text aggregators can also be used to manipulate text while it's being + aggregated (e.g. reasoning blocks can be removed). + + """ + + @property + @abstractmethod + def text(self) -> str: + """Returns the currently aggregated text.""" + pass + + @abstractmethod + def aggregate(self, text: str) -> Optional[str]: + """Aggregates the specified text with the currently accumulated text. + + This method should be implemented to define how the new text contributes + to the aggregation process. It returns the updated aggregated text if + it's ready to be processed, or None otherwise. + + Args: + text (str): The text to be aggregated. + + Returns: + Optional[str]: The updated aggregated text or None if aggregated + text is not ready. + + """ + pass + + @abstractmethod + def handle_interruption(self): + """Handles interruptions. When an interruption occurs it is possible + that we might want to discard the aggregated text or do some internal + modifications to the aggregated text. + + """ + pass + + @abstractmethod + def reset(self): + """Clears the internally aggregated text.""" + pass diff --git a/src/pipecat/utils/text/pattern_pair_aggregator.py b/src/pipecat/utils/text/pattern_pair_aggregator.py new file mode 100644 index 000000000..86f87103b --- /dev/null +++ b/src/pipecat/utils/text/pattern_pair_aggregator.py @@ -0,0 +1,262 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import re +from typing import Callable, Optional, Tuple + +from loguru import logger + +from pipecat.utils.string import match_endofsentence +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator + + +class PatternMatch: + """Represents a matched pattern pair with its content. + + A PatternMatch object is created when a complete pattern pair is found + in the text. It contains information about which pattern was matched, + the full matched text (including start and end patterns), and the + content between the patterns. + + Attributes: + pattern_id: The identifier of the matched pattern pair. + full_match: The complete text including start and end patterns. + content: The text content between the start and end patterns. + """ + + def __init__(self, pattern_id: str, full_match: str, content: str): + """Initialize a pattern match. + + Args: + pattern_id: ID of the pattern pair. + full_match: Complete matched text including start and end patterns. + content: Content between the start and end patterns. + """ + self.pattern_id = pattern_id + self.full_match = full_match + self.content = content + + def __str__(self) -> str: + """Return a string representation of the pattern match. + + Returns: + A string describing the pattern match. + """ + return f"PatternMatch(id={self.pattern_id}, content={self.content})" + + +class PatternPairAggregator(BaseTextAggregator): + """Aggregator that identifies and processes content between pattern pairs. + + This aggregator buffers text until it can identify complete pattern pairs + (defined by start and end patterns), processes the content between these + patterns using registered handlers, and returns text at sentence boundaries. + It's particularly useful for processing structured content in streaming text, + such as XML tags, markdown formatting, or custom delimiters. + + The aggregator ensures that patterns spanning multiple text chunks are + correctly identified and handles cases where patterns contain sentence + boundaries. + """ + + def __init__(self): + """Initialize the pattern pair aggregator. + + Creates an empty aggregator with no patterns or handlers registered. + """ + self._text = "" + self._patterns = {} + self._handlers = {} + + @property + def text(self) -> str: + """Get the currently buffered text. + + Returns: + The current text buffer content. + """ + return self._text + + def add_pattern_pair( + self, pattern_id: str, start_pattern: str, end_pattern: str, remove_match: bool = True + ) -> "PatternPairAggregator": + """Add a pattern pair to detect in the text. + + Registers a new pattern pair with a unique identifier. The aggregator + will look for text that starts with the start pattern and ends with + the end pattern, and treat the content between them as a match. + + Args: + pattern_id: Unique identifier for this pattern pair. + start_pattern: Pattern that marks the beginning of content. + end_pattern: Pattern that marks the end of content. + remove_match: Whether to remove the matched content from the text. + + Returns: + Self for method chaining. + """ + self._patterns[pattern_id] = { + "start": start_pattern, + "end": end_pattern, + "remove_match": remove_match, + } + return self + + def on_pattern_match( + self, pattern_id: str, handler: Callable[[PatternMatch], None] + ) -> "PatternPairAggregator": + """Register a handler for when a pattern pair is matched. + + The handler will be called whenever a complete match for the + specified pattern ID is found in the text. + + Args: + pattern_id: ID of the pattern pair to match. + handler: Function to call when pattern is matched. + The function should accept a PatternMatch object. + + Returns: + Self for method chaining. + """ + self._handlers[pattern_id] = handler + return self + + def _process_complete_patterns(self, text: str) -> Tuple[str, bool]: + """Process all complete pattern pairs in the text. + + Searches for all complete pattern pairs in the text, calls the + appropriate handlers, and optionally removes the matches. + + Args: + text: The text to process. + + Returns: + Tuple of (processed_text, was_modified) where: + - processed_text is the text after processing patterns + - was_modified indicates whether any changes were made + """ + processed_text = text + modified = False + + for pattern_id, pattern_info in self._patterns.items(): + # Escape special regex characters in the patterns + start = re.escape(pattern_info["start"]) + end = re.escape(pattern_info["end"]) + remove_match = pattern_info["remove_match"] + + # Create regex to match from start pattern to end pattern + # The .*? is non-greedy to handle nested patterns + regex = f"{start}(.*?){end}" + + # Find all matches + match_iter = re.finditer(regex, processed_text, re.DOTALL) + matches = list(match_iter) # Convert to list for safe iteration + + for match in matches: + content = match.group(1) # Content between patterns + full_match = match.group(0) # Full match including patterns + + # Create pattern match object + pattern_match = PatternMatch( + pattern_id=pattern_id, full_match=full_match, content=content + ) + + # Call the appropriate handler if registered + if pattern_id in self._handlers: + try: + self._handlers[pattern_id](pattern_match) + except Exception as e: + logger.error(f"Error in pattern handler for {pattern_id}: {e}") + + # Remove the pattern from the text if configured + if remove_match: + processed_text = processed_text.replace(full_match, "", 1) + modified = True + + return processed_text, modified + + def _has_incomplete_patterns(self, text: str) -> bool: + """Check if text contains incomplete pattern pairs. + + Determines whether the text contains any start patterns without + matching end patterns, which would indicate incomplete content. + + Args: + text: The text to check. + + Returns: + True if there are incomplete patterns, False otherwise. + """ + for pattern_id, pattern_info in self._patterns.items(): + start = pattern_info["start"] + end = pattern_info["end"] + + # Count occurrences + start_count = text.count(start) + end_count = text.count(end) + + # If there are more starts than ends, we have incomplete patterns + if start_count > end_count: + return True + + return False + + def aggregate(self, text: str) -> Optional[str]: + """Aggregate text and process pattern pairs. + + This method adds the new text to the buffer, processes any complete pattern + pairs, and returns processed text up to sentence boundaries if possible. + If there are incomplete patterns (start without matching end), it will + continue buffering text. + + Args: + text: New text to add to the buffer. + + Returns: + Processed text up to a sentence boundary, or None if more + text is needed to form a complete sentence or pattern. + """ + # Add new text to buffer + self._text += text + + # Process any complete patterns in the buffer + processed_text, modified = self._process_complete_patterns(self._text) + + # Only update the buffer if modifications were made + if modified: + self._text = processed_text + + # Check if we have incomplete patterns + if self._has_incomplete_patterns(self._text): + # Still waiting for complete patterns + return None + + # Find sentence boundary if no incomplete patterns + eos_marker = match_endofsentence(self._text) + if eos_marker: + # Extract text up to the sentence boundary + result = self._text[:eos_marker] + self._text = self._text[eos_marker:] + return result + + # No complete sentence found yet + return None + + def handle_interruption(self): + """Handle interruptions by clearing the buffer. + + Called when an interruption occurs in the processing pipeline, + to reset the state and discard any partially aggregated text. + """ + self._text = "" + + def reset(self): + """Clear the internally aggregated text. + + Resets the aggregator to its initial state, discarding any + buffered text. + """ + self._text = "" diff --git a/src/pipecat/utils/text/simple_text_aggregator.py b/src/pipecat/utils/text/simple_text_aggregator.py new file mode 100644 index 000000000..9022fc25a --- /dev/null +++ b/src/pipecat/utils/text/simple_text_aggregator.py @@ -0,0 +1,42 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import Optional + +from pipecat.utils.string import match_endofsentence +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator + + +class SimpleTextAggregator(BaseTextAggregator): + """This is a simple text aggregator. It aggregates text until an end of + sentence is found. + + """ + + def __init__(self): + self._text = "" + + @property + def text(self) -> str: + return self._text + + def aggregate(self, text: str) -> Optional[str]: + result: Optional[str] = None + + self._text += text + + eos_end_marker = match_endofsentence(self._text) + if eos_end_marker: + result = self._text[:eos_end_marker] + self._text = self._text[eos_end_marker:] + + return result + + def handle_interruption(self): + self._text = "" + + def reset(self): + self._text = "" diff --git a/src/pipecat/utils/text/skip_tags_aggregator.py b/src/pipecat/utils/text/skip_tags_aggregator.py new file mode 100644 index 000000000..00129028e --- /dev/null +++ b/src/pipecat/utils/text/skip_tags_aggregator.py @@ -0,0 +1,94 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import Optional, Sequence + +from pipecat.utils.string import StartEndTags, match_endofsentence, parse_start_end_tags +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator + + +class SkipTagsAggregator(BaseTextAggregator): + """Aggregator that prevents end of sentence matching between start/end tags. + + This aggregator buffers text until it finds an end of sentence or a start + tag. If a start tag is found the aggregator will keep aggregating text + unconditionally until the corresponding end tag is found. It's particularly + useful for processing content with custom delimiters that should prevent + text from being considered for end of sentence matching.. + + The aggregator ensures that tags spanning multiple text chunks are correctly + identified. + + """ + + def __init__(self, tags: Sequence[StartEndTags]): + """Initialize the pattern pair aggregator. + + Creates an empty aggregator with no patterns or handlers registered. + """ + self._text = "" + self._tags = tags + self._current_tag: Optional[StartEndTags] = None + self._current_tag_index: int = 0 + + @property + def text(self) -> str: + """Get the currently buffered text. + + Returns: + The current text buffer content. + """ + return self._text + + def aggregate(self, text: str) -> Optional[str]: + """Aggregate text and process pattern pairs. + + This method adds the new text to the buffer, processes any complete pattern + pairs, and returns processed text up to sentence boundaries if possible. + If there are incomplete patterns (start without matching end), it will + continue buffering text. + + Args: + text: New text to add to the buffer. + + Returns: + Processed text up to a sentence boundary, or None if more + text is needed to form a complete sentence or pattern. + """ + # Add new text to buffer + self._text += text + + (self._current_tag, self._current_tag_index) = parse_start_end_tags( + self._text, self._tags, self._current_tag, self._current_tag_index + ) + + # Find sentence boundary if no incomplete patterns + if not self._current_tag: + eos_marker = match_endofsentence(self._text) + if eos_marker: + # Extract text up to the sentence boundary + result = self._text[:eos_marker] + self._text = self._text[eos_marker:] + return result + + # No complete sentence found yet + return None + + def handle_interruption(self): + """Handle interruptions by clearing the buffer. + + Called when an interruption occurs in the processing pipeline, + to reset the state and discard any partially aggregated text. + """ + self._text = "" + + def reset(self): + """Clear the internally aggregated text. + + Resets the aggregator to its initial state, discarding any + buffered text. + """ + self._text = "" diff --git a/src/pipecat/vad/silero.py b/src/pipecat/vad/silero.py deleted file mode 100644 index 285e2e0e8..000000000 --- a/src/pipecat/vad/silero.py +++ /dev/null @@ -1,16 +0,0 @@ -# -# Copyright (c) 2024–2025, Daily -# -# SPDX-License-Identifier: BSD 2-Clause License -# - -import warnings - -with warnings.catch_warnings(): - warnings.simplefilter("always") - warnings.warn( - "Package `pipecat.vad` is deprecated, use `pipecat.audio.vad` instead", DeprecationWarning - ) - -from ..audio.vad.silero import SileroVADAnalyzer -from ..processors.audio.vad.silero import SileroVAD diff --git a/src/pipecat/vad/vad_analyzer.py b/src/pipecat/vad/vad_analyzer.py deleted file mode 100644 index 7c8fc4c31..000000000 --- a/src/pipecat/vad/vad_analyzer.py +++ /dev/null @@ -1,15 +0,0 @@ -# -# Copyright (c) 2024–2025, Daily -# -# SPDX-License-Identifier: BSD 2-Clause License -# - -import warnings - -with warnings.catch_warnings(): - warnings.simplefilter("always") - warnings.warn( - "Package `pipecat.vad` is deprecated, use `pipecat.audio.vad` instead", DeprecationWarning - ) - -from ..audio.vad.vad_analyzer import VADAnalyzer, VADParams, VADState diff --git a/test-requirements.txt b/test-requirements.txt index aae1c2dbb..b34a53ab9 100644 --- a/test-requirements.txt +++ b/test-requirements.txt @@ -1,30 +1 @@ -aiohttp~=3.10.3 -anthropic~=0.30.0 -azure-cognitiveservices-speech~=1.40.0 -boto3~=1.35.27 -daily-python~=0.11.0 -deepgram-sdk~=3.5.0 -fal-client~=0.4.1 -fastapi~=0.115.0 -faster-whisper~=1.0.3 -google-cloud-texttospeech~=2.21.1 -google-generativeai~=0.8.3 -langchain~=0.2.14 -livekit~=0.13.1 -lmnt~=1.1.4 -loguru~=0.7.2 -numpy~=1.26.4 -openai~=1.37.2 -openpipe~=4.24.0 -Pillow~=10.4.0 -pyaudio~=0.2.14 -pydantic~=2.8.2 -pyloudnorm~=0.1.1 -pyht~=0.1.4 -pytest-aiohttp==1.1.0 -python-dotenv~=1.0.1 -silero-vad~=5.1 -soxr~=0.5.0 -together~=1.2.7 -transformers~=4.44.0 -websockets~=13.1 +-e ".[anthropic,google,langchain]" diff --git a/tests/integration/integration_azure_llm.py b/tests/integration/integration_azure_llm.py deleted file mode 100644 index 8e49e9d04..000000000 --- a/tests/integration/integration_azure_llm.py +++ /dev/null @@ -1,33 +0,0 @@ -import asyncio -import os -import unittest - -from openai.types.chat import ( - ChatCompletionSystemMessageParam, -) - -from pipecat.processors.aggregators.openai_llm_context import ( - OpenAILLMContext, - OpenAILLMContextFrame, -) -from pipecat.services.azure import AzureLLMService - -if __name__ == "__main__": - - @unittest.skip("Skip azure integration test") - async def test_chat(): - llm = AzureLLMService( - api_key=os.getenv("AZURE_CHATGPT_API_KEY"), - endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), - model=os.getenv("AZURE_CHATGPT_MODEL"), - ) - context = OpenAILLMContext() - message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Please tell the world hello.", name="system", role="system" - ) - context.add_message(message) - frame = OpenAILLMContextFrame(context) - async for s in llm.process_frame(frame): - print(s) - - asyncio.run(test_chat()) diff --git a/tests/integration/integration_ollama_llm.py b/tests/integration/integration_ollama_llm.py deleted file mode 100644 index 085500cb8..000000000 --- a/tests/integration/integration_ollama_llm.py +++ /dev/null @@ -1,28 +0,0 @@ -import asyncio -import unittest - -from openai.types.chat import ( - ChatCompletionSystemMessageParam, -) - -from pipecat.processors.aggregators.openai_llm_context import ( - OpenAILLMContext, - OpenAILLMContextFrame, -) -from pipecat.services.ollama import OLLamaLLMService - -if __name__ == "__main__": - - @unittest.skip("Skip azure integration test") - async def test_chat(): - llm = OLLamaLLMService() - context = OpenAILLMContext() - message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Please tell the world hello.", name="system", role="system" - ) - context.add_message(message) - frame = OpenAILLMContextFrame(context) - async for s in llm.process_frame(frame): - print(s) - - asyncio.run(test_chat()) diff --git a/tests/integration/integration_openai_llm.py b/tests/integration/integration_openai_llm.py deleted file mode 100644 index c788936a1..000000000 --- a/tests/integration/integration_openai_llm.py +++ /dev/null @@ -1,128 +0,0 @@ -import asyncio -import json -import os -from typing import List - -from openai.types.chat import ( - ChatCompletionSystemMessageParam, - ChatCompletionToolParam, - ChatCompletionUserMessageParam, -) - -from pipecat.frames.frames import LLMFullResponseEndFrame, LLMFullResponseStartFrame, TextFrame -from pipecat.processors.frame_processor import FrameDirection, FrameProcessor -from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame, OpenAILLMService -from pipecat.utils.test_frame_processor import TestFrameProcessor - -tools = [ - ChatCompletionToolParam( - type="function", - function={ - "name": "get_current_weather", - "description": "Get the current weather", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - }, - "required": ["location", "format"], - }, - }, - ) -] - -if __name__ == "__main__": - - async def test_simple_functions(): - async def get_weather_from_api(llm, args): - return json.dumps({"conditions": "nice", "temperature": "75"}) - - api_key = os.getenv("OPENAI_API_KEY") - - llm = OpenAILLMService( - api_key=api_key or "", - model="gpt-4-1106-preview", - ) - - llm.register_function("get_current_weather", get_weather_from_api) - t = TestFrameProcessor([LLMFullResponseStartFrame, TextFrame, LLMFullResponseEndFrame]) - llm.link(t) - - context = OpenAILLMContext(tools=tools) - system_message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Ask the user to ask for a weather report", name="system", role="system" - ) - user_message: ChatCompletionUserMessageParam = ChatCompletionUserMessageParam( - content="Could you tell me the weather for Boulder, Colorado", - name="user", - role="user", - ) - context.add_message(system_message) - context.add_message(user_message) - frame = OpenAILLMContextFrame(context) - await llm.process_frame(frame, FrameDirection.DOWNSTREAM) - - async def test_advanced_functions(): - async def get_weather_from_api(llm, args): - return [ - { - "role": "system", - "content": "The user has asked for live weather. Respond by telling them we don't currently support live weather for that area, but it's coming soon.", - } - ] - - api_key = os.getenv("OPENAI_API_KEY") - - llm = OpenAILLMService( - api_key=api_key or "", - model="gpt-4-1106-preview", - ) - - llm.register_function("get_current_weather", get_weather_from_api) - t = TestFrameProcessor([LLMFullResponseStartFrame, TextFrame, LLMFullResponseEndFrame]) - llm.link(t) - - context = OpenAILLMContext(tools=tools) - system_message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Ask the user to ask for a weather report", name="system", role="system" - ) - user_message: ChatCompletionUserMessageParam = ChatCompletionUserMessageParam( - content="Could you tell me the weather for Boulder, Colorado", - name="user", - role="user", - ) - context.add_message(system_message) - context.add_message(user_message) - frame = OpenAILLMContextFrame(context) - await llm.process_frame(frame, FrameDirection.DOWNSTREAM) - - async def test_chat(): - api_key = os.getenv("OPENAI_API_KEY") - t = TestFrameProcessor([LLMFullResponseStartFrame, TextFrame, LLMFullResponseEndFrame]) - llm = OpenAILLMService( - api_key=api_key or "", - model="gpt-4o", - ) - llm.link(t) - context = OpenAILLMContext() - message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Please tell the world hello.", name="system", role="system" - ) - context.add_message(message) - frame = OpenAILLMContextFrame(context) - await llm.process_frame(frame, FrameDirection.DOWNSTREAM) - - async def run_tests(): - await test_simple_functions() - await test_advanced_functions() - await test_chat() - - asyncio.run(run_tests()) diff --git a/tests/integration/test_integration_unified_function_calling.py b/tests/integration/test_integration_unified_function_calling.py new file mode 100644 index 000000000..0696c531a --- /dev/null +++ b/tests/integration/test_integration_unified_function_calling.py @@ -0,0 +1,99 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import os +from unittest.mock import AsyncMock + +import pytest +from dotenv import load_dotenv + +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema +from pipecat.pipeline.pipeline import Pipeline +from pipecat.services.ai_services import LLMService +from pipecat.services.anthropic import AnthropicLLMService +from pipecat.services.google import GoogleLLMService +from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame, OpenAILLMService +from pipecat.tests.utils import run_test + +load_dotenv(override=True) + + +def standard_tools() -> ToolsSchema: + weather_function = FunctionSchema( + name="get_current_weather", + description="Get the current weather", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the user's location.", + }, + }, + required=["location"], + ) + tools_def = ToolsSchema(standard_tools=[weather_function]) + return tools_def + + +async def _test_llm_function_calling(llm: LLMService): + # Create an AsyncMock for the function + mock_fetch_weather = AsyncMock() + + llm.register_function(None, mock_fetch_weather) + + messages = [ + { + "role": "system", + "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation.", + }, + {"role": "user", "content": " How is the weather today in San Francisco, California?"}, + ] + context = OpenAILLMContext(messages, standard_tools()) + # This is done by default inside the create_context_aggregator + context.set_llm_adapter(llm.get_llm_adapter()) + + pipeline = Pipeline([llm]) + + frames_to_send = [OpenAILLMContextFrame(context)] + await run_test( + pipeline, + frames_to_send=frames_to_send, + expected_down_frames=None, + ) + + # Assert that the mock function was called + mock_fetch_weather.assert_called_once() + + +@pytest.mark.skipif(os.getenv("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY is not set") +@pytest.mark.asyncio +async def test_unified_function_calling_openai(): + llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") + # This will fail if an exception is raised + await _test_llm_function_calling(llm) + + +@pytest.mark.skipif(os.getenv("GOOGLE_API_KEY") is None, reason="GOOGLE_API_KEY is not set") +@pytest.mark.asyncio +async def test_unified_function_calling_gemini(): + llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001") + # This will fail if an exception is raised + await _test_llm_function_calling(llm) + + +@pytest.mark.skipif(os.getenv("ANTHROPIC_API_KEY") is None, reason="ANTHROPIC_API_KEY is not set") +@pytest.mark.asyncio +async def test_unified_function_calling_anthropic(): + llm = AnthropicLLMService( + api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620" + ) + # This will fail if an exception is raised + await _test_llm_function_calling(llm) diff --git a/tests/test_aggregators.py b/tests/test_aggregators.py index 5eb6cc40f..48650977e 100644 --- a/tests/test_aggregators.py +++ b/tests/test_aggregators.py @@ -29,9 +29,13 @@ class TestSentenceAggregator(unittest.IsolatedAsyncioTestCase): for word in sentence.split(" "): frames_to_send.append(TextFrame(text=word + " ")) - expected_returned_frames = [TextFrame, TextFrame, TextFrame] + expected_down_frames = [TextFrame, TextFrame, TextFrame] - (received_down, _) = await run_test(aggregator, frames_to_send, expected_returned_frames) + (received_down, _) = await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) assert received_down[-3].text == "Hello, world. " assert received_down[-2].text == "How are you? " assert received_down[-1].text == "I am fine! " @@ -55,7 +59,7 @@ class TestGatedAggregator(unittest.IsolatedAsyncioTestCase): LLMFullResponseEndFrame(), ] - expected_returned_frames = [ + expected_down_frames = [ OutputImageRawFrame, LLMFullResponseStartFrame, TextFrame, @@ -66,5 +70,7 @@ class TestGatedAggregator(unittest.IsolatedAsyncioTestCase): ] (received_down, _) = await run_test( - gated_aggregator, frames_to_send, expected_returned_frames + gated_aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, ) diff --git a/tests/test_context_aggregators.py b/tests/test_context_aggregators.py new file mode 100644 index 000000000..df00f11e4 --- /dev/null +++ b/tests/test_context_aggregators.py @@ -0,0 +1,738 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import json +import unittest +from typing import Any + +import google.ai.generativelanguage as glm + +from pipecat.frames.frames import ( + EmulateUserStartedSpeakingFrame, + EmulateUserStoppedSpeakingFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, + FunctionCallResultProperties, + InterimTranscriptionFrame, + LLMFullResponseEndFrame, + LLMFullResponseStartFrame, + OpenAILLMContextAssistantTimestampFrame, + StartInterruptionFrame, + TextFrame, + TranscriptionFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, +) +from pipecat.processors.aggregators.llm_response import LLMUserContextAggregator +from pipecat.processors.aggregators.openai_llm_context import ( + OpenAILLMContext, + OpenAILLMContextFrame, +) +from pipecat.services.anthropic import ( + AnthropicAssistantContextAggregator, + AnthropicLLMContext, + AnthropicUserContextAggregator, +) +from pipecat.services.google.google import ( + GoogleAssistantContextAggregator, + GoogleLLMContext, + GoogleUserContextAggregator, +) +from pipecat.services.openai import OpenAIAssistantContextAggregator, OpenAIUserContextAggregator +from pipecat.tests.utils import SleepFrame, run_test + +AGGREGATION_TIMEOUT = 0.1 +AGGREGATION_SLEEP = 0.15 + + +class BaseTestUserContextAggregator: + CONTEXT_CLASS = None # To be set in subclasses + AGGREGATOR_CLASS = None # To be set in subclasses + EXPECTED_CONTEXT_FRAMES = [OpenAILLMContextFrame] + + def check_message_content(self, context: OpenAILLMContext, index: int, content: str): + assert context.messages[index]["content"] == content + + def check_message_multi_content( + self, context: OpenAILLMContext, content_index: int, index: int, content: str + ): + assert context.messages[index]["content"] == content + + async def test_se(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [UserStartedSpeakingFrame(), UserStoppedSpeakingFrame()] + expected_down_frames = [UserStartedSpeakingFrame, UserStoppedSpeakingFrame] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + async def test_ste(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + UserStartedSpeakingFrame(), + TranscriptionFrame(text="Hello!", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "Hello!") + + async def test_site(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + UserStartedSpeakingFrame(), + InterimTranscriptionFrame(text="Hello", user_id="cat", timestamp=""), + TranscriptionFrame(text="Hello Pipecat!", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "Hello Pipecat!") + + async def test_st1iest2e(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + UserStartedSpeakingFrame(), + TranscriptionFrame(text="Hello Pipecat!", user_id="cat", timestamp=""), + InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + UserStartedSpeakingFrame(), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "Hello Pipecat! How are you?") + + async def test_siet(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + UserStartedSpeakingFrame(), + InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "How are you?") + + async def test_sieit(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + UserStartedSpeakingFrame(), + InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + InterimTranscriptionFrame(text="are you?", user_id="cat", timestamp=""), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "How are you?") + + async def test_set(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + UserStartedSpeakingFrame(), + UserStoppedSpeakingFrame(), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "How are you?") + + async def test_seit(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + UserStartedSpeakingFrame(), + UserStoppedSpeakingFrame(), + InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "How are you?") + + async def test_st1et2(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + UserStartedSpeakingFrame(), + TranscriptionFrame(text="Hello Pipecat!", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_multi_content(context, 0, 0, "Hello Pipecat!") + self.check_message_multi_content(context, 0, 1, "How are you?") + + async def test_set1t2(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + UserStartedSpeakingFrame(), + UserStoppedSpeakingFrame(), + TranscriptionFrame(text="Hello Pipecat!", user_id="cat", timestamp=""), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "Hello Pipecat! How are you?") + + async def test_siet1it2(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + UserStartedSpeakingFrame(), + InterimTranscriptionFrame(text="Hello ", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + TranscriptionFrame(text="Hello Pipecat!", user_id="cat", timestamp=""), + InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "Hello Pipecat! How are you?") + + async def test_t(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + TranscriptionFrame(text="Hello!", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [*self.EXPECTED_CONTEXT_FRAMES] + expected_up_frames = [EmulateUserStartedSpeakingFrame, EmulateUserStoppedSpeakingFrame] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + expected_up_frames=expected_up_frames, + ) + self.check_message_content(context, 0, "Hello!") + + async def test_it(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT) + frames_to_send = [ + InterimTranscriptionFrame(text="Hello ", user_id="cat", timestamp=""), + SleepFrame(), + TranscriptionFrame(text="Hello Pipecat!", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [*self.EXPECTED_CONTEXT_FRAMES] + expected_up_frames = [EmulateUserStartedSpeakingFrame, EmulateUserStoppedSpeakingFrame] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + expected_up_frames=expected_up_frames, + ) + self.check_message_content(context, 0, "Hello Pipecat!") + + async def test_sie_delay_it(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS( + context, + aggregation_timeout=AGGREGATION_TIMEOUT, + ) + frames_to_send = [ + UserStartedSpeakingFrame(), + InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""), + SleepFrame(), + UserStoppedSpeakingFrame(), + SleepFrame(AGGREGATION_SLEEP), + InterimTranscriptionFrame(text="are you?", user_id="cat", timestamp=""), + TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), + SleepFrame(sleep=AGGREGATION_SLEEP), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "How are you?") + + +class BaseTestAssistantContextAggreagator: + CONTEXT_CLASS = None # To be set in subclasses + AGGREGATOR_CLASS = None # To be set in subclasses + EXPECTED_CONTEXT_FRAMES = None # To be set in subclasses + + def check_message_content(self, context: OpenAILLMContext, index: int, content: str): + assert context.messages[index]["content"] == content + + def check_message_multi_content( + self, context: OpenAILLMContext, content_index: int, index: int, content: str + ): + assert context.messages[index]["content"] == content + + def check_function_call_result(self, context: OpenAILLMContext, index: int, content: str): + assert json.loads(context.messages[index]["content"]) == content + + async def test_empty(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [LLMFullResponseStartFrame(), LLMFullResponseEndFrame()] + expected_down_frames = [] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + async def test_single_text(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + LLMFullResponseStartFrame(), + TextFrame(text="Hello Pipecat!"), + LLMFullResponseEndFrame(), + ] + expected_down_frames = [*self.EXPECTED_CONTEXT_FRAMES] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "Hello Pipecat!") + + async def test_multiple_text(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, expect_stripped_words=False) + frames_to_send = [ + LLMFullResponseStartFrame(), + TextFrame(text="Hello "), + TextFrame(text="Pipecat. "), + TextFrame(text="How are "), + TextFrame(text="you?"), + LLMFullResponseEndFrame(), + ] + expected_down_frames = [*self.EXPECTED_CONTEXT_FRAMES] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "Hello Pipecat. How are you?") + + async def test_multiple_text_stripped(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + LLMFullResponseStartFrame(), + TextFrame(text="Hello"), + TextFrame(text="Pipecat."), + TextFrame(text="How are"), + TextFrame(text="you?"), + LLMFullResponseEndFrame(), + ] + expected_down_frames = [*self.EXPECTED_CONTEXT_FRAMES] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_content(context, 0, "Hello Pipecat. How are you?") + + async def test_multiple_llm_responses(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, expect_stripped_words=False) + frames_to_send = [ + LLMFullResponseStartFrame(), + TextFrame(text="Hello "), + TextFrame(text="Pipecat."), + LLMFullResponseEndFrame(), + LLMFullResponseStartFrame(), + TextFrame(text="How are "), + TextFrame(text="you?"), + LLMFullResponseEndFrame(), + ] + expected_down_frames = [*self.EXPECTED_CONTEXT_FRAMES, *self.EXPECTED_CONTEXT_FRAMES] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_multi_content(context, 0, 0, "Hello Pipecat.") + self.check_message_multi_content(context, 0, 1, "How are you?") + + async def test_multiple_llm_responses_interruption(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context, expect_stripped_words=False) + frames_to_send = [ + LLMFullResponseStartFrame(), + TextFrame(text="Hello "), + TextFrame(text="Pipecat."), + LLMFullResponseEndFrame(), + SleepFrame(AGGREGATION_SLEEP), + StartInterruptionFrame(), + LLMFullResponseStartFrame(), + TextFrame(text="How are "), + TextFrame(text="you?"), + LLMFullResponseEndFrame(), + ] + expected_down_frames = [ + *self.EXPECTED_CONTEXT_FRAMES, + StartInterruptionFrame, + *self.EXPECTED_CONTEXT_FRAMES, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_message_multi_content(context, 0, 0, "Hello Pipecat.") + self.check_message_multi_content(context, 0, 1, "How are you?") + + async def test_function_call(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + FunctionCallInProgressFrame( + function_name="get_weather", + tool_call_id="1", + arguments={"location": "Los Angeles"}, + cancel_on_interruption=False, + ), + SleepFrame(), + FunctionCallResultFrame( + function_name="get_weather", + tool_call_id="1", + arguments={"location": "Los Angeles"}, + result={"conditions": "Sunny"}, + ), + ] + expected_down_frames = [] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_function_call_result(context, -1, {"conditions": "Sunny"}) + + async def test_function_call_on_context_updated(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context_updated = False + + async def on_context_updated(): + nonlocal context_updated + context_updated = True + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + FunctionCallInProgressFrame( + function_name="get_weather", + tool_call_id="1", + arguments={"location": "Los Angeles"}, + cancel_on_interruption=False, + ), + SleepFrame(), + FunctionCallResultFrame( + function_name="get_weather", + tool_call_id="1", + arguments={"location": "Los Angeles"}, + result={"conditions": "Sunny"}, + properties=FunctionCallResultProperties(on_context_updated=on_context_updated), + ), + SleepFrame(), + ] + expected_down_frames = [] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_function_call_result(context, -1, {"conditions": "Sunny"}) + assert context_updated + + +# +# LLMUserContextAggregator +# + + +class TestLLMUserContextAggregator(BaseTestUserContextAggregator, unittest.IsolatedAsyncioTestCase): + CONTEXT_CLASS = OpenAILLMContext + AGGREGATOR_CLASS = LLMUserContextAggregator + + +# +# OpenAI +# + + +class TestOpenAIUserContextAggregator( + BaseTestUserContextAggregator, unittest.IsolatedAsyncioTestCase +): + CONTEXT_CLASS = OpenAILLMContext + AGGREGATOR_CLASS = OpenAIUserContextAggregator + + +class TestOpenAIAssistantContextAggregator( + BaseTestAssistantContextAggreagator, unittest.IsolatedAsyncioTestCase +): + CONTEXT_CLASS = OpenAILLMContext + AGGREGATOR_CLASS = OpenAIAssistantContextAggregator + EXPECTED_CONTEXT_FRAMES = [OpenAILLMContextFrame, OpenAILLMContextAssistantTimestampFrame] + + +# +# Anthropic +# + + +class TestAnthropicUserContextAggregator( + BaseTestUserContextAggregator, unittest.IsolatedAsyncioTestCase +): + CONTEXT_CLASS = AnthropicLLMContext + AGGREGATOR_CLASS = AnthropicUserContextAggregator + + def check_message_multi_content( + self, context: OpenAILLMContext, content_index: int, index: int, content: str + ): + messages = context.messages[content_index] + assert messages["content"][index]["text"] == content + + +class TestAnthropicAssistantContextAggregator( + BaseTestAssistantContextAggreagator, unittest.IsolatedAsyncioTestCase +): + CONTEXT_CLASS = AnthropicLLMContext + AGGREGATOR_CLASS = AnthropicAssistantContextAggregator + EXPECTED_CONTEXT_FRAMES = [OpenAILLMContextFrame, OpenAILLMContextAssistantTimestampFrame] + + def check_message_multi_content( + self, context: OpenAILLMContext, content_index: int, index: int, content: str + ): + messages = context.messages[content_index] + assert messages["content"][index]["text"] == content + + def check_function_call_result(self, context: OpenAILLMContext, index: int, content: Any): + assert context.messages[index]["content"][0]["content"] == json.dumps(content) + + +# +# Google +# + + +class TestGoogleUserContextAggregator( + BaseTestUserContextAggregator, unittest.IsolatedAsyncioTestCase +): + CONTEXT_CLASS = GoogleLLMContext + AGGREGATOR_CLASS = GoogleUserContextAggregator + + def check_message_content(self, context: OpenAILLMContext, index: int, content: str): + obj = glm.Content.to_dict(context.messages[index]) + assert obj["parts"][0]["text"] == content + + def check_message_multi_content( + self, context: OpenAILLMContext, content_index: int, index: int, content: str + ): + obj = glm.Content.to_dict(context.messages[index]) + assert obj["parts"][0]["text"] == content + + +class TestGoogleAssistantContextAggregator( + BaseTestAssistantContextAggreagator, unittest.IsolatedAsyncioTestCase +): + CONTEXT_CLASS = GoogleLLMContext + AGGREGATOR_CLASS = GoogleAssistantContextAggregator + EXPECTED_CONTEXT_FRAMES = [OpenAILLMContextFrame, OpenAILLMContextAssistantTimestampFrame] + + def check_message_content(self, context: OpenAILLMContext, index: int, content: str): + obj = glm.Content.to_dict(context.messages[index]) + assert obj["parts"][0]["text"] == content + + def check_message_multi_content( + self, context: OpenAILLMContext, content_index: int, index: int, content: str + ): + obj = glm.Content.to_dict(context.messages[index]) + assert obj["parts"][0]["text"] == content + + def check_function_call_result(self, context: OpenAILLMContext, index: int, content: Any): + obj = glm.Content.to_dict(context.messages[index]) + assert obj["parts"][0]["function_response"]["response"]["value"] == json.dumps(content) diff --git a/tests/test_filters.py b/tests/test_filters.py index 4a0fa6a25..a47903232 100644 --- a/tests/test_filters.py +++ b/tests/test_filters.py @@ -4,7 +4,6 @@ # SPDX-License-Identifier: BSD 2-Clause License # -import asyncio import unittest from pipecat.frames.frames import ( @@ -19,35 +18,52 @@ from pipecat.processors.filters.frame_filter import FrameFilter from pipecat.processors.filters.function_filter import FunctionFilter from pipecat.processors.filters.identity_filter import IdentityFilter from pipecat.processors.filters.wake_check_filter import WakeCheckFilter -from pipecat.tests.utils import EndTestFrame, run_test +from pipecat.tests.utils import run_test class TestIdentifyFilter(unittest.IsolatedAsyncioTestCase): async def test_identity(self): filter = IdentityFilter() frames_to_send = [UserStartedSpeakingFrame(), UserStoppedSpeakingFrame()] - expected_returned_frames = [UserStartedSpeakingFrame, UserStoppedSpeakingFrame] - await run_test(filter, frames_to_send, expected_returned_frames) + expected_down_frames = [UserStartedSpeakingFrame, UserStoppedSpeakingFrame] + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) class TestFrameFilter(unittest.IsolatedAsyncioTestCase): async def test_text_frame(self): - filter = FrameFilter(types=(TextFrame, EndTestFrame)) + filter = FrameFilter(types=(TextFrame,)) frames_to_send = [TextFrame(text="Hello Pipecat!")] - expected_returned_frames = [TextFrame] - await run_test(filter, frames_to_send, expected_returned_frames) + expected_down_frames = [TextFrame] + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) async def test_end_frame(self): - filter = FrameFilter(types=(EndFrame, EndTestFrame)) + filter = FrameFilter(types=(EndFrame,)) frames_to_send = [EndFrame()] - expected_returned_frames = [EndFrame] - await run_test(filter, frames_to_send, expected_returned_frames) + expected_down_frames = [EndFrame] + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + send_end_frame=False, + ) async def test_system_frame(self): - filter = FrameFilter(types=(EndTestFrame,)) + filter = FrameFilter(types=()) frames_to_send = [UserStartedSpeakingFrame()] - expected_returned_frames = [UserStartedSpeakingFrame] - await run_test(filter, frames_to_send, expected_returned_frames) + expected_down_frames = [UserStartedSpeakingFrame] + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) class TestFunctionFilter(unittest.IsolatedAsyncioTestCase): @@ -57,8 +73,12 @@ class TestFunctionFilter(unittest.IsolatedAsyncioTestCase): filter = FunctionFilter(filter=passthrough) frames_to_send = [TextFrame(text="Hello Pipecat!")] - expected_returned_frames = [TextFrame] - await run_test(filter, frames_to_send, expected_returned_frames) + expected_down_frames = [TextFrame] + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) async def test_no_passthrough(self): async def no_passthrough(frame: Frame): @@ -66,22 +86,24 @@ class TestFunctionFilter(unittest.IsolatedAsyncioTestCase): filter = FunctionFilter(filter=no_passthrough) frames_to_send = [TextFrame(text="Hello Pipecat!")] - expected_returned_frames = [TextFrame] - try: - await asyncio.wait_for( - run_test(filter, frames_to_send, expected_returned_frames), timeout=0.5 - ) - assert False - except asyncio.TimeoutError: - pass + expected_down_frames = [] + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) class TestWakeCheckFilter(unittest.IsolatedAsyncioTestCase): async def test_no_wake_word(self): filter = WakeCheckFilter(wake_phrases=["Hey, Pipecat"]) frames_to_send = [TranscriptionFrame(user_id="test", text="Phrase 1", timestamp="")] - expected_returned_frames = [] - await run_test(filter, frames_to_send, expected_returned_frames) + expected_down_frames = [] + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) async def test_wake_word(self): filter = WakeCheckFilter(wake_phrases=["Hey, Pipecat"]) @@ -89,6 +111,10 @@ class TestWakeCheckFilter(unittest.IsolatedAsyncioTestCase): TranscriptionFrame(user_id="test", text="Hey, Pipecat", timestamp=""), TranscriptionFrame(user_id="test", text="Phrase 1", timestamp=""), ] - expected_returned_frames = [TranscriptionFrame, TranscriptionFrame] - (received_down, _) = await run_test(filter, frames_to_send, expected_returned_frames) + expected_down_frames = [TranscriptionFrame, TranscriptionFrame] + (received_down, _) = await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) assert received_down[-1].text == "Phrase 1" diff --git a/tests/test_function_calling_adapters.py b/tests/test_function_calling_adapters.py new file mode 100644 index 000000000..5d6dafce3 --- /dev/null +++ b/tests/test_function_calling_adapters.py @@ -0,0 +1,176 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest + +from openai.types.chat import ChatCompletionToolParam + +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema +from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter +from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter +from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter +from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter + + +class TestFunctionAdapters(unittest.TestCase): + def setUp(self) -> None: + """Sets up a common tools schema for all tests.""" + function_def = FunctionSchema( + name="get_weather", + description="Get the weather in a given location", + properties={ + "location": {"type": "string", "description": "The city, e.g. San Francisco"}, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use.", + }, + }, + required=["location", "format"], + ) + self.tools_def = ToolsSchema(standard_tools=[function_def]) + + def test_openai_adapter(self): + """Test OpenAI adapter format transformation.""" + expected = [ + ChatCompletionToolParam( + type="function", + function={ + "name": "get_weather", + "description": "Get the weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city, e.g. San Francisco", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use.", + }, + }, + "required": ["location", "format"], + }, + }, + ) + ] + assert OpenAILLMAdapter().to_provider_tools_format(self.tools_def) == expected + + def test_anthropic_adapter(self): + """Test Anthropic adapter format transformation.""" + expected = [ + { + "name": "get_weather", + "description": "Get the weather in a given location", + "input_schema": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city, e.g. San Francisco", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use.", + }, + }, + "required": ["location", "format"], + }, + } + ] + assert AnthropicLLMAdapter().to_provider_tools_format(self.tools_def) == expected + + def test_gemini_adapter(self): + """Test Gemini adapter format transformation.""" + expected = [ + { + "function_declarations": [ + { + "name": "get_weather", + "description": "Get the weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city, e.g. San Francisco", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use.", + }, + }, + "required": ["location", "format"], + }, + } + ] + } + ] + assert GeminiLLMAdapter().to_provider_tools_format(self.tools_def) == expected + + def test_openai_realtime_adapter(self): + """Test Anthropic adapter format transformation.""" + expected = [ + { + "type": "function", + "name": "get_weather", + "description": "Get the weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city, e.g. San Francisco", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use.", + }, + }, + "required": ["location", "format"], + }, + } + ] + assert OpenAIRealtimeLLMAdapter().to_provider_tools_format(self.tools_def) == expected + + def test_gemini_adapter_with_custom_tools(self): + """Test Gemini adapter format transformation.""" + search_tool = {"google_search": {}} + expected = [ + { + "function_declarations": [ + { + "name": "get_weather", + "description": "Get the weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city, e.g. San Francisco", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use.", + }, + }, + "required": ["location", "format"], + }, + } + ] + }, + search_tool, + ] + tools_def = self.tools_def + tools_def.custom_tools = {AdapterType.GEMINI: [search_tool]} + assert GeminiLLMAdapter().to_provider_tools_format(tools_def) == expected diff --git a/tests/test_langchain.py b/tests/test_langchain.py index c94bc1c01..6534f6bb0 100644 --- a/tests/test_langchain.py +++ b/tests/test_langchain.py @@ -10,23 +10,22 @@ from langchain.prompts import ChatPromptTemplate from langchain_core.language_models import FakeStreamingListLLM from pipecat.frames.frames import ( - EndFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, + LLMMessagesFrame, TextFrame, TranscriptionFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) from pipecat.pipeline.pipeline import Pipeline -from pipecat.pipeline.runner import PipelineRunner -from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.llm_response import ( LLMAssistantResponseAggregator, LLMUserResponseAggregator, ) from pipecat.processors.frame_processor import FrameProcessor from pipecat.processors.frameworks.langchain import LangchainProcessor +from pipecat.tests.utils import SleepFrame, run_test class TestLangchain(unittest.IsolatedAsyncioTestCase): @@ -64,31 +63,26 @@ class TestLangchain(unittest.IsolatedAsyncioTestCase): self.mock_proc = self.MockProcessor("token_collector") tma_in = LLMUserResponseAggregator(messages) - tma_out = LLMAssistantResponseAggregator(messages) + tma_out = LLMAssistantResponseAggregator(messages, expect_stripped_words=False) - pipeline = Pipeline( - [ - tma_in, - proc, - self.mock_proc, - tma_out, - ] + pipeline = Pipeline([tma_in, proc, self.mock_proc, tma_out]) + + frames_to_send = [ + UserStartedSpeakingFrame(), + TranscriptionFrame(text="Hi World", user_id="user", timestamp="now"), + SleepFrame(), + UserStoppedSpeakingFrame(), + ] + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + LLMMessagesFrame, + ] + await run_test( + pipeline, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, ) - task = PipelineTask(pipeline, PipelineParams(allow_interruptions=False)) - await task.queue_frames( - [ - UserStartedSpeakingFrame(), - TranscriptionFrame(text="Hi World", user_id="user", timestamp="now"), - UserStoppedSpeakingFrame(), - EndFrame(), - ] - ) - - runner = PipelineRunner() - await runner.run(task) self.assertEqual("".join(self.mock_proc.token), self.expected_response) - # TODO: Address this issue - # This next one would fail with: - # AssertionError: ' H e l l o d e a r h u m a n' != 'Hello dear human' - # self.assertEqual(tma_out.messages[-1]["content"], self.expected_response) + self.assertEqual(tma_out.messages[-1]["content"], self.expected_response) diff --git a/tests/test_llm_response.py b/tests/test_llm_response.py new file mode 100644 index 000000000..93838a658 --- /dev/null +++ b/tests/test_llm_response.py @@ -0,0 +1,136 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest + +from pipecat.frames.frames import ( + LLMFullResponseEndFrame, + LLMFullResponseStartFrame, + LLMTextFrame, + StartInterruptionFrame, +) +from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator +from pipecat.tests.utils import SleepFrame, run_test + + +class TestLLMFullResponseAggregator(unittest.IsolatedAsyncioTestCase): + async def test_empty(self): + completion_ok = False + + aggregator = LLMFullResponseAggregator() + + @aggregator.event_handler("on_completion") + async def on_completion(aggregator, completion, completed): + nonlocal completion_ok + completion_ok = completion == "" and completed + + frames_to_send = [LLMFullResponseStartFrame(), LLMFullResponseEndFrame()] + expected_down_frames = [LLMFullResponseStartFrame, LLMFullResponseEndFrame] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + assert completion_ok + + async def test_simple(self): + completion_ok = False + + aggregator = LLMFullResponseAggregator() + + @aggregator.event_handler("on_completion") + async def on_completion(aggregator, completion, completed): + nonlocal completion_ok + completion_ok = completion == "Hello from Pipecat!" and completed + + frames_to_send = [ + LLMFullResponseStartFrame(), + LLMTextFrame("Hello from Pipecat!"), + LLMFullResponseEndFrame(), + ] + expected_down_frames = [LLMFullResponseStartFrame, LLMTextFrame, LLMFullResponseEndFrame] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + assert completion_ok + + async def test_multiple(self): + completion_ok = False + + aggregator = LLMFullResponseAggregator() + + @aggregator.event_handler("on_completion") + async def on_completion(aggregator, completion, completed): + nonlocal completion_ok + completion_ok = completion == "Hello from Pipecat!" and completed + + frames_to_send = [ + LLMFullResponseStartFrame(), + LLMTextFrame("Hello "), + LLMTextFrame("from "), + LLMTextFrame("Pipecat!"), + LLMFullResponseEndFrame(), + ] + expected_down_frames = [ + LLMFullResponseStartFrame, + LLMTextFrame, + LLMTextFrame, + LLMTextFrame, + LLMFullResponseEndFrame, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + assert completion_ok + + async def test_interruption(self): + completion_ok = True + + completion_result = [("Hello ", False), ("Hello there!", True)] + completion_index = 0 + + aggregator = LLMFullResponseAggregator() + + @aggregator.event_handler("on_completion") + async def on_completion(aggregator, completion, completed): + nonlocal completion_result, completion_index, completion_ok + (completion_expected, completion_completed) = completion_result[completion_index] + completion_ok = ( + completion_ok + and completion == completion_expected + and completed == completion_completed + ) + completion_index += 1 + + frames_to_send = [ + LLMFullResponseStartFrame(), + LLMTextFrame("Hello "), + SleepFrame(), + StartInterruptionFrame(), + LLMFullResponseStartFrame(), + LLMTextFrame("Hello "), + LLMTextFrame("there!"), + LLMFullResponseEndFrame(), + ] + expected_down_frames = [ + LLMFullResponseStartFrame, + LLMTextFrame, + StartInterruptionFrame, + LLMFullResponseStartFrame, + LLMTextFrame, + LLMTextFrame, + LLMFullResponseEndFrame, + ] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + assert completion_ok diff --git a/tests/test_markdown_text_filter.py b/tests/test_markdown_text_filter.py new file mode 100644 index 000000000..c8cae97cc --- /dev/null +++ b/tests/test_markdown_text_filter.py @@ -0,0 +1,230 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + + +import unittest + +from pipecat.utils.text.markdown_text_filter import MarkdownTextFilter + + +class TestMarkdownTextFilter(unittest.IsolatedAsyncioTestCase): + def setUp(self): + self.filter = MarkdownTextFilter() + + async def test_basic_markdown_removal(self): + """Test removal of basic Markdown formatting while preserving content.""" + input_text = """ + **Bold text** and *italic text* + 1. Numbered list item + - Bullet point + Some `inline code` here + """ + + expected_text = """ + Bold text and italic text + 1. Numbered list item + - Bullet point + Some inline code here + """ + + result = self.filter.filter(input_text) + self.assertEqual(result.strip(), expected_text.strip()) + + async def test_space_preservation(self): + """Test preservation of leading and trailing spaces (for + word-by-word streaming in bot-tts-text). + """ + input_text = [ + " Leading spaces", + "Trailing spaces ", + " Both ends ", + " Multiple spaces between words ", + ] + + for text in input_text: + result = self.filter.filter(text) + self.assertEqual( + len(result), len(text), f"Space preservation failed for: '{text}'\nGot: '{result}'" + ) + # Check if spaces are in the same positions + for i, char in enumerate(text): + if char == " ": + self.assertEqual( + result[i], " ", f"Space at position {i} was not preserved in: '{text}'" + ) + + async def test_repeated_character_removal(self): + """Test removal of repeated character sequences (5 or more).""" + test_cases = { + "Hello!!!!!World": "HelloWorld", # 5 exclamations removed + "Test####ing": "Test####ing", # 4 hashes preserved + "Normal text": "Normal text", # No repeated chars + "!!!!!": "", # All repeated chars removed + "Mixed!!!!!...../////": "Mixed", # Multiple repeated sequences + "Text^^^^test": "Text^^^^test", # 4 carets preserved + "Text^^^^^test": "Texttest", # 5 carets removed + "Dots....here": "Dots....here", # 4 dots preserved + "Dots.....here": "Dotshere", # 5 dots removed + } + + for input_text, expected in test_cases.items(): + result = self.filter.filter(input_text) + self.assertEqual( + result, + expected, + f"Failed to handle repeated characters in: '{input_text}'\nExpected: '{expected}'\nGot: '{result}'", + ) + + async def test_numbered_list_preservation(self): + """Test that numbered lists are preserved correctly.""" + input_text = """1. First item + 2. Second item + 3. Third item with **bold**""" + + expected = """1. First item + 2. Second item + 3. Third item with bold""" + + result = self.filter.filter(input_text) + self.assertEqual( + result.strip(), + expected.strip(), + f"Numbered list preservation failed.\nExpected:\n{expected}\nGot:\n{result}", + ) + + async def test_html_entity_conversion(self): + """Test conversion of HTML entities to their plain text equivalents.""" + test_cases = { + "This & that": "This & that", + "1 < 2": "1 < 2", + "2 > 1": "2 > 1", + "Line break": "Line break", + "Mixed & <entities>": "Mixed & ", + } + + for input_text, expected in test_cases.items(): + result = self.filter.filter(input_text) + self.assertEqual(result, expected, f"HTML entity conversion failed for: '{input_text}'") + + async def test_asterisk_removal(self): + """Test removal of Markdown asterisk formatting.""" + test_cases = { + "**bold text**": "bold text", # Double asterisks + "*italic text*": "italic text", # Single asterisks + "**bold** and *italic*": "bold and italic", # Mixed + "multiple**bold**words": "multipleboldwords", # No spaces + "edge**cases***here*": "edgecaseshere", # Adjacent asterisks + } + + for input_text, expected in test_cases.items(): + result = self.filter.filter(input_text) + self.assertEqual(result, expected, f"Asterisk removal failed for: '{input_text}'") + + async def test_newline_handling(self): + """Test handling of empty and whitespace-only lines.""" + test_cases = { + "Line 1\n\nLine 2": "Line 1\n Line 2", # Empty line becomes space + "Line 1\n \nLine 2": "Line 1\n Line 2", # Whitespace line becomes single space + "Text\n\n\nMore": "Text\n More", # Multiple empty lines become spaces + } + + for input_text, expected in test_cases.items(): + result = self.filter.filter(input_text) + self.assertEqual( + result, expected, f"Newline handling failed for:\n{input_text}\nGot:\n{result}" + ) + + async def test_numbered_list_marker_handling(self): + """Test handling of numbered lists with the special §NUM§ marker.""" + test_cases = { + "1. First\n2. Second": "1. First\n2. Second", # Basic numbered list + " 1. Indented": " 1. Indented", # Indented numbered list + "1. Item\nText\n2. Item": "1. Item\nText\n2. Item", # Text between items + "1.No space": "1.No space", # Not a list item (no space) + "12. Large number": "12. Large number", # Multi-digit numbers + } + + for input_text, expected in test_cases.items(): + result = self.filter.filter(input_text) + self.assertEqual( + result, + expected, + f"Numbered list handling failed for:\n{input_text}\nGot:\n{result}", + ) + + async def test_inline_code_handling(self): + """Test handling of inline code with backticks.""" + test_cases = { + "`code`": "code", # Basic inline code + "Text `code` more": "Text code more", # Inline code within text + "``nested`code``": "nested`code", # Nested backticks + "`code1` and `code2`": "code1 and code2", # Multiple inline codes + "No``space``between": "Nospacebetween", # No spaces around backticks + } + + for input_text, expected in test_cases.items(): + result = self.filter.filter(input_text) + self.assertEqual(result, expected, f"Inline code handling failed for: '{input_text}'") + + async def test_simple_table_removal(self): + """Test removal of a simple markdown table.""" + filter = MarkdownTextFilter(params=MarkdownTextFilter.InputParams(filter_tables=True)) + + input_text = "| Column 1 | Column 2 |\n|----------|----------|\n| Cell 1 | Cell 2 |" + + expected = "" + + result = filter.filter(input_text) + self.assertEqual( + result.strip(), + expected.strip(), + f"Simple table removal failed.\nExpected:\n{expected}\nGot:\n{result}", + ) + + async def test_feature_toggles(self): + """Test enabling/disabling specific filter features.""" + # Create a filter with all features disabled + filter = MarkdownTextFilter( + params=MarkdownTextFilter.InputParams( + enable_text_filter=False, + filter_code=False, + filter_tables=False, + ) + ) + + # Test with text filtering disabled + text_with_markdown = "**bold** and *italic* with `code`" + self.assertEqual( + filter.filter(text_with_markdown), + text_with_markdown, + "Disabled filter should not modify text", + ) + + # Enable just text filtering + filter.update_settings({"enable_text_filter": True}) + self.assertEqual( + filter.filter(text_with_markdown), + "bold and italic with code", + "Enabled filter should remove markdown", + ) + + async def test_settings_update(self): + """Test that filter settings can be updated at runtime.""" + filter = MarkdownTextFilter() + + # Initial state - formatting should be removed + input_text = "**bold** and *italic*" + self.assertEqual(filter.filter(input_text), "bold and italic") + + # Disable text filtering + filter.update_settings({"enable_text_filter": False}) + self.assertEqual(filter.filter(input_text), input_text, "Text filtering should be disabled") + + # Re-enable text filtering + filter.update_settings({"enable_text_filter": True}) + self.assertEqual( + filter.filter(input_text), "bold and italic", "Text filtering should be re-enabled" + ) diff --git a/tests/test_pattern_pair_aggregator.py b/tests/test_pattern_pair_aggregator.py new file mode 100644 index 000000000..e1086e577 --- /dev/null +++ b/tests/test_pattern_pair_aggregator.py @@ -0,0 +1,147 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest +from unittest.mock import Mock + +from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator + + +class TestPatternPairAggregator(unittest.IsolatedAsyncioTestCase): + def setUp(self): + self.aggregator = PatternPairAggregator() + self.test_handler = Mock() + + # Add a test pattern + self.aggregator.add_pattern_pair( + pattern_id="test_pattern", + start_pattern="", + end_pattern="", + remove_match=True, + ) + + # Register the mock handler + self.aggregator.on_pattern_match("test_pattern", self.test_handler) + + async def test_pattern_match_and_removal(self): + # First part doesn't complete the pattern + result = self.aggregator.aggregate("Hello pattern") + self.assertIsNone(result) + self.assertEqual(self.aggregator.text, "Hello pattern") + + # Second part completes the pattern and includes an exclamation point + result = self.aggregator.aggregate(" content!") + + # Verify the handler was called with correct PatternMatch object + self.test_handler.assert_called_once() + call_args = self.test_handler.call_args[0][0] + self.assertIsInstance(call_args, PatternMatch) + self.assertEqual(call_args.pattern_id, "test_pattern") + self.assertEqual(call_args.full_match, "pattern content") + self.assertEqual(call_args.content, "pattern content") + + # The exclamation point should be treated as a sentence boundary, + # so the result should include just text up to and including "!" + self.assertEqual(result, "Hello !") + + # Next sentence should be processed separately + result = self.aggregator.aggregate(" This is another sentence.") + self.assertEqual(result, " This is another sentence.") + + # Buffer should be empty after returning a complete sentence + self.assertEqual(self.aggregator.text, "") + + async def test_incomplete_pattern(self): + # Add text with incomplete pattern + result = self.aggregator.aggregate("Hello pattern content") + + # No complete pattern yet, so nothing should be returned + self.assertIsNone(result) + + # The handler should not be called yet + self.test_handler.assert_not_called() + + # Buffer should contain the incomplete text + self.assertEqual(self.aggregator.text, "Hello pattern content") + + # Reset and confirm buffer is cleared + self.aggregator.reset() + self.assertEqual(self.aggregator.text, "") + + async def test_multiple_patterns(self): + # Set up multiple patterns and handlers + voice_handler = Mock() + emphasis_handler = Mock() + + self.aggregator.add_pattern_pair( + pattern_id="voice", start_pattern="", end_pattern="", remove_match=True + ) + + self.aggregator.add_pattern_pair( + pattern_id="emphasis", + start_pattern="", + end_pattern="", + remove_match=False, # Keep emphasis tags + ) + + self.aggregator.on_pattern_match("voice", voice_handler) + self.aggregator.on_pattern_match("emphasis", emphasis_handler) + + # Test with multiple patterns in one text block + text = "Hello female I am very excited to meet you!" + result = self.aggregator.aggregate(text) + + # Both handlers should be called with correct data + voice_handler.assert_called_once() + voice_match = voice_handler.call_args[0][0] + self.assertEqual(voice_match.pattern_id, "voice") + self.assertEqual(voice_match.content, "female") + + emphasis_handler.assert_called_once() + emphasis_match = emphasis_handler.call_args[0][0] + self.assertEqual(emphasis_match.pattern_id, "emphasis") + self.assertEqual(emphasis_match.content, "very") + + # Voice pattern should be removed, emphasis pattern should remain + self.assertEqual(result, "Hello I am very excited to meet you!") + + # Buffer should be empty + self.assertEqual(self.aggregator.text, "") + + async def test_handle_interruption(self): + # Start with incomplete pattern + result = self.aggregator.aggregate("Hello pattern") + self.assertIsNone(result) + + # Simulate interruption + self.aggregator.handle_interruption() + + # Buffer should be cleared + self.assertEqual(self.aggregator.text, "") + + # Handler should not have been called + self.test_handler.assert_not_called() + + async def test_pattern_across_sentences(self): + # Test pattern that spans multiple sentences + result = self.aggregator.aggregate("Hello This is sentence one.") + + # First sentence contains start of pattern but no end, so no complete pattern yet + self.assertIsNone(result) + + # Add second part with pattern end + result = self.aggregator.aggregate(" This is sentence two. Final sentence.") + + # Handler should be called with entire content + self.test_handler.assert_called_once() + call_args = self.test_handler.call_args[0][0] + self.assertEqual(call_args.content, "This is sentence one. This is sentence two.") + + # Pattern should be removed, resulting in text with sentences merged + self.assertEqual(result, "Hello Final sentence.") + + # Buffer should be empty + self.assertEqual(self.aggregator.text, "") diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index 6c001b7e0..46114b5b2 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -5,14 +5,15 @@ # import asyncio +import time import unittest -from pipecat.frames.frames import EndFrame, HeartbeatFrame, TextFrame +from pipecat.frames.frames import EndFrame, HeartbeatFrame, StartFrame, TextFrame from pipecat.pipeline.parallel_pipeline import ParallelPipeline from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.filters.identity_filter import IdentityFilter -from pipecat.processors.frame_processor import FrameProcessor +from pipecat.processors.frame_processor import FrameDirection, FrameProcessor from pipecat.tests.utils import HeartbeatsObserver, run_test @@ -21,8 +22,12 @@ class TestPipeline(unittest.IsolatedAsyncioTestCase): pipeline = Pipeline([IdentityFilter()]) frames_to_send = [TextFrame(text="Hello from Pipecat!")] - expected_returned_frames = [TextFrame] - await run_test(pipeline, frames_to_send, expected_returned_frames) + expected_down_frames = [TextFrame] + await run_test( + pipeline, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) async def test_pipeline_multiple(self): identity1 = IdentityFilter() @@ -32,8 +37,26 @@ class TestPipeline(unittest.IsolatedAsyncioTestCase): pipeline = Pipeline([identity1, identity2, identity3]) frames_to_send = [TextFrame(text="Hello from Pipecat!")] - expected_returned_frames = [TextFrame] - await run_test(pipeline, frames_to_send, expected_returned_frames) + expected_down_frames = [TextFrame] + await run_test( + pipeline, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + async def test_pipeline_start_metadata(self): + pipeline = Pipeline([IdentityFilter()]) + + frames_to_send = [] + expected_down_frames = [StartFrame] + (received_down, _) = await run_test( + pipeline, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ignore_start=False, + start_metadata={"foo": "bar"}, + ) + assert "foo" in received_down[-1].metadata class TestParallelPipeline(unittest.IsolatedAsyncioTestCase): @@ -41,16 +64,24 @@ class TestParallelPipeline(unittest.IsolatedAsyncioTestCase): pipeline = ParallelPipeline([IdentityFilter()]) frames_to_send = [TextFrame(text="Hello from Pipecat!")] - expected_returned_frames = [TextFrame] - await run_test(pipeline, frames_to_send, expected_returned_frames) + expected_down_frames = [TextFrame] + await run_test( + pipeline, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) async def test_parallel_multiple(self): """Should only passthrough one instance of TextFrame.""" pipeline = ParallelPipeline([IdentityFilter()], [IdentityFilter()]) frames_to_send = [TextFrame(text="Hello from Pipecat!")] - expected_returned_frames = [TextFrame] - await run_test(pipeline, frames_to_send, expected_returned_frames) + expected_down_frames = [TextFrame] + await run_test( + pipeline, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) class TestPipelineTask(unittest.IsolatedAsyncioTestCase): @@ -64,6 +95,42 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase): await task.run() assert task.has_finished() + async def test_task_event_handlers(self): + upstream_received = False + downstream_received = False + + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask(pipeline, cancel_on_idle_timeout=False) + task.set_event_loop(asyncio.get_event_loop()) + task.set_reached_upstream_filter((TextFrame,)) + task.set_reached_downstream_filter((TextFrame,)) + + @task.event_handler("on_frame_reached_upstream") + async def on_frame_reached_upstream(task, frame): + nonlocal upstream_received + if isinstance(frame, TextFrame) and frame.text == "Hello Upstream!": + upstream_received = True + + @task.event_handler("on_frame_reached_downstream") + async def on_frame_reached_downstream(task, frame): + nonlocal downstream_received + if isinstance(frame, TextFrame) and frame.text == "Hello Downstream!": + downstream_received = True + await identity.push_frame( + TextFrame(text="Hello Upstream!"), FrameDirection.UPSTREAM + ) + + await task.queue_frame(TextFrame(text="Hello Downstream!")) + + try: + await asyncio.wait_for(asyncio.shield(task.run()), timeout=1.0) + except asyncio.TimeoutError: + pass + + assert upstream_received + assert downstream_received + async def test_task_heartbeats(self): heartbeats_counter = 0 @@ -79,8 +146,11 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase): task = PipelineTask( pipeline, params=PipelineParams( - enable_heartbeats=True, heartbeats_period_secs=0.2, observers=[heartbeats_observer] + enable_heartbeats=True, + heartbeats_period_secs=0.2, ), + observers=[heartbeats_observer], + cancel_on_idle_timeout=False, ) task.set_event_loop(asyncio.get_event_loop()) @@ -88,7 +158,90 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase): await task.queue_frame(TextFrame(text="Hello!")) try: - await asyncio.wait_for(task.run(), timeout=1.0) + await asyncio.wait_for(asyncio.shield(task.run()), timeout=1.0) except asyncio.TimeoutError: pass assert heartbeats_counter == expected_heartbeats + + async def test_idle_task(self): + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask(pipeline, idle_timeout_secs=0.2) + task.set_event_loop(asyncio.get_event_loop()) + await task.run() + assert True + + async def test_no_idle_task(self): + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask(pipeline, idle_timeout_secs=0.2, cancel_on_idle_timeout=False) + task.set_event_loop(asyncio.get_event_loop()) + try: + await asyncio.wait_for(asyncio.shield(task.run()), timeout=0.3) + except asyncio.TimeoutError: + assert True + else: + assert False + + async def test_idle_task_heartbeats(self): + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask( + pipeline, + params=PipelineParams( + enable_heartbeats=True, + heartbeats_period_secs=0.1, + ), + idle_timeout_secs=0.3, + ) + task.set_event_loop(asyncio.get_event_loop()) + await task.run() + assert True + + async def test_idle_task_event_handler(self): + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask(pipeline, idle_timeout_secs=0.2, cancel_on_idle_timeout=False) + task.set_event_loop(asyncio.get_event_loop()) + + idle_timeout = False + + @task.event_handler("on_idle_timeout") + async def on_idle_timeout(task: PipelineTask): + nonlocal idle_timeout + idle_timeout = True + await task.cancel() + + await task.run() + assert True + + async def test_idle_task_frames(self): + idle_timeout_secs = 0.2 + sleep_time_secs = idle_timeout_secs / 2 + + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask( + pipeline, + idle_timeout_secs=idle_timeout_secs, + idle_timeout_frames=(TextFrame,), + ) + task.set_event_loop(asyncio.get_event_loop()) + + async def delayed_frames(): + await asyncio.sleep(sleep_time_secs) + await task.queue_frame(TextFrame("Hello Pipecat!")) + await asyncio.sleep(sleep_time_secs) + await task.queue_frame(TextFrame("Hello Pipecat!")) + await asyncio.sleep(sleep_time_secs) + await task.queue_frame(TextFrame("Hello Pipecat!")) + + start_time = time.time() + + tasks = {asyncio.create_task(task.run()), asyncio.create_task(delayed_frames())} + + await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED) + + diff_time = time.time() - start_time + + self.assertGreater(diff_time, sleep_time_secs * 3) diff --git a/tests/test_simple_text_aggregator.py b/tests/test_simple_text_aggregator.py new file mode 100644 index 000000000..10c4c6a88 --- /dev/null +++ b/tests/test_simple_text_aggregator.py @@ -0,0 +1,29 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest + +from pipecat.utils.text.simple_text_aggregator import SimpleTextAggregator + + +class TestSimpleTextAggregator(unittest.IsolatedAsyncioTestCase): + def setUp(self): + self.aggregator = SimpleTextAggregator() + + async def test_reset_aggregations(self): + assert self.aggregator.aggregate("Hello ") == None + assert self.aggregator.text == "Hello " + self.aggregator.reset() + assert self.aggregator.text == "" + + async def test_simple_sentence(self): + assert self.aggregator.aggregate("Hello ") == None + assert self.aggregator.aggregate("Pipecat!") == "Hello Pipecat!" + assert self.aggregator.text == "" + + async def test_multiple_sentences(self): + assert self.aggregator.aggregate("Hello Pipecat! How are ") == "Hello Pipecat!" + assert self.aggregator.aggregate("you?") == " How are you?" diff --git a/tests/test_skip_tags_aggregator.py b/tests/test_skip_tags_aggregator.py new file mode 100644 index 000000000..8f36c4c05 --- /dev/null +++ b/tests/test_skip_tags_aggregator.py @@ -0,0 +1,54 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest + +from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator + + +class TestSkipTagsAggregator(unittest.IsolatedAsyncioTestCase): + def setUp(self): + self.aggregator = SkipTagsAggregator([("", "")]) + + async def test_no_tags(self): + self.aggregator.reset() + + # No tags involved, aggregate at end of sentence. + result = self.aggregator.aggregate("Hello Pipecat!") + self.assertEqual(result, "Hello Pipecat!") + self.assertEqual(self.aggregator.text, "") + + async def test_basic_tags(self): + self.aggregator.reset() + + # Tags involved, avoid aggregation during tags. + result = self.aggregator.aggregate("My email is foo@pipecat.ai.") + self.assertEqual(result, "My email is foo@pipecat.ai.") + self.assertEqual(self.aggregator.text, "") + + async def test_streaming_tags(self): + self.aggregator.reset() + + # Tags involved, stream small chunk of texts. + result = self.aggregator.aggregate("My email is foo.") + self.assertIsNone(result) + self.assertEqual(self.aggregator.text, "My email is foo.") + + result = self.aggregator.aggregate("bar@pipecat.") + self.assertIsNone(result) + self.assertEqual(self.aggregator.text, "My email is foo.bar@pipecat.") + + result = self.aggregator.aggregate("aifoo.bar@pipecat.ai.") + self.assertEqual(result, "My email is foo.bar@pipecat.ai.") + self.assertEqual(self.aggregator.text, "") diff --git a/tests/test_stt_mute_filter.py b/tests/test_stt_mute_filter.py new file mode 100644 index 000000000..a55c4609e --- /dev/null +++ b/tests/test_stt_mute_filter.py @@ -0,0 +1,248 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest + +from pipecat.frames.frames import ( + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, + InputAudioRawFrame, + STTMuteFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, +) +from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy +from pipecat.tests.utils import SleepFrame, run_test + + +class TestSTTMuteFilter(unittest.IsolatedAsyncioTestCase): + async def test_first_speech_strategy(self): + filter = STTMuteFilter(config=STTMuteConfig(strategies={STTMuteStrategy.FIRST_SPEECH})) + + frames_to_send = [ + BotStartedSpeakingFrame(), # First bot speech starts + UserStartedSpeakingFrame(), # Should be suppressed + InputAudioRawFrame( + audio=b"", sample_rate=16000, num_channels=1 + ), # Should be suppressed + UserStoppedSpeakingFrame(), # Should be suppressed + BotStoppedSpeakingFrame(), # First bot speech ends + BotStartedSpeakingFrame(), # Second bot speech + UserStartedSpeakingFrame(), # Should pass through + InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1), # Should pass through + UserStoppedSpeakingFrame(), # Should pass through + BotStoppedSpeakingFrame(), + ] + + expected_returned_frames = [ + BotStartedSpeakingFrame, + STTMuteFrame, # mute=True + BotStoppedSpeakingFrame, + STTMuteFrame, # mute=False + BotStartedSpeakingFrame, + UserStartedSpeakingFrame, # Now passes through + InputAudioRawFrame, # Now passes through + UserStoppedSpeakingFrame, # Now passes through + BotStoppedSpeakingFrame, + ] + + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_returned_frames, + ) + + async def test_always_strategy(self): + filter = STTMuteFilter(config=STTMuteConfig(strategies={STTMuteStrategy.ALWAYS})) + + frames_to_send = [ + BotStartedSpeakingFrame(), # First speech starts + UserStartedSpeakingFrame(), # Should be suppressed + InputAudioRawFrame( + audio=b"", sample_rate=16000, num_channels=1 + ), # Should be suppressed + UserStoppedSpeakingFrame(), # Should be suppressed + BotStoppedSpeakingFrame(), # First speech ends + UserStartedSpeakingFrame(), # Should pass through + InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1), # Should pass through + UserStoppedSpeakingFrame(), # Should pass through + BotStartedSpeakingFrame(), # Second speech starts + UserStartedSpeakingFrame(), # Should be suppressed again + InputAudioRawFrame( + audio=b"", sample_rate=16000, num_channels=1 + ), # Should be suppressed again + UserStoppedSpeakingFrame(), # Should be suppressed again + BotStoppedSpeakingFrame(), # Second speech ends + ] + + expected_returned_frames = [ + BotStartedSpeakingFrame, + STTMuteFrame, # mute=True + BotStoppedSpeakingFrame, + STTMuteFrame, # mute=False + UserStartedSpeakingFrame, + InputAudioRawFrame, + UserStoppedSpeakingFrame, + BotStartedSpeakingFrame, + STTMuteFrame, # mute=True + BotStoppedSpeakingFrame, + STTMuteFrame, # mute=False + ] + + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_returned_frames, + ) + + # TODO: Revisit once we figure out how to test SystemFrames and DataFrames + # async def test_function_call_strategy(self): + # filter = STTMuteFilter(config=STTMuteConfig(strategies={STTMuteStrategy.FUNCTION_CALL})) + + # frames_to_send = [ + # UserStartedSpeakingFrame(), # Should pass through initially + # UserStoppedSpeakingFrame(), + # FunctionCallInProgressFrame( + # function_name="get_weather", + # tool_call_id="call_123", + # arguments='{"location": "San Francisco"}', + # ), # Start function call + # UserStartedSpeakingFrame(), # Should be suppressed + # UserStoppedSpeakingFrame(), # Should be suppressed + # FunctionCallResultFrame( + # function_name="get_weather", + # tool_call_id="call_123", + # arguments='{"location": "San Francisco"}', + # result={"temperature": 22}, + # ), # End function call + # UserStartedSpeakingFrame(), # Should pass through again + # UserStoppedSpeakingFrame(), + # ] + + # expected_returned_frames = [ + # UserStartedSpeakingFrame, + # UserStoppedSpeakingFrame, + # FunctionCallInProgressFrame, + # STTMuteFrame, # mute=True + # FunctionCallResultFrame, + # STTMuteFrame, # mute=False + # UserStartedSpeakingFrame, + # UserStoppedSpeakingFrame, + # ] + + # await run_test( + # filter, + # frames_to_send=frames_to_send, + # expected_down_frames=expected_returned_frames, + # ) + + async def test_mute_until_first_bot_complete_strategy(self): + filter = STTMuteFilter( + config=STTMuteConfig(strategies={STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE}) + ) + + frames_to_send = [ + UserStartedSpeakingFrame(), # Should be suppressed (starts muted) + InputAudioRawFrame( + audio=b"", sample_rate=16000, num_channels=1 + ), # Should be suppressed + UserStoppedSpeakingFrame(), # Should be suppressed + BotStartedSpeakingFrame(), # First bot speech + UserStartedSpeakingFrame(), # Should be suppressed + InputAudioRawFrame( + audio=b"", sample_rate=16000, num_channels=1 + ), # Should be suppressed + UserStoppedSpeakingFrame(), # Should be suppressed + BotStoppedSpeakingFrame(), # First speech ends, unmutes + UserStartedSpeakingFrame(), # Should pass through + InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1), # Should pass through + UserStoppedSpeakingFrame(), # Should pass through + BotStartedSpeakingFrame(), # Second speech + UserStartedSpeakingFrame(), # Should pass through + InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1), # Should pass through + UserStoppedSpeakingFrame(), # Should pass through + BotStoppedSpeakingFrame(), + ] + + expected_returned_frames = [ + STTMuteFrame, # mute=True after first speech + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + STTMuteFrame, # mute=False after first speech + UserStartedSpeakingFrame, + InputAudioRawFrame, + UserStoppedSpeakingFrame, + BotStartedSpeakingFrame, + UserStartedSpeakingFrame, + InputAudioRawFrame, + UserStoppedSpeakingFrame, + BotStoppedSpeakingFrame, + ] + + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_returned_frames, + ) + + async def test_incompatible_strategies(self): + with self.assertRaises(ValueError): + STTMuteFilter( + config=STTMuteConfig( + strategies={ + STTMuteStrategy.FIRST_SPEECH, + STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE, + } + ) + ) + + async def test_custom_strategy(self): + async def custom_mute_logic(processor: STTMuteFilter) -> bool: + return processor._bot_is_speaking + + filter = STTMuteFilter( + config=STTMuteConfig( + strategies={STTMuteStrategy.CUSTOM}, + should_mute_callback=custom_mute_logic, + ) + ) + + frames_to_send = [ + UserStartedSpeakingFrame(), # Should pass through + InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1), # Should pass through + UserStoppedSpeakingFrame(), # Should pass through + BotStartedSpeakingFrame(), # Bot starts speaking + UserStartedSpeakingFrame(), # Should be suppressed + InputAudioRawFrame( + audio=b"", sample_rate=16000, num_channels=1 + ), # Should be suppressed + UserStoppedSpeakingFrame(), # Should be suppressed + BotStoppedSpeakingFrame(), # Bot stops speaking + UserStartedSpeakingFrame(), # Should pass through + InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1), # Should pass through + UserStoppedSpeakingFrame(), # Should pass through + ] + + expected_returned_frames = [ + UserStartedSpeakingFrame, + InputAudioRawFrame, + UserStoppedSpeakingFrame, + BotStartedSpeakingFrame, + STTMuteFrame, # mute=True + BotStoppedSpeakingFrame, + STTMuteFrame, # mute=False + UserStartedSpeakingFrame, + InputAudioRawFrame, + UserStoppedSpeakingFrame, + ] + + await run_test( + filter, + frames_to_send=frames_to_send, + expected_down_frames=expected_returned_frames, + ) diff --git a/tests/test_transcript_processor.py b/tests/test_transcript_processor.py new file mode 100644 index 000000000..d13246b2c --- /dev/null +++ b/tests/test_transcript_processor.py @@ -0,0 +1,480 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + + +import asyncio +import unittest +from datetime import datetime, timezone +from typing import List, Tuple, cast + +from pipecat.frames.frames import ( + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + CancelFrame, + StartInterruptionFrame, + TranscriptionFrame, + TranscriptionMessage, + TranscriptionUpdateFrame, + TTSTextFrame, +) +from pipecat.processors.transcript_processor import ( + AssistantTranscriptProcessor, + UserTranscriptProcessor, +) +from pipecat.tests.utils import SleepFrame, run_test + + +class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase): + """Tests for UserTranscriptProcessor""" + + async def test_basic_transcription(self): + """Test basic transcription frame processing""" + # Create processor + processor = UserTranscriptProcessor() + + # Create test timestamp + timestamp = datetime.now(timezone.utc).isoformat() + + # Create frames to send + frames_to_send = [ + TranscriptionFrame(text="Hello, world!", user_id="test_user", timestamp=timestamp) + ] + + # Expected frames downstream - note the order: + # 1. TranscriptionUpdateFrame (processor emits the update first) + # 2. TranscriptionFrame (original frame is passed through) + expected_down_frames = [TranscriptionUpdateFrame, TranscriptionFrame] + + # Run test + received_frames, _ = await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + # Verify the content of the TranscriptionUpdateFrame + update_frame = cast( + TranscriptionUpdateFrame, received_frames[0] + ) # Note: now checking first frame + self.assertIsInstance(update_frame, TranscriptionUpdateFrame) + self.assertEqual(len(update_frame.messages), 1) + message = update_frame.messages[0] + self.assertEqual(message.role, "user") + self.assertEqual(message.content, "Hello, world!") + self.assertEqual(message.timestamp, timestamp) + + async def test_event_handler(self): + """Test that event handlers are called with transcript updates""" + # Create processor + processor = UserTranscriptProcessor() + + # Track received updates + received_updates: List[TranscriptionMessage] = [] + + # Register event handler + @processor.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + received_updates.extend(frame.messages) + + # Create test data + timestamp = datetime.now(timezone.utc).isoformat() + frames_to_send = [ + TranscriptionFrame(text="First message", user_id="test_user", timestamp=timestamp), + TranscriptionFrame(text="Second message", user_id="test_user", timestamp=timestamp), + ] + + expected_down_frames = [ + TranscriptionUpdateFrame, + TranscriptionFrame, # First message + TranscriptionUpdateFrame, + TranscriptionFrame, # Second message + ] + + # Run test + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + # Verify event handler received updates + self.assertEqual(len(received_updates), 2) + + # Check first message + self.assertEqual(received_updates[0].role, "user") + self.assertEqual(received_updates[0].content, "First message") + self.assertEqual(received_updates[0].timestamp, timestamp) + + # Check second message + self.assertEqual(received_updates[1].role, "user") + self.assertEqual(received_updates[1].content, "Second message") + self.assertEqual(received_updates[1].timestamp, timestamp) + + async def test_text_aggregation(self): + """Test that TTSTextFrames are properly aggregated into a single message""" + # Create processor + processor = AssistantTranscriptProcessor() + + # Track received updates + received_updates: List[TranscriptionUpdateFrame] = [] + + @processor.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + received_updates.append(frame) + + # Create test frames simulating bot speaking multiple text chunks + frames_to_send = [ + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), # Wait for StartedSpeaking to process + TTSTextFrame(text="Hello"), + TTSTextFrame(text="world!"), + TTSTextFrame(text="How"), + TTSTextFrame(text="are"), + TTSTextFrame(text="you?"), + SleepFrame(sleep=0.1), # Wait for text frames to queue + BotStoppedSpeakingFrame(), + ] + + # Expected order: + # 1. BotStartedSpeakingFrame (system frame, immediate) + # 2. All queued TTSTextFrames + # 3. BotStoppedSpeakingFrame (system frame, immediate) + # 4. TranscriptionUpdateFrame (after aggregation) + expected_down_frames = [ + BotStartedSpeakingFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + BotStoppedSpeakingFrame, + TranscriptionUpdateFrame, + ] + + # Run test + received_frames, _ = await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + # Verify update was received + self.assertEqual(len(received_updates), 1) + + # Get the update frame + update_frame = received_updates[0] + + # Should have one aggregated message + self.assertEqual(len(update_frame.messages), 1) + + message = update_frame.messages[0] + self.assertEqual(message.role, "assistant") + self.assertEqual(message.content, "Hello world! How are you?") + + # Verify timestamp exists + self.assertIsNotNone(message.timestamp) + + # All frames should be passed through in order, with update at end + downstream_update = cast(TranscriptionUpdateFrame, received_frames[-1]) + self.assertEqual(downstream_update.messages[0].content, "Hello world! How are you?") + + async def test_empty_text_handling(self): + """Test that empty messages are not emitted""" + processor = AssistantTranscriptProcessor() + + received_updates: List[TranscriptionUpdateFrame] = [] + + @processor.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + received_updates.append(frame) + + frames_to_send = [ + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), + TTSTextFrame(text=""), # Empty text + TTSTextFrame(text=" "), # Just whitespace + TTSTextFrame(text="\n"), # Just newline + BotStoppedSpeakingFrame(), + # Pipeline ends here; run_test will automatically send EndFrame + ] + + # From our earlier tests, we know BotStoppedSpeakingFrame comes before TTSTextFrames + expected_down_frames = [ + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + TTSTextFrame, # empty + TTSTextFrame, # whitespace + TTSTextFrame, # newline + # No TranscriptionUpdateFrame since content is empty after stripping + ] + + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + self.assertEqual(len(received_updates), 0, "No updates should be emitted for empty content") + + async def test_interruption_handling(self): + """Test that messages are properly captured when bot is interrupted""" + processor = AssistantTranscriptProcessor() + + # Track received updates + received_updates: List[TranscriptionUpdateFrame] = [] + + @processor.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + received_updates.append(frame) + + # Simulate bot being interrupted mid-sentence + frames_to_send = [ + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), + TTSTextFrame(text="Hello"), + TTSTextFrame(text="world!"), + SleepFrame(sleep=0.1), + StartInterruptionFrame(), # User interrupts here + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), + TTSTextFrame(text="New"), + TTSTextFrame(text="response"), + SleepFrame(sleep=0.1), + BotStoppedSpeakingFrame(), + ] + + # Actual order of frames: + expected_down_frames = [ + BotStartedSpeakingFrame, + TTSTextFrame, # "Hello" + TTSTextFrame, # "world!" + TranscriptionUpdateFrame, # First message (emitted due to interruption) + StartInterruptionFrame, # Interruption frame comes after the update + BotStartedSpeakingFrame, + TTSTextFrame, # "New" + TTSTextFrame, # "response" + BotStoppedSpeakingFrame, + TranscriptionUpdateFrame, # Second message + ] + + # Run test + received_frames, _ = await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + # Should have received two updates + self.assertEqual(len(received_updates), 2) + + # First update should be interrupted message + first_message = received_updates[0].messages[0] + self.assertEqual(first_message.role, "assistant") + self.assertEqual(first_message.content, "Hello world!") + self.assertIsNotNone(first_message.timestamp) + + # Second update should be new response + second_message = received_updates[1].messages[0] + self.assertEqual(second_message.role, "assistant") + self.assertEqual(second_message.content, "New response") + self.assertIsNotNone(second_message.timestamp) + + # Verify timestamps are different + self.assertNotEqual(first_message.timestamp, second_message.timestamp) + + async def test_end_frame_handling(self): + """Test that final messages are captured when pipeline ends normally""" + processor = AssistantTranscriptProcessor() + + received_updates: List[TranscriptionUpdateFrame] = [] + + @processor.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + received_updates.append(frame) + + frames_to_send = [ + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), + TTSTextFrame(text="Hello"), + TTSTextFrame(text="world"), + # Pipeline ends here; run_test will automatically send EndFrame + ] + + expected_down_frames = [ + BotStartedSpeakingFrame, + TTSTextFrame, + TTSTextFrame, + TranscriptionUpdateFrame, # Final message emitted due to EndFrame + ] + + # Run test - EndFrame will be sent automatically + received_frames, _ = await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + self.assertEqual(len(received_updates), 1) + message = received_updates[0].messages[0] + self.assertEqual(message.role, "assistant") + self.assertEqual(message.content, "Hello world") + + async def test_cancel_frame_handling(self): + """Test that messages are properly captured when pipeline is cancelled""" + processor = AssistantTranscriptProcessor() + + # Track updates with timestamps to verify order + received_updates: List[Tuple[str, float]] = [] + + @processor.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + # Record message content and time received + received_updates.append((frame.messages[0].content, asyncio.get_event_loop().time())) + + frames_to_send = [ + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), + TTSTextFrame(text="Hello"), + TTSTextFrame(text="world"), + SleepFrame(sleep=0.1), # Ensure messages are processed + CancelFrame(), + ] + + # We don't need to verify frame order, just that CancelFrame triggers message emission + expected_down_frames = [ + BotStartedSpeakingFrame, + TTSTextFrame, + TTSTextFrame, + CancelFrame, + ] + + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + send_end_frame=False, + ) + + # Verify that we received an update + self.assertEqual(len(received_updates), 1, "Should receive one update before cancellation") + content, _ = received_updates[0] + self.assertEqual(content, "Hello world") + + async def test_transcript_processor_factory(self): + """Test that factory properly manages processors and event handlers""" + from pipecat.processors.transcript_processor import TranscriptProcessor + + factory = TranscriptProcessor() + received_updates: List[TranscriptionMessage] = [] + + # Register handler with factory + @factory.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + received_updates.extend(frame.messages) + + # Get processors and verify they're reused + user_proc1 = factory.user() + user_proc2 = factory.user() + self.assertIs(user_proc1, user_proc2, "User processor should be reused") + + asst_proc1 = factory.assistant() + asst_proc2 = factory.assistant() + self.assertIs(asst_proc1, asst_proc2, "Assistant processor should be reused") + + # Test user processor + timestamp = datetime.now(timezone.utc).isoformat() + frames_to_send = [ + TranscriptionFrame(text="User message", user_id="user1", timestamp=timestamp) + ] + + await run_test( + user_proc1, + frames_to_send=frames_to_send, + expected_down_frames=[TranscriptionUpdateFrame, TranscriptionFrame], + ) + + # Test assistant processor + frames_to_send = [ + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), + TTSTextFrame(text="Assistant"), + TTSTextFrame(text="message"), + BotStoppedSpeakingFrame(), + ] + + # The actual order we see in the output: + await run_test( + asst_proc1, + frames_to_send=frames_to_send, + expected_down_frames=[ + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + TTSTextFrame, + TTSTextFrame, + TranscriptionUpdateFrame, + ], + ) + + # Verify both processors triggered the same handler + self.assertEqual(len(received_updates), 2) + self.assertEqual(received_updates[0].role, "user") + self.assertEqual(received_updates[0].content, "User message") + self.assertEqual(received_updates[1].role, "assistant") + self.assertEqual(received_updates[1].content, "Assistant message") + + async def test_text_fragments_with_spaces(self): + """Test aggregating text fragments with various spacing patterns""" + processor = AssistantTranscriptProcessor() + + # Track received updates + received_updates = [] + + @processor.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + received_updates.append(frame) + + # Test the specific pattern shared + frames_to_send = [ + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), + TTSTextFrame(text="Hello"), + TTSTextFrame(text=" there"), + TTSTextFrame(text="!"), + TTSTextFrame(text=" How"), + TTSTextFrame(text="'s"), + TTSTextFrame(text=" it"), + TTSTextFrame(text=" going"), + TTSTextFrame(text="?"), + BotStoppedSpeakingFrame(), + ] + + expected_down_frames = [ + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TranscriptionUpdateFrame, + ] + + # Run test + received_frames, _ = await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + # Verify result + self.assertEqual(len(received_updates), 1) + message = received_updates[0].messages[0] + self.assertEqual(message.role, "assistant") + # Should be properly joined without extra spaces + self.assertEqual(message.content, "Hello there! How's it going?") diff --git a/tests/test_user_idle_processor.py b/tests/test_user_idle_processor.py new file mode 100644 index 000000000..a2f2fd386 --- /dev/null +++ b/tests/test_user_idle_processor.py @@ -0,0 +1,216 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import unittest + +from pipecat.frames.frames import ( + BotSpeakingFrame, + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, +) +from pipecat.processors.user_idle_processor import UserIdleProcessor +from pipecat.tests.utils import SleepFrame, run_test + + +class TestUserIdleProcessor(unittest.IsolatedAsyncioTestCase): + async def test_basic_idle_detection(self): + """Test that idle callback is triggered after timeout when user stops speaking.""" + callback_called = asyncio.Event() + + async def idle_callback(processor: UserIdleProcessor) -> None: + callback_called.set() + + # Create processor with a short timeout for testing + processor = UserIdleProcessor(callback=idle_callback, timeout=0.1) # 100ms timeout + + frames_to_send = [ + # Start conversation + UserStartedSpeakingFrame(), + UserStoppedSpeakingFrame(), + # Wait 200ms - double the idle timeout to ensure it triggers + SleepFrame(sleep=0.2), + ] + + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + ] + + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + assert callback_called.is_set(), "Idle callback was not called" + + async def test_active_listening_resets_idle(self): + """Test that bot speaking frames reset the idle timer because user is actively listening.""" + callback_called = asyncio.Event() + + async def idle_callback(processor: UserIdleProcessor) -> None: + callback_called.set() + + processor = UserIdleProcessor(callback=idle_callback, timeout=0.2) + + frames_to_send = [ + # Start conversation + UserStartedSpeakingFrame(), + UserStoppedSpeakingFrame(), + # Wait almost long enough for idle timeout + SleepFrame(sleep=0.1), + # Bot speaking frame should reset idle timer + BotSpeakingFrame(), + # Wait almost long enough for idle timeout again + SleepFrame(sleep=0.1), + # Another bot speaking frame resets timer again + BotSpeakingFrame(), + ] + + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + BotSpeakingFrame, + BotSpeakingFrame, + ] + + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + assert not callback_called.is_set(), ( + "Idle callback was called even though bot speaking frames reset the timer" + ) + + async def test_idle_retry_callback(self): + """Test that retry count increases until user activity resets it.""" + retry_counts = [] + + async def retry_callback(processor: UserIdleProcessor, retry_count: int) -> bool: + retry_counts.append(retry_count) + return True # Keep monitoring for idle events + + processor = UserIdleProcessor(callback=retry_callback, timeout=0.4) + + frames_to_send = [ + # Start conversation + UserStartedSpeakingFrame(), + UserStoppedSpeakingFrame(), + # Wait for first idle timeout (count=1) + SleepFrame(sleep=0.5), + # Wait for second idle timeout (count=2) + SleepFrame(sleep=0.5), + # User activity resets the count + UserStartedSpeakingFrame(), + UserStoppedSpeakingFrame(), + # Wait for new idle timeout (count should be 1 again) + SleepFrame(sleep=0.5), + ] + + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + ] + + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + assert retry_counts == [1, 2, 1], f"Expected retry counts [1, 2, 1], got {retry_counts}" + + async def test_idle_monitoring_stops_on_false_return(self): + """Test that idle monitoring stops when callback returns False.""" + retry_counts = [] + + async def retry_callback(processor: UserIdleProcessor, retry_count: int) -> bool: + retry_counts.append(retry_count) + return retry_count < 2 # Stop after second retry + + processor = UserIdleProcessor(callback=retry_callback, timeout=0.4) + + frames_to_send = [ + UserStartedSpeakingFrame(), + UserStoppedSpeakingFrame(), + SleepFrame(sleep=0.5), # First retry (count=1, returns True) + SleepFrame(sleep=0.5), # Second retry (count=2, returns False) + SleepFrame(sleep=0.5), # Should not trigger callback + ] + + expected_down_frames = [ + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + ] + + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + assert retry_counts == [1, 2], f"Expected retry counts [1, 2], got {retry_counts}" + + async def test_no_idle_before_conversation(self): + """Test that idle monitoring doesn't start before first conversation activity.""" + callback_called = asyncio.Event() + + async def idle_callback(processor: UserIdleProcessor) -> None: + callback_called.set() + + processor = UserIdleProcessor(callback=idle_callback, timeout=0.1) + + frames_to_send = [ + SleepFrame(sleep=0.2), # Should not trigger callback + # No conversation activity yet + ] + + expected_down_frames = [] + + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + assert not callback_called.is_set(), "Idle callback was called before conversation started" + + async def test_idle_starts_with_bot_speech(self): + """Test that monitoring starts with bot speaking frames, not just user speech.""" + callback_called = asyncio.Event() + + async def idle_callback(processor: UserIdleProcessor) -> None: + callback_called.set() + + processor = UserIdleProcessor(callback=idle_callback, timeout=0.1) + + frames_to_send = [ + BotStartedSpeakingFrame(), + BotSpeakingFrame(), + BotStoppedSpeakingFrame(), + SleepFrame(sleep=0.2), + ] + + expected_down_frames = [ + BotStartedSpeakingFrame, + BotSpeakingFrame, + BotStoppedSpeakingFrame, + ] + + await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + assert callback_called.is_set(), "Idle callback not called after bot speech" diff --git a/tests/test_utils_network.py b/tests/test_utils_network.py new file mode 100644 index 000000000..5b8d96939 --- /dev/null +++ b/tests/test_utils_network.py @@ -0,0 +1,34 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest + +from pipecat.utils.network import exponential_backoff_time + + +class TestUtilsNetwork(unittest.IsolatedAsyncioTestCase): + async def test_exponential_backoff_time(self): + # min_wait=4, max_wait=10, multiplier=1 + assert exponential_backoff_time(attempt=1, min_wait=4, max_wait=10, multiplier=1) == 4 + assert exponential_backoff_time(attempt=2, min_wait=4, max_wait=10, multiplier=1) == 4 + assert exponential_backoff_time(attempt=3, min_wait=4, max_wait=10, multiplier=1) == 4 + assert exponential_backoff_time(attempt=4, min_wait=4, max_wait=10, multiplier=1) == 8 + assert exponential_backoff_time(attempt=5, min_wait=4, max_wait=10, multiplier=1) == 10 + assert exponential_backoff_time(attempt=6, min_wait=4, max_wait=10, multiplier=1) == 10 + # min_wait=1, max_wait=10, multiplier=1 + assert exponential_backoff_time(attempt=1, min_wait=1, max_wait=10, multiplier=1) == 1 + assert exponential_backoff_time(attempt=2, min_wait=1, max_wait=10, multiplier=1) == 2 + assert exponential_backoff_time(attempt=3, min_wait=1, max_wait=10, multiplier=1) == 4 + assert exponential_backoff_time(attempt=4, min_wait=1, max_wait=10, multiplier=1) == 8 + assert exponential_backoff_time(attempt=5, min_wait=1, max_wait=10, multiplier=1) == 10 + assert exponential_backoff_time(attempt=6, min_wait=1, max_wait=10, multiplier=1) == 10 + # min_wait=1, max_wait=20, multiplier=2 + assert exponential_backoff_time(attempt=1, min_wait=1, max_wait=20, multiplier=2) == 2 + assert exponential_backoff_time(attempt=2, min_wait=1, max_wait=20, multiplier=2) == 4 + assert exponential_backoff_time(attempt=3, min_wait=1, max_wait=20, multiplier=2) == 8 + assert exponential_backoff_time(attempt=4, min_wait=1, max_wait=20, multiplier=2) == 16 + assert exponential_backoff_time(attempt=5, min_wait=1, max_wait=20, multiplier=2) == 20 + assert exponential_backoff_time(attempt=6, min_wait=1, max_wait=20, multiplier=2) == 20 diff --git a/tests/test_utils_string.py b/tests/test_utils_string.py index aa135a639..cabd88a36 100644 --- a/tests/test_utils_string.py +++ b/tests/test_utils_string.py @@ -6,16 +6,27 @@ import unittest -from pipecat.utils.string import match_endofsentence +from pipecat.utils.string import match_endofsentence, parse_start_end_tags class TestUtilsString(unittest.IsolatedAsyncioTestCase): async def test_endofsentence(self): - assert match_endofsentence("This is a sentence.") - assert match_endofsentence("This is a sentence! ") - assert match_endofsentence("This is a sentence?") - assert match_endofsentence("This is a sentence:") - assert match_endofsentence("This is a sentence;") + assert match_endofsentence("This is a sentence.") == 19 + assert match_endofsentence("This is a sentence!") == 19 + assert match_endofsentence("This is a sentence?") == 19 + assert match_endofsentence("This is a sentence;") == 19 + assert match_endofsentence("This is a sentence...") == 21 + assert match_endofsentence("This is a sentence . . .") == 24 + assert match_endofsentence("This is a sentence. ..") == 22 + assert match_endofsentence("This is for Mr. and Mrs. Jones.") == 31 + assert match_endofsentence("U.S.A and U.S.A..") == 17 + assert match_endofsentence("My emails are foo@pipecat.ai and bar@pipecat.ai.") == 48 + assert match_endofsentence("My email is foo.bar@pipecat.ai.") == 31 + assert match_endofsentence("My email is spell(foo.bar@pipecat.ai).") == 38 + assert match_endofsentence("My email is foo.bar@pipecat.ai.") == 46 + assert match_endofsentence("The number pi is 3.14159.") == 25 + assert match_endofsentence("Valid scientific notation 1.23e4.") == 33 + assert match_endofsentence("Valid scientific notation 0.e4.") == 31 assert not match_endofsentence("This is not a sentence") assert not match_endofsentence("This is not a sentence,") assert not match_endofsentence("This is not a sentence, ") @@ -26,6 +37,8 @@ class TestUtilsString(unittest.IsolatedAsyncioTestCase): assert not match_endofsentence("Heute ist Dienstag, der 3.") # 3. Juli 2024 assert not match_endofsentence("America, or the U.") # U.S.A. assert not match_endofsentence("It still early, it's 3:00 a.") # 3:00 a.m. + assert not match_endofsentence("My emails are foo@pipecat.ai and bar@pipecat.ai") + assert not match_endofsentence("The number pi is 3.14159") async def test_endofsentence_zh(self): chinese_sentences = [ @@ -33,7 +46,6 @@ class TestUtilsString(unittest.IsolatedAsyncioTestCase): "你好！", "吃了吗？", "安全第一；", - "他说：", ] for i in chinese_sentences: assert match_endofsentence(i) @@ -49,3 +61,50 @@ class TestUtilsString(unittest.IsolatedAsyncioTestCase): for i in hindi_sentences: assert match_endofsentence(i) assert not match_endofsentence("हैलो，") + + +class TestStartEndTags(unittest.IsolatedAsyncioTestCase): + async def test_empty(self): + assert parse_start_end_tags("", [], None, 0) == (None, 0) + assert parse_start_end_tags("Hello from Pipecat!", [], None, 0) == (None, 0) + + async def test_simple(self): + # (, ) + assert parse_start_end_tags("Hello from Pipecat!", [("", "")], None, 0) == ( + None, + 26, + ) + assert parse_start_end_tags("Hello from Pipecat", [("", "")], None, 0) == ( + ("", ""), + 21, + ) + assert parse_start_end_tags("Hello from Pipecat", [("", "")], None, 6) == ( + ("", ""), + 21, + ) + + # (spell(, )) + assert parse_start_end_tags("Hello from spell(Pipecat)!", [("spell(", ")")], None, 0) == ( + None, + 26, + ) + assert parse_start_end_tags("Hello from spell(Pipecat", [("spell(", ")")], None, 0) == ( + ("spell(", ")"), + 24, + ) + + async def test_multiple(self): + # (, ) + assert parse_start_end_tags( + "Hello from Pipecat! Hello World!", [("", "")], None, 0 + ) == ( + None, + 46, + ) + + assert parse_start_end_tags( + "Hello from Pipecat! Hello World", [("", "")], None, 0 + ) == ( + ("", ""), + 41, + )