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aleix/queu
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hush/moder
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4b142084b6 |
30
.gitignore
vendored
30
.gitignore
vendored
@@ -7,7 +7,7 @@ venv
|
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/.idea
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#*#
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|
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# Distribution / Packaging
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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@@ -30,24 +30,24 @@ MANIFEST
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.env
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fly.toml
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|
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# Examples
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examples/telnyx-chatbot/templates/streams.xml
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examples/twilio-chatbot/templates/streams.xml
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examples/**/node_modules/
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examples/**/.expo/
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examples/**/dist/
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||||
examples/**/npm-debug.*
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examples/**/*.jks
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||||
examples/**/*.p8
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||||
examples/**/*.p12
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examples/**/*.key
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examples/**/*.mobileprovision
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examples/**/*.orig.*
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examples/**/web-build/
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# Example files
|
||||
pipecat/examples/twilio-chatbot/templates/streams.xml
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pipecat/examples/bot-ready-signalling/client/react-native/node_modules/
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pipecat/examples/bot-ready-signalling/client/react-native/.expo/
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||||
pipecat/examples/bot-ready-signalling/client/react-native/dist/
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||||
pipecat/examples/bot-ready-signalling/client/react-native/npm-debug.*
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pipecat/examples/bot-ready-signalling/client/react-native/*.jks
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||||
pipecat/examples/bot-ready-signalling/client/react-native/*.p8
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.p12
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||||
pipecat/examples/bot-ready-signalling/client/react-native/*.key
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pipecat/examples/bot-ready-signalling/client/react-native/*.mobileprovision
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pipecat/examples/bot-ready-signalling/client/react-native/*.orig.*
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pipecat/examples/bot-ready-signalling/client/react-native/web-build/
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|
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# macOS
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.DS_Store
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|
||||
|
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# Documentation
|
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docs/api/_build/
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docs/api/api
|
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292
CHANGELOG.md
292
CHANGELOG.md
@@ -5,290 +5,17 @@ All notable changes to **Pipecat** will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
|
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|
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## [0.0.67] - 2025-05-07
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## [Unreleased]
|
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|
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### Added
|
||||
|
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- Added `DebugLogObserver` for detailed frame logging with configurable
|
||||
filtering by frame type and endpoint. This observer automatically extracts
|
||||
and formats all frame data fields for debug logging.
|
||||
|
||||
- `UserImageRequestFrame.video_source` field has been added to request an image
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||||
from the desired video source.
|
||||
|
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- Added support for the AWS Nova Sonic speech-to-speech model with the new
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||||
`AWSNovaSonicLLMService`.
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||||
See https://docs.aws.amazon.com/nova/latest/userguide/speech.html.
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||||
Note that it requires Python >= 3.12 and `pip install pipecat-ai[aws-nova-sonic]`.
|
||||
|
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- Added new AWS services `AWSBedrockLLMService` and `AWSTranscribeSTTService`.
|
||||
|
||||
- Added `on_active_speaker_changed` event handler to the `DailyTransport` class.
|
||||
|
||||
- Added `enable_ssml_parsing` and `enable_logging` to `InputParams` in
|
||||
`ElevenLabsTTSService`.
|
||||
|
||||
- Added support to `RimeHttpTTSService` for the `arcana` model.
|
||||
|
||||
### Changed
|
||||
|
||||
- Updated `ElevenLabsTTSService` to use the beta websocket API
|
||||
(multi-stream-input). This new API supports context_ids and cancelling those
|
||||
contexts, which greatly improves interruption handling.
|
||||
|
||||
- Observers `on_push_frame()` now take a single argument `FramePushed` instead
|
||||
of multiple arguments.
|
||||
|
||||
- Updated the default voice for `DeepgramTTSService` to `aura-2-helena-en`.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- `PollyTTSService` is now deprecated, use `AWSPollyTTSService` instead.
|
||||
|
||||
- Observer `on_push_frame(src, dst, frame, direction, timestamp)` is now
|
||||
deprecated, use `on_push_frame(data: FramePushed)` instead.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed a `DailyTransport` issue that was causing issues when multiple audio or
|
||||
video sources where being captured.
|
||||
|
||||
- Fixed a `UltravoxSTTService` issue that would cause the service to generate
|
||||
all tokens as one word.
|
||||
|
||||
- Fixed a `PipelineTask` issue that would cause tasks to not be cancelled if
|
||||
task was cancelled from outside of Pipecat.
|
||||
|
||||
- Fixed a `TaskManager` that was causing dangling tasks to be reported.
|
||||
|
||||
- Fixed an issue that could cause data to be sent to the transports when they
|
||||
were still not ready.
|
||||
|
||||
- Remove custom audio tracks from `DailyTransport` before leaving.
|
||||
|
||||
### Removed
|
||||
|
||||
- Removed `CanonicalMetricsService` as it's no longer maintained.
|
||||
|
||||
## [0.0.66] - 2025-05-02
|
||||
|
||||
### Added
|
||||
|
||||
- Added two new input parameters to `RimeTTSService`: `pause_between_brackets`
|
||||
and `phonemize_between_brackets`.
|
||||
|
||||
- Added support for cross-platform local smart turn detection. You can use
|
||||
`LocalSmartTurnAnalyzer` for on-device inference using Torch.
|
||||
|
||||
- `BaseOutputTransport` now allows multiple destinations if the transport
|
||||
implementation supports it (e.g. Daily's custom tracks). With multiple
|
||||
destinations it is possible to send different audio or video tracks with a
|
||||
single transport simultaneously. To do that, you need to set the new
|
||||
`Frame.transport_destination` field with your desired transport destination
|
||||
(e.g. custom track name), tell the transport you want a new destination with
|
||||
`TransportParams.audio_out_destinations` or
|
||||
`TransportParams.video_out_destinations` and the transport should take care of
|
||||
the rest.
|
||||
|
||||
- Similar to the new `Frame.transport_destination`, there's a new
|
||||
`Frame.transport_source` field which is set by the `BaseInputTransport` if the
|
||||
incoming data comes from a non-default source (e.g. custom tracks).
|
||||
|
||||
- `TTSService` has a new `transport_destination` constructor parameter. This
|
||||
parameter will be used to update the `Frame.transport_destination` field for
|
||||
each generated `TTSAudioRawFrame`. This allows sending multiple bots' audio to
|
||||
multiple destinations in the same pipeline.
|
||||
|
||||
- Added `DailyTransportParams.camera_out_enabled` and
|
||||
`DailyTransportParams.microphone_out_enabled` which allows you to
|
||||
enable/disable the main output camera or microphone tracks. This is useful if
|
||||
you only want to use custom tracks and not send the main tracks. Note that you
|
||||
still need `audio_out_enabled=True` or `video_out_enabled`.
|
||||
|
||||
- Added `DailyTransport.capture_participant_audio()` which allows you to capture
|
||||
an audio source (e.g. "microphone", "screenAudio" or a custom track name) from
|
||||
a remote participant.
|
||||
|
||||
- Added `DailyTransport.update_publishing()` which allows you to update the call
|
||||
video and audio publishing settings (e.g. audio and video quality).
|
||||
|
||||
- Added `RTVIObserverParams` which allows you to configure what RTVI messages
|
||||
are sent to the clients.
|
||||
|
||||
- Added a `context_window_compression` InputParam to
|
||||
`GeminiMultimodalLiveLLMService` which allows you to enable a sliding context
|
||||
window for the session as well as set the token limit of the sliding window.
|
||||
|
||||
- Updated `SmallWebRTCConnection` to support `ice_servers` with credentials.
|
||||
|
||||
- Added `VADUserStartedSpeakingFrame` and `VADUserStoppedSpeakingFrame`,
|
||||
indicating when the VAD detected the user to start and stop speaking. These
|
||||
events are helpful when using smart turn detection, as the user's stop time
|
||||
can differ from when their turn ends (signified by UserStoppedSpeakingFrame).
|
||||
|
||||
- Added `TranslationFrame`, a new frame type that contains a translated
|
||||
transcription.
|
||||
|
||||
- Added `TransportParams.audio_in_passthrough`. If set (the default), incoming
|
||||
audio will be pushed downstream.
|
||||
|
||||
- Added `MCPClient`; a way to connect to MCP servers and use the MCP servers'
|
||||
tools.
|
||||
|
||||
- Added `Mem0 OSS`, along with Mem0 cloud support now the OSS version is also
|
||||
available.
|
||||
|
||||
### Changed
|
||||
|
||||
- `TransportParams.audio_mixer` now supports a string and also a dictionary to
|
||||
provide a mixer per destination. For example:
|
||||
|
||||
```python
|
||||
audio_out_mixer={
|
||||
"track-1": SoundfileMixer(...),
|
||||
"track-2": SoundfileMixer(...),
|
||||
"track-N": SoundfileMixer(...),
|
||||
},
|
||||
```
|
||||
|
||||
- The `STTMuteFilter` now mutes `InterimTranscriptionFrame` and
|
||||
`TranscriptionFrame` which allows the `STTMuteFilter` to be used in
|
||||
conjunction with transports that generate transcripts, e.g. `DailyTransport`.
|
||||
|
||||
- Function calls now receive a single parameter `FunctionCallParams` instead of
|
||||
`(function_name, tool_call_id, args, llm, context, result_callback)` which is
|
||||
now deprecated.
|
||||
|
||||
- Changed the user aggregator timeout for late transcriptions from 1.0s to 0.5s
|
||||
(`LLMUserAggregatorParams.aggregation_timeout`). Sometimes, the STT services
|
||||
might give us more than one transcription which could come after the user
|
||||
stopped speaking. We still want to include these additional transcriptions
|
||||
with the first one because it's part of the user turn. This is what this
|
||||
timeout is helpful with.
|
||||
|
||||
- Short utterances not detected by VAD while the bot is speaking are now
|
||||
ignored. This reduces the amount of bot interruptions significantly providing
|
||||
a more natural conversation experience.
|
||||
|
||||
- Updated `GladiaSTTService` to output a `TranslationFrame` when specifying a
|
||||
`translation` and `translation_config`.
|
||||
|
||||
- STT services now passthrough audio frames by default. This allows you to add
|
||||
audio recording without worrying about what's wrong in your pipeline when it
|
||||
doesn't work the first time.
|
||||
|
||||
- Input transports now always push audio downstream unless disabled with
|
||||
`TransportParams.audio_in_passthrough`. After many Pipecat releases, we
|
||||
realized this is the common use case. There are use cases where the input
|
||||
transport already provides STT and you also don't want recordings, in which
|
||||
case there's no need to push audio to the rest of the pipeline, but this is
|
||||
not a very common case.
|
||||
|
||||
- Added `RivaSegmentedSTTService`, which allows Riva offline/batch models, such
|
||||
as to be "canary-1b-asr" used in Pipecat.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- Function calls with parameters
|
||||
`(function_name, tool_call_id, args, llm, context, result_callback)` are
|
||||
deprectated, use a single `FunctionCallParams` parameter instead.
|
||||
|
||||
- `TransportParams.camera_*` parameters are now deprecated, use
|
||||
`TransportParams.video_*` instead.
|
||||
|
||||
- `TransportParams.vad_enabled` parameter is now deprecated, use
|
||||
`TransportParams.audio_in_enabled` and `TransportParams.vad_analyzer` instead.
|
||||
|
||||
- `TransportParams.vad_audio_passthrough` parameter is now deprecated, use
|
||||
`TransportParams.audio_in_passthrough` instead.
|
||||
|
||||
- `ParakeetSTTService` is now deprecated, use `RivaSTTService` instead, which uses
|
||||
the model "parakeet-ctc-1.1b-asr" by default.
|
||||
|
||||
- `FastPitchTTSService` is now deprecated, use `RivaTTSService` instead, which uses
|
||||
the model "magpie-tts-multilingual" by default.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed an issue with `SimliVideoService` where the bot was continuously outputting
|
||||
audio, which prevents the `BotStoppedSpeakingFrame` from being emitted.
|
||||
|
||||
- Fixed an issue where `OpenAIRealtimeBetaLLMService` would add two assistant
|
||||
messages to the context.
|
||||
|
||||
- Fixed an issue with `GeminiMultimodalLiveLLMService` where the context
|
||||
contained tokens instead of words.
|
||||
|
||||
- Fixed an issue with HTTP Smart Turn handling, where the service returns a 500
|
||||
error. Previously, this would cause an unhandled exception. Now, a 500 error
|
||||
is treated as an incomplete response.
|
||||
|
||||
- Fixed a TTS services issue that could cause assistant output not to be
|
||||
aggregated to the context when also using `TTSSpeakFrame`s.
|
||||
|
||||
- Fixed an issue where the `SmartTurnMetricsData` was reporting 0ms for
|
||||
inference and processing time when using the `FalSmartTurnAnalyzer`.
|
||||
|
||||
### Other
|
||||
|
||||
- Added `examples/daily-custom-tracks` to show how to send and receive Daily
|
||||
custom tracks.
|
||||
|
||||
- Added `examples/daily-multi-translation` to showcase how to send multiple
|
||||
simulataneous translations with the same transport.
|
||||
|
||||
- Added 04 foundational examples for client/server transports. Also, renamed
|
||||
`29-livekit-audio-chat.py` to `04b-transports-livekit.py`.
|
||||
|
||||
- Added foundational example `13c-gladia-translation.py` showing how to use
|
||||
`TranscriptionFrame` and `TranslationFrame`.
|
||||
|
||||
## [0.0.65] - 2025-04-23 "Sant Jordi's release" 🌹📕
|
||||
|
||||
https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
|
||||
|
||||
### Added
|
||||
|
||||
- Added automatic hangup logic to the Telnyx serializer. This feature hangs up
|
||||
the Telnyx call when an `EndFrame` or `CancelFrame` is received. It is
|
||||
enabled by default and is configurable via the `auto_hang_up` `InputParam`.
|
||||
|
||||
- Added a keepalive task to `GladiaSTTService` to prevent the websocket from
|
||||
disconnecting after 30 seconds of no audio input.
|
||||
|
||||
### Changed
|
||||
|
||||
- The `InputParams` for `ElevenLabsTTSService` and `ElevenLabsHttpTTSService`
|
||||
no longer require that `stability` and `similarity_boost` be set. You can
|
||||
individually set each param.
|
||||
|
||||
- In `TwilioFrameSerializer`, `call_sid` is Optional so as to avoid a breaking
|
||||
changed. `call_sid` is required to automatically hang up.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed an issue where `TwilioFrameSerializer` would send two hang up commands:
|
||||
one for the `EndFrame` and one for the `CancelFrame`.
|
||||
|
||||
## [0.0.64] - 2025-04-22
|
||||
|
||||
### Added
|
||||
|
||||
- Added automatic hangup logic to the Twilio serializer. This feature hangs up
|
||||
the Twilio call when an `EndFrame` or `CancelFrame` is received. It is
|
||||
enabled by default and is configurable via the `auto_hang_up` `InputParam`.
|
||||
|
||||
- Added `SmartTurnMetricsData`, which contains end-of-turn prediction metrics,
|
||||
to the `MetricsFrame`. Using `MetricsFrame`, you can now retrieve prediction
|
||||
confidence scores and processing time metrics from the smart turn analyzers.
|
||||
|
||||
- Added support for Application Default Credentials in Google services,
|
||||
`GoogleSTTService`, `GoogleTTSService`, and `GoogleVertexLLMService`.
|
||||
|
||||
- Added support for Smart Turn Detection via the `turn_analyzer` transport
|
||||
parameter. You can now choose between `HttpSmartTurnAnalyzer()` or
|
||||
`FalSmartTurnAnalyzer()` for remote inference or
|
||||
`LocalCoreMLSmartTurnAnalyzer()` for on-device inference using Core ML.
|
||||
parameter. You can now choose between `SmartTurnAnalyzer()` for remote
|
||||
inference or `LocalCoreMLSmartTurnAnalyzer()` for on-device inference using
|
||||
Core ML.
|
||||
|
||||
- `DeepgramTTSService` accepts `base_url` argument again, allowing you to
|
||||
connect to an on-prem service.
|
||||
@@ -313,8 +40,6 @@ https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
|
||||
|
||||
### Changed
|
||||
|
||||
- `GrokLLMService` now uses `grok-3-beta` as its default model.
|
||||
|
||||
- Daily's REST helpers now include an `eject_at_token_exp` param, which ejects
|
||||
the user when their token expires. This new parameter defaults to False.
|
||||
Also, the default value for `enable_prejoin_ui` changed to False and
|
||||
@@ -346,13 +71,8 @@ https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
|
||||
- Fixed an issue in `SmallWebRTCTransport` where an error was thrown if the
|
||||
client did not create a video transceiver.
|
||||
|
||||
- Fixed an issue where LLM input parameters were not working and applied
|
||||
correctly in `GoogleVertexLLMService`, causing unexpected behavior during
|
||||
inference.
|
||||
|
||||
### Other
|
||||
|
||||
- Updated the `twilio-chatbot` example to use the auto-hangup feature.
|
||||
- Fixed an issue where LLM input parameters were not working and applied correctly in `GoogleVertexLLMService`, causing
|
||||
unexpected behavior during inference.
|
||||
|
||||
## [0.0.63] - 2025-04-11
|
||||
|
||||
|
||||
30
README.md
30
README.md
@@ -49,18 +49,18 @@ You can connect to Pipecat from any platform using our official SDKs:
|
||||
|
||||
## 🧩 Available services
|
||||
|
||||
| Category | Services |
|
||||
|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [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) |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [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), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
|
||||
| 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), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [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) |
|
||||
| Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
|
||||
| Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
|
||||
| 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) |
|
||||
| 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) |
|
||||
| Analytics & Metrics | [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
|
||||
| Category | Services |
|
||||
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| 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) |
|
||||
| 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), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
|
||||
| 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), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [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) |
|
||||
| Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
|
||||
| Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
|
||||
| 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) |
|
||||
| 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) |
|
||||
| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
|
||||
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
|
||||
|
||||
@@ -130,12 +130,6 @@ pip install "pipecat-ai[option,...]"
|
||||
|
||||
### Running tests
|
||||
|
||||
Install the test dependencies:
|
||||
|
||||
```shell
|
||||
pip install -r test-requirements.txt
|
||||
```
|
||||
|
||||
From the root directory, run:
|
||||
|
||||
```shell
|
||||
|
||||
@@ -50,6 +50,7 @@ autodoc_mock_imports = [
|
||||
"pyht.protos",
|
||||
"pyht.protos.api_pb2",
|
||||
"pipecat_ai_playht", # PlayHT wrapper
|
||||
"vllm",
|
||||
"aiortc",
|
||||
"aiortc.mediastreams",
|
||||
"cv2",
|
||||
@@ -75,6 +76,7 @@ autodoc_mock_imports = [
|
||||
"openpipe",
|
||||
"simli",
|
||||
"soundfile",
|
||||
# Existing mocks
|
||||
"pipecat_ai_krisp",
|
||||
"pyaudio",
|
||||
"_tkinter",
|
||||
@@ -85,66 +87,6 @@ autodoc_mock_imports = [
|
||||
"pydantic.Field",
|
||||
"pydantic._internal._model_construction",
|
||||
"pydantic._internal._fields",
|
||||
# Moondream dependencies
|
||||
"torch",
|
||||
"transformers",
|
||||
"intel_extension_for_pytorch",
|
||||
# Ultravox dependencies
|
||||
"huggingface_hub",
|
||||
"vllm",
|
||||
"vllm.engine.arg_utils",
|
||||
"transformers.AutoTokenizer",
|
||||
# Langchain dependencies
|
||||
"langchain_core",
|
||||
"langchain_core.messages",
|
||||
"langchain_core.runnables",
|
||||
"langchain_core.messages.AIMessageChunk",
|
||||
"langchain_core.runnables.Runnable",
|
||||
# LiveKit dependencies
|
||||
"livekit",
|
||||
"livekit.rtc",
|
||||
"livekit_api",
|
||||
"livekit_protocol",
|
||||
"tenacity",
|
||||
"tenacity.retry",
|
||||
"tenacity.stop_after_attempt",
|
||||
"tenacity.wait_exponential",
|
||||
"rtc",
|
||||
"rtc.Room",
|
||||
"rtc.RoomOptions",
|
||||
"rtc.AudioSource",
|
||||
"rtc.LocalAudioTrack",
|
||||
"rtc.TrackPublishOptions",
|
||||
"rtc.TrackSource",
|
||||
"rtc.AudioStream",
|
||||
"rtc.AudioFrameEvent",
|
||||
"rtc.AudioFrame",
|
||||
"rtc.Track",
|
||||
"rtc.TrackKind",
|
||||
"rtc.RemoteParticipant",
|
||||
"rtc.RemoteTrackPublication",
|
||||
"rtc.DataPacket",
|
||||
# Riva dependencies
|
||||
"riva",
|
||||
"riva.client",
|
||||
"riva.client.Auth",
|
||||
"riva.client.ASRService",
|
||||
"riva.client.StreamingRecognitionConfig",
|
||||
"riva.client.RecognitionConfig",
|
||||
"riva.client.AudioEncoding",
|
||||
"riva.client.proto.riva_tts_pb2",
|
||||
"riva.client.SpeechSynthesisService",
|
||||
# Local CoreML Smart Turn dependencies
|
||||
"coremltools",
|
||||
"coremltools.models",
|
||||
"coremltools.models.MLModel",
|
||||
"torch",
|
||||
"torch.nn",
|
||||
"torch.nn.functional",
|
||||
"transformers",
|
||||
"transformers.AutoFeatureExtractor",
|
||||
# Also add specific classes that are imported
|
||||
"AutoFeatureExtractor",
|
||||
]
|
||||
|
||||
# HTML output settings
|
||||
@@ -176,25 +118,12 @@ def verify_modules():
|
||||
},
|
||||
}
|
||||
|
||||
# Skip importing modules that are in autodoc_mock_imports
|
||||
skipped_modules = set(autodoc_mock_imports)
|
||||
|
||||
missing = []
|
||||
for category, modules in required_modules.items():
|
||||
if isinstance(modules, dict):
|
||||
# Handle nested structure
|
||||
for subcategory, submodules in modules.items():
|
||||
for module in submodules:
|
||||
# Check if module is in autodoc_mock_imports
|
||||
if (
|
||||
f"pipecat.{category}.{subcategory}.{module}" in skipped_modules
|
||||
or module in skipped_modules
|
||||
):
|
||||
logger.info(
|
||||
f"Skipping import of mocked module: pipecat.{category}.{subcategory}.{module}"
|
||||
)
|
||||
continue
|
||||
|
||||
try:
|
||||
__import__(f"pipecat.{category}.{subcategory}.{module}")
|
||||
logger.info(
|
||||
@@ -208,11 +137,6 @@ def verify_modules():
|
||||
else:
|
||||
# Handle flat structure
|
||||
for module in modules:
|
||||
# Check if module is in autodoc_mock_imports
|
||||
if f"pipecat.{category}.{module}" in skipped_modules or module in skipped_modules:
|
||||
logger.info(f"Skipping import of mocked module: pipecat.{category}.{module}")
|
||||
continue
|
||||
|
||||
try:
|
||||
__import__(f"pipecat.{category}.{module}")
|
||||
logger.info(f"Successfully imported pipecat.{category}.{module}")
|
||||
|
||||
@@ -10,6 +10,7 @@ pipecat-ai[anthropic]
|
||||
pipecat-ai[assemblyai]
|
||||
pipecat-ai[aws]
|
||||
pipecat-ai[azure]
|
||||
pipecat-ai[canonical]
|
||||
pipecat-ai[cartesia]
|
||||
pipecat-ai[cerebras]
|
||||
pipecat-ai[deepseek]
|
||||
@@ -25,23 +26,20 @@ pipecat-ai[grok]
|
||||
pipecat-ai[groq]
|
||||
# pipecat-ai[krisp] # Mocked
|
||||
pipecat-ai[koala]
|
||||
# pipecat-ai[langchain] # Mocked
|
||||
# pipecat-ai[livekit] # Mocked
|
||||
pipecat-ai[langchain]
|
||||
pipecat-ai[livekit]
|
||||
pipecat-ai[lmnt]
|
||||
pipecat-ai[local]
|
||||
# pipecat-ai[local-smart-turn] # Mocked
|
||||
# pipecat-ai[mem0] # Mocked
|
||||
# pipecat-ai[mlx-whisper] # Mocked
|
||||
# pipecat-ai[moondream] # Mocked
|
||||
pipecat-ai[moondream]
|
||||
pipecat-ai[nim]
|
||||
# pipecat-ai[neuphonic] # Mocked
|
||||
pipecat-ai[noisereduce]
|
||||
pipecat-ai[openai]
|
||||
# pipecat-ai[openpipe]
|
||||
# pipecat-ai[playht] # Mocked due to grpcio conflict with riva
|
||||
pipecat-ai[qwen]
|
||||
pipecat-ai[remote-smart-turn]
|
||||
# pipecat-ai[riva] # Mocked
|
||||
pipecat-ai[riva]
|
||||
pipecat-ai[silero]
|
||||
pipecat-ai[simli]
|
||||
pipecat-ai[soundfile]
|
||||
|
||||
@@ -96,8 +96,4 @@ PIPER_BASE_URL=...
|
||||
|
||||
# Smart turn
|
||||
LOCAL_SMART_TURN_MODEL_PATH=
|
||||
FAL_SMART_TURN_API_KEY=...
|
||||
|
||||
# Twilio
|
||||
TWILIO_ACCOUNT_SID=
|
||||
TWILIO_AUTH_TOKEN=
|
||||
REMOTE_SMART_TURN_URL=
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
161
examples/canonical-metrics/.gitignore
vendored
Normal file
161
examples/canonical-metrics/.gitignore
vendored
Normal file
@@ -0,0 +1,161 @@
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
recordings/
|
||||
# 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
|
||||
@@ -1,12 +1,7 @@
|
||||
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
|
||||
|
||||
66
examples/canonical-metrics/README.md
Normal file
66
examples/canonical-metrics/README.md
Normal file
@@ -0,0 +1,66 @@
|
||||
# Chatbot with canonical-metrics
|
||||
|
||||
This project implements a chatbot using a pipeline architecture that integrates audio processing, transcription, and a language model for conversational interactions. The chatbot operates within a daily communication environment, utilizing various services for text-to-speech and language model responses.
|
||||
|
||||
## Features
|
||||
|
||||
- **Audio Input and Output**: Captures microphone input and plays back audio responses.
|
||||
- **Voice Activity Detection**: Utilizes Silero VAD to manage audio input intelligently.
|
||||
- **Text-to-Speech**: Integrates ElevenLabs TTS service to convert text responses into audio.
|
||||
- **Language Model Interaction**: Uses OpenAI's GPT-4 model to generate responses based on user input.
|
||||
- **Transcription Services**: Captures and transcribes participant speech for analytics.
|
||||
- **Metrics Collection**: Sends audio data for analysis via Canonical Metrics Service.
|
||||
|
||||
## Requirements
|
||||
|
||||
- Python 3.10+
|
||||
- `python-dotenv`
|
||||
- Additional libraries from the `pipecat` package.
|
||||
|
||||
## Setup
|
||||
|
||||
1. Clone the repository.
|
||||
2. Install the required packages.
|
||||
3. Set up environment variables for API keys:
|
||||
- `OPENAI_API_KEY`
|
||||
- `ELEVENLABS_API_KEY`
|
||||
- `CANONICAL_API_KEY`
|
||||
- `CANONICAL_API_URL`
|
||||
4. Run the script.
|
||||
|
||||
## Usage
|
||||
|
||||
The chatbot introduces itself and engages in conversations, providing brief and creative responses. Designed for flexibility, it can support multiple languages with appropriate configuration.
|
||||
|
||||
## Events
|
||||
|
||||
- Participants joining or leaving the call are handled dynamically, adjusting the chatbot's behavior accordingly.
|
||||
|
||||
|
||||
ℹ️ The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
|
||||
|
||||
## 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
|
||||
```
|
||||
148
examples/canonical-metrics/bot.py
Normal file
148
examples/canonical-metrics/bot.py
Normal file
@@ -0,0 +1,148 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
import uuid
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from runner import configure
|
||||
|
||||
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.processors.audio.audio_buffer_processor import AudioBufferProcessor
|
||||
from pipecat.services.canonical.metrics import CanonicalMetricsService
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
|
||||
from pipecat.services.openai.llm 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,
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
# transcription_settings=DailyTranscriptionSettings(
|
||||
# language="es",
|
||||
# tier="nova",
|
||||
# model="2-general"
|
||||
# )
|
||||
),
|
||||
)
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
#
|
||||
# English
|
||||
#
|
||||
voice_id="cgSgspJ2msm6clMCkdW9",
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
# model="eleven_multilingual_v2",
|
||||
# voice_id="gD1IexrzCvsXPHUuT0s3",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
#
|
||||
# English
|
||||
#
|
||||
"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. Keep all your responses to 12 words or fewer.",
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
# "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
|
||||
},
|
||||
]
|
||||
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
"""
|
||||
CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
|
||||
call completion, CanonicalMetrics will send the audio buffer to Canonical for
|
||||
analysis. Visit https://voice.canonical.chat to learn more.
|
||||
"""
|
||||
audio_buffer_processor = AudioBufferProcessor(num_channels=2)
|
||||
canonical = CanonicalMetricsService(
|
||||
audio_buffer_processor=audio_buffer_processor,
|
||||
aiohttp_session=session,
|
||||
api_key=os.getenv("CANONICAL_API_KEY"),
|
||||
call_id=str(uuid.uuid4()),
|
||||
assistant="pipecat-chatbot",
|
||||
assistant_speaks_first=True,
|
||||
context=context,
|
||||
)
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # microphone
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
transport.output(),
|
||||
canonical, # uploads audio buffer to Canonical AI for metrics
|
||||
audio_buffer_processor, # captures audio into a buffer
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
await audio_buffer_processor.start_recording()
|
||||
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()
|
||||
|
||||
@transport.event_handler("on_call_state_updated")
|
||||
async def on_call_state_updated(transport, state):
|
||||
if state == "left":
|
||||
# Here we don't want to cancel, we just want to finish sending
|
||||
# whatever is queued, so we use an EndFrame().
|
||||
await task.queue_frame(EndFrame())
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1,5 +1,6 @@
|
||||
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...
|
||||
DEEPGRAM_API_KEY=efb...
|
||||
CARTESIA_API_KEY=aeb...
|
||||
ELEVENLABS_API_KEY=aeb...
|
||||
CANONICAL_API_KEY=can...
|
||||
CANONICAL_API_URL=
|
||||
5
examples/canonical-metrics/requirements.txt
Normal file
5
examples/canonical-metrics/requirements.txt
Normal file
@@ -0,0 +1,5 @@
|
||||
python-dotenv
|
||||
fastapi[all]
|
||||
uvicorn
|
||||
pipecat-ai[daily,openai,silero,elevenlabs,canonical]
|
||||
|
||||
@@ -66,7 +66,9 @@ async def main():
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
audio_in_enabled=True,
|
||||
video_out_enabled=False,
|
||||
camera_out_enabled=False,
|
||||
vad_enabled=True,
|
||||
vad_audio_passthrough=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
transcription_enabled=True,
|
||||
#
|
||||
|
||||
@@ -53,3 +53,4 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
|
||||
token = await daily_rest_helper.get_token(url, expiry_time)
|
||||
|
||||
return (url, token)
|
||||
return (url, token)
|
||||
|
||||
@@ -1,39 +0,0 @@
|
||||
# Daily Custom Tracks
|
||||
|
||||
This example shows how to send and receive Daily custom tracks. We will run a simple `daily-python` application to send an audio file with a custom track (named "pipecat") to a room. Then, the Pipecat bot will mirror that custom track into another custom track (named "pipecat-mirror") in the same room.
|
||||
|
||||
## Get started
|
||||
|
||||
```python
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## Run the bot
|
||||
|
||||
Start the bot by giving it a Daily room URL.
|
||||
|
||||
```bash
|
||||
python bot.py -u ROOM_URL
|
||||
```
|
||||
|
||||
The bot will wait for the first participant to join. Then, it will mirror a custom track named "pipecat" into a new custom track named "pipecat-mirror".
|
||||
|
||||
## Run the sender
|
||||
|
||||
Now, run the custom track sender. This is a simple `daily-python` application that opens and audio file and sends it as a custom track to the same Daily room.
|
||||
|
||||
```bash
|
||||
python custom_track_sender.py -u ROOM_URL -i office-ambience-mono-16000.mp3
|
||||
```
|
||||
|
||||
## Open client
|
||||
|
||||
Finally, open the client so you can hear both custom tracks.
|
||||
|
||||
```bash
|
||||
open index.html
|
||||
```
|
||||
|
||||
Once the client is opened, copy the URL of the Daily room and join it. You should be able to select which custom track you want to hear.
|
||||
@@ -1,87 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import sys
|
||||
|
||||
import aiohttp
|
||||
from loguru import logger
|
||||
from runner import configure
|
||||
|
||||
from pipecat.frames.frames import Frame, InputAudioRawFrame, OutputAudioRawFrame
|
||||
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.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
class CustomTrackMirrorProcessor(FrameProcessor):
|
||||
def __init__(self, transport_destination: str, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self._transport_destination = transport_destination
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, InputAudioRawFrame) and frame.transport_source:
|
||||
output_frame = OutputAudioRawFrame(
|
||||
audio=frame.audio,
|
||||
sample_rate=frame.sample_rate,
|
||||
num_channels=frame.num_channels,
|
||||
)
|
||||
output_frame.transport_destination = self._transport_destination
|
||||
await self.push_frame(output_frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
None,
|
||||
"Custom tracks mirror",
|
||||
DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
microphone_out_enabled=False, # Disable since we just use custom tracks
|
||||
audio_out_destinations=["pipecat-mirror"],
|
||||
),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
CustomTrackMirrorProcessor("pipecat-mirror"),
|
||||
transport.output(), # Transport bot output
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
audio_in_sample_rate=16000,
|
||||
audio_out_sample_rate=16000,
|
||||
),
|
||||
)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
await transport.capture_participant_audio(participant["id"], audio_source="pipecat")
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1,74 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import time
|
||||
|
||||
from daily import CallClient, CustomAudioSource, Daily
|
||||
from pydub import AudioSegment
|
||||
|
||||
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
|
||||
parser.add_argument("-u", "--url", type=str, required=True, help="URL of the Daily room to join")
|
||||
parser.add_argument(
|
||||
"-i", "--input", type=str, required=True, help="Input audio file (needs 16000 sample rate)"
|
||||
)
|
||||
|
||||
args, _ = parser.parse_known_args()
|
||||
|
||||
audio = AudioSegment.from_mp3(args.input)
|
||||
|
||||
raw_bytes = audio.raw_data
|
||||
sample_rate = audio.frame_rate
|
||||
channels = audio.channels
|
||||
|
||||
print(f"Length: {len(raw_bytes)} bytes")
|
||||
print(f"Sample rate: {sample_rate}, Channels: {channels}")
|
||||
|
||||
# Initialize the Daily context & create call client
|
||||
Daily.init()
|
||||
|
||||
client = CallClient()
|
||||
|
||||
# Join the room and indicate we have a custom track named "pipecat".
|
||||
client.join(
|
||||
args.url,
|
||||
client_settings={
|
||||
"publishing": {
|
||||
"camera": False,
|
||||
"microphone": False,
|
||||
"customAudio": {"pipecat": True},
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
# Just sleep for a couple of seconds. To do this well we should really use
|
||||
# completions.
|
||||
time.sleep(2)
|
||||
|
||||
# Create the custom audio source. This is where we will write our audio.
|
||||
audio_source = CustomAudioSource(sample_rate, channels)
|
||||
|
||||
# Create an audio track and assign it our audio source.
|
||||
client.add_custom_audio_track("pipecat", audio_source)
|
||||
|
||||
# Just sleep for a second. To do this well we should really use completions.
|
||||
time.sleep(1)
|
||||
|
||||
try:
|
||||
# Just write one second of audio until we have read all the file.
|
||||
chunk_size = sample_rate * channels * 2
|
||||
while len(raw_bytes) > 0:
|
||||
chunk = raw_bytes[:chunk_size]
|
||||
raw_bytes = raw_bytes[chunk_size:]
|
||||
audio_source.write_frames(chunk)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
client.leave()
|
||||
|
||||
# Just sleep for a second. To do this well we should really use completions.
|
||||
time.sleep(1)
|
||||
|
||||
client.release()
|
||||
@@ -1,173 +0,0 @@
|
||||
<html>
|
||||
<head>
|
||||
<title>daily custom tracks</title>
|
||||
</head>
|
||||
<script crossorigin src="https://unpkg.com/@daily-co/daily-js"></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/fomantic-ui/2.8.6/semantic.min.js"></script>
|
||||
<link
|
||||
rel="stylesheet"
|
||||
type="text/css"
|
||||
href="https://cdnjs.cloudflare.com/ajax/libs/fomantic-ui/2.8.6/semantic.min.css"
|
||||
/>
|
||||
<script>
|
||||
function enableButton(buttonId, enable) {
|
||||
const button = document.getElementById(buttonId);
|
||||
button.disabled = !enable;
|
||||
}
|
||||
|
||||
function enableJoinButton(enable) {
|
||||
enableButton("join-button", enable);
|
||||
}
|
||||
|
||||
function enableLeaveButton(enable) {
|
||||
enableButton("leave-button", enable);
|
||||
}
|
||||
|
||||
function destroyPlayers(query) {
|
||||
const items = document.querySelectorAll(query);
|
||||
if (items) {
|
||||
for (const item of items) {
|
||||
item.remove();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function destroyParticipantPlayers(participantId) {
|
||||
destroyPlayers(`audio[data-participant-id="${participantId}"]`);
|
||||
destroyPlayers(`button[data-participant-id="${participantId}"]`);
|
||||
}
|
||||
|
||||
async function startPlayer(player, track) {
|
||||
player.muted = false;
|
||||
player.autoplay = true;
|
||||
if (track != null) {
|
||||
player.srcObject = new MediaStream([track]);
|
||||
}
|
||||
}
|
||||
|
||||
async function buildAudioPlayer(track, participantId) {
|
||||
const audioContainer = document.getElementById("audio-container");
|
||||
const player = document.createElement("audio");
|
||||
player.dataset.participantId = participantId;
|
||||
|
||||
// Create a new button for controlling audio
|
||||
const audioControlButton = document.createElement("button");
|
||||
audioControlButton.className = "ui primary green button"
|
||||
audioControlButton.innerText = track._mediaTag == "cam-audio" ? "english" : track._mediaTag;
|
||||
audioControlButton.dataset.participantId = participantId;
|
||||
audioControlButton.onclick = () => {
|
||||
if (player.paused) {
|
||||
|
||||
player.play();
|
||||
audioControlButton.className = "ui primary red button"
|
||||
} else {
|
||||
player.pause();
|
||||
audioControlButton.className = "ui primary green button"
|
||||
}
|
||||
};
|
||||
|
||||
audioContainer.appendChild(player);
|
||||
audioContainer.appendChild(audioControlButton);
|
||||
|
||||
await startPlayer(player, track);
|
||||
player.pause()
|
||||
|
||||
return player;
|
||||
}
|
||||
|
||||
function subscribeToTracks(participantId) {
|
||||
console.log(`subscribing to track`);
|
||||
|
||||
if (participantId === "local") {
|
||||
return;
|
||||
}
|
||||
|
||||
callObject.updateParticipant(participantId, {
|
||||
setSubscribedTracks: {
|
||||
audio: true,
|
||||
video: false,
|
||||
custom: true,
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
function startDaily() {
|
||||
enableJoinButton(true);
|
||||
enableLeaveButton(false);
|
||||
|
||||
window.callObject = window.DailyIframe.createCallObject({});
|
||||
|
||||
callObject.on("participant-joined", (e) => {
|
||||
if (!e.participant.local) {
|
||||
console.log("participant-joined", e.participant);
|
||||
subscribeToTracks(e.participant.session_id);
|
||||
}
|
||||
});
|
||||
|
||||
callObject.on("participant-left", (e) => {
|
||||
console.log("participant-left", e.participant.session_id);
|
||||
destroyParticipantPlayers(e.participant.session_id);
|
||||
});
|
||||
|
||||
callObject.on("track-started", async (e) => {
|
||||
console.log("track-started", e.track);
|
||||
if (e.track.kind === "audio") {
|
||||
await buildAudioPlayer(e.track, e.participant.session_id);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
async function joinRoom() {
|
||||
enableJoinButton(false);
|
||||
enableLeaveButton(true);
|
||||
|
||||
const meetingUrl = document.getElementById("meeting-url").value;
|
||||
|
||||
callObject.join({
|
||||
url: meetingUrl,
|
||||
startVideoOff: true,
|
||||
startAudioOff: true,
|
||||
subscribeToTracksAutomatically: false,
|
||||
receiveSettings: {
|
||||
base: { video: { layer: 0 } },
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
async function leaveRoom() {
|
||||
enableJoinButton(true);
|
||||
enableLeaveButton(false);
|
||||
|
||||
callObject.leave();
|
||||
|
||||
const audioContainer = document.getElementById("audio-container");
|
||||
audioContainer.replaceChildren();
|
||||
}
|
||||
</script>
|
||||
|
||||
<body onload="startDaily()">
|
||||
<div class="ui centered page grid" style="margin-top: 30px">
|
||||
<div class="ten wide column">
|
||||
<div class="ui form" style="margin-top: 30px">
|
||||
<div class="field">
|
||||
<label>Meeting URL</label>
|
||||
<input id="meeting-url" value="" />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="ui centered aligned header" style="margin-top: 30px">
|
||||
<button id="join-button" class="ui primary button" onclick="joinRoom()">
|
||||
Join
|
||||
</button>
|
||||
<button id="leave-button" class="ui button" onclick="leaveRoom()">
|
||||
Leave
|
||||
</button>
|
||||
</div>
|
||||
<div id="tile" class="ui container" style="margin-top: 30px">
|
||||
<div id="tile" class="ui center aligned grid">
|
||||
<div id="audio-container"></div><br/>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
Binary file not shown.
@@ -1,2 +0,0 @@
|
||||
pydub
|
||||
pipecat-ai[daily]
|
||||
@@ -1,39 +0,0 @@
|
||||
# Daily Multi Translation
|
||||
|
||||
This example shows how to use Daily to stream multiple simultaneous translations using a single transport. Daily provides custom tracks and in this example we will simultaneously translate incoming audio in English to Spanish, French and German, each of them being sent to a custom track.
|
||||
|
||||
## 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. This will open a Daily Prebuilt room where you will speak in English (make sure you are not muted).
|
||||
|
||||
## Open client
|
||||
|
||||
Next, you need to open the client that will listen to the translations.
|
||||
|
||||
```bash
|
||||
open index.html
|
||||
```
|
||||
|
||||
Once the client is opened, copy the URL of the Daily room created above and join it. You should be able to select which translation you want to hear.
|
||||
|
||||
## Build and test the Docker image
|
||||
|
||||
```
|
||||
docker build -t daily-multi-translation .
|
||||
docker run --env-file .env -p 7860:7860 daily-multi-translation
|
||||
```
|
||||
@@ -1,165 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from runner import configure
|
||||
|
||||
from pipecat.audio.mixers.soundfile_mixer import SoundfileMixer
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
|
||||
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
|
||||
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.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
BACKGROUND_SOUND_FILE = "office-ambience-mono-16000.mp3"
|
||||
|
||||
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Multi translation bot",
|
||||
DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
audio_out_mixer={
|
||||
"spanish": SoundfileMixer(
|
||||
sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
|
||||
),
|
||||
"french": SoundfileMixer(
|
||||
sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
|
||||
),
|
||||
"german": SoundfileMixer(
|
||||
sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
|
||||
),
|
||||
},
|
||||
audio_out_destinations=["spanish", "french", "german"],
|
||||
microphone_out_enabled=False, # Disable since we just use custom tracks
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts_spanish = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="cefcb124-080b-4655-b31f-932f3ee743de",
|
||||
transport_destination="spanish",
|
||||
)
|
||||
tts_french = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="8832a0b5-47b2-4751-bb22-6a8e2149303d",
|
||||
transport_destination="french",
|
||||
)
|
||||
tts_german = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="38aabb6a-f52b-4fb0-a3d1-988518f4dc06",
|
||||
transport_destination="german",
|
||||
)
|
||||
|
||||
messages_spanish = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You will be provided with a sentence in English, and your task is to only translate it into Spanish.",
|
||||
},
|
||||
]
|
||||
messages_french = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You will be provided with a sentence in English, and your task is to only translate it into French.",
|
||||
},
|
||||
]
|
||||
messages_german = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You will be provided with a sentence in English, and your task is to only translate it into German.",
|
||||
},
|
||||
]
|
||||
|
||||
llm_spanish = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm_french = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm_german = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
context_spanish = OpenAILLMContext(messages_spanish)
|
||||
context_aggregator_spanish = llm_spanish.create_context_aggregator(context_spanish)
|
||||
|
||||
context_french = OpenAILLMContext(messages_french)
|
||||
context_aggregator_french = llm_french.create_context_aggregator(context_french)
|
||||
|
||||
context_german = OpenAILLMContext(messages_german)
|
||||
context_aggregator_german = llm_german.create_context_aggregator(context_german)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
ParallelPipeline(
|
||||
# Spanish pipeline.
|
||||
[
|
||||
context_aggregator_spanish.user(),
|
||||
llm_spanish,
|
||||
tts_spanish,
|
||||
context_aggregator_spanish.assistant(),
|
||||
],
|
||||
# French pipeline.
|
||||
[
|
||||
context_aggregator_french.user(),
|
||||
llm_french,
|
||||
tts_french,
|
||||
context_aggregator_french.assistant(),
|
||||
],
|
||||
# German pipeline.
|
||||
[
|
||||
context_aggregator_german.user(),
|
||||
llm_german,
|
||||
tts_german,
|
||||
context_aggregator_german.assistant(),
|
||||
],
|
||||
),
|
||||
transport.output(), # Transport bot output
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
audio_in_sample_rate=16000,
|
||||
audio_out_sample_rate=16000,
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=True,
|
||||
),
|
||||
observers=[TranscriptionLogObserver()],
|
||||
)
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1,202 +0,0 @@
|
||||
<html>
|
||||
<head>
|
||||
<title>daily multi translation</title>
|
||||
</head>
|
||||
<script crossorigin src="https://unpkg.com/@daily-co/daily-js"></script>
|
||||
<script
|
||||
src="https://code.jquery.com/jquery-3.1.1.min.js"
|
||||
integrity="sha256-hVVnYaiADRTO2PzUGmuLJr8BLUSjGIZsDYGmIJLv2b8="
|
||||
crossorigin="anonymous"
|
||||
></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/fomantic-ui/2.8.6/semantic.min.js"></script>
|
||||
<link
|
||||
rel="stylesheet"
|
||||
type="text/css"
|
||||
href="https://cdnjs.cloudflare.com/ajax/libs/fomantic-ui/2.8.6/semantic.min.css"
|
||||
/>
|
||||
<script>
|
||||
function enableButton(buttonId, enable) {
|
||||
const button = document.getElementById(buttonId);
|
||||
button.disabled = !enable;
|
||||
}
|
||||
|
||||
function enableJoinButton(enable) {
|
||||
enableButton("join-button", enable);
|
||||
}
|
||||
|
||||
function enableLeaveButton(enable) {
|
||||
enableButton("leave-button", enable);
|
||||
}
|
||||
|
||||
function destroyPlayers(query) {
|
||||
const items = document.querySelectorAll(query);
|
||||
if (items) {
|
||||
for (const item of items) {
|
||||
item.remove();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function destroyParticipantPlayers(participantId) {
|
||||
destroyPlayers(`video[data-participant-id="${participantId}"]`);
|
||||
destroyPlayers(`audio[data-participant-id="${participantId}"]`);
|
||||
destroyPlayers(`button[data-participant-id="${participantId}"]`);
|
||||
}
|
||||
|
||||
async function startPlayer(player, track) {
|
||||
player.muted = false;
|
||||
player.autoplay = true;
|
||||
if (track != null) {
|
||||
player.srcObject = new MediaStream([track]);
|
||||
}
|
||||
}
|
||||
|
||||
async function buildVideoPlayer(track, participantId) {
|
||||
const videoContainer = document.getElementById("video-container");
|
||||
const player = document.createElement("video");
|
||||
player.dataset.participantId = participantId;
|
||||
|
||||
videoContainer.appendChild(player);
|
||||
|
||||
await startPlayer(player, track);
|
||||
await player.play();
|
||||
|
||||
return player;
|
||||
}
|
||||
|
||||
async function buildAudioPlayer(track, participantId) {
|
||||
const audioContainer = document.getElementById("audio-container");
|
||||
const player = document.createElement("audio");
|
||||
player.dataset.participantId = participantId;
|
||||
|
||||
// Create a new button for controlling audio
|
||||
const audioControlButton = document.createElement("button");
|
||||
audioControlButton.className = "ui primary green button"
|
||||
audioControlButton.innerText = track._mediaTag == "cam-audio" ? "english" : track._mediaTag;
|
||||
audioControlButton.dataset.participantId = participantId;
|
||||
audioControlButton.onclick = () => {
|
||||
if (player.paused) {
|
||||
|
||||
player.play();
|
||||
audioControlButton.className = "ui primary red button"
|
||||
} else {
|
||||
player.pause();
|
||||
audioControlButton.className = "ui primary green button"
|
||||
}
|
||||
};
|
||||
|
||||
audioContainer.appendChild(player);
|
||||
audioContainer.appendChild(audioControlButton);
|
||||
|
||||
await startPlayer(player, track);
|
||||
player.pause()
|
||||
|
||||
return player;
|
||||
}
|
||||
|
||||
function subscribeToTracks(participantId) {
|
||||
console.log(`subscribing to track`);
|
||||
|
||||
if (participantId === "local") {
|
||||
return;
|
||||
}
|
||||
|
||||
callObject.updateParticipant(participantId, {
|
||||
setSubscribedTracks: {
|
||||
audio: true,
|
||||
video: true,
|
||||
custom: true,
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
function startDaily() {
|
||||
enableJoinButton(true);
|
||||
enableLeaveButton(false);
|
||||
|
||||
window.callObject = window.DailyIframe.createCallObject({});
|
||||
|
||||
callObject.on("participant-joined", (e) => {
|
||||
if (!e.participant.local) {
|
||||
console.log("participant-joined", e.participant);
|
||||
subscribeToTracks(e.participant.session_id);
|
||||
}
|
||||
});
|
||||
|
||||
callObject.on("participant-left", (e) => {
|
||||
console.log("participant-left", e.participant.session_id);
|
||||
destroyParticipantPlayers(e.participant.session_id);
|
||||
});
|
||||
|
||||
callObject.on("track-started", async (e) => {
|
||||
console.log("track-started", e.track);
|
||||
if (e.track.kind === "video") {
|
||||
await buildVideoPlayer(e.track, e.participant.session_id);
|
||||
} else if (e.track.kind === "audio") {
|
||||
await buildAudioPlayer(e.track, e.participant.session_id);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
async function joinRoom() {
|
||||
enableJoinButton(false);
|
||||
enableLeaveButton(true);
|
||||
|
||||
const meetingUrl = document.getElementById("meeting-url").value;
|
||||
|
||||
callObject.join({
|
||||
url: meetingUrl,
|
||||
startVideoOff: true,
|
||||
startAudioOff: true,
|
||||
subscribeToTracksAutomatically: false,
|
||||
receiveSettings: {
|
||||
base: { video: { layer: 0 } },
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
async function leaveRoom() {
|
||||
enableJoinButton(true);
|
||||
enableLeaveButton(false);
|
||||
|
||||
callObject.leave();
|
||||
|
||||
const videoContainer = document.getElementById("video-container");
|
||||
videoContainer.replaceChildren();
|
||||
|
||||
const audioContainer = document.getElementById("audio-container");
|
||||
audioContainer.replaceChildren();
|
||||
}
|
||||
</script>
|
||||
|
||||
<body onload="startDaily()">
|
||||
<div class="ui centered page grid" style="margin-top: 30px">
|
||||
<div class="ten wide column">
|
||||
<div class="ui form" style="margin-top: 30px">
|
||||
<div class="field">
|
||||
<label>Meeting URL</label>
|
||||
<input id="meeting-url" value="" />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="ui centered aligned header" style="margin-top: 30px">
|
||||
<button id="join-button" class="ui primary button" onclick="joinRoom()">
|
||||
Join
|
||||
</button>
|
||||
<button id="leave-button" class="ui button" onclick="leaveRoom()">
|
||||
Leave
|
||||
</button>
|
||||
</div>
|
||||
<div id="tile" class="ui container" style="margin-top: 30px">
|
||||
<div id="tile" class="ui center aligned grid">
|
||||
<div id="audio-container"></div><br/>
|
||||
</div>
|
||||
</div>
|
||||
<div id="tile" class="ui container" style="margin-top: 30px">
|
||||
<div id="tile" class="ui center aligned grid">
|
||||
<div id="video-container" class="ui segment"></div>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
Binary file not shown.
@@ -1,5 +0,0 @@
|
||||
aiofiles
|
||||
python-dotenv
|
||||
fastapi[all]
|
||||
uvicorn
|
||||
pipecat-ai[daily,deepgram,openai,silero,cartesia]
|
||||
@@ -1,55 +0,0 @@
|
||||
#
|
||||
# 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)
|
||||
@@ -41,7 +41,8 @@ async def main(room_url: str, token: str):
|
||||
api_key=daily_api_key,
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=False,
|
||||
camera_out_enabled=False,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
transcription_enabled=True,
|
||||
),
|
||||
|
||||
@@ -32,9 +32,9 @@ async def main(room_url: str, token: str):
|
||||
token,
|
||||
"bot",
|
||||
DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
python-dotenv==1.0.1
|
||||
modal==0.71.3
|
||||
pipecat-ai[daily,silero,cartesia,openai]
|
||||
pipecat-ai[daily,silero,cartesia,openai]==0.0.52
|
||||
fastapi==0.115.6
|
||||
aiohttp==3.11.11
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
@@ -22,23 +21,44 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
# Check if we're in local development mode
|
||||
LOCAL_RUN = os.getenv("LOCAL_RUN")
|
||||
if LOCAL_RUN:
|
||||
import asyncio
|
||||
import webbrowser
|
||||
|
||||
try:
|
||||
from local_runner import configure
|
||||
except ImportError:
|
||||
logger.error("Could not import local_runner module. Local development mode may not work.")
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv(override=True)
|
||||
|
||||
# Check if we're in local development mode
|
||||
LOCAL_RUN = os.getenv("LOCAL_RUN")
|
||||
|
||||
|
||||
async def main(transport: DailyTransport):
|
||||
async def main(room_url: str, token: str):
|
||||
"""Main pipeline setup and execution function.
|
||||
|
||||
Args:
|
||||
transport: The DailyTransport object for the bot
|
||||
room_url: The Daily room URL
|
||||
token: The Daily room token
|
||||
"""
|
||||
logger.debug("Starting bot")
|
||||
logger.debug("Starting bot in room: {}", room_url)
|
||||
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
|
||||
api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
@@ -106,25 +126,10 @@ async def bot(args: DailySessionArguments):
|
||||
body: The configuration object from the request body
|
||||
session_id: The session ID for logging
|
||||
"""
|
||||
from pipecat.audio.filters.krisp_filter import KrispFilter
|
||||
|
||||
logger.info(f"Bot process initialized {args.room_url} {args.token}")
|
||||
|
||||
transport = DailyTransport(
|
||||
args.room_url,
|
||||
args.token,
|
||||
"Pipecat Bot",
|
||||
DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_in_filter=None if LOCAL_RUN else KrispFilter(),
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
try:
|
||||
await main(transport)
|
||||
await main(args.room_url, args.token)
|
||||
logger.info("Bot process completed")
|
||||
except Exception as e:
|
||||
logger.exception(f"Error in bot process: {str(e)}")
|
||||
@@ -132,27 +137,18 @@ async def bot(args: DailySessionArguments):
|
||||
|
||||
|
||||
# Local development functions
|
||||
async def local_daily():
|
||||
async def local_main():
|
||||
"""Function for local development testing."""
|
||||
from local_runner import configure
|
||||
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Pipecat Bot",
|
||||
DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
await main(transport)
|
||||
|
||||
logger.warning("_")
|
||||
logger.warning("_")
|
||||
logger.warning(f"Talk to your voice agent here: {room_url}")
|
||||
logger.warning("_")
|
||||
logger.warning("_")
|
||||
webbrowser.open(room_url)
|
||||
await main(room_url, token)
|
||||
except Exception as e:
|
||||
logger.exception(f"Error in local development mode: {e}")
|
||||
|
||||
@@ -160,6 +156,6 @@ async def local_daily():
|
||||
# Local development entry point
|
||||
if LOCAL_RUN and __name__ == "__main__":
|
||||
try:
|
||||
asyncio.run(local_daily())
|
||||
asyncio.run(local_main())
|
||||
except Exception as e:
|
||||
logger.exception(f"Failed to run in local mode: {e}")
|
||||
|
||||
@@ -1,4 +1,2 @@
|
||||
CARTESIA_API_KEY=
|
||||
OPENAI_API_KEY=
|
||||
# Local dev only
|
||||
DAILY_API_KEY=
|
||||
OPENAI_API_KEY=
|
||||
@@ -7,7 +7,6 @@
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from fastapi import HTTPException
|
||||
|
||||
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
|
||||
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
agent_name = "my-first-agent"
|
||||
image = "your-username/my-first-agent:0.1"
|
||||
image_credentials = "your-dockerhub-creds"
|
||||
secret_set = "my-first-agent-secrets"
|
||||
enable_krisp = true
|
||||
|
||||
[scaling]
|
||||
min_instances = 0
|
||||
|
||||
51
examples/fal-smart-turn/.gitignore
vendored
51
examples/fal-smart-turn/.gitignore
vendored
@@ -1,51 +0,0 @@
|
||||
# Python
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
*.so
|
||||
.Python
|
||||
build/
|
||||
dist/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
.pytest_cache/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.env
|
||||
.venv
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
|
||||
# JavaScript/Node.js
|
||||
node_modules/
|
||||
dist/
|
||||
dist-ssr/
|
||||
*.local
|
||||
.env.local
|
||||
.env.development.local
|
||||
.env.test.local
|
||||
.env.production.local
|
||||
|
||||
# Logs
|
||||
logs/
|
||||
*.log
|
||||
npm-debug.log*
|
||||
yarn-debug.log*
|
||||
yarn-error.log*
|
||||
pnpm-debug.log*
|
||||
|
||||
# Editor/IDE
|
||||
.vscode/*
|
||||
!.vscode/extensions.json
|
||||
.idea/
|
||||
*.swp
|
||||
*.swo
|
||||
.DS_Store
|
||||
|
||||
# Project specific
|
||||
runpod.toml
|
||||
@@ -1,152 +0,0 @@
|
||||
# Smart Turn Detection Demo
|
||||
|
||||
This demo showcases Pipecat's Smart Turn Detection feature - an advanced conversational turn detection system that uses machine learning to identify when a speaker has finished their turn in a conversation. Unlike basic Voice Activity Detection (VAD) which only detects speech vs. silence, Smart Turn detects natural conversational cues like intonation patterns, pacing, and linguistic signals.
|
||||
|
||||
This demo uses the [pipecat-ai/smart-turn](https://huggingface.co/pipecat-ai/smart-turn) model - an open-source, community-driven conversational turn detection model designed to provide more natural turn-taking in voice interactions. The model is being hosted on Fal's infrastructure for GPU acceleration, offering inference times between 50-60ms.
|
||||
|
||||
In the client UI, you can see the transcription messages along with the smart-turn model's prediction results in real-time.
|
||||
|
||||
## Try the demo
|
||||
|
||||
Try the hosted version of the demo here: https://pcc-smart-turn.vercel.app/.
|
||||
|
||||
## Run the demo locally
|
||||
|
||||
### Run the Server
|
||||
|
||||
1. Set up and activate your virtual environment:
|
||||
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate # On Windows: venv\Scripts\activate
|
||||
```
|
||||
|
||||
2. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
3. Create your .env file and set your env vars:
|
||||
|
||||
```bash
|
||||
cp env.example .env
|
||||
```
|
||||
|
||||
Keys to provide:
|
||||
|
||||
- GOOGLE_API_KEY
|
||||
- CARTESIA_API_KEY
|
||||
- DEEPGRAM_API_KEY
|
||||
- DAILY_API_KEY
|
||||
- FAL_SMART_TURN_API_KEY
|
||||
|
||||
4. Run the server:
|
||||
|
||||
```bash
|
||||
LOCAL=1 python server.py
|
||||
```
|
||||
|
||||
### Run the client
|
||||
|
||||
1. Open a new terminal and navigate to the client directory:
|
||||
|
||||
```bash
|
||||
cd client
|
||||
```
|
||||
|
||||
2. Install dependencies:
|
||||
|
||||
```bash
|
||||
npm install
|
||||
```
|
||||
|
||||
3. Create your .env.local file:
|
||||
|
||||
```bash
|
||||
cp env.local.example .env.local
|
||||
```
|
||||
|
||||
> Note: No keys need to be modified. `NEXT_PUBLIC_API_BASE_URL` is already configured for local use.
|
||||
|
||||
4. Start the development server:
|
||||
|
||||
```bash
|
||||
npm run dev
|
||||
```
|
||||
|
||||
5. Open [http://localhost:3000](http://localhost:3000) in your browser.
|
||||
|
||||
## Deploy the app
|
||||
|
||||
### Deploy the server to Pipecat Cloud
|
||||
|
||||
1. Navigate to server
|
||||
|
||||
```bash
|
||||
cd server
|
||||
```
|
||||
|
||||
2. You should already have a .env set up from running locally. If not, do that now.
|
||||
|
||||
3. Update your build and deploy scripts.
|
||||
|
||||
- In build.sh, set `DOCKER_USERNAME` and `AGENT_NAME`.
|
||||
- In pcc-deploy.toml, set `image`, which specifies where your Docker image is stored.
|
||||
|
||||
4. Build your Docker image by running the build script:
|
||||
|
||||
```bash
|
||||
./build.sh
|
||||
```
|
||||
|
||||
> Note: This builds, tags and pushes your docker image and assumes Docker Hub is the container registry.
|
||||
|
||||
5. Make sure you have the Pipecat Cloud CLI installed:
|
||||
|
||||
```bash
|
||||
pip install pipecatcloud
|
||||
```
|
||||
|
||||
6. Login via the Pipecat Cloud CLI:
|
||||
|
||||
```bash
|
||||
pcc auth login
|
||||
```
|
||||
|
||||
> Note: If you don't have an account, sign up at https://pipecat.daily.co.
|
||||
|
||||
7. Add a secrets set:
|
||||
|
||||
```bash
|
||||
pcc secrets set pcc-smart-turn-secrets --file .env
|
||||
```
|
||||
|
||||
8. Deploy your agent:
|
||||
|
||||
```bash
|
||||
pcc deploy
|
||||
```
|
||||
|
||||
> Note: This uses your pcc-deploy.toml settings. Modify as needed.
|
||||
|
||||
### Deploy the client to Vercel
|
||||
|
||||
This project uses TypeScript, React, and Next.js, making it a perfect fit for [Vercel](https://vercel.com/).
|
||||
|
||||
- In your client directory, install Vercel's CLI tool: `npm install -g vercel`
|
||||
- Verify it's installed using `vercel --version`
|
||||
- Log in your Vercel account using `vercel login`
|
||||
- Deploy your client to Vercel using `vercel`
|
||||
|
||||
Follow the vercel prompts to deploy your project.
|
||||
|
||||
### Test your deployed app
|
||||
|
||||
Now with the client and server deployed, you can join the call using your Vercel URL.
|
||||
|
||||
See the debug information for the Smart Turn data. It prints a log line for each smart-turn inference:
|
||||
|
||||
```
|
||||
Smart Turn: COMPLETE, Probability: 95.3%, Model inference: 65.23ms, Server processing: 82.09ms, End-to-end: 245.43ms
|
||||
```
|
||||
41
examples/fal-smart-turn/client/.gitignore
vendored
41
examples/fal-smart-turn/client/.gitignore
vendored
@@ -1,41 +0,0 @@
|
||||
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
|
||||
|
||||
# dependencies
|
||||
/node_modules
|
||||
/.pnp
|
||||
.pnp.*
|
||||
.yarn/*
|
||||
!.yarn/patches
|
||||
!.yarn/plugins
|
||||
!.yarn/releases
|
||||
!.yarn/versions
|
||||
|
||||
# testing
|
||||
/coverage
|
||||
|
||||
# next.js
|
||||
/.next/
|
||||
/out/
|
||||
|
||||
# production
|
||||
/build
|
||||
|
||||
# misc
|
||||
.DS_Store
|
||||
*.pem
|
||||
|
||||
# debug
|
||||
npm-debug.log*
|
||||
yarn-debug.log*
|
||||
yarn-error.log*
|
||||
.pnpm-debug.log*
|
||||
|
||||
# env files (can opt-in for committing if needed)
|
||||
.env*
|
||||
|
||||
# vercel
|
||||
.vercel
|
||||
|
||||
# typescript
|
||||
*.tsbuildinfo
|
||||
next-env.d.ts
|
||||
@@ -1,3 +0,0 @@
|
||||
NEXT_PUBLIC_API_BASE_URL=http://localhost:7860
|
||||
PIPECAT_CLOUD_API_KEY=
|
||||
AGENT_NAME=pcc-smart-turn
|
||||
@@ -1,16 +0,0 @@
|
||||
import { dirname } from "path";
|
||||
import { fileURLToPath } from "url";
|
||||
import { FlatCompat } from "@eslint/eslintrc";
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = dirname(__filename);
|
||||
|
||||
const compat = new FlatCompat({
|
||||
baseDirectory: __dirname,
|
||||
});
|
||||
|
||||
const eslintConfig = [
|
||||
...compat.extends("next/core-web-vitals", "next/typescript"),
|
||||
];
|
||||
|
||||
export default eslintConfig;
|
||||
@@ -1,7 +0,0 @@
|
||||
import type { NextConfig } from "next";
|
||||
|
||||
const nextConfig: NextConfig = {
|
||||
/* config options here */
|
||||
};
|
||||
|
||||
export default nextConfig;
|
||||
5174
examples/fal-smart-turn/client/package-lock.json
generated
5174
examples/fal-smart-turn/client/package-lock.json
generated
File diff suppressed because it is too large
Load Diff
@@ -1,28 +0,0 @@
|
||||
{
|
||||
"name": "my-nextjs-app",
|
||||
"version": "0.1.0",
|
||||
"private": true,
|
||||
"scripts": {
|
||||
"dev": "next dev",
|
||||
"build": "next build",
|
||||
"start": "next start",
|
||||
"lint": "next lint"
|
||||
},
|
||||
"dependencies": {
|
||||
"@pipecat-ai/client-js": "^0.3.5",
|
||||
"@pipecat-ai/client-react": "^0.3.5",
|
||||
"@pipecat-ai/daily-transport": "^0.3.10",
|
||||
"next": "15.3.1",
|
||||
"react": "^19.0.0",
|
||||
"react-dom": "^19.0.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@eslint/eslintrc": "^3",
|
||||
"@types/node": "^20",
|
||||
"@types/react": "^19",
|
||||
"@types/react-dom": "^19",
|
||||
"eslint": "^9",
|
||||
"eslint-config-next": "15.2.3",
|
||||
"typescript": "^5"
|
||||
}
|
||||
}
|
||||
@@ -1,7 +0,0 @@
|
||||
<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M3.3088 5.05615C3.64682 4.92779 4.02833 5.02411 4.26653 5.29797L7.36884 8.86461H16.6312L19.7335 5.29797C19.9717 5.02411 20.3532 4.92779 20.6912 5.05615C21.0292 5.18452 21.253 5.51072 21.253 5.87504V13.75H24V15.5H19.5181V8.19909L17.6762 10.3167C17.5115 10.506 17.2738 10.6146 17.0241 10.6146H6.9759C6.72616 10.6146 6.48854 10.506 6.32383 10.3167L4.48193 8.19909V15.5H0V13.75H2.74699V5.87504C2.74699 5.51072 2.97078 5.18452 3.3088 5.05615Z" fill="black"/>
|
||||
<path d="M19.5181 17.25H24V19H19.5181V17.25Z" fill="black"/>
|
||||
<path d="M0 17.25H4.48193V19H0V17.25Z" fill="black"/>
|
||||
<path d="M9.25301 14.3333C9.25301 14.9777 8.73517 15.5 8.09639 15.5C7.4576 15.5 6.93976 14.9777 6.93976 14.3333C6.93976 13.689 7.4576 13.1667 8.09639 13.1667C8.73517 13.1667 9.25301 13.689 9.25301 14.3333Z" fill="black"/>
|
||||
<path d="M17.0602 14.3333C17.0602 14.9777 16.5424 15.5 15.9036 15.5C15.2648 15.5 14.747 14.9777 14.747 14.3333C14.747 13.689 15.2648 13.1667 15.9036 13.1667C16.5424 13.1667 17.0602 13.689 17.0602 14.3333Z" fill="black"/>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 1.1 KiB |
@@ -1,44 +0,0 @@
|
||||
import { NextResponse, NextRequest } from 'next/server';
|
||||
|
||||
export async function POST(request: NextRequest) {
|
||||
const { MY_CUSTOM_DATA } = await request.json();
|
||||
|
||||
try {
|
||||
const response = await fetch(
|
||||
`https://api.pipecat.daily.co/v1/public/${process.env.AGENT_NAME}/start`,
|
||||
{
|
||||
method: 'POST',
|
||||
headers: {
|
||||
Authorization: `Bearer ${process.env.PIPECAT_CLOUD_API_KEY}`,
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
// Create Daily room
|
||||
createDailyRoom: true,
|
||||
// Optionally set Daily room properties
|
||||
dailyRoomProperties: { start_video_off: true },
|
||||
// Optionally pass custom data to the bot
|
||||
body: { MY_CUSTOM_DATA },
|
||||
}),
|
||||
}
|
||||
);
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`API responded with status: ${response.status}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
|
||||
// Transform the response to match what RTVI client expects
|
||||
return NextResponse.json({
|
||||
room_url: data.dailyRoom,
|
||||
token: data.dailyToken,
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('API error:', error);
|
||||
return NextResponse.json(
|
||||
{ error: 'Failed to start agent' },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -1,82 +0,0 @@
|
||||
body {
|
||||
margin: 0;
|
||||
padding: 20px;
|
||||
font-family: Arial, sans-serif;
|
||||
background-color: #f0f0f0;
|
||||
}
|
||||
|
||||
.app {
|
||||
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;
|
||||
}
|
||||
|
||||
button:disabled {
|
||||
opacity: 0.5;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
|
||||
.connect-btn {
|
||||
background-color: #4caf50;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.disconnect-btn {
|
||||
background-color: #f44336;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.main-content {
|
||||
background-color: #fff;
|
||||
border-radius: 8px;
|
||||
padding: 20px;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
.bot-container {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.video-container {
|
||||
width: 640px;
|
||||
height: 360px;
|
||||
background-color: #ddd;
|
||||
margin-bottom: 20px;
|
||||
border-radius: 8px;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.video-container video {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
object-fit: cover;
|
||||
}
|
||||
|
||||
.mic-enabled {
|
||||
background-color: #4caf50;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.mic-disabled {
|
||||
background-color: #f44336;
|
||||
color: white;
|
||||
}
|
||||
@@ -1,27 +0,0 @@
|
||||
import './globals.css';
|
||||
import { RTVIProvider } from '@/providers/RTVIProvider';
|
||||
|
||||
export const metadata = {
|
||||
title: 'Pipecat React Client',
|
||||
description: 'Pipecat RTVI Client using Next.js',
|
||||
icons: {
|
||||
icon: [{ url: '/favicon.svg', type: 'image/svg+xml' }],
|
||||
},
|
||||
};
|
||||
|
||||
export default function RootLayout({
|
||||
children,
|
||||
}: {
|
||||
children: React.ReactNode;
|
||||
}) {
|
||||
return (
|
||||
<html lang="en">
|
||||
<head>
|
||||
<link rel="icon" href="/favicon.svg" type="image/svg+xml" />
|
||||
</head>
|
||||
<body>
|
||||
<RTVIProvider>{children}</RTVIProvider>
|
||||
</body>
|
||||
</html>
|
||||
);
|
||||
}
|
||||
@@ -1,41 +0,0 @@
|
||||
'use client';
|
||||
|
||||
import {
|
||||
RTVIClientAudio,
|
||||
RTVIClientVideo,
|
||||
useRTVIClientTransportState,
|
||||
} from '@pipecat-ai/client-react';
|
||||
import { ConnectButton } from '../components/ConnectButton';
|
||||
import { StatusDisplay } from '../components/StatusDisplay';
|
||||
import { DebugDisplay } from '../components/DebugDisplay';
|
||||
|
||||
function BotVideo() {
|
||||
const transportState = useRTVIClientTransportState();
|
||||
const isConnected = transportState !== 'disconnected';
|
||||
|
||||
return (
|
||||
<div className="bot-container">
|
||||
<div className="video-container">
|
||||
{isConnected && <RTVIClientVideo participant="bot" fit="cover" />}
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
export default function Home() {
|
||||
return (
|
||||
<div className="app">
|
||||
<div className="status-bar">
|
||||
<StatusDisplay />
|
||||
<ConnectButton />
|
||||
</div>
|
||||
|
||||
<div className="main-content">
|
||||
<BotVideo />
|
||||
</div>
|
||||
|
||||
<DebugDisplay />
|
||||
<RTVIClientAudio />
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1,40 +0,0 @@
|
||||
import {
|
||||
useRTVIClient,
|
||||
useRTVIClientTransportState,
|
||||
} from '@pipecat-ai/client-react';
|
||||
|
||||
export function ConnectButton() {
|
||||
const client = useRTVIClient();
|
||||
const transportState = useRTVIClientTransportState();
|
||||
const isConnected = ['connected', 'ready'].includes(transportState);
|
||||
|
||||
const handleClick = async () => {
|
||||
if (!client) {
|
||||
console.error('RTVI client is not initialized');
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
if (isConnected) {
|
||||
await client.disconnect();
|
||||
} else {
|
||||
await client.connect();
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Connection error:', error);
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="controls">
|
||||
<button
|
||||
className={isConnected ? 'disconnect-btn' : 'connect-btn'}
|
||||
onClick={handleClick}
|
||||
disabled={
|
||||
!client || ['connecting', 'disconnecting'].includes(transportState)
|
||||
}>
|
||||
{isConnected ? 'Disconnect' : 'Connect'}
|
||||
</button>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1,26 +0,0 @@
|
||||
.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: 200px;
|
||||
overflow-y: auto;
|
||||
background-color: #f8f8f8;
|
||||
padding: 10px;
|
||||
border-radius: 4px;
|
||||
font-family: monospace;
|
||||
font-size: 12px;
|
||||
line-height: 1.4;
|
||||
}
|
||||
|
||||
.debug-log div {
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
@@ -1,171 +0,0 @@
|
||||
import { useRef, useCallback } from 'react';
|
||||
import {
|
||||
Participant,
|
||||
RTVIEvent,
|
||||
TransportState,
|
||||
TranscriptData,
|
||||
BotLLMTextData,
|
||||
} from '@pipecat-ai/client-js';
|
||||
import { useRTVIClient, useRTVIClientEvent } from '@pipecat-ai/client-react';
|
||||
import './DebugDisplay.css';
|
||||
|
||||
interface SmartTurnResultData {
|
||||
type: 'smart_turn_result';
|
||||
is_complete: boolean;
|
||||
probability: number;
|
||||
inference_time_ms: number; // Pure model inference time
|
||||
server_total_time_ms: number; // Server processing time
|
||||
e2e_processing_time_ms: number; // Complete end-to-end time
|
||||
}
|
||||
|
||||
export function DebugDisplay() {
|
||||
const debugLogRef = useRef<HTMLDivElement>(null);
|
||||
const client = useRTVIClient();
|
||||
|
||||
const log = useCallback((message: string) => {
|
||||
if (!debugLogRef.current) return;
|
||||
|
||||
const entry = document.createElement('div');
|
||||
entry.textContent = `${new Date().toISOString()} - ${message}`;
|
||||
|
||||
// Add styling based on message type
|
||||
if (message.startsWith('User: ')) {
|
||||
entry.style.color = '#2196F3'; // blue for user
|
||||
} else if (message.startsWith('Bot: ')) {
|
||||
entry.style.color = '#4CAF50'; // green for bot
|
||||
} else if (message.includes('Smart Turn:')) {
|
||||
entry.style.color = '#9C27B0'; // purple for smart turn
|
||||
}
|
||||
|
||||
debugLogRef.current.appendChild(entry);
|
||||
debugLogRef.current.scrollTop = debugLogRef.current.scrollHeight;
|
||||
}, []);
|
||||
|
||||
// Log transport state changes
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.TransportStateChanged,
|
||||
useCallback(
|
||||
(state: TransportState) => {
|
||||
log(`Transport state changed: ${state}`);
|
||||
},
|
||||
[log]
|
||||
)
|
||||
);
|
||||
|
||||
// Log bot connection events
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.BotConnected,
|
||||
useCallback(
|
||||
(participant?: Participant) => {
|
||||
log(`Bot connected: ${JSON.stringify(participant)}`);
|
||||
},
|
||||
[log]
|
||||
)
|
||||
);
|
||||
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.BotDisconnected,
|
||||
useCallback(
|
||||
(participant?: Participant) => {
|
||||
log(`Bot disconnected: ${JSON.stringify(participant)}`);
|
||||
},
|
||||
[log]
|
||||
)
|
||||
);
|
||||
|
||||
// Log track events
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.TrackStarted,
|
||||
useCallback(
|
||||
(track: MediaStreamTrack, participant?: Participant) => {
|
||||
log(
|
||||
`Track started: ${track.kind} from ${participant?.name || 'unknown'}`
|
||||
);
|
||||
},
|
||||
[log]
|
||||
)
|
||||
);
|
||||
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.TrackStopped,
|
||||
useCallback(
|
||||
(track: MediaStreamTrack, participant?: Participant) => {
|
||||
log(
|
||||
`Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`
|
||||
);
|
||||
},
|
||||
[log]
|
||||
)
|
||||
);
|
||||
|
||||
// Log bot ready state and check tracks
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.BotReady,
|
||||
useCallback(() => {
|
||||
log(`Bot ready`);
|
||||
|
||||
if (!client) return;
|
||||
|
||||
const tracks = client.tracks();
|
||||
log(
|
||||
`Available tracks: ${JSON.stringify({
|
||||
local: {
|
||||
audio: !!tracks.local.audio,
|
||||
video: !!tracks.local.video,
|
||||
},
|
||||
bot: {
|
||||
audio: !!tracks.bot?.audio,
|
||||
video: !!tracks.bot?.video,
|
||||
},
|
||||
})}`
|
||||
);
|
||||
}, [client, log])
|
||||
);
|
||||
|
||||
// Log transcripts
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.UserTranscript,
|
||||
useCallback(
|
||||
(data: TranscriptData) => {
|
||||
// Only log final transcripts
|
||||
if (data.final) {
|
||||
log(`User: ${data.text}`);
|
||||
}
|
||||
},
|
||||
[log]
|
||||
)
|
||||
);
|
||||
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.BotTranscript,
|
||||
useCallback(
|
||||
(data: BotLLMTextData) => {
|
||||
log(`Bot: ${data.text}`);
|
||||
},
|
||||
[log]
|
||||
)
|
||||
);
|
||||
|
||||
useRTVIClientEvent(
|
||||
RTVIEvent.ServerMessage,
|
||||
useCallback(
|
||||
(data: SmartTurnResultData) => {
|
||||
log(
|
||||
`Smart Turn:
|
||||
${data.is_complete ? 'COMPLETE' : 'INCOMPLETE'},
|
||||
Probability: ${(data.probability * 100).toFixed(1)}%,
|
||||
Model inference: ${data.inference_time_ms?.toFixed(2) || 'N/A'}ms,
|
||||
Server processing: ${data.server_total_time_ms?.toFixed(2) || 'N/A'}ms,
|
||||
End-to-end: ${data.e2e_processing_time_ms?.toFixed(2) || 'N/A'}ms`
|
||||
);
|
||||
},
|
||||
[log]
|
||||
)
|
||||
);
|
||||
return (
|
||||
<div className="debug-panel">
|
||||
<h3>Debug Info</h3>
|
||||
<div ref={debugLogRef} className="debug-log" />
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1,11 +0,0 @@
|
||||
import { useRTVIClientTransportState } from '@pipecat-ai/client-react';
|
||||
|
||||
export function StatusDisplay() {
|
||||
const transportState = useRTVIClientTransportState();
|
||||
|
||||
return (
|
||||
<div className="status">
|
||||
Status: <span>{transportState}</span>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1,43 +0,0 @@
|
||||
'use client';
|
||||
|
||||
import { RTVIClient } from '@pipecat-ai/client-js';
|
||||
import { DailyTransport } from '@pipecat-ai/daily-transport';
|
||||
import { RTVIClientProvider } from '@pipecat-ai/client-react';
|
||||
import { PropsWithChildren, useEffect, useState } from 'react';
|
||||
|
||||
// Get the API base URL from environment variables
|
||||
// Default to "/api" if not specified
|
||||
// "/api" is the default for Next.js API routes and used
|
||||
// for the Pipecat Cloud deployed agent
|
||||
const API_BASE_URL = process.env.NEXT_PUBLIC_API_BASE_URL || '/api';
|
||||
|
||||
console.log('Using API base URL:', API_BASE_URL);
|
||||
|
||||
export function RTVIProvider({ children }: PropsWithChildren) {
|
||||
const [client, setClient] = useState<RTVIClient | null>(null);
|
||||
|
||||
useEffect(() => {
|
||||
const transport = new DailyTransport();
|
||||
|
||||
const rtviClient = new RTVIClient({
|
||||
transport,
|
||||
params: {
|
||||
baseUrl: API_BASE_URL,
|
||||
endpoints: {
|
||||
connect: '/connect',
|
||||
},
|
||||
requestData: { foo: 'bar' },
|
||||
},
|
||||
enableMic: true,
|
||||
enableCam: false,
|
||||
});
|
||||
|
||||
setClient(rtviClient);
|
||||
}, []);
|
||||
|
||||
if (!client) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return <RTVIClientProvider client={client}>{children}</RTVIClientProvider>;
|
||||
}
|
||||
@@ -1,28 +0,0 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
"target": "ES2017",
|
||||
"lib": ["dom", "dom.iterable", "esnext"],
|
||||
"allowJs": true,
|
||||
"skipLibCheck": true,
|
||||
"strict": true,
|
||||
"noEmit": true,
|
||||
"esModuleInterop": true,
|
||||
"module": "esnext",
|
||||
"moduleResolution": "bundler",
|
||||
"resolveJsonModule": true,
|
||||
"isolatedModules": true,
|
||||
"jsx": "preserve",
|
||||
"incremental": true,
|
||||
"plugins": [
|
||||
{
|
||||
"name": "next"
|
||||
}
|
||||
],
|
||||
"paths": {
|
||||
"@/components/*": ["./src/components/*"],
|
||||
"@/providers/*": ["./src/providers/*"]
|
||||
}
|
||||
},
|
||||
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
|
||||
"exclude": ["node_modules"]
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
FROM dailyco/pipecat-base:latest
|
||||
|
||||
COPY ./requirements.txt requirements.txt
|
||||
|
||||
RUN pip install --no-cache-dir --upgrade -r requirements.txt
|
||||
|
||||
COPY ./assets assets
|
||||
COPY ./bot.py bot.py
|
||||
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@@ -1,299 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
from pipecatcloud.agent import DailySessionArguments
|
||||
|
||||
from pipecat.audio.turn.smart_turn.fal_smart_turn import FalSmartTurnAnalyzer
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import (
|
||||
BotStartedSpeakingFrame,
|
||||
BotStoppedSpeakingFrame,
|
||||
Frame,
|
||||
MetricsFrame,
|
||||
OutputImageRawFrame,
|
||||
SpriteFrame,
|
||||
)
|
||||
from pipecat.metrics.metrics import SmartTurnMetricsData
|
||||
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.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.processors.frameworks.rtvi import (
|
||||
RTVIConfig,
|
||||
RTVIObserver,
|
||||
RTVIProcessor,
|
||||
RTVIServerMessageFrame,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# Check if we're in local development mode
|
||||
LOCAL = os.getenv("LOCAL")
|
||||
|
||||
logger.remove()
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
sprites = []
|
||||
script_dir = os.path.dirname(__file__)
|
||||
|
||||
# Load sequential animation frames
|
||||
for i in range(1, 26):
|
||||
# Build the full path to the image file
|
||||
full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
|
||||
# Get the filename without the extension to use as the dictionary key
|
||||
# Open the image and convert it to bytes
|
||||
with Image.open(full_path) as img:
|
||||
sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
|
||||
|
||||
# Create a smooth animation by adding reversed frames
|
||||
flipped = sprites[::-1]
|
||||
sprites.extend(flipped)
|
||||
|
||||
# Define static and animated states
|
||||
quiet_frame = sprites[0] # Static frame for when bot is listening
|
||||
talking_frame = SpriteFrame(images=sprites) # Animation sequence for when bot is talking
|
||||
|
||||
|
||||
class TalkingAnimation(FrameProcessor):
|
||||
"""Manages the bot's visual animation states.
|
||||
|
||||
Switches between static (listening) and animated (talking) states based on
|
||||
the bot's current speaking status.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._is_talking = False
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
"""Process incoming frames and update animation state.
|
||||
|
||||
Args:
|
||||
frame: The incoming frame to process
|
||||
direction: The direction of frame flow in the pipeline
|
||||
"""
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
# Switch to talking animation when bot starts speaking
|
||||
if isinstance(frame, BotStartedSpeakingFrame):
|
||||
if not self._is_talking:
|
||||
await self.push_frame(talking_frame)
|
||||
self._is_talking = True
|
||||
# Return to static frame when bot stops speaking
|
||||
elif isinstance(frame, BotStoppedSpeakingFrame):
|
||||
await self.push_frame(quiet_frame)
|
||||
self._is_talking = False
|
||||
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class SmartTurnMetricsProcessor(FrameProcessor):
|
||||
"""Processes the metrics data from Smart Turn Analyzer.
|
||||
|
||||
This processor is responsible for handling smart turn metrics data
|
||||
and forwarding it to the client UI via RTVI.
|
||||
"""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
"""Process incoming frames and handle Smart Turn metrics.
|
||||
|
||||
Args:
|
||||
frame: The incoming frame to process
|
||||
direction: The direction of frame flow in the pipeline
|
||||
"""
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
# Handle Smart Turn metrics
|
||||
if isinstance(frame, MetricsFrame):
|
||||
for metrics in frame.data:
|
||||
if isinstance(metrics, SmartTurnMetricsData):
|
||||
logger.info(f"Smart Turn metrics: {metrics}")
|
||||
|
||||
# Create a payload with the smart turn prediction data
|
||||
smart_turn_data = {
|
||||
"type": "smart_turn_result",
|
||||
"is_complete": metrics.is_complete,
|
||||
"probability": metrics.probability,
|
||||
"inference_time_ms": metrics.inference_time_ms,
|
||||
"server_total_time_ms": metrics.server_total_time_ms,
|
||||
"e2e_processing_time_ms": metrics.e2e_processing_time_ms,
|
||||
}
|
||||
|
||||
# Send the data to the client via RTVI
|
||||
rtvi_frame = RTVIServerMessageFrame(data=smart_turn_data)
|
||||
await self.push_frame(rtvi_frame)
|
||||
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
async def main(transport: DailyTransport):
|
||||
# Configure your STT, LLM, and TTS services here
|
||||
# Swap out different processors or properties to customize your bot
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
# Set up the initial context for the conversation
|
||||
# You can specified initial system and assistant messages here
|
||||
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.",
|
||||
},
|
||||
]
|
||||
|
||||
# This sets up the LLM context by providing messages and tools
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
ta = TalkingAnimation()
|
||||
smart_turn_metrics_processor = SmartTurnMetricsProcessor()
|
||||
|
||||
# RTVI events for Pipecat client UI
|
||||
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
|
||||
|
||||
# A core voice AI pipeline
|
||||
# Add additional processors to customize the bot's behavior
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
rtvi,
|
||||
smart_turn_metrics_processor,
|
||||
stt,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
ta,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
observers=[RTVIObserver(rtvi)],
|
||||
)
|
||||
|
||||
@rtvi.event_handler("on_client_ready")
|
||||
async def on_client_ready(rtvi):
|
||||
logger.debug("Client ready event received")
|
||||
await rtvi.set_bot_ready()
|
||||
# Kick off the conversation
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
logger.info("First participant joined: {}", participant["id"])
|
||||
# Push a static frame to show the bot is listening
|
||||
await task.queue_frame(quiet_frame)
|
||||
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
logger.info("Participant left: {}", participant)
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False, force_gc=True)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(args: DailySessionArguments):
|
||||
"""Main bot entry point compatible with the FastAPI route handler.
|
||||
|
||||
Args:
|
||||
room_url: The Daily room URL
|
||||
token: The Daily room token
|
||||
body: The configuration object from the request body
|
||||
session_id: The session ID for logging
|
||||
"""
|
||||
from pipecat.audio.filters.krisp_filter import KrispFilter
|
||||
|
||||
logger.info(f"Bot process initialized {args.room_url} {args.token}")
|
||||
async with aiohttp.ClientSession() as session:
|
||||
transport = DailyTransport(
|
||||
args.room_url,
|
||||
args.token,
|
||||
"Smart Turn Bot",
|
||||
params=DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_in_filter=KrispFilter(),
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=576,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=FalSmartTurnAnalyzer(
|
||||
api_key=os.getenv("FAL_SMART_TURN_API_KEY"), aiohttp_session=session
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
try:
|
||||
await main(transport)
|
||||
logger.info("Bot process completed")
|
||||
except Exception as e:
|
||||
logger.exception(f"Error in bot process: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
# Local development
|
||||
async def local_daily():
|
||||
"""Daily transport for local development."""
|
||||
from runner import configure
|
||||
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Smart Turn Bot",
|
||||
params=DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=576,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=FalSmartTurnAnalyzer(
|
||||
api_key=os.getenv("FAL_SMART_TURN_API_KEY"), aiohttp_session=session
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
await main(transport)
|
||||
except Exception as e:
|
||||
logger.exception(f"Error in local development mode: {e}")
|
||||
|
||||
|
||||
# Local development entry point
|
||||
if LOCAL and __name__ == "__main__":
|
||||
try:
|
||||
asyncio.run(local_daily())
|
||||
except Exception as e:
|
||||
logger.exception(f"Failed to run in local mode: {e}")
|
||||
@@ -1,19 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
VERSION="0.1"
|
||||
DOCKER_USERNAME=""
|
||||
AGENT_NAME="pcc-smart-turn"
|
||||
|
||||
# Build the Docker image with the correct context
|
||||
echo "Building Docker image..."
|
||||
docker build --platform=linux/arm64 -t "$DOCKER_USERNAME/$AGENT_NAME:$VERSION" -t "$DOCKER_USERNAME/$AGENT_NAME:latest" .
|
||||
|
||||
# Push the Docker images
|
||||
echo "Pushing Docker image $DOCKER_USERNAME/$AGENT_NAME:$VERSION..."
|
||||
docker push "$DOCKER_USERNAME/$AGENT_NAME:$VERSION"
|
||||
|
||||
echo "Pushing Docker image $DOCKER_USERNAME/$AGENT_NAME:latest..."
|
||||
docker push "$DOCKER_USERNAME/$AGENT_NAME:latest"
|
||||
|
||||
echo "Successfully built and pushed $DOCKER_USERNAME/$AGENT_NAME:$VERSION and $DOCKER_USERNAME/$AGENT_NAME:latest"
|
||||
@@ -1,5 +0,0 @@
|
||||
GOOGLE_API_KEY=
|
||||
CARTESIA_API_KEY=
|
||||
DEEPGRAM_API_KEY=
|
||||
DAILY_API_KEY=
|
||||
FAL_SMART_TURN_API_KEY=
|
||||
@@ -1,7 +0,0 @@
|
||||
agent_name = "pcc-smart-turn"
|
||||
image = "your-username/pcc-smart-turn:0.1"
|
||||
secret_set = "pcc-smart-turn-secrets"
|
||||
enable_krisp = true
|
||||
|
||||
[scaling]
|
||||
min_instances = 0
|
||||
@@ -1,3 +0,0 @@
|
||||
pipecatcloud
|
||||
pipecat-ai[google,daily,deepgram,cartesia,silero]
|
||||
python-dotenv
|
||||
@@ -1,56 +0,0 @@
|
||||
#
|
||||
# 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):
|
||||
"""Configure the Daily room and Daily REST helper."""
|
||||
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)
|
||||
@@ -1,228 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""RTVI Bot Server Implementation.
|
||||
|
||||
This FastAPI server manages RTVI bot instances and provides endpoints for both
|
||||
direct browser access and RTVI client connections. It handles:
|
||||
- Creating Daily rooms
|
||||
- Managing bot processes
|
||||
- Providing connection credentials
|
||||
- Monitoring bot status
|
||||
|
||||
Requirements:
|
||||
- Daily API key (set in .env file)
|
||||
- Python 3.10+
|
||||
- FastAPI
|
||||
- Running bot implementation
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
from contextlib import asynccontextmanager
|
||||
from typing import Any, Dict
|
||||
|
||||
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
|
||||
|
||||
# Load environment variables from .env file
|
||||
load_dotenv(override=True)
|
||||
|
||||
# Maximum number of bot instances allowed per room
|
||||
MAX_BOTS_PER_ROOM = 1
|
||||
|
||||
# Dictionary to track bot processes: {pid: (process, room_url)}
|
||||
bot_procs = {}
|
||||
|
||||
# Store Daily API helpers
|
||||
daily_helpers = {}
|
||||
|
||||
|
||||
def cleanup():
|
||||
"""Cleanup function to terminate all bot processes.
|
||||
|
||||
Called during server shutdown.
|
||||
"""
|
||||
for entry in bot_procs.values():
|
||||
proc = entry[0]
|
||||
proc.terminate()
|
||||
proc.wait()
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
"""FastAPI lifespan manager that handles startup and shutdown tasks.
|
||||
|
||||
- Creates aiohttp session
|
||||
- Initializes Daily API helper
|
||||
- Cleans up resources on shutdown
|
||||
"""
|
||||
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()
|
||||
|
||||
|
||||
# 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=["*"],
|
||||
)
|
||||
|
||||
|
||||
async def create_room_and_token() -> tuple[str, str]:
|
||||
"""Helper function to create a Daily room and generate an access token.
|
||||
|
||||
Returns:
|
||||
tuple[str, str]: A tuple containing (room_url, token)
|
||||
|
||||
Raises:
|
||||
HTTPException: If room creation or token generation fails
|
||||
"""
|
||||
room = await daily_helpers["rest"].create_room(DailyRoomParams())
|
||||
if not room.url:
|
||||
raise HTTPException(status_code=500, detail="Failed to create 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}")
|
||||
|
||||
return room.url, token
|
||||
|
||||
|
||||
@app.get("/")
|
||||
async def start_agent(request: Request):
|
||||
"""Endpoint for direct browser access to the bot.
|
||||
|
||||
Creates a room, starts a bot instance, and redirects to the Daily room URL.
|
||||
|
||||
Returns:
|
||||
RedirectResponse: Redirects to the Daily room URL
|
||||
|
||||
Raises:
|
||||
HTTPException: If room creation, token generation, or bot startup fails
|
||||
"""
|
||||
print("Creating room")
|
||||
room_url, token = await create_room_and_token()
|
||||
print(f"Room URL: {room_url}")
|
||||
|
||||
# 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 limit reached for room: {room_url}")
|
||||
|
||||
# Spawn a new bot process
|
||||
try:
|
||||
proc = subprocess.Popen(
|
||||
[f"python3 bot.py -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.post("/connect")
|
||||
async def rtvi_connect(request: Request) -> Dict[Any, Any]:
|
||||
"""RTVI connect endpoint that creates a room and returns connection credentials.
|
||||
|
||||
This endpoint is called by RTVI clients to establish a connection.
|
||||
|
||||
Returns:
|
||||
Dict[Any, Any]: Authentication bundle containing room_url and token
|
||||
|
||||
Raises:
|
||||
HTTPException: If room creation, token generation, or bot startup fails
|
||||
"""
|
||||
print("Creating room for RTVI connection")
|
||||
room_url, token = await create_room_and_token()
|
||||
print(f"Room URL: {room_url}")
|
||||
|
||||
# Start the bot process
|
||||
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 the authentication bundle in format expected by DailyTransport
|
||||
return {"room_url": room_url, "token": token}
|
||||
|
||||
|
||||
@app.get("/status/{pid}")
|
||||
def get_status(pid: int):
|
||||
"""Get the status of a specific bot process.
|
||||
|
||||
Args:
|
||||
pid (int): Process ID of the bot
|
||||
|
||||
Returns:
|
||||
JSONResponse: Status information for the bot
|
||||
|
||||
Raises:
|
||||
HTTPException: If the specified bot process is not found
|
||||
"""
|
||||
# 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
|
||||
status = "running" if proc[0].poll() is None else "finished"
|
||||
return JSONResponse({"bot_id": pid, "status": status})
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
# Parse command line arguments for server configuration
|
||||
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()
|
||||
|
||||
# Start the FastAPI server
|
||||
uvicorn.run(
|
||||
"server:app",
|
||||
host=config.host,
|
||||
port=config.port,
|
||||
reload=config.reload,
|
||||
)
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
@@ -23,7 +22,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
@@ -23,7 +22,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
@@ -22,7 +21,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
@@ -22,7 +21,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
@@ -23,7 +22,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
@@ -23,16 +22,16 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
transport = SmallWebRTCTransport(
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=1024,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -33,7 +33,9 @@ async def main():
|
||||
|
||||
transport = TkLocalTransport(
|
||||
tk_root,
|
||||
TkTransportParams(video_out_enabled=True, video_out_width=1024, video_out_height=1024),
|
||||
TkTransportParams(
|
||||
camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024
|
||||
),
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
@@ -22,16 +21,16 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
transport = SmallWebRTCTransport(
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=1024,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
86
examples/foundational/04-utterance-and-speech.py
Normal file
86
examples/foundational/04-utterance-and-speech.py
Normal file
@@ -0,0 +1,86 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
#
|
||||
# This example broken on latest pipecat and needs updating.
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
|
||||
import aiohttp
|
||||
from daily_runner import configure
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import EndPipeFrame, LLMMessagesFrame, TextFrame
|
||||
from pipecat.pipeline.merge_pipeline import SequentialMergePipeline
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.services.azure import AzureLLMService, AzureTTSService
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.transport_services import TransportServiceOutput
|
||||
from pipecat.services.transports.daily_transport import 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, _) = await configure(session)
|
||||
|
||||
transport = DailyTransport(room_url, None, "Static And Dynamic Speech")
|
||||
|
||||
meeting = TransportServiceOutput(transport, mic_enabled=True)
|
||||
|
||||
llm = AzureLLMService(
|
||||
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
)
|
||||
azure_tts = AzureTTSService(
|
||||
api_key=os.getenv("AZURE_SPEECH_API_KEY"),
|
||||
region=os.getenv("AZURE_SPEECH_REGION"),
|
||||
)
|
||||
|
||||
elevenlabs_tts = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
||||
)
|
||||
|
||||
messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
|
||||
|
||||
# Start a task to run the LLM to create a joke, and convert the LLM
|
||||
# output to audio frames. This task will run in parallel with generating
|
||||
# and speaking the audio for static text, so there's no delay to speak
|
||||
# the LLM response.
|
||||
llm_pipeline = Pipeline([llm, elevenlabs_tts])
|
||||
llm_task = PipelineTask(llm_pipeline)
|
||||
await llm_task.queue_frames([LLMMessagesFrame(messages), EndPipeFrame()])
|
||||
|
||||
simple_tts_pipeline = Pipeline([azure_tts])
|
||||
await simple_tts_pipeline.queue_frames(
|
||||
[
|
||||
TextFrame("My friend the LLM is going to tell a joke about llamas."),
|
||||
EndPipeFrame(),
|
||||
]
|
||||
)
|
||||
|
||||
merge_pipeline = SequentialMergePipeline([simple_tts_pipeline, llm_pipeline])
|
||||
|
||||
await asyncio.gather(
|
||||
transport.run(merge_pipeline),
|
||||
simple_tts_pipeline.run_pipeline(),
|
||||
llm_pipeline.run_pipeline(),
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1,102 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
|
||||
import aiohttp
|
||||
from daily_runner import configure
|
||||
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.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.openai.llm 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,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. 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.",
|
||||
},
|
||||
]
|
||||
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=True,
|
||||
),
|
||||
)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
await transport.capture_participant_transcription(participant["id"])
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -4,7 +4,6 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import os
|
||||
from dataclasses import dataclass
|
||||
|
||||
@@ -64,7 +63,7 @@ class MonthPrepender(FrameProcessor):
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
"""Run the Calendar Month Narration bot using WebRTC transport.
|
||||
|
||||
Args:
|
||||
@@ -78,9 +77,9 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=1024,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user