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v0.0.92
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hush/TTSSp
| Author | SHA1 | Date | |
|---|---|---|---|
|
1d3ae6b029 |
93
CHANGELOG.md
93
CHANGELOG.md
@@ -5,34 +5,10 @@ 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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## [0.0.92] - 2025-10-31 🎃 "The Haunted Edition" 👻
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## [Unreleased]
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### Added
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- Added a new `DeepgramHttpTTSService`, which delivers a meaningful reduction
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in latency when compared to the `DeepgramTTSService`.
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- Add support for `speaking_rate` input parameter in `GoogleHttpTTSService`.
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- Added `enable_speaker_diarization` and `enable_language_identification` to
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`SonioxSTTService`.
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- Added `SpeechmaticsTTSService`, which uses Speechmatic's TTS API. Updated
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examples 07a\* to use the new TTS service.
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- Added support for including images or audio to LLM context messages using
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`LLMContext.create_image_message()` or `LLMContext.create_image_url_message()`
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(not all LLMs support URLs) and `LLMContext.create_audio_message()`. For
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example, when creating `LLMMessagesAppendFrame`:
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```python
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message = LLMContext.create_image_message(image=..., size= ...)
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await self.push_frame(LLMMessagesAppendFrame(messages=[message], run_llm=True))
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```
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- New event handlers for the `DeepgramFluxSTTService`: `on_start_of_turn`,
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`on_turn_resumed`, `on_end_of_turn`, `on_eager_end_of_turn`, `on_update`.
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- Added `generation_config` parameter support to `CartesiaTTSService` and
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`CartesiaHttpTTSService` for Cartesia Sonic-3 models. Includes a new
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`GenerationConfig` class with `volume` (0.5-2.0), `speed` (0.6-1.5),
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@@ -44,7 +20,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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```python
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context = LLMContext(messages, tools)
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context_aggregator = LLMContextAggregatorPair(context)
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context_aggregator = LLMContextAggregatorPair(
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context,
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# This part is `OpenAIRealtimeLLMService`-specific.
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# `expect_stripped_words=False` needed when OpenAI Realtime used with
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# "audio" modality (the default).
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assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
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)
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```
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(Note that even though `OpenAIRealtimeLLMService` now supports the universal
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@@ -121,7 +103,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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```python
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context = LLMContext(messages, tools)
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context_aggregator = LLMContextAggregatorPair(context)
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context_aggregator = LLMContextAggregatorPair(
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context,
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# This part is `GeminiLiveLLMService`-specific.
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# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
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# modality (the default).
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assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
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)
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```
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(Note that even though `GeminiLiveLLMService` now supports the universal
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@@ -163,26 +151,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Changed
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- The development runner's `/start` endpoint now supports passing
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`dailyRoomProperties` and `dailyMeetingTokenProperties` in the request body
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when `createDailyRoom` is true. Properties are validated against the
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`DailyRoomProperties` and `DailyMeetingTokenProperties` types respectively
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and passed to Daily's room and token creation APIs.
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- `UserImageRawFrame` new fields `append_to_context` and `text`. The
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`append_to_context` field indicates if this image and text should be added to
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the LLM context (by the LLM assistant aggregator). The `text` field, if set,
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might also guide the LLM or the vision service on how to analyze the image.
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- `UserImageRequestFrame` new fiels `append_to_context` and `text`. Both fields
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will be used to set the same fields on the captured `UserImageRawFrame`.
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- `UserImageRequestFrame` don't require function call name and ID anymore.
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- Updated `MoondreamService` to process `UserImageRawFrame`.
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- `VisionService` expects `UserImageRawFrame` in order to analyze images.
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- `DailyTransport` triggers `on_error` event if transcription can't be started
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or stopped.
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@@ -207,15 +175,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Deprecated
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- The `expect_stripped_words` parameter of `LLMAssistantAggregatorParams` is
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ignored when used with the newer `LLMAssistantAggregator`, which now handles
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word spacing automatically.
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- `LLMService.request_image_frame()` is deprecated, push a
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`UserImageRequestFrame` instead.
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- `UserResponseAggregator` is deprecated and will be removed in a future version.
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- The `send_transcription_frames` argument to `OpenAIRealtimeLLMService` is
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deprecated. Transcription frames are now always sent. They go upstream, to be
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handled by the user context aggregator. See "Added" section for details.
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@@ -229,24 +188,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Removed
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- Removed `enable_non_final_tokens` and `max_non_final_tokens_duration_ms` from
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`SonioxSTTService`.
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- Removed the `aiohttp_session` arg from `SarvamTTSService` as it's no longer
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used.
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### Fixed
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- Fixed a `PipelineTask` issue that was causing an idle timeout for frames that
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were being generated but not reaching the end of the pipeline. Since the exact
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point when frames are discarded is unknown, we now monitor pipeline frames
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using an observer. If the observer detects frames are being generated, it will
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prevent the pipeline from being considered idle.
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- Fixed an issue in `HumeTTSService` that was only using Octave 2, which does
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not support the `description` field. Now, if a description is provided, it
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switches to Octave 1.
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- Fixed an issue where `DailyTransport` would timeout prematurely on join and on
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leave.
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@@ -256,12 +202,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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- Fixed an issue in `ServiceSwitcher` where the `STTService`s would result in
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all STT services producing `TranscriptionFrame`s.
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### Other
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- Updated all vision 12-series foundational examples to load images from a file.
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- Added 14-series video examples for different services. These new examples
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request an image from the user camera through a function call.
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- Fixed an issue in `HumeTTSService` that was only using Octave 2, which does not support the `description` field. Now, if a description is provided, it switches to Octave 1.
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## [0.0.91] - 2025-10-21
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@@ -1403,8 +1344,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Added
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- Added `SonioxSTTService` using Soniox's STT websocket API.
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- Added `enable_emulated_vad_interruptions` to `LLMUserAggregatorParams`.
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When user speech is emulated (e.g. when a transcription is received but
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VAD doesn't detect speech), this parameter controls whether the emulated
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26
README.md
26
README.md
@@ -72,19 +72,19 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
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## 🧩 Available services
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| Category | Services |
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| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| 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), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [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), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
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| 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), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [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), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
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| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [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), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
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| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) |
|
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| 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 |
|
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| Serializers | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx) |
|
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| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
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| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
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| 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) |
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| 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), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter) |
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| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
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| Category | Services |
|
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| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
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| 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), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [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), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
|
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| 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), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [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), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
|
||||
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [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), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
|
||||
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [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 |
|
||||
| Serializers | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx) |
|
||||
| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [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), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter) |
|
||||
| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
|
||||
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
|
||||
|
||||
|
||||
@@ -6,7 +6,6 @@
|
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|
||||
import os
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|
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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|
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@@ -21,10 +20,10 @@ from pipecat.processors.aggregators.llm_response import (
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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from pipecat.services.openai.base_llm import BaseOpenAILLMService
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||||
from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
|
||||
from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
|
||||
from pipecat.transcriptions.language import Language
|
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from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -52,127 +51,121 @@ transport_params = {
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
"""Speechmatics STT and TTS Service Example
|
||||
"""Speechmatics STT Service Example
|
||||
|
||||
This example demonstrates using Speechmatics Speech-to-Text and Text-to-Speech services
|
||||
with speaker diarization and intelligent speaker management. Key features:
|
||||
This example demonstrates using Speechmatics Speech-to-Text service with speaker diarization and intelligent speaker management. Key features:
|
||||
|
||||
1. Speaker Diarization (STT)
|
||||
1. Speaker Diarization
|
||||
- Automatically identifies and distinguishes between different speakers
|
||||
- First speaker is identified as 'S1', others get subsequent IDs
|
||||
- Uses `enable_diarization` parameter to manage speaker detection
|
||||
|
||||
2. Smart Speaker Control (STT)
|
||||
2. Smart Speaker Control
|
||||
- `focus_speakers` parameter lets you target specific speakers (e.g. ["S1"])
|
||||
- Other speakers will be wrapped in PASSIVE tags
|
||||
- Only processes speech from focused speakers
|
||||
- Words from all speakers are wrapped with XML tags for clear speaker identification
|
||||
- Other speakers' speech only sent when focused speaker is active
|
||||
|
||||
3. Voice Activity Detection (STT)
|
||||
3. Voice Activity Detection
|
||||
- Built-in VAD using `enable_vad` parameter
|
||||
- Remove `vad_analyzer` from `transport` config to use module's VAD
|
||||
- Emits speaker started/stopped events
|
||||
|
||||
4. Text-to-Speech (TTS)
|
||||
- Low latency streaming audio synthesis
|
||||
- Multiple voice options available including `sarah`, `theo`, and `megan`
|
||||
|
||||
5. Configuration Options
|
||||
4. Configuration Options
|
||||
- `operating_point` parameter defaults to `ENHANCED` for optimal accuracy
|
||||
- Configurable `end_of_utterance_silence_trigger` (default 0.5s)
|
||||
- Customizable speaker formatting
|
||||
- Additional diarization settings available
|
||||
|
||||
For detailed information:
|
||||
- STT: https://docs.speechmatics.com/rt-api-ref
|
||||
- TTS: https://docs.speechmatics.com/text-to-speech/quickstart
|
||||
For detailed information about operating points and configuration:
|
||||
https://docs.speechmatics.com/rt-api-ref
|
||||
"""
|
||||
|
||||
logger.info(f"Starting bot")
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_vad=True,
|
||||
enable_diarization=True,
|
||||
focus_speakers=["S1"],
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
|
||||
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_vad=True,
|
||||
enable_diarization=True,
|
||||
focus_speakers=["S1"],
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
|
||||
),
|
||||
)
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
||||
model="eleven_turbo_v2_5",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Alfred. "
|
||||
"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. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
|
||||
"Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
|
||||
),
|
||||
)
|
||||
},
|
||||
]
|
||||
|
||||
tts = SpeechmaticsTTSService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
voice_id="sarah",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Sarah. "
|
||||
"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. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
|
||||
"Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
|
||||
),
|
||||
},
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
|
||||
@@ -6,7 +6,6 @@
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
@@ -25,10 +24,10 @@ from pipecat.processors.aggregators.llm_response import (
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
|
||||
from pipecat.services.openai.base_llm import BaseOpenAILLMService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
|
||||
from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
|
||||
from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -62,106 +61,100 @@ transport_params = {
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
"""Run example using Speechmatics STT and TTS.
|
||||
"""Run example using Speechmatics STT.
|
||||
|
||||
This example demonstrates a complete Speechmatics integration with both Speech-to-Text
|
||||
and Text-to-Speech services:
|
||||
This example will use diarization within our STT service and output the words spoken by
|
||||
each individual speaker and wrap them with XML tags for the LLM to process. Note the
|
||||
instructions in the system context for the LLM. This greatly improves the conversation
|
||||
experience by allowing the LLM to understand who is speaking in a multi-party call.
|
||||
|
||||
STT Features:
|
||||
- Diarization to identify and distinguish between different speakers
|
||||
- Words spoken by each speaker are wrapped with XML tags for LLM processing
|
||||
- System context instructions help the LLM understand multi-party conversations
|
||||
- ENHANCED operating point by default for optimal accuracy
|
||||
By default, this example will use our ENHANCED operating point, which is optimized for
|
||||
high accuracy. You can change this by setting the `operating_point` parameter to a different
|
||||
value.
|
||||
|
||||
TTS Features:
|
||||
- Low latency streaming audio synthesis
|
||||
- Multiple voice options available including `sarah`, `theo`, and `megan`
|
||||
|
||||
For more information:
|
||||
- STT: https://docs.speechmatics.com/rt-api-ref
|
||||
- TTS: https://docs.speechmatics.com/text-to-speech/quickstart
|
||||
For more information on operating points, see the Speechmatics documentation:
|
||||
https://docs.speechmatics.com/rt-api-ref
|
||||
"""
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_diarization=True,
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_diarization=True,
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
),
|
||||
)
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
||||
model="eleven_turbo_v2_5",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Alfred. "
|
||||
"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. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
|
||||
),
|
||||
)
|
||||
},
|
||||
]
|
||||
|
||||
tts = SpeechmaticsTTSService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
voice_id="sarah",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Sarah. "
|
||||
"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. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
|
||||
),
|
||||
},
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
|
||||
@@ -101,10 +101,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
@stt.event_handler("on_update")
|
||||
async def on_deepgram_flux_update(stt, transcript):
|
||||
logger.debug(f"On deeggram flux update: {transcript}")
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
@@ -1,132 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.deepgram.tts import DeepgramHttpTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = DeepgramHttpTTSService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
voice="aura-2-andromeda-en",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
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 = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
180
examples/foundational/12-describe-video.py
Normal file
180
examples/foundational/12-describe-video.py
Normal file
@@ -0,0 +1,180 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.moondream.vision import MoondreamService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
# If you run into weird description, try with use_cpu=True
|
||||
moondream = MoondreamService()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
moondream,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected: {client}")
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -5,23 +5,29 @@
|
||||
#
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
@@ -31,37 +37,53 @@ from pipecat.runner.utils import (
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image and push it to the LLM.
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream which will be added to the context by the LLM
|
||||
assistant aggregator.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
# Request a user image frame and indicate that it should be added to the
|
||||
# context.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
await params.result_callback(None)
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -88,53 +110,33 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
# Google Gemini model for vision analysis
|
||||
google = GoogleLLMService(model="gemini-2.0-flash-001", 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
|
||||
)
|
||||
|
||||
# Google Gemini model for vision analysis
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
properties={
|
||||
"user_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
google,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -155,15 +157,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
@@ -1,148 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.aws.llm import AWSBedrockLLMService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = AWSBedrockLLMService(
|
||||
aws_region="us-west-2",
|
||||
model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
|
||||
# Note: usually, prefer providing latency="optimized" param.
|
||||
# Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
|
||||
# which we need for image input.
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
)
|
||||
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
|
||||
# Kick off the conversation.
|
||||
image = Image.open(image_path)
|
||||
message = LLMContext.create_image_message(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -4,25 +4,36 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
@@ -32,6 +43,49 @@ from pipecat.transports.daily.transport import DailyParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
@@ -39,12 +93,14 @@ transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
@@ -54,34 +110,33 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
# OpenAI GPT-4o for vision analysis
|
||||
openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
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"))
|
||||
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
openai,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -96,28 +151,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
logger.info(f"Client connected: {client}")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Kick off the conversation.
|
||||
image = Image.open(image_path)
|
||||
message = LLMContext.create_image_message(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
@@ -1,141 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
|
||||
# Kick off the conversation.
|
||||
image = Image.open(image_path)
|
||||
message = LLMContext.create_image_message(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -4,25 +4,36 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.anthropic.llm import AnthropicLLMService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
@@ -32,6 +43,49 @@ from pipecat.transports.daily.transport import DailyParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
@@ -39,12 +93,14 @@ transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
@@ -54,34 +110,33 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
# Anthropic for vision analysis
|
||||
anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
|
||||
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
anthropic,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -96,28 +151,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
logger.info(f"Client connected: {client}")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Kick off the conversation.
|
||||
image = Image.open(image_path)
|
||||
message = LLMContext.create_image_message(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
@@ -1,122 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import UserImageRawFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.moondream.vision import MoondreamService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
vision = MoondreamService()
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
vision, # Vision
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
|
||||
# Describe the image.
|
||||
image = Image.open(image_path)
|
||||
await task.queue_frames(
|
||||
[
|
||||
UserImageRawFrame(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -5,23 +5,29 @@
|
||||
#
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
@@ -31,37 +37,54 @@ from pipecat.runner.utils import (
|
||||
from pipecat.services.aws.llm import AWSBedrockLLMService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image and push it to the LLM.
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream which will be added to the context by the LLM
|
||||
assistant aggregator.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
# Request a user image frame and indicate that it should be added to the
|
||||
# context.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
await params.result_callback(None)
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
# Note: AWS Bedrock does not yet support the universal LLMContext
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -88,15 +111,17 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
# AWS for vision analysis
|
||||
llm = AWSBedrockLLMService(
|
||||
aws = AWSBedrockLLMService(
|
||||
aws_region="us-west-2",
|
||||
model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
|
||||
# Note: usually, prefer providing latency="optimized" param.
|
||||
@@ -104,44 +129,22 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# which we need for image input.
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
)
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
properties={
|
||||
"user_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
aws,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -162,15 +165,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
@@ -4,6 +4,8 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
@@ -15,13 +17,12 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
@@ -38,30 +39,34 @@ from pipecat.transports.daily.transport import DailyParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image and push it to the LLM.
|
||||
# Global variable to store the client ID
|
||||
client_id = ""
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream which will be added to the context by the LLM
|
||||
assistant aggregator.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
|
||||
async def get_weather(params: FunctionCallParams):
|
||||
location = params.arguments["location"]
|
||||
await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
|
||||
|
||||
|
||||
async def get_image(params: FunctionCallParams):
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
logger.debug(f"Requesting image with user_id={client_id}, question={question}")
|
||||
|
||||
# Request a user image frame and indicate that it should be added to the
|
||||
# context.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
|
||||
FrameDirection.UPSTREAM,
|
||||
# Request the image frame
|
||||
await params.llm.request_image_frame(
|
||||
user_id=client_id,
|
||||
function_name=params.function_name,
|
||||
tool_call_id=params.tool_call_id,
|
||||
text_content=question,
|
||||
)
|
||||
|
||||
await params.result_callback(None)
|
||||
# Wait a short time for the frame to be processed
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
# Return a result to complete the function call
|
||||
await params.result_callback(
|
||||
f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
|
||||
)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -95,32 +100,70 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
# Anthropic for vision analysis
|
||||
llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
llm = AnthropicLLMService(
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"),
|
||||
model="claude-3-7-sonnet-latest",
|
||||
params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
|
||||
)
|
||||
llm.register_function("get_weather", get_weather)
|
||||
llm.register_function("get_image", get_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
weather_function = FunctionSchema(
|
||||
name="get_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"user_id": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
required=["location"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
get_image_function = FunctionSchema(
|
||||
name="get_image",
|
||||
description="Get an image from the video stream.",
|
||||
properties={
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image.",
|
||||
}
|
||||
},
|
||||
required=["question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
|
||||
|
||||
system_prompt = """\
|
||||
You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
|
||||
|
||||
Your response will be turned into speech so use only simple words and punctuation.
|
||||
|
||||
You have access to two tools: get_weather and get_image.
|
||||
|
||||
You can respond to questions about the weather using the get_weather tool.
|
||||
|
||||
You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
|
||||
indicate you should use the get_image tool are:
|
||||
- What do you see?
|
||||
- What's in the video?
|
||||
- Can you describe the video?
|
||||
- Tell me about what you see.
|
||||
- Tell me something interesting about what you see.
|
||||
- What's happening in the video?
|
||||
|
||||
If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
|
||||
"""
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": system_prompt,
|
||||
}
|
||||
],
|
||||
},
|
||||
{"role": "user", "content": "Start the conversation by introducing yourself."},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
@@ -130,11 +173,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
context_aggregator.user(), # User speech to text
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses and tool context
|
||||
]
|
||||
)
|
||||
|
||||
@@ -153,16 +196,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
global client_id
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,190 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.moondream.vision import MoondreamService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image.
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
|
||||
# Request a user image frame. In this case, we don't want the requested
|
||||
# image to be added to the context because we will process it with
|
||||
# Moondream.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=False),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
|
||||
await params.result_callback(None)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
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"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
properties={
|
||||
"user_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
# If you run into weird description, try with use_cpu=True
|
||||
moondream = MoondreamService()
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
ParallelPipeline(
|
||||
[llm], # LLM
|
||||
[moondream],
|
||||
),
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected: {client}")
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -5,6 +5,7 @@
|
||||
#
|
||||
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
@@ -16,13 +17,12 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
@@ -39,30 +39,34 @@ from pipecat.transports.daily.transport import DailyParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image and push it to the LLM.
|
||||
# Global variable to store the client ID
|
||||
client_id = ""
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream which will be added to the context by the LLM
|
||||
assistant aggregator.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
|
||||
async def get_weather(params: FunctionCallParams):
|
||||
location = params.arguments["location"]
|
||||
await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
|
||||
|
||||
|
||||
async def get_image(params: FunctionCallParams):
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
logger.debug(f"Requesting image with user_id={client_id}, question={question}")
|
||||
|
||||
# Request a user image frame and indicate that it should be added to the
|
||||
# context.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
|
||||
FrameDirection.UPSTREAM,
|
||||
# Request the image frame
|
||||
await params.llm.request_image_frame(
|
||||
user_id=client_id,
|
||||
function_name=params.function_name,
|
||||
tool_call_id=params.tool_call_id,
|
||||
text_content=question,
|
||||
)
|
||||
|
||||
await params.result_callback(None)
|
||||
# Wait a short time for the frame to be processed
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
# Return a result to complete the function call
|
||||
await params.result_callback(
|
||||
f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
|
||||
)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -97,30 +101,58 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
llm.register_function("get_weather", get_weather)
|
||||
llm.register_function("get_image", get_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
weather_function = FunctionSchema(
|
||||
name="get_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"user_id": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
get_image_function = FunctionSchema(
|
||||
name="get_image",
|
||||
description="Get an image from the video stream.",
|
||||
properties={
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
},
|
||||
"description": "The question that the user is asking about the image.",
|
||||
}
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
required=["question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
|
||||
|
||||
system_prompt = """\
|
||||
You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
|
||||
|
||||
Your response will be turned into speech so use only simple words and punctuation.
|
||||
|
||||
You have access to two tools: get_weather and get_image.
|
||||
|
||||
You can respond to questions about the weather using the get_weather tool.
|
||||
|
||||
You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
|
||||
indicate you should use the get_image tool are:
|
||||
- What do you see?
|
||||
- What's in the video?
|
||||
- Can you describe the video?
|
||||
- Tell me about what you see.
|
||||
- Tell me something interesting about what you see.
|
||||
- What's happening in the video?
|
||||
"""
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
{"role": "system", "content": system_prompt},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
@@ -128,13 +160,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
transport.input(),
|
||||
stt,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -153,15 +185,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
global client_id
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -82,25 +82,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
async def handle_user_idle(user_idle: UserIdleProcessor, retry_count: int) -> bool:
|
||||
if retry_count == 1:
|
||||
# First attempt: Add a gentle prompt to the conversation
|
||||
message = {
|
||||
"role": "system",
|
||||
"content": "The user has been quiet. Politely and briefly ask if they're still there.",
|
||||
}
|
||||
await user_idle.push_frame(LLMMessagesAppendFrame([message], run_llm=True))
|
||||
await user_idle.push_frame(TTSSpeakFrame("First check."))
|
||||
return True
|
||||
elif retry_count == 2:
|
||||
# Second attempt: More direct prompt
|
||||
message = {
|
||||
"role": "system",
|
||||
"content": "The user is still inactive. Ask if they'd like to continue our conversation.",
|
||||
}
|
||||
await user_idle.push_frame(LLMMessagesAppendFrame([message], run_llm=True))
|
||||
await user_idle.push_frame(TTSSpeakFrame("Second check."))
|
||||
return True
|
||||
else:
|
||||
# Third attempt: End the conversation
|
||||
await user_idle.push_frame(
|
||||
TTSSpeakFrame("It seems like you're busy right now. Have a nice day!")
|
||||
)
|
||||
await user_idle.push_frame(TTSSpeakFrame("Third check."))
|
||||
await task.queue_frame(EndFrame())
|
||||
return False
|
||||
|
||||
@@ -128,6 +118,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
turn_observer = task.turn_tracking_observer
|
||||
if turn_observer:
|
||||
|
||||
@turn_observer.event_handler("on_turn_ended")
|
||||
async def on_turn_ended(observer, turn_number, duration, was_interrupted):
|
||||
# Log the context after every turn
|
||||
logger.info(f"📝 Context after turn {turn_number}: {context.get_messages()}")
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
@@ -187,7 +187,12 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
|
||||
tools,
|
||||
)
|
||||
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when OpenAI Realtime used with
|
||||
# "audio" modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -175,7 +175,12 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
|
||||
tools,
|
||||
)
|
||||
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when OpenAI Realtime used with
|
||||
# "audio" modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -92,7 +92,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# },
|
||||
],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
transcript = TranscriptProcessor()
|
||||
|
||||
|
||||
@@ -144,7 +144,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
context = LLMContext(
|
||||
[{"role": "user", "content": "Say hello."}],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -75,7 +75,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
},
|
||||
],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -100,7 +100,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
}
|
||||
],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -164,7 +164,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
|
||||
# Create context aggregator
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
# Build the pipeline
|
||||
pipeline = Pipeline(
|
||||
|
||||
@@ -127,7 +127,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
# Set up conversation context and management
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -140,7 +140,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
|
||||
context = LLMContext([{"role": "user", "content": "Say hello."}])
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -157,7 +157,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
context = LLMContext(
|
||||
[{"role": "user", "content": "Say hello."}],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -111,7 +111,12 @@ async def run_bot(pipecat_transport):
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
|
||||
# modality (the default)
|
||||
assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
|
||||
)
|
||||
|
||||
# RTVI events for Pipecat client UI
|
||||
rtvi = RTVIProcessor()
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 63 KiB |
@@ -10,10 +10,9 @@ import os
|
||||
import re
|
||||
import time
|
||||
import wave
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Any, List, Optional, Tuple
|
||||
from typing import List, Optional, Tuple
|
||||
|
||||
import aiofiles
|
||||
from deepgram import LiveOptions
|
||||
@@ -54,14 +53,6 @@ EVAL_TIMEOUT_SECS = 120
|
||||
EvalPrompt = str | Tuple[str, ImageFile]
|
||||
|
||||
|
||||
@dataclass
|
||||
class EvalConfig:
|
||||
prompt: EvalPrompt
|
||||
eval: str
|
||||
eval_speaks_first: bool = False
|
||||
runner_args_body: Optional[Any] = None
|
||||
|
||||
|
||||
class EvalRunner:
|
||||
def __init__(
|
||||
self,
|
||||
@@ -102,7 +93,9 @@ class EvalRunner:
|
||||
async def run_eval(
|
||||
self,
|
||||
example_file: str,
|
||||
eval_config: EvalConfig,
|
||||
prompt: EvalPrompt,
|
||||
eval: str,
|
||||
user_speaks_first: bool = False,
|
||||
):
|
||||
if not re.match(self._pattern, example_file):
|
||||
return
|
||||
@@ -119,8 +112,10 @@ class EvalRunner:
|
||||
|
||||
try:
|
||||
tasks = [
|
||||
asyncio.create_task(run_example_pipeline(script_path, eval_config)),
|
||||
asyncio.create_task(run_eval_pipeline(self, example_file, eval_config)),
|
||||
asyncio.create_task(run_example_pipeline(script_path)),
|
||||
asyncio.create_task(
|
||||
run_eval_pipeline(self, example_file, prompt, eval, user_speaks_first)
|
||||
),
|
||||
]
|
||||
_, pending = await asyncio.wait(tasks, timeout=EVAL_TIMEOUT_SECS)
|
||||
if pending:
|
||||
@@ -182,7 +177,7 @@ class EvalRunner:
|
||||
return os.path.join(self._recordings_dir, f"{base_name}.wav")
|
||||
|
||||
|
||||
async def run_example_pipeline(script_path: Path, eval_config: EvalConfig):
|
||||
async def run_example_pipeline(script_path: Path):
|
||||
room_url = os.getenv("DAILY_SAMPLE_ROOM_URL")
|
||||
|
||||
module = load_module_from_path(script_path)
|
||||
@@ -201,7 +196,6 @@ async def run_example_pipeline(script_path: Path, eval_config: EvalConfig):
|
||||
|
||||
runner_args = RunnerArguments()
|
||||
runner_args.pipeline_idle_timeout_secs = PIPELINE_IDLE_TIMEOUT_SECS
|
||||
runner_args.body = eval_config.runner_args_body
|
||||
|
||||
await module.run_bot(transport, runner_args)
|
||||
|
||||
@@ -209,7 +203,9 @@ async def run_example_pipeline(script_path: Path, eval_config: EvalConfig):
|
||||
async def run_eval_pipeline(
|
||||
eval_runner: EvalRunner,
|
||||
example_file: str,
|
||||
eval_config: EvalConfig,
|
||||
prompt: EvalPrompt,
|
||||
eval: str,
|
||||
user_speaks_first: bool = False,
|
||||
):
|
||||
logger.info(f"Starting eval bot")
|
||||
|
||||
@@ -266,16 +262,17 @@ async def run_eval_pipeline(
|
||||
# Load example prompt depending on image.
|
||||
example_prompt = ""
|
||||
example_image: Optional[ImageFile] = None
|
||||
if isinstance(eval_config.prompt, str):
|
||||
example_prompt = eval_config.prompt
|
||||
elif isinstance(eval_config.prompt, tuple):
|
||||
example_prompt, example_image = eval_config.prompt
|
||||
if isinstance(prompt, str):
|
||||
example_prompt = prompt
|
||||
elif isinstance(prompt, tuple):
|
||||
example_prompt, example_image = prompt
|
||||
|
||||
eval_prompt = f"The answer is correct if it matches: {eval}."
|
||||
common_system_prompt = (
|
||||
"The user might say things other than the answer and that's allowed. "
|
||||
f"You should only call the eval function when the user: {eval_config.eval}"
|
||||
f"You should only call the eval function with your assessment when the user actually answers the question. {eval_prompt}"
|
||||
)
|
||||
if eval_config.eval_speaks_first:
|
||||
if user_speaks_first:
|
||||
system_prompt = f"You are an LLM eval, be extremly brief. You will start the conversation by saying: '{example_prompt}'. {common_system_prompt}"
|
||||
else:
|
||||
system_prompt = f"You are an LLM eval, be extremly brief. Your goal is to first ask one question: {example_prompt}. {common_system_prompt}"
|
||||
@@ -333,9 +330,9 @@ async def run_eval_pipeline(
|
||||
|
||||
# Default behavior is for the bot to speak first
|
||||
# If the eval bot speaks first, we append the prompt to the messages
|
||||
if eval_config.eval_speaks_first:
|
||||
if user_speaks_first:
|
||||
messages.append(
|
||||
{"role": "user", "content": f"Start by saying this exactly: '{eval_config.prompt}'"}
|
||||
{"role": "user", "content": f"Start by saying this exactly: '{prompt}'"}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
|
||||
@@ -11,7 +11,7 @@ from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from eval import EvalConfig, EvalRunner
|
||||
from eval import EvalRunner
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
from utils import check_env_variables
|
||||
@@ -24,184 +24,189 @@ ASSETS_DIR = SCRIPT_DIR / "assets"
|
||||
|
||||
FOUNDATIONAL_DIR = SCRIPT_DIR.parent.parent / "examples" / "foundational"
|
||||
|
||||
EVAL_SIMPLE_MATH = EvalConfig(
|
||||
prompt="A simple math addition.",
|
||||
eval="The user answers the math addition correctly.",
|
||||
# Speaking order constants
|
||||
USER_SPEAKS_FIRST = True
|
||||
BOT_SPEAKS_FIRST = False
|
||||
|
||||
# Math
|
||||
PROMPT_SIMPLE_MATH = "A simple math addition."
|
||||
EVAL_SIMPLE_MATH = "Correct math addition."
|
||||
|
||||
# Weather
|
||||
PROMPT_WEATHER = "What's the weather in San Francisco?"
|
||||
EVAL_WEATHER = (
|
||||
"Something specific about the current weather in San Francisco, including the degrees."
|
||||
)
|
||||
|
||||
EVAL_WEATHER = EvalConfig(
|
||||
prompt="What's the weather in San Francisco?",
|
||||
eval="The user says something specific about the current weather in San Francisco, including the degrees.",
|
||||
)
|
||||
# Online search
|
||||
PROMPT_ONLINE_SEARCH = "What's the date right now in London?"
|
||||
EVAL_ONLINE_SEARCH = f"Today is {datetime.now(timezone.utc).strftime('%B %d, %Y')}."
|
||||
|
||||
EVAL_ONLINE_SEARCH = EvalConfig(
|
||||
prompt="What's the date right now in London?",
|
||||
eval=f"The user says today is {datetime.now(timezone.utc).strftime('%B %d, %Y')} in London.",
|
||||
)
|
||||
# Switch language
|
||||
PROMPT_SWITCH_LANGUAGE = "Say something in Spanish."
|
||||
EVAL_SWITCH_LANGUAGE = "The user is now talking in Spanish."
|
||||
|
||||
EVAL_SWITCH_LANGUAGE = EvalConfig(
|
||||
prompt="Say something in Spanish.",
|
||||
eval="The user talks in Spanish.",
|
||||
)
|
||||
|
||||
EVAL_VISION_CAMERA = EvalConfig(
|
||||
prompt=("Briefly describe what you see.", Image.open(ASSETS_DIR / "cat.jpg")),
|
||||
eval="The user provides a cat description.",
|
||||
)
|
||||
|
||||
|
||||
def EVAL_VISION_IMAGE(*, eval_speaks_first: bool = False):
|
||||
return EvalConfig(
|
||||
prompt="Briefly describe this image.",
|
||||
eval="The user provides a cat description.",
|
||||
eval_speaks_first=eval_speaks_first,
|
||||
runner_args_body={
|
||||
"image_path": ASSETS_DIR / "cat.jpg",
|
||||
"question": "Briefly describe this image.",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
EVAL_VOICEMAIL = EvalConfig(
|
||||
prompt="Please leave a message.",
|
||||
eval="The user leaves a voicemail message.",
|
||||
eval_speaks_first=True,
|
||||
)
|
||||
|
||||
EVAL_CONVERSATION = EvalConfig(
|
||||
prompt="Hello, this is Mark.",
|
||||
eval="The user replies with a greeting.",
|
||||
eval_speaks_first=True,
|
||||
)
|
||||
# Vision
|
||||
PROMPT_VISION = ("What do you see?", Image.open(ASSETS_DIR / "cat.jpg"))
|
||||
EVAL_VISION = "A cat description."
|
||||
|
||||
# Voicemail
|
||||
PROMPT_VOICEMAIL = "Please leave a message after the beep."
|
||||
EVAL_VOICEMAIL = "Assess the conversation and determine if it is a voicemail."
|
||||
PROMPT_CONVERSATION = "Hello, this is Mark."
|
||||
EVAL_CONVERSATION = "A start of a conversation, not a voicemail."
|
||||
|
||||
TESTS_07 = [
|
||||
# 07 series
|
||||
("07-interruptible.py", EVAL_SIMPLE_MATH),
|
||||
("07-interruptible-cartesia-http.py", EVAL_SIMPLE_MATH),
|
||||
("07a-interruptible-speechmatics.py", EVAL_SIMPLE_MATH),
|
||||
("07aa-interruptible-soniox.py", EVAL_SIMPLE_MATH),
|
||||
("07ab-interruptible-inworld-http.py", EVAL_SIMPLE_MATH),
|
||||
("07ac-interruptible-asyncai.py", EVAL_SIMPLE_MATH),
|
||||
("07ac-interruptible-asyncai-http.py", EVAL_SIMPLE_MATH),
|
||||
("07b-interruptible-langchain.py", EVAL_SIMPLE_MATH),
|
||||
("07c-interruptible-deepgram.py", EVAL_SIMPLE_MATH),
|
||||
("07c-interruptible-deepgram-flux.py", EVAL_SIMPLE_MATH),
|
||||
("07c-interruptible-deepgram-http.py", EVAL_SIMPLE_MATH),
|
||||
("07d-interruptible-elevenlabs.py", EVAL_SIMPLE_MATH),
|
||||
("07d-interruptible-elevenlabs-http.py", EVAL_SIMPLE_MATH),
|
||||
("07f-interruptible-azure.py", EVAL_SIMPLE_MATH),
|
||||
("07g-interruptible-openai.py", EVAL_SIMPLE_MATH),
|
||||
("07h-interruptible-openpipe.py", EVAL_SIMPLE_MATH),
|
||||
("07j-interruptible-gladia.py", EVAL_SIMPLE_MATH),
|
||||
("07k-interruptible-lmnt.py", EVAL_SIMPLE_MATH),
|
||||
("07l-interruptible-groq.py", EVAL_SIMPLE_MATH),
|
||||
("07m-interruptible-aws.py", EVAL_SIMPLE_MATH),
|
||||
("07m-interruptible-aws-strands.py", EVAL_WEATHER),
|
||||
("07n-interruptible-gemini.py", EVAL_SIMPLE_MATH),
|
||||
("07n-interruptible-google.py", EVAL_SIMPLE_MATH),
|
||||
("07o-interruptible-assemblyai.py", EVAL_SIMPLE_MATH),
|
||||
("07q-interruptible-rime.py", EVAL_SIMPLE_MATH),
|
||||
("07q-interruptible-rime-http.py", EVAL_SIMPLE_MATH),
|
||||
("07r-interruptible-riva-nim.py", EVAL_SIMPLE_MATH),
|
||||
("07s-interruptible-google-audio-in.py", EVAL_SIMPLE_MATH),
|
||||
("07t-interruptible-fish.py", EVAL_SIMPLE_MATH),
|
||||
("07v-interruptible-neuphonic.py", EVAL_SIMPLE_MATH),
|
||||
("07v-interruptible-neuphonic-http.py", EVAL_SIMPLE_MATH),
|
||||
("07w-interruptible-fal.py", EVAL_SIMPLE_MATH),
|
||||
("07y-interruptible-minimax.py", EVAL_SIMPLE_MATH),
|
||||
("07z-interruptible-sarvam.py", EVAL_SIMPLE_MATH),
|
||||
("07ae-interruptible-hume.py", EVAL_SIMPLE_MATH),
|
||||
("07-interruptible.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07-interruptible-cartesia-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07a-interruptible-speechmatics.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07aa-interruptible-soniox.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07ab-interruptible-inworld-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07ac-interruptible-asyncai.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07ac-interruptible-asyncai-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07b-interruptible-langchain.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07c-interruptible-deepgram.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07c-interruptible-deepgram-flux.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07d-interruptible-elevenlabs.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"07d-interruptible-elevenlabs-http.py",
|
||||
PROMPT_SIMPLE_MATH,
|
||||
EVAL_SIMPLE_MATH,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
("07f-interruptible-azure.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07g-interruptible-openai.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07h-interruptible-openpipe.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07j-interruptible-gladia.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07k-interruptible-lmnt.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07l-interruptible-groq.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07m-interruptible-aws.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07m-interruptible-aws-strands.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("07n-interruptible-gemini.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07n-interruptible-google.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07o-interruptible-assemblyai.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07q-interruptible-rime.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07q-interruptible-rime-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07r-interruptible-riva-nim.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"07s-interruptible-google-audio-in.py",
|
||||
PROMPT_SIMPLE_MATH,
|
||||
EVAL_SIMPLE_MATH,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
("07t-interruptible-fish.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07v-interruptible-neuphonic.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07v-interruptible-neuphonic-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07w-interruptible-fal.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07y-interruptible-minimax.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07z-interruptible-sarvam.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07ae-interruptible-hume.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
# Needs a local XTTS docker instance running.
|
||||
# ("07i-interruptible-xtts.py", EVAL_SIMPLE_MATH),
|
||||
# ("07i-interruptible-xtts.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
# Needs a Krisp license.
|
||||
# ("07p-interruptible-krisp.py", EVAL_SIMPLE_MATH),
|
||||
# ("07p-interruptible-krisp.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
# Needs GPU resources.
|
||||
# ("07u-interruptible-ultravox.py", EVAL_SIMPLE_MATH),
|
||||
# ("07u-interruptible-ultravox.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_12 = [
|
||||
("12-describe-image-openai.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
|
||||
("12a-describe-image-anthropic.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
|
||||
("12b-describe-image-aws.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
|
||||
("12c-describe-image-gemini-flash.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
|
||||
("12d-describe-image-moondream.py", EVAL_VISION_IMAGE()),
|
||||
("12-describe-video.py", PROMPT_VISION, EVAL_VISION, BOT_SPEAKS_FIRST),
|
||||
("12a-describe-video-gemini-flash.py", PROMPT_VISION, EVAL_VISION, BOT_SPEAKS_FIRST),
|
||||
("12b-describe-video-gpt-4o.py", PROMPT_VISION, EVAL_VISION, BOT_SPEAKS_FIRST),
|
||||
("12c-describe-video-anthropic.py", PROMPT_VISION, EVAL_VISION, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_14 = [
|
||||
("14-function-calling.py", EVAL_WEATHER),
|
||||
("14a-function-calling-anthropic.py", EVAL_WEATHER),
|
||||
("14e-function-calling-google.py", EVAL_WEATHER),
|
||||
("14f-function-calling-groq.py", EVAL_WEATHER),
|
||||
("14g-function-calling-grok.py", EVAL_WEATHER),
|
||||
("14h-function-calling-azure.py", EVAL_WEATHER),
|
||||
("14i-function-calling-fireworks.py", EVAL_WEATHER),
|
||||
("14j-function-calling-nim.py", EVAL_WEATHER),
|
||||
("14k-function-calling-cerebras.py", EVAL_WEATHER),
|
||||
("14m-function-calling-openrouter.py", EVAL_WEATHER),
|
||||
("14n-function-calling-perplexity.py", EVAL_WEATHER),
|
||||
("14p-function-calling-gemini-vertex-ai.py", EVAL_WEATHER),
|
||||
("14q-function-calling-qwen.py", EVAL_WEATHER),
|
||||
("14r-function-calling-aws.py", EVAL_WEATHER),
|
||||
("14v-function-calling-openai.py", EVAL_WEATHER),
|
||||
("14w-function-calling-mistral.py", EVAL_WEATHER),
|
||||
("14x-function-calling-openpipe.py", EVAL_WEATHER),
|
||||
# Video
|
||||
("14d-function-calling-anthropic-video.py", EVAL_VISION_CAMERA),
|
||||
("14d-function-calling-aws-video.py", EVAL_VISION_CAMERA),
|
||||
("14d-function-calling-gemini-flash-video.py", EVAL_VISION_CAMERA),
|
||||
("14d-function-calling-moondream-video.py", EVAL_VISION_CAMERA),
|
||||
("14d-function-calling-openai-video.py", EVAL_VISION_CAMERA),
|
||||
("14-function-calling.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14a-function-calling-anthropic.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14b-function-calling-anthropic-video.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14d-function-calling-video.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14e-function-calling-google.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14f-function-calling-groq.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14g-function-calling-grok.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14h-function-calling-azure.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14i-function-calling-fireworks.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14j-function-calling-nim.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14k-function-calling-cerebras.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14m-function-calling-openrouter.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14n-function-calling-perplexity.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14p-function-calling-gemini-vertex-ai.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14q-function-calling-qwen.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14r-function-calling-aws.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14v-function-calling-openai.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14w-function-calling-mistral.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14x-function-calling-openpipe.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
# Currently not working.
|
||||
# ("14c-function-calling-together.py", EVAL_WEATHER),
|
||||
# ("14l-function-calling-deepseek.py", EVAL_WEATHER),
|
||||
# ("14o-function-calling-gemini-openai-format.py", EVAL_WEATHER),
|
||||
# ("14c-function-calling-together.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
# ("14l-function-calling-deepseek.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
# ("14o-function-calling-gemini-openai-format.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_15 = [
|
||||
("15a-switch-languages.py", EVAL_SWITCH_LANGUAGE),
|
||||
("15a-switch-languages.py", PROMPT_SWITCH_LANGUAGE, EVAL_SWITCH_LANGUAGE, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_19 = [
|
||||
("19-openai-realtime.py", EVAL_WEATHER),
|
||||
("19-openai-realtime-beta.py", EVAL_WEATHER),
|
||||
("19-openai-realtime.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("19-openai-realtime-beta.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
# OpenAI Realtime not released on Azure yet
|
||||
# ("19a-azure-realtime.py", EVAL_WEATHER),
|
||||
("19a-azure-realtime-beta.py", EVAL_WEATHER),
|
||||
("19b-openai-realtime-text.py", EVAL_WEATHER),
|
||||
("19b-openai-realtime-beta-text.py", EVAL_WEATHER),
|
||||
# ("19a-azure-realtime.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("19a-azure-realtime-beta.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("19b-openai-realtime-text.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("19b-openai-realtime-beta-text.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_21 = [
|
||||
("21a-tavus-video-service.py", EVAL_SIMPLE_MATH),
|
||||
("21a-tavus-video-service.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_26 = [
|
||||
("26-gemini-live.py", EVAL_SIMPLE_MATH),
|
||||
("26a-gemini-live-transcription.py", EVAL_SIMPLE_MATH),
|
||||
("26b-gemini-live-function-calling.py", EVAL_WEATHER),
|
||||
("26c-gemini-live-video.py", EVAL_SIMPLE_MATH),
|
||||
("26e-gemini-live-google-search.py", EVAL_ONLINE_SEARCH),
|
||||
("26h-gemini-live-vertex-function-calling.py", EVAL_WEATHER),
|
||||
("26-gemini-multimodal-live.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"26a-gemini-live-transcription.py",
|
||||
PROMPT_SIMPLE_MATH,
|
||||
EVAL_SIMPLE_MATH,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
(
|
||||
"26b-gemini-live-function-calling.py",
|
||||
PROMPT_WEATHER,
|
||||
EVAL_WEATHER,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
("26c-gemini-live-video.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"26e-gemini-multimodal-google-search.py",
|
||||
PROMPT_ONLINE_SEARCH,
|
||||
EVAL_ONLINE_SEARCH,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
# Currently not working.
|
||||
# ("26d-gemini-live-text.py", EVAL_SIMPLE_MATH),
|
||||
# ("26d-gemini-live-text.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"26h-gemini-live-vertex-function-calling.py",
|
||||
PROMPT_WEATHER,
|
||||
EVAL_WEATHER,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
]
|
||||
|
||||
TESTS_27 = [
|
||||
("27-simli-layer.py", EVAL_SIMPLE_MATH),
|
||||
("27-simli-layer.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_40 = [
|
||||
("40-aws-nova-sonic.py", EVAL_SIMPLE_MATH),
|
||||
("40-aws-nova-sonic.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_43 = [
|
||||
("43a-heygen-video-service.py", EVAL_SIMPLE_MATH),
|
||||
("43a-heygen-video-service.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_44 = [
|
||||
("44-voicemail-detection.py", EVAL_VOICEMAIL),
|
||||
("44-voicemail-detection.py", EVAL_CONVERSATION),
|
||||
("44-voicemail-detection.py", PROMPT_VOICEMAIL, EVAL_VOICEMAIL, USER_SPEAKS_FIRST),
|
||||
("44-voicemail-detection.py", PROMPT_CONVERSATION, EVAL_CONVERSATION, USER_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS = [
|
||||
@@ -239,9 +244,9 @@ async def main(args: argparse.Namespace):
|
||||
|
||||
# Parse test config: (test, prompt, eval, user_speaks_first)
|
||||
for test_config in TESTS:
|
||||
test, eval_config = test_config
|
||||
test, prompt, eval, user_speaks_first = test_config
|
||||
|
||||
await runner.run_eval(test, eval_config)
|
||||
await runner.run_eval(test, prompt, eval, user_speaks_first)
|
||||
|
||||
runner.print_results()
|
||||
|
||||
|
||||
@@ -245,25 +245,13 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
|
||||
item["text"] = "(empty)"
|
||||
# handle image_url -> image conversion
|
||||
if item["type"] == "image_url":
|
||||
if item["image_url"]["url"].startswith("data:"):
|
||||
item["type"] = "image"
|
||||
item["source"] = {
|
||||
"type": "base64",
|
||||
"media_type": "image/jpeg",
|
||||
"data": item["image_url"]["url"].split(",")[1],
|
||||
}
|
||||
del item["image_url"]
|
||||
elif item["image_url"]["url"].startswith("http"):
|
||||
item["type"] = "image"
|
||||
item["source"] = {
|
||||
"type": "url",
|
||||
"url": item["image_url"]["url"],
|
||||
}
|
||||
del item["image_url"]
|
||||
else:
|
||||
url = item["image_url"]["url"]
|
||||
logger.warning(f"Unsupported 'image_url': {url}")
|
||||
|
||||
item["type"] = "image"
|
||||
item["source"] = {
|
||||
"type": "base64",
|
||||
"media_type": "image/jpeg",
|
||||
"data": item["image_url"]["url"].split(",")[1],
|
||||
}
|
||||
del item["image_url"]
|
||||
# In the case where there's a single image in the list (like what
|
||||
# would result from a UserImageRawFrame), ensure that the image
|
||||
# comes before text, as recommended by Anthropic docs
|
||||
|
||||
@@ -256,22 +256,15 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
|
||||
new_content.append({"text": text_content})
|
||||
# handle image_url -> image conversion
|
||||
if item["type"] == "image_url":
|
||||
if item["image_url"]["url"].startswith("data:"):
|
||||
new_item = {
|
||||
"image": {
|
||||
"format": "jpeg",
|
||||
"source": {
|
||||
"bytes": base64.b64decode(
|
||||
item["image_url"]["url"].split(",")[1]
|
||||
)
|
||||
},
|
||||
}
|
||||
new_item = {
|
||||
"image": {
|
||||
"format": "jpeg",
|
||||
"source": {
|
||||
"bytes": base64.b64decode(item["image_url"]["url"].split(",")[1])
|
||||
},
|
||||
}
|
||||
new_content.append(new_item)
|
||||
else:
|
||||
url = item["image_url"]["url"]
|
||||
logger.warning(f"Unsupported 'image_url': {url}")
|
||||
|
||||
}
|
||||
new_content.append(new_item)
|
||||
# In the case where there's a single image in the list (like what
|
||||
# would result from a UserImageRawFrame), ensure that the image
|
||||
# comes before text
|
||||
|
||||
@@ -343,7 +343,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
for c in content:
|
||||
if c["type"] == "text":
|
||||
parts.append(Part(text=c["text"]))
|
||||
elif c["type"] == "image_url" and c["image_url"]["url"].startswith("data:"):
|
||||
elif c["type"] == "image_url":
|
||||
parts.append(
|
||||
Part(
|
||||
inline_data=Blob(
|
||||
@@ -352,9 +352,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
)
|
||||
)
|
||||
)
|
||||
elif c["type"] == "image_url":
|
||||
url = c["image_url"]["url"]
|
||||
logger.warning(f"Unsupported 'image_url': {url}")
|
||||
elif c["type"] == "input_audio":
|
||||
input_audio = c["input_audio"]
|
||||
audio_bytes = base64.b64decode(input_audio["data"])
|
||||
|
||||
@@ -1201,23 +1201,26 @@ class TransportMessageUrgentFrame(OutputTransportMessageUrgentFrame):
|
||||
class UserImageRequestFrame(SystemFrame):
|
||||
"""Frame requesting an image from a specific user.
|
||||
|
||||
A frame to request an image from the given user. The request might come with
|
||||
a text that can be later used to describe the requested image.
|
||||
A frame to request an image from the given user. The frame might be
|
||||
generated by a function call in which case the corresponding fields will be
|
||||
properly set.
|
||||
|
||||
Parameters:
|
||||
user_id: Identifier of the user to request image from.
|
||||
text: An optional text associated to the image request.
|
||||
append_to_context: Whether the requested image should be appended to the LLM context.
|
||||
context: Optional context for the image request.
|
||||
function_name: Name of function that generated this request (if any).
|
||||
tool_call_id: Tool call ID if generated by function call.
|
||||
video_source: Specific video source to capture from.
|
||||
"""
|
||||
|
||||
user_id: str
|
||||
text: Optional[str] = None
|
||||
append_to_context: Optional[bool] = None
|
||||
context: Optional[Any] = None
|
||||
function_name: Optional[str] = None
|
||||
tool_call_id: Optional[str] = None
|
||||
video_source: Optional[str] = None
|
||||
|
||||
def __str__(self):
|
||||
return f"{self.name}(user: {self.user_id}, text: {self.text}, append_to_context: {self.append_to_context}, {self.video_source})"
|
||||
return f"{self.name}(user: {self.user_id}, video_source: {self.video_source}, function: {self.function_name}, request: {self.tool_call_id})"
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -1291,17 +1294,15 @@ class UserImageRawFrame(InputImageRawFrame):
|
||||
|
||||
Parameters:
|
||||
user_id: Identifier of the user who provided this image.
|
||||
text: An optional text associated to this image.
|
||||
append_to_context: Whether the requested image should be appended to the LLM context.
|
||||
request: The original image request frame if this is a response.
|
||||
"""
|
||||
|
||||
user_id: str = ""
|
||||
text: Optional[str] = None
|
||||
append_to_context: Optional[bool] = None
|
||||
request: Optional[UserImageRequestFrame] = None
|
||||
|
||||
def __str__(self):
|
||||
pts = format_pts(self.pts)
|
||||
return f"{self.name}(pts: {pts}, user: {self.user_id}, source: {self.transport_source}, size: {self.size}, format: {self.format}, text: {self.text}, append_to_context: {self.append_to_context})"
|
||||
return f"{self.name}(pts: {pts}, user: {self.user_id}, source: {self.transport_source}, size: {self.size}, format: {self.format}, request: {self.request})"
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
@@ -12,6 +12,7 @@ including heartbeats, idle detection, and observer integration.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import time
|
||||
from typing import Any, AsyncIterable, Dict, Iterable, List, Optional, Tuple, Type
|
||||
|
||||
from loguru import logger
|
||||
@@ -38,7 +39,7 @@ from pipecat.frames.frames import (
|
||||
UserSpeakingFrame,
|
||||
)
|
||||
from pipecat.metrics.metrics import ProcessingMetricsData, TTFBMetricsData
|
||||
from pipecat.observers.base_observer import BaseObserver, FramePushed
|
||||
from pipecat.observers.base_observer import BaseObserver
|
||||
from pipecat.observers.turn_tracking_observer import TurnTrackingObserver
|
||||
from pipecat.pipeline.base_task import BasePipelineTask, PipelineTaskParams
|
||||
from pipecat.pipeline.pipeline import Pipeline, PipelineSink, PipelineSource
|
||||
@@ -56,43 +57,6 @@ IDLE_TIMEOUT_SECS = 300
|
||||
CANCEL_TIMEOUT_SECS = 20.0
|
||||
|
||||
|
||||
class IdleFrameObserver(BaseObserver):
|
||||
"""Idle timeout observer.
|
||||
|
||||
This observer waits for specific frames being generated in the pipeline. If
|
||||
the frames are generated the given asyncio event is set. If the event is not
|
||||
set it means the pipeline is probably idle.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, *, idle_event: asyncio.Event, idle_timeout_frames: Tuple[Type[Frame], ...]):
|
||||
"""Initialize the observer.
|
||||
|
||||
Args:
|
||||
idle_event: The event to set if the idle timeout frames are being pushed.
|
||||
idle_timeout_frames: A tuple with the frames that should set the event when received
|
||||
"""
|
||||
super().__init__()
|
||||
self._idle_event = idle_event
|
||||
self._idle_timeout_frames = idle_timeout_frames
|
||||
self._processed_frames = set()
|
||||
|
||||
async def on_push_frame(self, data: FramePushed):
|
||||
"""Callback executed when a frame is pushed in the pipeline.
|
||||
|
||||
Args:
|
||||
data: The frame push event data.
|
||||
"""
|
||||
# Skip already processed frames
|
||||
if data.frame.id in self._processed_frames:
|
||||
return
|
||||
|
||||
self._processed_frames.add(data.frame.id)
|
||||
|
||||
if isinstance(data.frame, StartFrame) or isinstance(data.frame, self._idle_timeout_frames):
|
||||
self._idle_event.set()
|
||||
|
||||
|
||||
class PipelineParams(BaseModel):
|
||||
"""Configuration parameters for pipeline execution.
|
||||
|
||||
@@ -251,6 +215,7 @@ class PipelineTask(BasePipelineTask):
|
||||
self._conversation_id = conversation_id
|
||||
self._enable_tracing = enable_tracing and is_tracing_available()
|
||||
self._enable_turn_tracking = enable_turn_tracking
|
||||
self._idle_timeout_frames = idle_timeout_frames
|
||||
self._idle_timeout_secs = idle_timeout_secs
|
||||
if self._params.observers:
|
||||
import warnings
|
||||
@@ -285,24 +250,16 @@ class PipelineTask(BasePipelineTask):
|
||||
# This queue is the queue used to push frames to the pipeline.
|
||||
self._push_queue = asyncio.Queue()
|
||||
self._process_push_task: Optional[asyncio.Task] = None
|
||||
|
||||
# This is the heartbeat queue. When a heartbeat frame is received in the
|
||||
# down queue we add it to the heartbeat queue for processing.
|
||||
self._heartbeat_queue = asyncio.Queue()
|
||||
self._heartbeat_push_task: Optional[asyncio.Task] = None
|
||||
self._heartbeat_monitor_task: Optional[asyncio.Task] = None
|
||||
|
||||
# This is the idle event. When selected frames are pushed from any
|
||||
# processor we consider the pipeline is not idle. We use an observer
|
||||
# which will be listening any part of the pipeline.
|
||||
self._idle_event = asyncio.Event()
|
||||
# This is the idle queue. When frames are received downstream they are
|
||||
# put in the queue. If no frame is received the pipeline is considered
|
||||
# idle.
|
||||
self._idle_queue = asyncio.Queue()
|
||||
self._idle_monitor_task: Optional[asyncio.Task] = None
|
||||
if self._idle_timeout_secs:
|
||||
idle_frame_observer = IdleFrameObserver(
|
||||
idle_event=self._idle_event,
|
||||
idle_timeout_frames=idle_timeout_frames,
|
||||
)
|
||||
observers.append(idle_frame_observer)
|
||||
|
||||
# This event is used to indicate the StartFrame has been received at the
|
||||
# end of the pipeline.
|
||||
@@ -573,7 +530,7 @@ class PipelineTask(BasePipelineTask):
|
||||
|
||||
async def _maybe_cancel_idle_task(self):
|
||||
"""Cancel idle monitoring task if it is running."""
|
||||
if self._idle_monitor_task:
|
||||
if self._idle_timeout_secs and self._idle_monitor_task:
|
||||
await self._task_manager.cancel_task(self._idle_monitor_task)
|
||||
self._idle_monitor_task = None
|
||||
|
||||
@@ -749,6 +706,10 @@ class PipelineTask(BasePipelineTask):
|
||||
processors have handled the EndFrame and therefore we can exit the task
|
||||
cleanly.
|
||||
"""
|
||||
# Queue received frame to the idle queue so we can monitor idle
|
||||
# pipelines.
|
||||
await self._idle_queue.put(frame)
|
||||
|
||||
if isinstance(frame, self._reached_downstream_types):
|
||||
await self._call_event_handler("on_frame_reached_downstream", frame)
|
||||
|
||||
@@ -811,10 +772,33 @@ class PipelineTask(BasePipelineTask):
|
||||
Note: Heartbeats are excluded from idle detection.
|
||||
"""
|
||||
running = True
|
||||
last_frame_time = 0
|
||||
|
||||
while running:
|
||||
try:
|
||||
await asyncio.wait_for(self._idle_event.wait(), timeout=self._idle_timeout_secs)
|
||||
self._idle_event.clear()
|
||||
frame = await asyncio.wait_for(
|
||||
self._idle_queue.get(), timeout=self._idle_timeout_secs
|
||||
)
|
||||
|
||||
if isinstance(frame, StartFrame) or isinstance(frame, self._idle_timeout_frames):
|
||||
# If we find a StartFrame or one of the frames that prevents a
|
||||
# time out we update the time.
|
||||
last_frame_time = time.time()
|
||||
else:
|
||||
# If we find any other frame we check if the pipeline is
|
||||
# idle by checking the last time we received one of the
|
||||
# valid frames.
|
||||
diff_time = time.time() - last_frame_time
|
||||
if diff_time >= self._idle_timeout_secs:
|
||||
running = await self._idle_timeout_detected()
|
||||
# Reset `last_frame_time` so we don't trigger another
|
||||
# immediate idle timeout if we are not cancelling. For
|
||||
# example, we might want to force the bot to say goodbye
|
||||
# and then clean nicely with an `EndFrame`.
|
||||
last_frame_time = time.time()
|
||||
|
||||
self._idle_queue.task_done()
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
running = await self._idle_timeout_detected()
|
||||
|
||||
@@ -826,7 +810,7 @@ class PipelineTask(BasePipelineTask):
|
||||
"""
|
||||
# If we are cancelling, just exit the task.
|
||||
if self._cancelled:
|
||||
return False
|
||||
return True
|
||||
|
||||
logger.warning("Idle timeout detected.")
|
||||
await self._call_event_handler("on_idle_timeout")
|
||||
|
||||
@@ -129,7 +129,7 @@ class TaskObserver(BaseObserver):
|
||||
for proxy in self._proxies:
|
||||
await proxy.cleanup()
|
||||
|
||||
async def on_process_frame(self, data: FrameProcessed):
|
||||
async def on_process_frame(self, data: FramePushed):
|
||||
"""Queue frame data for all managed observers.
|
||||
|
||||
Args:
|
||||
|
||||
@@ -16,7 +16,6 @@ service-specific adapter.
|
||||
|
||||
import base64
|
||||
import io
|
||||
import wave
|
||||
from dataclasses import dataclass
|
||||
from typing import TYPE_CHECKING, Any, List, Optional, TypeAlias, Union
|
||||
|
||||
@@ -114,89 +113,6 @@ class LLMContext:
|
||||
self._tools: ToolsSchema | NotGiven = LLMContext._normalize_and_validate_tools(tools)
|
||||
self._tool_choice: LLMContextToolChoice | NotGiven = tool_choice
|
||||
|
||||
@staticmethod
|
||||
def create_image_url_message(
|
||||
*,
|
||||
role: str = "user",
|
||||
url: str,
|
||||
text: Optional[str] = None,
|
||||
) -> LLMContextMessage:
|
||||
"""Create a context message containing an image URL.
|
||||
|
||||
Args:
|
||||
role: The role of this message (defaults to "user").
|
||||
url: The URL of the image.
|
||||
text: Optional text to include with the image.
|
||||
"""
|
||||
content = []
|
||||
if text:
|
||||
content.append({"type": "text", "text": text})
|
||||
|
||||
content.append({"type": "image_url", "image_url": {"url": url}})
|
||||
|
||||
return {"role": role, "content": content}
|
||||
|
||||
@staticmethod
|
||||
def create_image_message(
|
||||
*,
|
||||
role: str = "user",
|
||||
format: str,
|
||||
size: tuple[int, int],
|
||||
image: bytes,
|
||||
text: Optional[str] = None,
|
||||
) -> LLMContextMessage:
|
||||
"""Create a context message containing an image.
|
||||
|
||||
Args:
|
||||
role: The role of this message (defaults to "user").
|
||||
format: Image format (e.g., 'RGB', 'RGBA').
|
||||
size: Image dimensions as (width, height) tuple.
|
||||
image: Raw image bytes.
|
||||
text: Optional text to include with the image.
|
||||
"""
|
||||
buffer = io.BytesIO()
|
||||
Image.frombytes(format, size, image).save(buffer, format="JPEG")
|
||||
encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||||
url = f"data:image/jpeg;base64,{encoded_image}"
|
||||
|
||||
return LLMContext.create_image_url_message(role=role, url=url, text=text)
|
||||
|
||||
@staticmethod
|
||||
def create_audio_message(
|
||||
*, role: str = "user", audio_frames: list[AudioRawFrame], text: str = "Audio follows"
|
||||
) -> LLMContextMessage:
|
||||
"""Create a context message containing audio.
|
||||
|
||||
Args:
|
||||
role: The role of this message (defaults to "user").
|
||||
audio_frames: List of audio frame objects to include.
|
||||
text: Optional text to include with the audio.
|
||||
"""
|
||||
sample_rate = audio_frames[0].sample_rate
|
||||
num_channels = audio_frames[0].num_channels
|
||||
|
||||
content = []
|
||||
content.append({"type": "text", "text": text})
|
||||
data = b"".join(frame.audio for frame in audio_frames)
|
||||
|
||||
with io.BytesIO() as buffer:
|
||||
with wave.open(buffer, "wb") as wf:
|
||||
wf.setsampwidth(2)
|
||||
wf.setnchannels(num_channels)
|
||||
wf.setframerate(sample_rate)
|
||||
wf.writeframes(data)
|
||||
|
||||
encoded_audio = base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||||
|
||||
content.append(
|
||||
{
|
||||
"type": "input_audio",
|
||||
"input_audio": {"data": encoded_audio, "format": "wav"},
|
||||
}
|
||||
)
|
||||
|
||||
return {"role": role, "content": content}
|
||||
|
||||
@property
|
||||
def messages(self) -> List[LLMContextMessage]:
|
||||
"""Get the current messages list.
|
||||
@@ -322,7 +238,7 @@ class LLMContext:
|
||||
self._tool_choice = tool_choice
|
||||
|
||||
def add_image_frame_message(
|
||||
self, *, format: str, size: tuple[int, int], image: bytes, text: Optional[str] = None
|
||||
self, *, format: str, size: tuple[int, int], image: bytes, text: str = None
|
||||
):
|
||||
"""Add a message containing an image frame.
|
||||
|
||||
@@ -332,8 +248,17 @@ class LLMContext:
|
||||
image: Raw image bytes.
|
||||
text: Optional text to include with the image.
|
||||
"""
|
||||
message = LLMContext.create_image_message(format=format, size=size, image=image, text=text)
|
||||
self.add_message(message)
|
||||
buffer = io.BytesIO()
|
||||
Image.frombytes(format, size, image).save(buffer, format="JPEG")
|
||||
encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||||
|
||||
content = []
|
||||
if text:
|
||||
content.append({"type": "text", "text": text})
|
||||
content.append(
|
||||
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}},
|
||||
)
|
||||
self.add_message({"role": "user", "content": content})
|
||||
|
||||
def add_audio_frames_message(
|
||||
self, *, audio_frames: list[AudioRawFrame], text: str = "Audio follows"
|
||||
@@ -344,8 +269,66 @@ class LLMContext:
|
||||
audio_frames: List of audio frame objects to include.
|
||||
text: Optional text to include with the audio.
|
||||
"""
|
||||
message = LLMContext.create_audio_message(audio_frames=audio_frames, text=text)
|
||||
self.add_message(message)
|
||||
if not audio_frames:
|
||||
return
|
||||
|
||||
sample_rate = audio_frames[0].sample_rate
|
||||
num_channels = audio_frames[0].num_channels
|
||||
|
||||
content = []
|
||||
content.append({"type": "text", "text": text})
|
||||
data = b"".join(frame.audio for frame in audio_frames)
|
||||
data = bytes(
|
||||
self._create_wav_header(
|
||||
sample_rate,
|
||||
num_channels,
|
||||
16,
|
||||
len(data),
|
||||
)
|
||||
+ data
|
||||
)
|
||||
encoded_audio = base64.b64encode(data).decode("utf-8")
|
||||
content.append(
|
||||
{
|
||||
"type": "input_audio",
|
||||
"input_audio": {"data": encoded_audio, "format": "wav"},
|
||||
}
|
||||
)
|
||||
self.add_message({"role": "user", "content": content})
|
||||
|
||||
def _create_wav_header(self, sample_rate, num_channels, bits_per_sample, data_size):
|
||||
"""Create a WAV file header for audio data.
|
||||
|
||||
Args:
|
||||
sample_rate: Audio sample rate in Hz.
|
||||
num_channels: Number of audio channels.
|
||||
bits_per_sample: Bits per audio sample.
|
||||
data_size: Size of audio data in bytes.
|
||||
|
||||
Returns:
|
||||
WAV header as a bytearray.
|
||||
"""
|
||||
# RIFF chunk descriptor
|
||||
header = bytearray()
|
||||
header.extend(b"RIFF") # ChunkID
|
||||
header.extend((data_size + 36).to_bytes(4, "little")) # ChunkSize: total size - 8
|
||||
header.extend(b"WAVE") # Format
|
||||
# "fmt " sub-chunk
|
||||
header.extend(b"fmt ") # Subchunk1ID
|
||||
header.extend((16).to_bytes(4, "little")) # Subchunk1Size (16 for PCM)
|
||||
header.extend((1).to_bytes(2, "little")) # AudioFormat (1 for PCM)
|
||||
header.extend(num_channels.to_bytes(2, "little")) # NumChannels
|
||||
header.extend(sample_rate.to_bytes(4, "little")) # SampleRate
|
||||
# Calculate byte rate and block align
|
||||
byte_rate = sample_rate * num_channels * (bits_per_sample // 8)
|
||||
block_align = num_channels * (bits_per_sample // 8)
|
||||
header.extend(byte_rate.to_bytes(4, "little")) # ByteRate
|
||||
header.extend(block_align.to_bytes(2, "little")) # BlockAlign
|
||||
header.extend(bits_per_sample.to_bytes(2, "little")) # BitsPerSample
|
||||
# "data" sub-chunk
|
||||
header.extend(b"data") # Subchunk2ID
|
||||
header.extend(data_size.to_bytes(4, "little")) # Subchunk2Size
|
||||
return header
|
||||
|
||||
@staticmethod
|
||||
def _normalize_and_validate_tools(tools: ToolsSchema | NotGiven) -> ToolsSchema | NotGiven:
|
||||
|
||||
@@ -89,9 +89,7 @@ class LLMAssistantAggregatorParams:
|
||||
|
||||
Parameters:
|
||||
expect_stripped_words: Whether to expect and handle stripped words
|
||||
in text frames by adding spaces between tokens. This parameter is
|
||||
ignored when used with the newer LLMAssistantAggregator, which
|
||||
handles word spacing automatically.
|
||||
in text frames by adding spaces between tokens.
|
||||
"""
|
||||
|
||||
expect_stripped_words: bool = True
|
||||
|
||||
@@ -13,7 +13,6 @@ LLM processing, and text-to-speech components in conversational AI pipelines.
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import warnings
|
||||
from abc import abstractmethod
|
||||
from typing import Any, Dict, List, Literal, Optional, Set
|
||||
|
||||
@@ -66,7 +65,6 @@ from pipecat.processors.aggregators.llm_response import (
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.utils.string import concatenate_aggregated_text
|
||||
from pipecat.utils.time import time_now_iso8601
|
||||
|
||||
|
||||
@@ -90,7 +88,7 @@ class LLMContextAggregator(FrameProcessor):
|
||||
self._context = context
|
||||
self._role = role
|
||||
|
||||
self._aggregation: List[str] = []
|
||||
self._aggregation: str = ""
|
||||
|
||||
@property
|
||||
def messages(self) -> List[LLMContextMessage]:
|
||||
@@ -170,21 +168,13 @@ class LLMContextAggregator(FrameProcessor):
|
||||
|
||||
async def reset(self):
|
||||
"""Reset the aggregation state."""
|
||||
self._aggregation = []
|
||||
self._aggregation = ""
|
||||
|
||||
@abstractmethod
|
||||
async def push_aggregation(self):
|
||||
"""Push the current aggregation downstream."""
|
||||
pass
|
||||
|
||||
def aggregation_string(self) -> str:
|
||||
"""Get the current aggregation as a string.
|
||||
|
||||
Returns:
|
||||
The concatenated aggregation string.
|
||||
"""
|
||||
return concatenate_aggregated_text(self._aggregation)
|
||||
|
||||
|
||||
class LLMUserAggregator(LLMContextAggregator):
|
||||
"""User LLM aggregator that processes speech-to-text transcriptions.
|
||||
@@ -222,6 +212,8 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
self._turn_params: Optional[SmartTurnParams] = None
|
||||
|
||||
if "aggregation_timeout" in kwargs:
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
@@ -315,7 +307,7 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
|
||||
async def _process_aggregation(self):
|
||||
"""Process the current aggregation and push it downstream."""
|
||||
aggregation = self.aggregation_string()
|
||||
aggregation = self._aggregation
|
||||
await self.reset()
|
||||
self._context.add_message({"role": self.role, "content": aggregation})
|
||||
frame = LLMContextFrame(self._context)
|
||||
@@ -363,7 +355,7 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
"""
|
||||
|
||||
async def should_interrupt(strategy: BaseInterruptionStrategy):
|
||||
await strategy.append_text(self.aggregation_string())
|
||||
await strategy.append_text(self._aggregation)
|
||||
return await strategy.should_interrupt()
|
||||
|
||||
return any([await should_interrupt(s) for s in self._interruption_strategies])
|
||||
@@ -433,7 +425,7 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
if not text.strip():
|
||||
return
|
||||
|
||||
self._aggregation.append(text)
|
||||
self._aggregation += f" {text}" if self._aggregation else text
|
||||
# We just got a final result, so let's reset interim results.
|
||||
self._seen_interim_results = False
|
||||
# Reset aggregation timer.
|
||||
@@ -558,31 +550,23 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
Args:
|
||||
context: The OpenAI LLM context for conversation storage.
|
||||
params: Configuration parameters for aggregation behavior.
|
||||
**kwargs: Additional arguments.
|
||||
**kwargs: Additional arguments. Supports deprecated 'expect_stripped_words'.
|
||||
"""
|
||||
super().__init__(context=context, role="assistant", **kwargs)
|
||||
self._params = params or LLMAssistantAggregatorParams()
|
||||
|
||||
if "expect_stripped_words" in kwargs:
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"Parameter 'expect_stripped_words' is deprecated. "
|
||||
"LLMAssistantAggregator now handles word spacing automatically.",
|
||||
"Parameter 'expect_stripped_words' is deprecated, use 'params' instead.",
|
||||
DeprecationWarning,
|
||||
)
|
||||
|
||||
self._params.expect_stripped_words = kwargs["expect_stripped_words"]
|
||||
|
||||
if params and not params.expect_stripped_words:
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"params.expect_stripped_words is deprecated. "
|
||||
"LLMAssistantAggregator now handles word spacing automatically.",
|
||||
DeprecationWarning,
|
||||
)
|
||||
|
||||
self._started = 0
|
||||
self._function_calls_in_progress: Dict[str, Optional[FunctionCallInProgressFrame]] = {}
|
||||
self._context_updated_tasks: Set[asyncio.Task] = set()
|
||||
@@ -632,7 +616,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
await self._handle_function_call_result(frame)
|
||||
elif isinstance(frame, FunctionCallCancelFrame):
|
||||
await self._handle_function_call_cancel(frame)
|
||||
elif isinstance(frame, UserImageRawFrame):
|
||||
elif isinstance(frame, UserImageRawFrame) and frame.request and frame.request.tool_call_id:
|
||||
await self._handle_user_image_frame(frame)
|
||||
elif isinstance(frame, BotStoppedSpeakingFrame):
|
||||
await self.push_aggregation()
|
||||
@@ -645,7 +629,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
if not self._aggregation:
|
||||
return
|
||||
|
||||
aggregation = self.aggregation_string()
|
||||
aggregation = self._aggregation.strip()
|
||||
await self.reset()
|
||||
|
||||
if aggregation:
|
||||
@@ -783,16 +767,27 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
message["content"] = result
|
||||
|
||||
async def _handle_user_image_frame(self, frame: UserImageRawFrame):
|
||||
if not frame.append_to_context:
|
||||
logger.debug(
|
||||
f"{self} UserImageRawFrame: [{frame.request.function_name}:{frame.request.tool_call_id}]"
|
||||
)
|
||||
|
||||
if frame.request.tool_call_id not in self._function_calls_in_progress:
|
||||
logger.warning(
|
||||
f"UserImageRawFrame tool_call_id [{frame.request.tool_call_id}] is not running"
|
||||
)
|
||||
return
|
||||
|
||||
logger.debug(f"{self} Appending UserImageRawFrame to LLM context (size: {frame.size})")
|
||||
del self._function_calls_in_progress[frame.request.tool_call_id]
|
||||
|
||||
# Update context with the image frame
|
||||
self._update_function_call_result(
|
||||
frame.request.function_name, frame.request.tool_call_id, "COMPLETED"
|
||||
)
|
||||
self._context.add_image_frame_message(
|
||||
format=frame.format,
|
||||
size=frame.size,
|
||||
image=frame.image,
|
||||
text=frame.text,
|
||||
text=frame.request.context,
|
||||
)
|
||||
|
||||
await self.push_aggregation()
|
||||
@@ -809,11 +804,10 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
if not self._started:
|
||||
return
|
||||
|
||||
# Make sure we really have text (spaces count, too!)
|
||||
if len(frame.text) == 0:
|
||||
return
|
||||
|
||||
self._aggregation.append(frame.text)
|
||||
if self._params.expect_stripped_words:
|
||||
self._aggregation += f" {frame.text}" if self._aggregation else frame.text
|
||||
else:
|
||||
self._aggregation += frame.text
|
||||
|
||||
def _context_updated_task_finished(self, task: asyncio.Task):
|
||||
self._context_updated_tasks.discard(task)
|
||||
|
||||
@@ -27,24 +27,11 @@ class UserResponseAggregator(LLMUserAggregator):
|
||||
def __init__(self, **kwargs):
|
||||
"""Initialize the user response aggregator.
|
||||
|
||||
.. deprecated:: 0.0.92
|
||||
`UserResponseAggregator` is deprecated and will be removed in a future version.
|
||||
|
||||
Args:
|
||||
**kwargs: Additional arguments passed to parent LLMUserAggregator.
|
||||
"""
|
||||
super().__init__(context=LLMContext(), **kwargs)
|
||||
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"`UserResponseAggregator` is deprecated and will be removed in a future version.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
async def push_aggregation(self):
|
||||
"""Push the aggregated user response as a TextFrame.
|
||||
|
||||
|
||||
@@ -26,7 +26,6 @@ from pipecat.frames.frames import (
|
||||
TTSTextFrame,
|
||||
)
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.utils.string import concatenate_aggregated_text
|
||||
from pipecat.utils.time import time_now_iso8601
|
||||
|
||||
|
||||
@@ -141,7 +140,29 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
|
||||
Result: "Hello there how are you"
|
||||
"""
|
||||
if self._current_text_parts and self._aggregation_start_time:
|
||||
content = concatenate_aggregated_text(self._current_text_parts)
|
||||
# Check specifically for space characters, previously isspace() was used
|
||||
# but that includes all whitespace characters (e.g. \n), not just spaces.
|
||||
has_leading_spaces = any(
|
||||
part and part[0] == " " for part in self._current_text_parts[1:]
|
||||
)
|
||||
has_trailing_spaces = any(
|
||||
part and part[-1] == " " for part in self._current_text_parts[:-1]
|
||||
)
|
||||
|
||||
# If there are embedded spaces in the fragments, use direct concatenation
|
||||
contains_spacing_between_fragments = has_leading_spaces or has_trailing_spaces
|
||||
|
||||
# Apply corresponding joining method
|
||||
if contains_spacing_between_fragments:
|
||||
# Fragments already have spacing - just concatenate
|
||||
content = "".join(self._current_text_parts)
|
||||
else:
|
||||
# Word-by-word fragments - join with spaces
|
||||
content = " ".join(self._current_text_parts)
|
||||
|
||||
# Clean up any excessive whitespace
|
||||
content = content.strip()
|
||||
|
||||
if content:
|
||||
logger.trace(f"Emitting aggregated assistant message: {content}")
|
||||
message = TranscriptionMessage(
|
||||
|
||||
@@ -44,8 +44,6 @@ from loguru import logger
|
||||
from pydantic import BaseModel
|
||||
|
||||
from pipecat.transports.daily.utils import (
|
||||
DailyMeetingTokenParams,
|
||||
DailyMeetingTokenProperties,
|
||||
DailyRESTHelper,
|
||||
DailyRoomParams,
|
||||
DailyRoomProperties,
|
||||
@@ -86,7 +84,6 @@ async def configure(
|
||||
sip_num_endpoints: Optional[int] = 1,
|
||||
sip_codecs: Optional[Dict[str, List[str]]] = None,
|
||||
room_properties: Optional[DailyRoomProperties] = None,
|
||||
token_properties: Optional["DailyMeetingTokenProperties"] = None,
|
||||
) -> DailyRoomConfig:
|
||||
"""Configure Daily room URL and token with optional SIP capabilities.
|
||||
|
||||
@@ -109,9 +106,6 @@ async def configure(
|
||||
individual parameters. When provided, this overrides room_exp_duration and
|
||||
SIP-related parameters. If not provided, properties are built from the
|
||||
individual parameters as before.
|
||||
token_properties: Optional DailyMeetingTokenProperties to customize the meeting
|
||||
token. When provided, these properties are passed to the token creation API.
|
||||
Note that room_name, exp, and is_owner will be set automatically.
|
||||
|
||||
Returns:
|
||||
DailyRoomConfig: Object with room_url, token, and optional sip_endpoint.
|
||||
@@ -185,10 +179,7 @@ async def configure(
|
||||
|
||||
# Create token and return standard format
|
||||
expiry_time: float = token_exp_duration * 60 * 60
|
||||
token_params = None
|
||||
if token_properties:
|
||||
token_params = DailyMeetingTokenParams(properties=token_properties)
|
||||
token = await daily_rest_helper.get_token(room_url, expiry_time, params=token_params)
|
||||
token = await daily_rest_helper.get_token(room_url, expiry_time)
|
||||
return DailyRoomConfig(room_url=room_url, token=token)
|
||||
|
||||
# Create a new room
|
||||
@@ -230,12 +221,7 @@ async def configure(
|
||||
|
||||
# Create meeting token
|
||||
token_expiry_seconds = token_exp_duration * 60 * 60
|
||||
token_params = None
|
||||
if token_properties:
|
||||
token_params = DailyMeetingTokenParams(properties=token_properties)
|
||||
token = await daily_rest_helper.get_token(
|
||||
room_url, token_expiry_seconds, params=token_params
|
||||
)
|
||||
token = await daily_rest_helper.get_token(room_url, token_expiry_seconds)
|
||||
|
||||
if sip_enabled:
|
||||
# Return SIP configuration object
|
||||
|
||||
@@ -555,7 +555,6 @@ def _setup_daily_routes(app: FastAPI):
|
||||
{
|
||||
"createDailyRoom": true,
|
||||
"dailyRoomProperties": { "start_video_off": true },
|
||||
"dailyMeetingTokenProperties": { "is_owner": true, "user_name": "Bot" },
|
||||
"body": { "custom_data": "value" }
|
||||
}
|
||||
"""
|
||||
@@ -571,8 +570,6 @@ def _setup_daily_routes(app: FastAPI):
|
||||
|
||||
create_daily_room = request_data.get("createDailyRoom", False)
|
||||
body = request_data.get("body", {})
|
||||
daily_room_properties_dict = request_data.get("dailyRoomProperties", None)
|
||||
daily_token_properties_dict = request_data.get("dailyMeetingTokenProperties", None)
|
||||
|
||||
bot_module = _get_bot_module()
|
||||
|
||||
@@ -587,37 +584,9 @@ def _setup_daily_routes(app: FastAPI):
|
||||
import aiohttp
|
||||
|
||||
from pipecat.runner.daily import configure
|
||||
from pipecat.transports.daily.utils import (
|
||||
DailyMeetingTokenProperties,
|
||||
DailyRoomProperties,
|
||||
)
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
# Parse dailyRoomProperties if provided
|
||||
room_properties = None
|
||||
if daily_room_properties_dict:
|
||||
try:
|
||||
room_properties = DailyRoomProperties(**daily_room_properties_dict)
|
||||
logger.debug(f"Using custom room properties: {room_properties}")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to parse dailyRoomProperties: {e}")
|
||||
# Continue without custom properties
|
||||
|
||||
# Parse dailyMeetingTokenProperties if provided
|
||||
token_properties = None
|
||||
if daily_token_properties_dict:
|
||||
try:
|
||||
token_properties = DailyMeetingTokenProperties(
|
||||
**daily_token_properties_dict
|
||||
)
|
||||
logger.debug(f"Using custom token properties: {token_properties}")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to parse dailyMeetingTokenProperties: {e}")
|
||||
# Continue without custom properties
|
||||
|
||||
room_url, token = await configure(
|
||||
session, room_properties=room_properties, token_properties=token_properties
|
||||
)
|
||||
room_url, token = await configure(session)
|
||||
runner_args = DailyRunnerArguments(room_url=room_url, token=token, body=body)
|
||||
result = {
|
||||
"dailyRoom": room_url,
|
||||
|
||||
@@ -156,12 +156,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
|
||||
self._language = Language.EN
|
||||
self._websocket_url = None
|
||||
self._receive_task = None
|
||||
# Flux event handlers
|
||||
self._register_event_handler("on_start_of_turn")
|
||||
self._register_event_handler("on_turn_resumed")
|
||||
self._register_event_handler("on_end_of_turn")
|
||||
self._register_event_handler("on_eager_end_of_turn")
|
||||
self._register_event_handler("on_update")
|
||||
|
||||
async def _connect(self):
|
||||
"""Connect to WebSocket and start background tasks.
|
||||
@@ -529,7 +523,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
|
||||
await self.push_frame(UserStartedSpeakingFrame(), FrameDirection.DOWNSTREAM)
|
||||
await self.push_frame(UserStartedSpeakingFrame(), FrameDirection.UPSTREAM)
|
||||
await self.start_metrics()
|
||||
await self._call_event_handler("on_start_of_turn", transcript)
|
||||
if transcript:
|
||||
logger.trace(f"Start of turn transcript: {transcript}")
|
||||
|
||||
@@ -544,7 +537,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
|
||||
event: The event type string for logging purposes.
|
||||
"""
|
||||
logger.trace(f"Received event TurnResumed: {event}")
|
||||
await self._call_event_handler("on_turn_resumed")
|
||||
|
||||
async def _handle_end_of_turn(self, transcript: str, data: Dict[str, Any]):
|
||||
"""Handle EndOfTurn events from Deepgram Flux.
|
||||
@@ -579,7 +571,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
|
||||
await self.stop_processing_metrics()
|
||||
await self.push_frame(UserStoppedSpeakingFrame(), FrameDirection.DOWNSTREAM)
|
||||
await self.push_frame(UserStoppedSpeakingFrame(), FrameDirection.UPSTREAM)
|
||||
await self._call_event_handler("on_end_of_turn", transcript)
|
||||
|
||||
async def _handle_eager_end_of_turn(self, transcript: str, data: Dict[str, Any]):
|
||||
"""Handle EagerEndOfTurn events from Deepgram Flux.
|
||||
@@ -624,7 +615,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
|
||||
result=data,
|
||||
)
|
||||
)
|
||||
await self._call_event_handler("on_eager_end_of_turn", transcript)
|
||||
|
||||
async def _handle_update(self, transcript: str):
|
||||
"""Handle Update events from Deepgram Flux.
|
||||
@@ -648,4 +638,3 @@ class DeepgramFluxSTTService(WebsocketSTTService):
|
||||
# both the "user started speaking" event and the first transcript simultaneously,
|
||||
# making this timing measurement meaningless in this context.
|
||||
# await self.stop_ttfb_metrics()
|
||||
await self._call_event_handler("on_update", transcript)
|
||||
|
||||
@@ -12,7 +12,6 @@ for generating speech from text using various voice models.
|
||||
|
||||
from typing import AsyncGenerator, Optional
|
||||
|
||||
import aiohttp
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import (
|
||||
@@ -118,114 +117,3 @@ class DeepgramTTSService(TTSService):
|
||||
except Exception as e:
|
||||
logger.exception(f"{self} exception: {e}")
|
||||
yield ErrorFrame(f"Error getting audio: {str(e)}")
|
||||
|
||||
|
||||
class DeepgramHttpTTSService(TTSService):
|
||||
"""Deepgram HTTP text-to-speech service.
|
||||
|
||||
Provides text-to-speech synthesis using Deepgram's HTTP TTS API.
|
||||
Supports various voice models and audio encoding formats with
|
||||
configurable sample rates and quality settings.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str,
|
||||
voice: str = "aura-2-helena-en",
|
||||
aiohttp_session: aiohttp.ClientSession,
|
||||
base_url: str = "https://api.deepgram.com",
|
||||
sample_rate: Optional[int] = None,
|
||||
encoding: str = "linear16",
|
||||
**kwargs,
|
||||
):
|
||||
"""Initialize the Deepgram TTS service.
|
||||
|
||||
Args:
|
||||
api_key: Deepgram API key for authentication.
|
||||
voice: Voice model to use for synthesis. Defaults to "aura-2-helena-en".
|
||||
aiohttp_session: Shared aiohttp session for HTTP requests with connection pooling.
|
||||
base_url: Custom base URL for Deepgram API. Defaults to "https://api.deepgram.com".
|
||||
sample_rate: Audio sample rate in Hz. If None, uses service default.
|
||||
encoding: Audio encoding format. Defaults to "linear16".
|
||||
**kwargs: Additional arguments passed to parent TTSService class.
|
||||
"""
|
||||
super().__init__(sample_rate=sample_rate, **kwargs)
|
||||
|
||||
self._api_key = api_key
|
||||
self._session = aiohttp_session
|
||||
self._base_url = base_url
|
||||
self._settings = {
|
||||
"encoding": encoding,
|
||||
}
|
||||
self.set_voice(voice)
|
||||
|
||||
def can_generate_metrics(self) -> bool:
|
||||
"""Check if the service can generate metrics.
|
||||
|
||||
Returns:
|
||||
True, as Deepgram TTS service supports metrics generation.
|
||||
"""
|
||||
return True
|
||||
|
||||
@traced_tts
|
||||
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
|
||||
"""Generate speech from text using Deepgram's TTS API.
|
||||
|
||||
Args:
|
||||
text: The text to synthesize into speech.
|
||||
|
||||
Yields:
|
||||
Frame: Audio frames containing the synthesized speech, plus start/stop frames.
|
||||
"""
|
||||
logger.debug(f"{self}: Generating TTS [{text}]")
|
||||
|
||||
# Build URL with parameters
|
||||
url = f"{self._base_url}/v1/speak"
|
||||
|
||||
headers = {"Authorization": f"Token {self._api_key}", "Content-Type": "application/json"}
|
||||
|
||||
params = {
|
||||
"model": self._voice_id,
|
||||
"encoding": self._settings["encoding"],
|
||||
"sample_rate": self.sample_rate,
|
||||
"container": "none",
|
||||
}
|
||||
|
||||
payload = {
|
||||
"text": text,
|
||||
}
|
||||
|
||||
try:
|
||||
await self.start_ttfb_metrics()
|
||||
|
||||
async with self._session.post(
|
||||
url, headers=headers, json=payload, params=params
|
||||
) as response:
|
||||
if response.status != 200:
|
||||
error_text = await response.text()
|
||||
raise Exception(f"HTTP {response.status}: {error_text}")
|
||||
|
||||
await self.start_tts_usage_metrics(text)
|
||||
yield TTSStartedFrame()
|
||||
|
||||
CHUNK_SIZE = self.chunk_size
|
||||
|
||||
first_chunk = True
|
||||
async for chunk in response.content.iter_chunked(CHUNK_SIZE):
|
||||
if first_chunk:
|
||||
await self.stop_ttfb_metrics()
|
||||
first_chunk = False
|
||||
|
||||
if chunk:
|
||||
yield TTSAudioRawFrame(
|
||||
audio=chunk,
|
||||
sample_rate=self.sample_rate,
|
||||
num_channels=1,
|
||||
)
|
||||
|
||||
yield TTSStoppedFrame()
|
||||
|
||||
except Exception as e:
|
||||
logger.exception(f"{self} exception: {e}")
|
||||
yield ErrorFrame(f"Error getting audio: {str(e)}")
|
||||
|
||||
@@ -22,7 +22,7 @@ from pipecat.utils.tracing.service_decorators import traced_tts
|
||||
# Suppress gRPC fork warnings
|
||||
os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
|
||||
|
||||
from typing import Any, AsyncGenerator, List, Literal, Mapping, Optional
|
||||
from typing import AsyncGenerator, List, Literal, Optional
|
||||
|
||||
from loguru import logger
|
||||
from pydantic import BaseModel
|
||||
@@ -248,8 +248,7 @@ class GoogleHttpTTSService(TTSService):
|
||||
|
||||
Parameters:
|
||||
pitch: Voice pitch adjustment (e.g., "+2st", "-50%").
|
||||
rate: Speaking rate adjustment (e.g., "slow", "fast", "125%"). Used for SSML prosody tags (non-Chirp voices).
|
||||
speaking_rate: Speaking rate for AudioConfig (Chirp/Journey voices). Range [0.25, 2.0].
|
||||
rate: Speaking rate adjustment (e.g., "slow", "fast", "125%").
|
||||
volume: Volume adjustment (e.g., "loud", "soft", "+6dB").
|
||||
emphasis: Emphasis level for the text.
|
||||
language: Language for synthesis. Defaults to English.
|
||||
@@ -259,7 +258,6 @@ class GoogleHttpTTSService(TTSService):
|
||||
|
||||
pitch: Optional[str] = None
|
||||
rate: Optional[str] = None
|
||||
speaking_rate: Optional[float] = None
|
||||
volume: Optional[str] = None
|
||||
emphasis: Optional[Literal["strong", "moderate", "reduced", "none"]] = None
|
||||
language: Optional[Language] = Language.EN
|
||||
@@ -293,7 +291,6 @@ class GoogleHttpTTSService(TTSService):
|
||||
self._settings = {
|
||||
"pitch": params.pitch,
|
||||
"rate": params.rate,
|
||||
"speaking_rate": params.speaking_rate,
|
||||
"volume": params.volume,
|
||||
"emphasis": params.emphasis,
|
||||
"language": self.language_to_service_language(params.language)
|
||||
@@ -363,22 +360,6 @@ class GoogleHttpTTSService(TTSService):
|
||||
"""
|
||||
return language_to_google_tts_language(language)
|
||||
|
||||
async def _update_settings(self, settings: Mapping[str, Any]):
|
||||
"""Override to handle speaking_rate updates for Chirp/Journey voices.
|
||||
|
||||
Args:
|
||||
settings: Dictionary of settings to update. Can include 'speaking_rate' (float)
|
||||
"""
|
||||
if "speaking_rate" in settings:
|
||||
rate_value = float(settings["speaking_rate"])
|
||||
if 0.25 <= rate_value <= 2.0:
|
||||
self._settings["speaking_rate"] = rate_value
|
||||
else:
|
||||
logger.warning(
|
||||
f"Invalid speaking_rate value: {rate_value}. Must be between 0.25 and 2.0"
|
||||
)
|
||||
await super()._update_settings(settings)
|
||||
|
||||
def _construct_ssml(self, text: str) -> str:
|
||||
ssml = "<speak>"
|
||||
|
||||
@@ -455,17 +436,10 @@ class GoogleHttpTTSService(TTSService):
|
||||
voice = texttospeech_v1.VoiceSelectionParams(
|
||||
language_code=self._settings["language"], name=self._voice_id
|
||||
)
|
||||
# Build audio config with conditional speaking_rate
|
||||
audio_config_params = {
|
||||
"audio_encoding": texttospeech_v1.AudioEncoding.LINEAR16,
|
||||
"sample_rate_hertz": self.sample_rate,
|
||||
}
|
||||
|
||||
# For Chirp and Journey voices, include speaking_rate in AudioConfig
|
||||
if (is_chirp_voice or is_journey_voice) and self._settings["speaking_rate"] is not None:
|
||||
audio_config_params["speaking_rate"] = self._settings["speaking_rate"]
|
||||
|
||||
audio_config = texttospeech_v1.AudioConfig(**audio_config_params)
|
||||
audio_config = texttospeech_v1.AudioConfig(
|
||||
audio_encoding=texttospeech_v1.AudioEncoding.LINEAR16,
|
||||
sample_rate_hertz=self.sample_rate,
|
||||
)
|
||||
|
||||
request = texttospeech_v1.SynthesizeSpeechRequest(
|
||||
input=synthesis_input, voice=voice, audio_config=audio_config
|
||||
@@ -526,7 +500,7 @@ class GoogleTTSService(TTSService):
|
||||
|
||||
Parameters:
|
||||
language: Language for synthesis. Defaults to English.
|
||||
speaking_rate: The speaking rate, in the range [0.25, 2.0].
|
||||
speaking_rate: The speaking rate, in the range [0.25, 4.0].
|
||||
"""
|
||||
|
||||
language: Optional[Language] = Language.EN
|
||||
@@ -617,22 +591,6 @@ class GoogleTTSService(TTSService):
|
||||
"""
|
||||
return language_to_google_tts_language(language)
|
||||
|
||||
async def _update_settings(self, settings: Mapping[str, Any]):
|
||||
"""Override to handle speaking_rate updates for streaming API.
|
||||
|
||||
Args:
|
||||
settings: Dictionary of settings to update. Can include 'speaking_rate' (float)
|
||||
"""
|
||||
if "speaking_rate" in settings:
|
||||
rate_value = float(settings["speaking_rate"])
|
||||
if 0.25 <= rate_value <= 2.0:
|
||||
self._settings["speaking_rate"] = rate_value
|
||||
else:
|
||||
logger.warning(
|
||||
f"Invalid speaking_rate value: {rate_value}. Must be between 0.25 and 2.0"
|
||||
)
|
||||
await super()._update_settings(settings)
|
||||
|
||||
@traced_tts
|
||||
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
|
||||
"""Generate streaming speech from text using Google's streaming API.
|
||||
|
||||
@@ -492,19 +492,11 @@ class LLMService(AIService):
|
||||
tool_call_id: Optional[str] = None,
|
||||
text_content: Optional[str] = None,
|
||||
video_source: Optional[str] = None,
|
||||
timeout: Optional[float] = 10.0,
|
||||
):
|
||||
"""Request an image from a user.
|
||||
|
||||
Pushes a UserImageRequestFrame upstream to request an image from the
|
||||
specified user. The user image can then be processed by the LLM.
|
||||
|
||||
Use this function from a function call if you want the LLM to process
|
||||
the image. If you expect the image to be processed by a vision service,
|
||||
you might want to push a UserImageRequestFrame upstream directly.
|
||||
|
||||
.. deprecated:: 0.0.92
|
||||
This method is deprecated, push a `UserImageRequestFrame` instead.
|
||||
specified user.
|
||||
|
||||
Args:
|
||||
user_id: The ID of the user to request an image from.
|
||||
@@ -512,19 +504,15 @@ class LLMService(AIService):
|
||||
tool_call_id: Optional tool call ID associated with the request.
|
||||
text_content: Optional text content/context for the image request.
|
||||
video_source: Optional video source identifier.
|
||||
timeout: Optional timeout for the requested image to be added to the LLM context.
|
||||
|
||||
"""
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"Method `request_image_frame()` is deprecated, push a `UserImageRequestFrame` instead.",
|
||||
DeprecationWarning,
|
||||
)
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=text_content),
|
||||
UserImageRequestFrame(
|
||||
user_id=user_id,
|
||||
function_name=function_name,
|
||||
tool_call_id=tool_call_id,
|
||||
context=text_content,
|
||||
video_source=video_source,
|
||||
),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
|
||||
|
||||
@@ -11,17 +11,15 @@ for image analysis and description generation.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
from io import BytesIO
|
||||
from typing import AsyncGenerator, Optional
|
||||
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.frames.frames import (
|
||||
ErrorFrame,
|
||||
Frame,
|
||||
TextFrame,
|
||||
UserImageRawFrame,
|
||||
)
|
||||
from pipecat.frames.frames import ErrorFrame, Frame, TextFrame
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.services.vision_service import VisionService
|
||||
|
||||
try:
|
||||
@@ -94,16 +92,16 @@ class MoondreamService(VisionService):
|
||||
trust_remote_code=True,
|
||||
revision=revision,
|
||||
device_map={"": device},
|
||||
dtype=dtype,
|
||||
torch_dtype=dtype,
|
||||
).eval()
|
||||
|
||||
logger.debug("Loaded Moondream model")
|
||||
|
||||
async def run_vision(self, frame: UserImageRawFrame) -> AsyncGenerator[Frame, None]:
|
||||
async def run_vision(self, context: LLMContext) -> AsyncGenerator[Frame, None]:
|
||||
"""Analyze an image and generate a description.
|
||||
|
||||
Args:
|
||||
frame: The image frame to process.
|
||||
context: The context to process, containing image data.
|
||||
|
||||
Yields:
|
||||
Frame: TextFrame containing the generated image description, or ErrorFrame
|
||||
@@ -114,14 +112,45 @@ class MoondreamService(VisionService):
|
||||
yield ErrorFrame("Moondream model not available")
|
||||
return
|
||||
|
||||
logger.debug(f"Analyzing image (bytes length: {len(frame.image)})")
|
||||
image_bytes = None
|
||||
text = None
|
||||
try:
|
||||
messages = context.get_messages()
|
||||
last_message = messages[-1]
|
||||
last_message_content = last_message.get("content")
|
||||
|
||||
def get_image_description(image_bytes: bytes, text: Optional[str]) -> str:
|
||||
image = Image.frombytes(frame.format, frame.size, image_bytes)
|
||||
for item in last_message_content:
|
||||
if isinstance(item, dict):
|
||||
if (
|
||||
"image_url" in item
|
||||
and isinstance(item["image_url"], dict)
|
||||
and item["image_url"].get("url")
|
||||
):
|
||||
image_bytes = base64.b64decode(item["image_url"]["url"].split(",")[1])
|
||||
elif "text" in item and isinstance(item["text"], str):
|
||||
text = item["text"]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Exception during image extraction: {e}")
|
||||
yield ErrorFrame("Failed to extract image from context")
|
||||
return
|
||||
|
||||
if not image_bytes:
|
||||
logger.error("No image found in context")
|
||||
yield ErrorFrame("No image found in context")
|
||||
return
|
||||
|
||||
logger.debug(
|
||||
f"Analyzing image (bytes length: {len(image_bytes) if image_bytes else 'None'})"
|
||||
)
|
||||
|
||||
def get_image_description(bytes: bytes, text: Optional[str]) -> str:
|
||||
image_buffer = BytesIO(bytes)
|
||||
image = Image.open(image_buffer)
|
||||
image_embeds = self._model.encode_image(image)
|
||||
description = self._model.query(image_embeds, text)["answer"]
|
||||
return description
|
||||
|
||||
description = await asyncio.to_thread(get_image_description, frame.image, frame.text)
|
||||
description = await asyncio.to_thread(get_image_description, image_bytes, text)
|
||||
|
||||
yield TextFrame(text=description)
|
||||
|
||||
@@ -49,33 +49,6 @@ END_TOKEN = "<end>"
|
||||
FINALIZED_TOKEN = "<fin>"
|
||||
|
||||
|
||||
class SonioxContextGeneralItem(BaseModel):
|
||||
"""Represents a key-value pair for structured general context information."""
|
||||
|
||||
key: str
|
||||
value: str
|
||||
|
||||
|
||||
class SonioxContextTranslationTerm(BaseModel):
|
||||
"""Represents a custom translation mapping for ambiguous or domain-specific terms."""
|
||||
|
||||
source: str
|
||||
target: str
|
||||
|
||||
|
||||
class SonioxContextObject(BaseModel):
|
||||
"""Context object for models with context_version 2, for Soniox stt-rt-v3-preview and higher.
|
||||
|
||||
Learn more about context in the documentation:
|
||||
https://soniox.com/docs/stt/concepts/context
|
||||
"""
|
||||
|
||||
general: Optional[List[SonioxContextGeneralItem]] = None
|
||||
text: Optional[str] = None
|
||||
terms: Optional[List[str]] = None
|
||||
translation_terms: Optional[List[SonioxContextTranslationTerm]] = None
|
||||
|
||||
|
||||
class SonioxInputParams(BaseModel):
|
||||
"""Real-time transcription settings.
|
||||
|
||||
@@ -87,9 +60,9 @@ class SonioxInputParams(BaseModel):
|
||||
audio_format: Audio format to use for transcription.
|
||||
num_channels: Number of channels to use for transcription.
|
||||
language_hints: List of language hints to use for transcription.
|
||||
context: Customization for transcription. String for models with context_version 1 and ContextObject for models with context_version 2.
|
||||
enable_speaker_diarization: Whether to enable speaker diarization. Tokens are annotated with speaker IDs.
|
||||
enable_language_identification: Whether to enable language identification. Tokens are annotated with language IDs.
|
||||
context: Customization for transcription.
|
||||
enable_non_final_tokens: Whether to enable non-final tokens. If false, only final tokens will be returned.
|
||||
max_non_final_tokens_duration_ms: Maximum duration of non-final tokens.
|
||||
client_reference_id: Client reference ID to use for transcription.
|
||||
"""
|
||||
|
||||
@@ -99,10 +72,10 @@ class SonioxInputParams(BaseModel):
|
||||
num_channels: Optional[int] = 1
|
||||
|
||||
language_hints: Optional[List[Language]] = None
|
||||
context: Optional[SonioxContextObject | str] = None
|
||||
context: Optional[str] = None
|
||||
|
||||
enable_speaker_diarization: Optional[bool] = False
|
||||
enable_language_identification: Optional[bool] = False
|
||||
enable_non_final_tokens: Optional[bool] = True
|
||||
max_non_final_tokens_duration_ms: Optional[int] = None
|
||||
|
||||
client_reference_id: Optional[str] = None
|
||||
|
||||
@@ -200,10 +173,6 @@ class SonioxSTTService(STTService):
|
||||
# Either one or the other is required.
|
||||
enable_endpoint_detection = not self._vad_force_turn_endpoint
|
||||
|
||||
context = self._params.context
|
||||
if isinstance(context, SonioxContextObject):
|
||||
context = context.model_dump()
|
||||
|
||||
# Send the initial configuration message.
|
||||
config = {
|
||||
"api_key": self._api_key,
|
||||
@@ -213,9 +182,9 @@ class SonioxSTTService(STTService):
|
||||
"enable_endpoint_detection": enable_endpoint_detection,
|
||||
"sample_rate": self.sample_rate,
|
||||
"language_hints": _prepare_language_hints(self._params.language_hints),
|
||||
"context": context,
|
||||
"enable_speaker_diarization": self._params.enable_speaker_diarization,
|
||||
"enable_language_identification": self._params.enable_language_identification,
|
||||
"context": self._params.context,
|
||||
"enable_non_final_tokens": self._params.enable_non_final_tokens,
|
||||
"max_non_final_tokens_duration_ms": self._params.max_non_final_tokens_duration_ms,
|
||||
"client_reference_id": self._params.client_reference_id,
|
||||
}
|
||||
|
||||
|
||||
@@ -1,189 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""Speechmatics TTS service integration."""
|
||||
|
||||
from typing import AsyncGenerator, Optional
|
||||
from urllib.parse import urlencode
|
||||
|
||||
import aiohttp
|
||||
from loguru import logger
|
||||
from pydantic import BaseModel
|
||||
|
||||
from pipecat.frames.frames import (
|
||||
ErrorFrame,
|
||||
Frame,
|
||||
TTSAudioRawFrame,
|
||||
TTSStartedFrame,
|
||||
TTSStoppedFrame,
|
||||
)
|
||||
from pipecat.services.tts_service import TTSService
|
||||
from pipecat.utils.tracing.service_decorators import traced_tts
|
||||
|
||||
try:
|
||||
from speechmatics.rt import __version__
|
||||
except ModuleNotFoundError as e:
|
||||
logger.error(f"Exception: {e}")
|
||||
logger.error(
|
||||
"In order to use Speechmatics, you need to `pip install pipecat-ai[speechmatics]`."
|
||||
)
|
||||
raise Exception(f"Missing module: {e}")
|
||||
|
||||
|
||||
class SpeechmaticsTTSService(TTSService):
|
||||
"""Speechmatics TTS service implementation.
|
||||
|
||||
This service provides text-to-speech synthesis using the Speechmatics HTTP API.
|
||||
It converts text to speech and returns raw PCM audio data for real-time playback.
|
||||
"""
|
||||
|
||||
SPEECHMATICS_SAMPLE_RATE = 16000
|
||||
|
||||
class InputParams(BaseModel):
|
||||
"""Optional input parameters for Speechmatics TTS configuration."""
|
||||
|
||||
pass
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str,
|
||||
base_url: str = "https://preview.tts.speechmatics.com",
|
||||
voice_id: str = "sarah",
|
||||
aiohttp_session: aiohttp.ClientSession,
|
||||
sample_rate: Optional[int] = SPEECHMATICS_SAMPLE_RATE,
|
||||
params: Optional[InputParams] = None,
|
||||
**kwargs,
|
||||
):
|
||||
"""Initialize the Speechmatics TTS service.
|
||||
|
||||
Args:
|
||||
api_key: Speechmatics API key for authentication.
|
||||
base_url: Base URL for Speechmatics TTS API.
|
||||
voice_id: Voice model to use for synthesis.
|
||||
aiohttp_session: Shared aiohttp session for HTTP requests.
|
||||
sample_rate: Audio sample rate in Hz.
|
||||
params: Optional[InputParams]: Input parameters for the service.
|
||||
**kwargs: Additional arguments passed to TTSService.
|
||||
"""
|
||||
if sample_rate and sample_rate != self.SPEECHMATICS_SAMPLE_RATE:
|
||||
logger.warning(
|
||||
f"Speechmatics TTS only supports {self.SPEECHMATICS_SAMPLE_RATE}Hz sample rate. "
|
||||
f"Current rate of {sample_rate}Hz may cause issues."
|
||||
)
|
||||
super().__init__(sample_rate=sample_rate, **kwargs)
|
||||
|
||||
# Service parameters
|
||||
self._api_key: str = api_key
|
||||
self._base_url: str = base_url
|
||||
self._session = aiohttp_session
|
||||
|
||||
# Check we have required attributes
|
||||
if not self._api_key:
|
||||
raise ValueError("Missing Speechmatics API key")
|
||||
|
||||
# Default parameters
|
||||
self._params = params or SpeechmaticsTTSService.InputParams()
|
||||
|
||||
# Set voice from constructor parameter
|
||||
self.set_voice(voice_id)
|
||||
|
||||
def can_generate_metrics(self) -> bool:
|
||||
"""Check if this service can generate processing metrics.
|
||||
|
||||
Returns:
|
||||
True, as Speechmatics service supports metrics generation.
|
||||
"""
|
||||
return True
|
||||
|
||||
@traced_tts
|
||||
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
|
||||
"""Generate speech from text using Speechmatics' HTTP API.
|
||||
|
||||
Args:
|
||||
text: The text to synthesize into speech.
|
||||
|
||||
Yields:
|
||||
Frame: Audio frames containing the synthesized speech.
|
||||
"""
|
||||
logger.debug(f"{self}: Generating TTS [{text}]")
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self._api_key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
payload = {
|
||||
"text": text,
|
||||
}
|
||||
|
||||
url = _get_endpoint_url(self._base_url, self._voice_id, self.sample_rate)
|
||||
|
||||
try:
|
||||
await self.start_ttfb_metrics()
|
||||
|
||||
async with self._session.post(url, json=payload, headers=headers) as response:
|
||||
if response.status != 200:
|
||||
error_message = f"Speechmatics TTS error: HTTP {response.status}"
|
||||
logger.error(error_message)
|
||||
yield ErrorFrame(error=error_message)
|
||||
return
|
||||
|
||||
await self.start_tts_usage_metrics(text)
|
||||
|
||||
yield TTSStartedFrame()
|
||||
|
||||
# Process the response in streaming chunks
|
||||
first_chunk = True
|
||||
buffer = b""
|
||||
|
||||
async for chunk in response.content.iter_any():
|
||||
if not chunk:
|
||||
continue
|
||||
if first_chunk:
|
||||
await self.stop_ttfb_metrics()
|
||||
first_chunk = False
|
||||
|
||||
buffer += chunk
|
||||
|
||||
# Emit all complete 2-byte int16 samples from buffer
|
||||
if len(buffer) >= 2:
|
||||
complete_samples = len(buffer) // 2
|
||||
complete_bytes = complete_samples * 2
|
||||
|
||||
audio_data = buffer[:complete_bytes]
|
||||
buffer = buffer[complete_bytes:] # Keep remaining bytes for next iteration
|
||||
|
||||
yield TTSAudioRawFrame(
|
||||
audio=audio_data,
|
||||
sample_rate=self.sample_rate,
|
||||
num_channels=1,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.exception(f"Error generating TTS: {e}")
|
||||
yield ErrorFrame(error=f"Speechmatics TTS error: {str(e)}")
|
||||
finally:
|
||||
yield TTSStoppedFrame()
|
||||
|
||||
|
||||
def _get_endpoint_url(base_url: str, voice: str, sample_rate: int) -> str:
|
||||
"""Format the TTS endpoint URL with voice, output format, and version params.
|
||||
|
||||
Args:
|
||||
base_url: The base URL for the TTS endpoint.
|
||||
voice: The voice model to use.
|
||||
sample_rate: The audio sample rate.
|
||||
|
||||
Returns:
|
||||
str: The formatted TTS endpoint URL.
|
||||
"""
|
||||
query_params = {}
|
||||
query_params["output_format"] = f"pcm_{sample_rate}"
|
||||
query_params["sm-app"] = f"pipecat/{__version__}"
|
||||
query = urlencode(query_params)
|
||||
|
||||
return f"{base_url}/generate/{voice}?{query}"
|
||||
@@ -14,7 +14,8 @@ visual content.
|
||||
from abc import abstractmethod
|
||||
from typing import AsyncGenerator
|
||||
|
||||
from pipecat.frames.frames import Frame, UserImageRawFrame
|
||||
from pipecat.frames.frames import Frame, LLMContextFrame
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.services.ai_service import AIService
|
||||
|
||||
@@ -37,15 +38,15 @@ class VisionService(AIService):
|
||||
self._describe_text = None
|
||||
|
||||
@abstractmethod
|
||||
async def run_vision(self, frame: UserImageRawFrame) -> AsyncGenerator[Frame, None]:
|
||||
"""Process the given vision image and generate results.
|
||||
async def run_vision(self, context: LLMContext) -> AsyncGenerator[Frame, None]:
|
||||
"""Process the latest image in the context and generate results.
|
||||
|
||||
This method must be implemented by subclasses to provide actual computer
|
||||
vision functionality such as image description, object detection, or
|
||||
visual question answering.
|
||||
|
||||
Args:
|
||||
frame: The image frame to process.
|
||||
context: The context to process, containing image data.
|
||||
|
||||
Yields:
|
||||
Frame: Frames containing the vision analysis results, typically TextFrame
|
||||
@@ -56,7 +57,7 @@ class VisionService(AIService):
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
"""Process frames, handling vision image frames for analysis.
|
||||
|
||||
Automatically processes UserImageRawFrame objects by calling run_vision
|
||||
Automatically processes VisionImageRawFrame objects by calling run_vision
|
||||
and handles metrics tracking. Other frames are passed through unchanged.
|
||||
|
||||
Args:
|
||||
@@ -65,9 +66,9 @@ class VisionService(AIService):
|
||||
"""
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame) and frame.text:
|
||||
if isinstance(frame, LLMContextFrame):
|
||||
await self.start_processing_metrics()
|
||||
await self.process_generator(self.run_vision(frame))
|
||||
await self.process_generator(self.run_vision(frame.context))
|
||||
await self.stop_processing_metrics()
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
@@ -8,7 +8,7 @@
|
||||
|
||||
import asyncio
|
||||
from dataclasses import dataclass
|
||||
from typing import Awaitable, Callable, List, Optional, Sequence, Tuple
|
||||
from typing import Any, Awaitable, Callable, Dict, List, Optional, Sequence, Tuple
|
||||
|
||||
from pipecat.frames.frames import (
|
||||
EndFrame,
|
||||
|
||||
@@ -1839,11 +1839,10 @@ class DailyInputTransport(BaseInputTransport):
|
||||
if render_frame:
|
||||
frame = UserImageRawFrame(
|
||||
user_id=participant_id,
|
||||
request=request_frame,
|
||||
image=video_frame.buffer,
|
||||
size=(video_frame.width, video_frame.height),
|
||||
format=video_frame.color_format,
|
||||
text=request_frame.text if request_frame else None,
|
||||
append_to_context=request_frame.append_to_context if request_frame else None,
|
||||
)
|
||||
frame.transport_source = video_source
|
||||
await self.push_video_frame(frame)
|
||||
|
||||
@@ -15,7 +15,7 @@ import asyncio
|
||||
import fractions
|
||||
import time
|
||||
from collections import deque
|
||||
from typing import Any, Awaitable, Callable, List, Optional
|
||||
from typing import Any, Awaitable, Callable, Optional
|
||||
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
@@ -567,7 +567,7 @@ class SmallWebRTCInputTransport(BaseInputTransport):
|
||||
self._receive_audio_task = None
|
||||
self._receive_video_task = None
|
||||
self._receive_screen_video_task = None
|
||||
self._image_requests: List[UserImageRequestFrame] = []
|
||||
self._image_requests = {}
|
||||
|
||||
# Whether we have seen a StartFrame already.
|
||||
self._initialized = False
|
||||
@@ -657,27 +657,23 @@ class SmallWebRTCInputTransport(BaseInputTransport):
|
||||
if video_frame:
|
||||
await self.push_video_frame(video_frame)
|
||||
|
||||
# Check if there are any pending image requests and create
|
||||
# UserImageRawFrame. Use a shallow copy so we can remove
|
||||
# elements.
|
||||
for request_frame in self._image_requests[:]:
|
||||
request_text = request_frame.text if request_frame else None
|
||||
add_to_context = request_frame.append_to_context if request_frame else None
|
||||
if request_frame.video_source == video_source:
|
||||
# Create UserImageRawFrame using the current video frame
|
||||
image_frame = UserImageRawFrame(
|
||||
user_id=request_frame.user_id,
|
||||
image=video_frame.image,
|
||||
size=video_frame.size,
|
||||
format=video_frame.format,
|
||||
text=request_text,
|
||||
append_to_context=add_to_context,
|
||||
)
|
||||
image_frame.transport_source = video_source
|
||||
# Push the frame to the pipeline
|
||||
await self.push_video_frame(image_frame)
|
||||
# Remove from pending requests
|
||||
self._image_requests.remove(request_frame)
|
||||
# Check if there are any pending image requests and create UserImageRawFrame
|
||||
if self._image_requests:
|
||||
for req_id, request_frame in list(self._image_requests.items()):
|
||||
if request_frame.video_source == video_source:
|
||||
# Create UserImageRawFrame using the current video frame
|
||||
image_frame = UserImageRawFrame(
|
||||
user_id=request_frame.user_id,
|
||||
request=request_frame,
|
||||
image=video_frame.image,
|
||||
size=video_frame.size,
|
||||
format=video_frame.format,
|
||||
)
|
||||
image_frame.transport_source = video_source
|
||||
# Push the frame to the pipeline
|
||||
await self.push_video_frame(image_frame)
|
||||
# Remove from pending requests
|
||||
del self._image_requests[req_id]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self} exception receiving data: {e.__class__.__name__} ({e})")
|
||||
@@ -705,7 +701,8 @@ class SmallWebRTCInputTransport(BaseInputTransport):
|
||||
logger.debug(f"Requesting image from participant: {frame.user_id}")
|
||||
|
||||
# Store the request
|
||||
self._image_requests.append(frame)
|
||||
request_id = f"{frame.function_name}:{frame.tool_call_id}"
|
||||
self._image_requests[request_id] = frame
|
||||
|
||||
# Default to camera if no source specified
|
||||
if frame.video_source is None:
|
||||
|
||||
@@ -18,7 +18,7 @@ Dependencies:
|
||||
"""
|
||||
|
||||
import re
|
||||
from typing import FrozenSet, List, Optional, Sequence, Tuple
|
||||
from typing import FrozenSet, Optional, Sequence, Tuple
|
||||
|
||||
import nltk
|
||||
from loguru import logger
|
||||
@@ -196,40 +196,3 @@ def parse_start_end_tags(
|
||||
return (None, len(text))
|
||||
|
||||
return (None, current_tag_index)
|
||||
|
||||
|
||||
def concatenate_aggregated_text(text_parts: List[str]) -> str:
|
||||
"""Concatenate a list of text parts into a single string.
|
||||
|
||||
This function joins the provided list of text parts into a single string,
|
||||
taking into account whether or not the parts already contain spacing.
|
||||
|
||||
This function is useful for aggregating text segments received from LLMs or
|
||||
transcription services.
|
||||
|
||||
Args:
|
||||
text_parts: A list of strings representing parts of text to concatenate.
|
||||
|
||||
Returns:
|
||||
A single concatenated string.
|
||||
"""
|
||||
# Check specifically for space characters, previously isspace() was used
|
||||
# but that includes all whitespace characters (e.g. \n), not just spaces.
|
||||
has_leading_spaces = any(part and part[0] == " " for part in text_parts[1:])
|
||||
has_trailing_spaces = any(part and part[-1] == " " for part in text_parts[:-1])
|
||||
|
||||
# If there are embedded spaces in the fragments, use direct concatenation
|
||||
contains_spacing_between_fragments = has_leading_spaces or has_trailing_spaces
|
||||
|
||||
# Apply corresponding joining method
|
||||
if contains_spacing_between_fragments:
|
||||
# Fragments already have spacing - just concatenate
|
||||
result = "".join(text_parts)
|
||||
else:
|
||||
# Word-by-word fragments - join with spaces
|
||||
result = " ".join(text_parts)
|
||||
|
||||
# Clean up any excessive whitespace
|
||||
result = result.strip()
|
||||
|
||||
return result
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
|
||||
import json
|
||||
import unittest
|
||||
from typing import Any, Optional
|
||||
from typing import Any
|
||||
|
||||
from pipecat.audio.interruptions.min_words_interruption_strategy import MinWordsInterruptionStrategy
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
@@ -22,8 +22,6 @@ from pipecat.frames.frames import (
|
||||
InterimTranscriptionFrame,
|
||||
InterruptionFrame,
|
||||
InterruptionTaskFrame,
|
||||
LLMContextAssistantTimestampFrame,
|
||||
LLMContextFrame,
|
||||
LLMFullResponseEndFrame,
|
||||
LLMFullResponseStartFrame,
|
||||
OpenAILLMContextAssistantTimestampFrame,
|
||||
@@ -40,7 +38,6 @@ from pipecat.processors.aggregators.llm_response import (
|
||||
LLMUserAggregatorParams,
|
||||
LLMUserContextAggregator,
|
||||
)
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMAssistantAggregator
|
||||
from pipecat.processors.aggregators.openai_llm_context import (
|
||||
OpenAILLMContext,
|
||||
OpenAILLMContextFrame,
|
||||
@@ -589,16 +586,11 @@ class BaseTestUserContextAggregator:
|
||||
assert context_processor.context_received
|
||||
|
||||
|
||||
class BaseTestAssistantContextAggregator:
|
||||
class BaseTestAssistantContextAggreagator:
|
||||
CONTEXT_CLASS = None # To be set in subclasses
|
||||
AGGREGATOR_CLASS = None # To be set in subclasses
|
||||
EXPECTED_CONTEXT_FRAMES = None # To be set in subclasses
|
||||
|
||||
def create_assistant_aggregator_params(
|
||||
self, **kwargs
|
||||
) -> Optional[LLMAssistantAggregatorParams]:
|
||||
return LLMAssistantAggregatorParams(**kwargs)
|
||||
|
||||
def check_message_content(self, context: OpenAILLMContext, index: int, content: str):
|
||||
assert context.messages[index]["content"] == content
|
||||
|
||||
@@ -649,7 +641,7 @@ class BaseTestAssistantContextAggregator:
|
||||
|
||||
context = self.CONTEXT_CLASS()
|
||||
aggregator = self.AGGREGATOR_CLASS(
|
||||
context, params=self.create_assistant_aggregator_params(expect_stripped_words=False)
|
||||
context, params=LLMAssistantAggregatorParams(expect_stripped_words=False)
|
||||
)
|
||||
frames_to_send = [
|
||||
LLMFullResponseStartFrame(),
|
||||
@@ -695,7 +687,7 @@ class BaseTestAssistantContextAggregator:
|
||||
|
||||
context = self.CONTEXT_CLASS()
|
||||
aggregator = self.AGGREGATOR_CLASS(
|
||||
context, params=self.create_assistant_aggregator_params(expect_stripped_words=False)
|
||||
context, params=LLMAssistantAggregatorParams(expect_stripped_words=False)
|
||||
)
|
||||
frames_to_send = [
|
||||
LLMFullResponseStartFrame(),
|
||||
@@ -722,7 +714,7 @@ class BaseTestAssistantContextAggregator:
|
||||
|
||||
context = self.CONTEXT_CLASS()
|
||||
aggregator = self.AGGREGATOR_CLASS(
|
||||
context, params=self.create_assistant_aggregator_params(expect_stripped_words=False)
|
||||
context, params=LLMAssistantAggregatorParams(expect_stripped_words=False)
|
||||
)
|
||||
frames_to_send = [
|
||||
LLMFullResponseStartFrame(),
|
||||
@@ -846,7 +838,7 @@ class TestAnthropicUserContextAggregator(
|
||||
|
||||
|
||||
class TestAnthropicAssistantContextAggregator(
|
||||
BaseTestAssistantContextAggregator, unittest.IsolatedAsyncioTestCase
|
||||
BaseTestAssistantContextAggreagator, unittest.IsolatedAsyncioTestCase
|
||||
):
|
||||
CONTEXT_CLASS = AnthropicLLMContext
|
||||
AGGREGATOR_CLASS = AnthropicAssistantContextAggregator
|
||||
@@ -881,7 +873,7 @@ class TestAWSBedrockUserContextAggregator(
|
||||
|
||||
|
||||
class TestAWSBedrockAssistantContextAggregator(
|
||||
BaseTestAssistantContextAggregator, unittest.IsolatedAsyncioTestCase
|
||||
BaseTestAssistantContextAggreagator, unittest.IsolatedAsyncioTestCase
|
||||
):
|
||||
CONTEXT_CLASS = AWSBedrockLLMContext
|
||||
AGGREGATOR_CLASS = AWSBedrockAssistantContextAggregator
|
||||
@@ -922,7 +914,7 @@ class TestGoogleUserContextAggregator(
|
||||
|
||||
|
||||
class TestGoogleAssistantContextAggregator(
|
||||
BaseTestAssistantContextAggregator, unittest.IsolatedAsyncioTestCase
|
||||
BaseTestAssistantContextAggreagator, unittest.IsolatedAsyncioTestCase
|
||||
):
|
||||
CONTEXT_CLASS = GoogleLLMContext
|
||||
AGGREGATOR_CLASS = GoogleAssistantContextAggregator
|
||||
@@ -956,27 +948,8 @@ class TestOpenAIUserContextAggregator(
|
||||
|
||||
|
||||
class TestOpenAIAssistantContextAggregator(
|
||||
BaseTestAssistantContextAggregator, unittest.IsolatedAsyncioTestCase
|
||||
BaseTestAssistantContextAggreagator, unittest.IsolatedAsyncioTestCase
|
||||
):
|
||||
CONTEXT_CLASS = OpenAILLMContext
|
||||
AGGREGATOR_CLASS = OpenAIAssistantContextAggregator
|
||||
EXPECTED_CONTEXT_FRAMES = [OpenAILLMContextFrame, OpenAILLMContextAssistantTimestampFrame]
|
||||
|
||||
|
||||
#
|
||||
# Universal
|
||||
#
|
||||
class TestLLMAssistantAggregator(
|
||||
BaseTestAssistantContextAggregator, unittest.IsolatedAsyncioTestCase
|
||||
):
|
||||
CONTEXT_CLASS = OpenAILLMContext
|
||||
AGGREGATOR_CLASS = LLMAssistantAggregator
|
||||
EXPECTED_CONTEXT_FRAMES = [LLMContextFrame, LLMContextAssistantTimestampFrame]
|
||||
|
||||
# Override to remove 'expect_stripped_words' parameter, which is deprecated
|
||||
# for LLMAssistantAggregator
|
||||
def create_assistant_aggregator_params(
|
||||
self, **kwargs
|
||||
) -> Optional[LLMAssistantAggregatorParams]:
|
||||
kwargs.pop("expect_stripped_words", None)
|
||||
return LLMAssistantAggregatorParams(**kwargs) if kwargs else None
|
||||
|
||||
@@ -65,7 +65,9 @@ class TestLangchain(unittest.IsolatedAsyncioTestCase):
|
||||
self.mock_proc = self.MockProcessor("token_collector")
|
||||
|
||||
context = LLMContext()
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context, assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False)
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[context_aggregator.user(), proc, self.mock_proc, context_aggregator.assistant()]
|
||||
|
||||
@@ -24,7 +24,6 @@ from pipecat.pipeline.base_task import PipelineTaskParams
|
||||
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.filters.frame_filter import FrameFilter
|
||||
from pipecat.processors.filters.identity_filter import IdentityFilter
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.tests.utils import HeartbeatsObserver, run_test
|
||||
@@ -384,7 +383,6 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
|
||||
idle_timeout_secs = 0.2
|
||||
sleep_time_secs = idle_timeout_secs / 2
|
||||
|
||||
# Use the identify filter so the frames just reach the end of the pipeline.
|
||||
identity = IdentityFilter()
|
||||
pipeline = Pipeline([identity])
|
||||
task = PipelineTask(
|
||||
@@ -394,12 +392,6 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
|
||||
)
|
||||
|
||||
async def delayed_frames():
|
||||
"""Sending multiple text frames.
|
||||
|
||||
The total amount of elapsed time in this function should be greater
|
||||
than the task idle timeout. If an idle timeout event is triggered it
|
||||
means we haven't detected that the TextFrames have been pushed.
|
||||
"""
|
||||
await asyncio.sleep(sleep_time_secs)
|
||||
await task.queue_frame(TextFrame("Hello Pipecat!"))
|
||||
await asyncio.sleep(sleep_time_secs)
|
||||
@@ -423,51 +415,6 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
|
||||
# Wait for the pending tasks to complete.
|
||||
await asyncio.gather(*pending)
|
||||
|
||||
async def test_idle_task_swallowed_frames(self):
|
||||
idle_timeout_secs = 0.2
|
||||
sleep_time_secs = idle_timeout_secs / 2
|
||||
|
||||
# Block all frames (except system frames). Here, we are testing that
|
||||
# generated frames don't trigger an idle timeout (they don't need to
|
||||
# reach the end of the pipeline).
|
||||
filter = FrameFilter(types=())
|
||||
pipeline = Pipeline([filter])
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
idle_timeout_secs=idle_timeout_secs,
|
||||
idle_timeout_frames=(TextFrame,),
|
||||
)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
async def delayed_frames():
|
||||
"""Sending multiple text frames.
|
||||
|
||||
The total amount of elapsed time in this function should be greater
|
||||
than the task idle timeout. If an idle timeout event is triggered it
|
||||
means we haven't detected that the TextFrames have been pushed.
|
||||
"""
|
||||
await asyncio.sleep(sleep_time_secs)
|
||||
await task.queue_frame(TextFrame("Hello Pipecat!"))
|
||||
await asyncio.sleep(sleep_time_secs)
|
||||
await task.queue_frame(TextFrame("Hello Pipecat!"))
|
||||
await asyncio.sleep(sleep_time_secs)
|
||||
await task.queue_frame(TextFrame("Hello Pipecat!"))
|
||||
|
||||
tasks = [
|
||||
asyncio.create_task(task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))),
|
||||
asyncio.create_task(delayed_frames()),
|
||||
]
|
||||
|
||||
_, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
|
||||
|
||||
diff_time = time.time() - start_time
|
||||
|
||||
self.assertGreater(diff_time, sleep_time_secs * 3)
|
||||
|
||||
# Wait for the pending tasks to complete.
|
||||
await asyncio.gather(*pending)
|
||||
|
||||
async def test_task_cancel_timeout(self):
|
||||
class CancelFilter(FrameProcessor):
|
||||
def __init__(self, **kwargs):
|
||||
|
||||
Reference in New Issue
Block a user