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Author SHA1 Message Date
kompfner
c861beb066 Fix await usage in transcription timeout task 2026-01-23 11:15:16 -05:00
420 changed files with 5560 additions and 12744 deletions

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@@ -7,30 +7,23 @@ Create changelog files for the important commits in this PR. The PR number is pr
## Instructions
1. Skip changelog for: documentation-only, internal refactoring, test-only, CI changes.
2. First, check what commits are on the current branch compared to main:
1. First, check what commits are on the current branch compared to main:
```
git log main..HEAD --oneline
```
3. For each significant change, create a changelog file in the `changelog/` folder using the format:
Allowed types: `added`, `changed`, `deprecated`, `removed`, `fixed`, `security`, `performance`, `other`
2. For each significant change, create a changelog file in the `changelog/` folder using the format:
- `{PR_NUMBER}.added.md` - for new features
- `{PR_NUMBER}.added.2.md`, `{PR_NUMBER}.added.3.md` - for additional entries of the same type
- `{PR_NUMBER}.added.2.md`, `{PR_NUMBER}.added.3.md` - for additional new features
- `{PR_NUMBER}.changed.md` - for changes to existing functionality
- `{PR_NUMBER}.fixed.md` - for bug fixes
- `{PR_NUMBER}.deprecated.md` - for deprecations
- `{PR_NUMBER}.removed.md` - for removed features
- `{PR_NUMBER}.security.md` - for security fixes
- `{PR_NUMBER}.performance.md` - for performance improvements
- `{PR_NUMBER}.other.md` - for other changes
4. Each changelog file should at least contain a main single line starting with `- ` followed by a clear description of the change.
3. Each changelog file should at least contain a main single line starting with `- ` followed by a clear description of the change.
5. If the change is complicated, changelog files can have indented lines after the main line with additional details or code samples.
4. If the change is complicated, changelog files can have indented lines after the main line with additional details or code samples.
6. Use ⚠️ emoji prefix for breaking changes.
5. Use ⚠️ emoji prefix for breaking changes.
## Example

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@@ -33,14 +33,7 @@ jobs:
- name: Install dependencies
run: |
uv sync --group dev \
--extra anthropic \
--extra aws \
--extra google \
--extra langchain \
--extra livekit \
--extra piper \
--extra websocket
uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain --extra livekit --extra websocket
- name: Run tests with coverage
run: |

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@@ -37,14 +37,7 @@ jobs:
- name: Install dependencies
run: |
uv sync --group dev \
--extra anthropic \
--extra aws \
--extra google \
--extra langchain \
--extra livekit \
--extra piper \
--extra websocket
uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain --extra livekit --extra websocket
- name: Test with pytest
run: |

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@@ -7,258 +7,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
<!-- towncrier release notes start -->
## [0.0.101] - 2026-01-30
### Added
- Additions for `AICFilter` and `AICVADAnalyzer`:
- Added model downloading support to `AICFilter` with `model_id` and
`model_download_dir` parameters.
- Added `model_path` parameter to `AICFilter` for loading local `.aicmodel`
files.
- Added unit tests for `AICFilter` and `AICVADAnalyzer`.
(PR [#3408](https://github.com/pipecat-ai/pipecat/pull/3408))
- Added handling for `server_content.interrupted` signal in the Gemini Live
service for faster interruption response in the case where there isn't
already turn tracking in the pipeline, e.g. local VAD + context aggregators.
When there is already turn tracking in the pipeline, the additional
interruption does no harm.
(PR [#3429](https://github.com/pipecat-ai/pipecat/pull/3429))
- Added new `GenesysFrameSerializer` for the Genesys AudioHook WebSocket
protocol, enabling bidirectional audio streaming between Pipecat pipelines
and Genesys Cloud contact center.
(PR [#3500](https://github.com/pipecat-ai/pipecat/pull/3500))
- Added `reached_upstream_types` and `reached_downstream_types` read-only
properties to `PipelineTask` for inspecting current frame filters.
(PR [#3510](https://github.com/pipecat-ai/pipecat/pull/3510))
- Added `add_reached_upstream_filter()` and `add_reached_downstream_filter()`
methods to `PipelineTask` for appending frame types.
(PR [#3510](https://github.com/pipecat-ai/pipecat/pull/3510))
- Added `UserTurnCompletionLLMServiceMixin` for LLM services to detect and
filter incomplete user turns. When enabled via `filter_incomplete_user_turns`
in `LLMUserAggregatorParams`, the LLM outputs a turn completion marker at the
start of each response: ✓ (complete), ○ (incomplete short), or ◐ (incomplete
long). Incomplete turns are suppressed, and configurable timeouts
automatically re-prompt the user.
(PR [#3518](https://github.com/pipecat-ai/pipecat/pull/3518))
- Added `FrameProcessor.broadcast_frame_instance(frame)` method to broadcast a
frame instance by extracting its fields and creating new instances for each
direction.
(PR [#3519](https://github.com/pipecat-ai/pipecat/pull/3519))
- `PipelineTask` now automatically adds `RTVIProcessor` and registers
`RTVIObserver` when `enable_rtvi=True` (default), simplifying pipeline setup.
(PR [#3519](https://github.com/pipecat-ai/pipecat/pull/3519))
- Added `RTVIProcessor.create_rtvi_observer()` factory method for creating RTVI
observers.
(PR [#3519](https://github.com/pipecat-ai/pipecat/pull/3519))
- Added `video_out_codec` parameter to `TransportParams` allowing configuration
of the preferred video codec (e.g., `"VP8"`, `"H264"`, `"H265"`) for video
output in `DailyTransport`.
(PR [#3520](https://github.com/pipecat-ai/pipecat/pull/3520))
- Added `location` parameter to Google TTS services (`GoogleHttpTTSService`,
`GoogleTTSService`, `GeminiTTSService`) for regional endpoint support.
(PR [#3523](https://github.com/pipecat-ai/pipecat/pull/3523))
- Added new `PIPECAT_SMART_TURN_LOG_DATA` environment variable, which causes
Smart Turn input data to be saved to disk
(PR [#3525](https://github.com/pipecat-ai/pipecat/pull/3525))
- Added `result_callback` parameter to `UserImageRequestFrame` to support
deferred function call results.
(PR [#3571](https://github.com/pipecat-ai/pipecat/pull/3571))
- Added `function_call_timeout_secs` parameter to `LLMService` to configure
timeout for deferred function calls (defaults to 10.0 seconds).
(PR [#3571](https://github.com/pipecat-ai/pipecat/pull/3571))
- Added `vad_analyzer` parameter to `LLMUserAggregatorParams`. VAD analysis is
now handled inside the `LLMUserAggregator` rather than in the transport,
keeping voice activity detection closer to where it is consumed. The
`vad_analyzer` on `BaseInputTransport` is now deprecated.
```python
context_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(
vad_analyzer=SileroVADAnalyzer(),
),
)
```
(PR [#3583](https://github.com/pipecat-ai/pipecat/pull/3583))
- Added `VADProcessor` for detecting speech in audio streams within a pipeline.
Pushes `VADUserStartedSpeakingFrame`, `VADUserStoppedSpeakingFrame`, and
`UserSpeakingFrame` downstream based on VAD state changes.
(PR [#3583](https://github.com/pipecat-ai/pipecat/pull/3583))
- Added `VADController` for managing voice activity detection state and
emitting speech events independently of transport or pipeline processors.
(PR [#3583](https://github.com/pipecat-ai/pipecat/pull/3583))
- Added local `PiperTTSService` for offline text-to-speech using Piper voice
models. The existing HTTP-based service has been renamed to
`PiperHttpTTSService`.
(PR [#3585](https://github.com/pipecat-ai/pipecat/pull/3585))
- `main()` in `pipecat.runner.run` now accepts an optional
`argparse.ArgumentParser`, allowing bots to define custom CLI arguments
accessible via `runner_args.cli_args`.
(PR [#3590](https://github.com/pipecat-ai/pipecat/pull/3590))
- Added `KokoroTTSService` for local text-to-speech synthesis using the
Kokoro-82M model.
(PR [#3595](https://github.com/pipecat-ai/pipecat/pull/3595))
### Changed
- Updated `AICFilter` and `AICVADAnalyzer` to use aic-sdk ~= 2.0.1.
(PR [#3408](https://github.com/pipecat-ai/pipecat/pull/3408))
- Improved the STT TTFB (Time To First Byte) measurement, reporting the delay
between when the user stops speaking and when the final transcription is
received. Note: Unlike traditional TTFB which measures from a discrete
request, STT services receive continuous audio input—so we measure from
speech end to final transcript, which captures the latency that matters for
voice AI applications. In support of this change, added `finalized` field to
`TranscriptionFrame` to indicate when a transcript is the final result for an
utterance.
(PR [#3495](https://github.com/pipecat-ai/pipecat/pull/3495))
- `SarvamSTTService` now defaults `vad_signals` and `high_vad_sensitivity` to
`None` (omitted from connection parameters), improving latency by ~300ms
compared to the previous defaults.
(PR [#3495](https://github.com/pipecat-ai/pipecat/pull/3495))
- Changed frame filter storage from tuples to sets in `PipelineTask`.
(PR [#3510](https://github.com/pipecat-ai/pipecat/pull/3510))
- Changed default Inworld TTS model from `inworld-tts-1` to
`inworld-tts-1.5-max`.
(PR [#3531](https://github.com/pipecat-ai/pipecat/pull/3531))
- `FrameSerializer` now subclasses from `BaseObject` to enable event support.
(PR [#3560](https://github.com/pipecat-ai/pipecat/pull/3560))
- Added support for TTFS in `SpeechmaticsSTTService` and set the default mode
to `EXTERNAL` to support Pipecat-controlled VAD.
- Changed dependency to `speechmatics-voice[smart]>=0.2.8`
(PR [#3562](https://github.com/pipecat-ai/pipecat/pull/3562))
- ⚠️ Changed function call handling to use timeout-based completion instead of
immediate callback execution.
- Function calls that defer their results (e.g., `UserImageRequestFrame`)
now use a timeout mechanism
- The `result_callback` is invoked automatically when the deferred
operation completes or after timeout
- This change affects examples using `UserImageRequestFrame` - the
`result_callback` should now be passed to the frame instead of being called
immediately
(PR [#3571](https://github.com/pipecat-ai/pipecat/pull/3571))
- Pipecat runner now uses `DAILY_ROOM_URL` instead of `DAILY_SAMPLE_ROOM_URL`.
(PR [#3582](https://github.com/pipecat-ai/pipecat/pull/3582))
- Updates to `GradiumSTTService`:
- Now flushes pending transcriptions when VAD detects the user stopped
speaking, improving response latency.
- `GradiumSTTService` now supports `InputParams` for configuring `language`
and `delay_in_frames` settings.
(PR [#3587](https://github.com/pipecat-ai/pipecat/pull/3587))
### Deprecated
- ⚠️ Deprecated `vad_analyzer` parameter on `BaseInputTransport`. Pass
`vad_analyzer` to `LLMUserAggregatorParams` instead or use `VADProcessor` in
the pipeline.
(PR [#3583](https://github.com/pipecat-ai/pipecat/pull/3583))
### Removed
- Removed deprecated `AICFilter` parameters: `enhancement_level`, `voice_gain`,
`noise_gate_enable`.
(PR [#3408](https://github.com/pipecat-ai/pipecat/pull/3408))
### Fixed
- Fixed an issue where if you were using `OpenRouterLLMService` with a Gemini
model, it wouldn't handle multiple `"system"` messages as expected (and as we
do in `GoogleLLMService`), which is to convert subsequent ones into `"user"`
messages. Instead, the latest `"system"` message would overwrite the previous
ones.
(PR [#3406](https://github.com/pipecat-ai/pipecat/pull/3406))
- Transports now properly broadcast `InputTransportMessageFrame` frames both
upstream and downstream instead of only pushing downstream.
(PR [#3519](https://github.com/pipecat-ai/pipecat/pull/3519))
- Fixed `FrameProcessor.broadcast_frame()` to deep copy kwargs, preventing
shared mutable references between the downstream and upstream frame
instances.
(PR [#3519](https://github.com/pipecat-ai/pipecat/pull/3519))
- Fixed OpenAI LLM services to emit `ErrorFrame` on completion timeout,
enabling proper error handling and LLMSwitcher failover.
(PR [#3529](https://github.com/pipecat-ai/pipecat/pull/3529))
- Fixed a logging issue where non-ASCII characters (e.g., Japanese, Chinese,
etc.) were being unnecessarily escaped to Unicode sequences when function
call occurred.
(PR [#3536](https://github.com/pipecat-ai/pipecat/pull/3536))
- Fixed how audio tracks are synchronized inside the `AudioBufferProcessor` to
fix timing issues where silence and audio were misaligned between user and
bot buffers.
(PR [#3541](https://github.com/pipecat-ai/pipecat/pull/3541))
- Fixed race condition in `OpenAIRealtimeBetaLLMService` that could cause an
error when truncating the conversation.
(PR [#3567](https://github.com/pipecat-ai/pipecat/pull/3567))
- Fixed an infinite loop in `WebsocketService` that blocked the event loop when
a remote server closed the connection gracefully.
(PR [#3574](https://github.com/pipecat-ai/pipecat/pull/3574))
- Fixed `LLMUserAggregator` and `LLMAssistantAggregator` not emitting pending
transcripts via `on_user_turn_stopped` and `on_assistant_turn_stopped` events
when the conversation ends (`EndFrame`) or is cancelled (`CancelFrame`).
(PR [#3575](https://github.com/pipecat-ai/pipecat/pull/3575))
- Added missing `LiveKitRunnerArguments` and `LiveKitTransport` support in
runner utilities to enable LiveKit transport configuration.
(PR [#3580](https://github.com/pipecat-ai/pipecat/pull/3580))
- Fixed race condition in `OpenAIRealtimeLLMService` that could cause an error
when truncating the conversation.
(PR [#3581](https://github.com/pipecat-ai/pipecat/pull/3581))
- Fixed `PiperHttpTTSService` (olf `PiperTTSService`) to resample audio output
based on the model's sample rate parsed from the WAV header.
(PR [#3585](https://github.com/pipecat-ai/pipecat/pull/3585))
- Fixed `UserTurnController` to reset user turn timeout when interim
transcriptions are received.
(PR [#3594](https://github.com/pipecat-ai/pipecat/pull/3594))
- Fixed an issue in the `IVRNavigator` where the `TextFrame`s pushed had
incorrect spacing. Now, the internal `IVRProcessor` pushes
`AggregatedTextFrame`s when in conversation mode. This allows for controlling
spacing of the outputted, aggregated text.
(PR [#3604](https://github.com/pipecat-ai/pipecat/pull/3604))
- Fixed `GeminiLiveLLMService` transcription timeout handler not being
scheduled by yielding to the event loop after task creation.
(PR [#3605](https://github.com/pipecat-ai/pipecat/pull/3605))
## [0.0.100] - 2026-01-20
### Added

143
CLAUDE.md
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@@ -1,143 +0,0 @@
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. It orchestrates audio/video, AI services, transports, and conversation pipelines using a frame-based architecture.
## Common Commands
```bash
# Setup development environment
uv sync --group dev --all-extras --no-extra gstreamer --no-extra krisp
# Install pre-commit hooks
uv run pre-commit install
# Run all tests
uv run pytest
# Run a single test file
uv run pytest tests/test_name.py
# Run a specific test
uv run pytest tests/test_name.py::test_function_name
# Preview changelog
towncrier build --draft --version Unreleased
# Lint and format check
uv run ruff check
uv run ruff format --check
# Update dependencies (after editing pyproject.toml)
uv lock && uv sync
```
## Architecture
### Frame-Based Pipeline Processing
All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
```
Transport Input → Pipeline Source → [Processor1] → [Processor2] → ... → Pipeline Sink → Transport Output
```
**Key components:**
- **Frames** (`src/pipecat/frames/frames.py`): Data units (audio, text, video) and control signals. Flow DOWNSTREAM (input→output) or UPSTREAM (acknowledgments/errors).
- **FrameProcessor** (`src/pipecat/processors/frame_processor.py`): Base processing unit. Each processor receives frames, processes them, and pushes results downstream.
- **Pipeline** (`src/pipecat/pipeline/pipeline.py`): Chains processors together.
- **ParallelPipeline** (`src/pipecat/pipeline/parallel_pipeline.py`): Runs multiple pipelines in parallel.
- **Transports** (`src/pipecat/transports/`): External I/O layer (Daily WebRTC, LiveKit WebRTC, WebSocket, Local). Abstract interface via `BaseTransport`.
- **Services** (`src/pipecat/services/`): 60+ AI provider integrations (STT, TTS, LLM, etc.). Extend base classes: `AIService`, `LLMService`, `STTService`, `TTSService`, `VisionService`.
- **Serializers** (`src/pipecat/serializers/`): Convert frames to/from wire formats for WebSocket transports. `FrameSerializer` base class defines `serialize()` and `deserialize()`. Telephony serializers (Twilio, Plivo, Vonage, Telnyx, Exotel, Genesys) handle provider-specific protocols and audio encoding (e.g., μ-law).
- **RTVI** (`src/pipecat/processors/frameworks/rtvi.py`): Real-Time Voice Interface protocol bridging clients and the pipeline. `RTVIProcessor` handles incoming client messages (text input, audio, function call results). `RTVIObserver` converts pipeline frames to outgoing messages: user/bot speaking events, transcriptions, LLM/TTS lifecycle, function calls, metrics, and audio levels.
### Important Patterns
- **Context Aggregation**: `LLMContext` accumulates messages for LLM calls; `UserResponse` aggregates user input
- **Turn Management**: Turn management is done through `LLMUserAggregator` and
`LLMAssistantAggregator`, created with `LLMContextAggregatorPair`
- **User turn strategies**: Detection of when the user starts and stops speaking is done via user turn start/stop strategies. They push `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` respectively.
- **Interruptions**: Interruptions are usually triggered by a user turn start strategy (e.g. `VADUserTurnStartStrategy`) but they can be triggered by other processors as well, in which case the user turn start strategies don't need to. An `InterruptionFrame` carries an optional `asyncio.Event` that is set when the frame reaches the pipeline sink. If a processor stops an `InterruptionFrame` from propagating downstream (i.e., doesn't push it), it **must** call `frame.complete()` to avoid stalling `push_interruption_task_frame_and_wait()` callers.
- **Uninterruptible Frames**: These are frames that will not be removed from internal queues even if there's an interruption. For example, `EndFrame` and `StopFrame`.
- **Events**: Most classes in Pipecat have `BaseObject` as the very base class. `BaseObject` has support for events. Events can run in the background in an async task (default) or synchronously (`sync=True`) if we want immediate action. Synchronous event handlers need to exectue fast.
### Key Directories
| Directory | Purpose |
|---------------------------|----------------------------------------------------|
| `src/pipecat/frames/` | Frame definitions (100+ types) |
| `src/pipecat/processors/` | FrameProcessor base + aggregators, filters, audio |
| `src/pipecat/pipeline/` | Pipeline orchestration |
| `src/pipecat/services/` | AI service integrations (60+ providers) |
| `src/pipecat/transports/` | Transport layer (Daily, LiveKit, WebSocket, Local) |
| `src/pipecat/serializers/`| Frame serialization for WebSocket protocols |
| `src/pipecat/audio/` | VAD, filters, mixers, turn detection, DTMF |
| `src/pipecat/turns/` | User turn management |
## Code Style
- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
- **Type hints**: Required for complex async code.
### Docstring Example
```python
class MyService(LLMService):
"""Description of what the service does.
More detailed description.
Event handlers available:
- on_connected: Called when we are connected
Example::
@service.event_handler("on_connected")
async def on_connected(service, frame):
...
"""
def __init__(self, param1: str, **kwargs):
"""Initialize the service.
Args:
param1: Description of param1.
**kwargs: Additional arguments passed to parent.
"""
super().__init__(**kwargs)
```
## Service Implementation
When adding a new service:
1. Extend the appropriate base class (`STTService`, `TTSService`, `LLMService`, etc.)
2. Implement required abstract methods
3. Handle necessary frames
4. By default, all frames should be pushed in the direction they came
5. Push `ErrorFrame` on failures
6. Add metrics tracking via `MetricsData` if relevant
7. Follow the pattern of existing services in `src/pipecat/services/`
## Pull Requests
After creating a PR, use `/changelog <pr_number>` to generate the changelog file and `/pr-description <pr_number>` to update the PR description.

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@@ -75,7 +75,7 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [Hathora](https://docs.pipecat.ai/server/services/stt/hathora), [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), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [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), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [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), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hathora](https://docs.pipecat.ai/server/services/tts/hathora), [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), [Resemble](https://docs.pipecat.ai/server/services/tts/resemble), [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) |
| 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), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [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), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hathora](https://docs.pipecat.ai/server/services/tts/hathora), [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) |
| 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), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox), |
| 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 | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [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), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage) |

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@@ -1 +0,0 @@
- Added `ResembleAITTSService` for text-to-speech using Resemble AI's streaming WebSocket API with word-level timestamps and jitter buffering for smooth audio playback.

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@@ -1 +0,0 @@
- Added `UserBotLatencyObserver` for tracking user-to-bot response latency. When tracing is enabled, latency measurements are automatically recorded as `turn.user_bot_latency_seconds` attributes on OpenTelemetry turn spans.

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@@ -1 +0,0 @@
- Deprecated `UserBotLatencyLogObserver`. Use `UserBotLatencyObserver` directly with its `on_latency_measured` event handler instead.

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@@ -0,0 +1 @@
- Added `add_reached_upstream_filter()` and `add_reached_downstream_filter()` methods to `PipelineTask` for appending frame types.

1
changelog/3510.added.md Normal file
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@@ -0,0 +1 @@
- Added `reached_upstream_types` and `reached_downstream_types` read-only properties to `PipelineTask` for inspecting current frame filters.

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@@ -0,0 +1 @@
- Changed frame filter storage from tuples to sets in `PipelineTask`.

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@@ -0,0 +1 @@
- Added `RTVIProcessor.create_rtvi_observer()` factory method for creating RTVI observers.

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@@ -0,0 +1 @@
- Added `FrameProcessor.broadcast_frame_instance(frame)` method to broadcast a frame instance by extracting its fields and creating new instances for each direction.

1
changelog/3519.added.md Normal file
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@@ -0,0 +1 @@
- `PipelineTask` now automatically adds `RTVIProcessor` and registers `RTVIObserver` when `enable_rtvi=True` (default), simplifying pipeline setup.

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@@ -0,0 +1 @@
- Fixed `FrameProcessor.broadcast_frame()` to deep copy kwargs, preventing shared mutable references between the downstream and upstream frame instances.

1
changelog/3519.fixed.md Normal file
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@@ -0,0 +1 @@
- Transports now properly broadcast `InputTransportMessageFrame` frames both upstream and downstream instead of only pushing downstream.

1
changelog/3520.added.md Normal file
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@@ -0,0 +1 @@
- Added `video_out_codec` parameter to `TransportParams` allowing configuration of the preferred video codec (e.g., `"VP8"`, `"H264"`, `"H265"`) for video output in `DailyTransport`.

1
changelog/3523.added.md Normal file
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@@ -0,0 +1 @@
- Added `location` parameter to Google TTS services (`GoogleHttpTTSService`, `GoogleTTSService`, `GeminiTTSService`) for regional endpoint support.

1
changelog/3525.added.md Normal file
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@@ -0,0 +1 @@
- Added new `SMART_TURN_LOG_DATA` environment variable, which causes Smart Turn input data to be saved to disk

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@@ -0,0 +1,2 @@
- Changed default Inworld TTS model from `inworld-tts-1` to
`inworld-tts-1.5-max`.

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@@ -1 +0,0 @@
- Fixed pipeline freeze when `InterruptionFrame` discards `EndFrame` or `StopFrame` by making terminal frames uninterruptible.

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@@ -1 +0,0 @@
- Fixed OpenAI LLM stream not being closed on cancellation/exception, which could leak sockets.

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@@ -1 +0,0 @@
- Added support for Inworld TTS Websocket Auto Mode for improved latency

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@@ -1 +0,0 @@
- Updated timestamps to be cumulative within an agent turn, using flushCompleted message as an indication of when timestamps from the server are reset to 0

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@@ -1 +0,0 @@
- Fixed `PipelineTask` adding duplicate `RTVIProcessor` and `RTVIObserver` when they were already provided in the pipeline or observers list. They are now detected and skipped, with appropriate warnings and errors logged for mismatched configurations.

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@@ -1 +0,0 @@
- Changed `KokoroTTSService` to use `kokoro-onnx` instead of `kokoro` as the underlying TTS engine.

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@@ -1 +0,0 @@
- Fixed function call timeout task not being cancelled when the handler completes without calling `result_callback` or is cancelled externally, which caused `RuntimeWarning: coroutine was never awaited`.

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@@ -1,5 +0,0 @@
- Fixed sentence splitting for Japanese, Chinese, Korean, and other non-Latin
languages in TTS pipeline. NLTK's sentence tokenizer does not support CJK
languages, causing text to accumulate until flush instead of being split at
sentence boundaries. Added fallback detection for unambiguous non-Latin
sentence-ending punctuation (e.g., `。`, ``, ``).

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@@ -1 +0,0 @@
- Fixed `PipelineTask` to also call `set_bot_ready()` when an external `RTVIProcessor` is provided.

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@@ -1 +0,0 @@
- Fixed `VADController` not broadcasting `SpeechControlParamsFrame` on startup, which prevented STT services from receiving VAD params needed for TTFB measurement.

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@@ -1 +0,0 @@
- Fixed `StopAsyncIteration` exceptions in `parse_telephony_websocket()` when WebSocket connections close before sending expected messages.

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@@ -1 +0,0 @@
- Added RTVI function call lifecycle events (`llm-function-call-started`, `llm-function-call-in-progress`, `llm-function-call-stopped`) with configurable security levels via `RTVIObserverParams.function_call_report_level`. Supports per-function control over what information is exposed (`DISABLED`, `NONE`, `NAME`, or `FULL`).

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@@ -1 +0,0 @@
- Deprecated `RTVILLMFunctionCallMessage`, `RTVILLMFunctionCallMessageData`, and `RTVIProcessor.handle_function_call()`. Use the new `llm-function-call-in-progress` event sent automatically by `RTVIObserver` instead.

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@@ -1 +0,0 @@
- Fixed WebSocket transport error when broadcasting `InputTransportMessageFrame` by correctly instantiating the frame with its message parameter.

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@@ -1 +0,0 @@
- Fixed orphan OpenTelemetry spans during flow initialization and transitions in tracing.

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@@ -1 +0,0 @@
- Upgraded the `pipecat-ai-small-webrtc-prebuilt` package to v2.1.0.

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@@ -1 +0,0 @@
- Added `OpenAIRealtimeSTTService` for real-time streaming speech-to-text using OpenAI's Realtime API WebSocket transcription sessions. Supports local VAD and server-side VAD modes, noise reduction, and automatic reconnection.

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@@ -1,10 +0,0 @@
- ⚠️ The default `VADParams` `stop_secs` default is changing from `0.8` seconds
to `0.2` seconds. This change both simplifies the developer experience and
improves the performance of STT services. With a shorter `stop_secs` value,
STT services using a local VAD can finalize sooner, resulting in faster
transcription.
- `SpeechTimeoutUserTurnStopStrategy`: control how long to wait for
additional user speech using `user_speech_timeout` (default: 0.6 sec).
- `TurnAnalyzerUserTurnStopStrategy`: the turn analyzer automatically adjusts
the user wait time based on the audio input.

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@@ -1 +0,0 @@
- Moved interruption wait event from per-processor instance state to `InterruptionFrame` itself. Added `InterruptionFrame.complete()` to signal when the interruption has fully traversed the pipeline. Custom processors that block or consume an `InterruptionFrame` before it reaches the pipeline sink must call `frame.complete()` to avoid stalling `push_interruption_task_frame_and_wait()`. A warning is logged if completion does not happen within 2 seconds.

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@@ -1 +0,0 @@
- Fixed `SambaNovaLLMService` and `GoogleLLMOpenAIBetaService` streams not being closed on cancellation/exception, which could leak sockets.

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@@ -1 +0,0 @@
- Update the default model to `scribe_v2` for `ElevenLabsSTTService`.

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@@ -1 +0,0 @@
- Changed the `DeepgramSTTService` default setting for `smart_format` to `False`, as agents don't need smart formatting. Disabling this setting provides a small performance improvement, as well.

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@@ -1 +0,0 @@
- Fixed an issue in `InworldTTSService` where punctuation was pronounced. Now, the `InworldTTSService` ensures proper spacing between sentences, resolving pronunciation issues.

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@@ -1 +0,0 @@
- Fixed `ParallelPipeline` allowing frames pushed by internal processors to escape during lifecycle frame (`StartFrame`/`EndFrame`/`CancelFrame`) synchronization. These frames are now buffered and flushed after all branches complete.

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@@ -1 +0,0 @@
- Added pyright basic type checking configuration for the core framework.

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@@ -43,7 +43,7 @@ CEREBRAS_API_KEY=...
# Daily
DAILY_API_KEY=...
DAILY_ROOM_URL=https://...
DAILY_SAMPLE_ROOM_URL=https://...
# Deepgram
DEEPGRAM_API_KEY=...
@@ -156,10 +156,6 @@ PLIVO_AUTH_TOKEN=...
# Qwen
QWEN_API_KEY=...
# Resemble AI
RESEMBLE_API_KEY=
RESEMBLE_VOICE_UUID=
# Rime
RIME_API_KEY=...
RIME_VOICE_ID=...

View File

@@ -16,7 +16,7 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.piper.tts import PiperHttpTTSService
from pipecat.services.piper.tts import PiperTTSService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -24,8 +24,9 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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),
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
@@ -38,7 +39,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
# Create an HTTP session
async with aiohttp.ClientSession() as session:
tts = PiperHttpTTSService(
tts = PiperTTSService(
base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
)

View File

@@ -23,8 +23,9 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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),
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),

View File

@@ -23,8 +23,9 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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),
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),

View File

@@ -23,8 +23,9 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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),
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),

View File

@@ -25,8 +25,9 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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),
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),

View File

@@ -23,8 +23,9 @@ from pipecat.transports.daily.transport import DailyParams
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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(
video_out_enabled=True,

View File

@@ -22,8 +22,9 @@ from pipecat.transports.daily.transport import DailyParams
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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(
video_out_enabled=True,

View File

@@ -19,6 +19,7 @@ from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
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
@@ -63,6 +64,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
)
@@ -89,7 +91,6 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -14,6 +14,7 @@ from loguru import logger
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
@@ -48,6 +49,7 @@ async def main():
audio_in_enabled=True,
audio_out_enabled=True,
transcription_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
)
@@ -74,7 +76,6 @@ async def main():
TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -14,6 +14,7 @@ from loguru import logger
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 (
InterruptionFrame,
TranscriptionFrame,
@@ -53,6 +54,7 @@ async def main():
params=LiveKitParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
)
@@ -82,7 +84,6 @@ async def main():
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -65,8 +65,9 @@ class MonthPrepender(FrameProcessor):
await self.push_frame(frame, direction)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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,

View File

@@ -11,6 +11,7 @@ from loguru import logger
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, LLMRunFrame, MetricsFrame
from pipecat.metrics.metrics import (
LLMUsageMetricsData,
@@ -61,20 +62,24 @@ class MetricsLogger(FrameProcessor):
await self.push_frame(frame, direction)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -107,7 +112,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from PIL import Image
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 (
BotStartedSpeakingFrame,
BotStoppedSpeakingFrame,
@@ -76,8 +77,9 @@ class ImageSyncAggregator(FrameProcessor):
await self.push_frame(frame, direction)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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,
@@ -85,6 +87,7 @@ transport_params = {
video_out_enabled=True,
video_out_width=1024,
video_out_height=1024,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
@@ -92,6 +95,7 @@ transport_params = {
video_out_enabled=True,
video_out_width=1024,
video_out_height=1024,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -122,7 +126,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -11,6 +11,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -78,7 +83,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -11,6 +11,7 @@ from loguru import logger
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
@@ -33,20 +34,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -77,7 +82,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -33,8 +33,9 @@ from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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,

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -36,20 +37,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -120,7 +125,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -17,6 +17,7 @@ from loguru import logger
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 LLMMessagesUpdateFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
@@ -49,20 +50,24 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
return message_store[session_id]
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -104,7 +109,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -32,8 +32,9 @@ from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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,

View File

@@ -13,6 +13,7 @@ from loguru import logger
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
@@ -36,20 +37,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -84,7 +89,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -87,7 +92,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -33,8 +33,9 @@ from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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,

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -76,7 +81,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -13,6 +13,7 @@ from loguru import logger
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
@@ -36,20 +37,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -88,7 +93,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -79,7 +84,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -79,7 +84,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -81,7 +86,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -85,7 +90,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -85,7 +90,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -1,135 +0,0 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
from dotenv import load_dotenv
from loguru import logger
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,
LLMUserAggregatorParams,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.openai.stt import OpenAISTTService
from pipecat.services.openai.tts import OpenAITTSService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
),
}
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot")
stt = OpenAISTTService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o-transcribe",
prompt="Expect words related to dogs, such as breed names.",
)
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
"content": "You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
},
]
context = LLMContext(messages)
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
user_aggregator, # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
assistant_aggregator, # Assistant spoken responses
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
audio_out_sample_rate=24000,
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()

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -24,9 +25,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.openai.stt import OpenAIRealtimeSTTService
from pipecat.services.openai.stt import OpenAISTTService
from pipecat.services.openai.tts import OpenAITTSService
from pipecat.transcriptions.language import Language
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -35,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -56,15 +60,10 @@ transport_params = {
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot")
stt = OpenAIRealtimeSTTService(
stt = OpenAISTTService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o-transcribe",
prompt="Expect words related to dogs, such as breed names.",
language=Language.EN,
# Uses local VAD by default.
# To enable server-side VAD, set turn_detection=None or
# a dict with server_vad settings.
# turn_detection={"type": "server_vad", "threshold": 0.5},
)
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
@@ -85,7 +84,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -13,6 +13,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -84,7 +89,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -13,6 +13,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -84,7 +89,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -11,6 +11,7 @@ from dotenv import load_dotenv
from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -83,10 +88,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
context = LLMContext(messages)
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(
user_turn_strategies=ExternalUserTurnStrategies(),
vad_analyzer=SileroVADAnalyzer(),
),
user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
)
pipeline = Pipeline(

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -36,20 +37,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -88,7 +93,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -75,7 +80,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -77,7 +82,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -41,20 +41,24 @@ except ImportError:
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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(),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
}
@@ -122,7 +126,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -10,6 +10,7 @@ from loguru import logger
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
@@ -32,20 +33,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -81,7 +86,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -27,6 +27,7 @@ from loguru import logger
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
@@ -49,8 +50,9 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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,
@@ -58,6 +60,7 @@ transport_params = {
video_out_enabled=True,
video_out_width=1024,
video_out_height=1024,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
@@ -65,6 +68,7 @@ transport_params = {
video_out_enabled=True,
video_out_width=1024,
video_out_height=1024,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -103,7 +107,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -108,7 +113,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -91,7 +96,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -91,7 +96,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -81,7 +86,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -30,6 +30,7 @@ from loguru import logger
from pipecat.audio.filters.krisp_viva_filter import KrispVivaFilter
from pipecat.audio.turn.krisp_viva_turn import KrispVivaTurn
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
@@ -52,22 +53,26 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
audio_in_filter=KrispVivaFilter(),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
audio_in_filter=KrispVivaFilter(),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
audio_in_filter=KrispVivaFilter(),
),
}
@@ -96,7 +101,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=KrispVivaTurn())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -13,6 +13,7 @@ from loguru import logger
from pipecat.audio.filters.krisp_filter import KrispFilter
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
@@ -35,22 +36,26 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
audio_in_filter=KrispFilter(),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
audio_in_filter=KrispFilter(),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
audio_in_filter=KrispFilter(),
),
}
@@ -79,7 +84,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -13,6 +13,7 @@ from loguru import logger
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
@@ -36,20 +37,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -86,7 +91,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -78,7 +83,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -77,7 +82,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -14,6 +14,7 @@ from loguru import logger
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,
InputAudioRawFrame,
@@ -196,20 +197,24 @@ class TranscriptionContextFixup(FrameProcessor):
await self.push_frame(frame, direction)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -250,7 +255,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)
audio_collector = UserAudioCollector(context, user_aggregator)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -35,20 +36,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -79,7 +84,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -13,6 +13,7 @@ from loguru import logger
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
@@ -36,20 +37,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -85,7 +90,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

View File

@@ -12,6 +12,7 @@ from loguru import logger
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
@@ -34,20 +35,24 @@ from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
# 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)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
@@ -78,7 +83,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
vad_analyzer=SileroVADAnalyzer(),
),
)

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