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Author SHA1 Message Date
James Hush
230d92850a example: realtime with transcripts 2025-02-26 16:29:07 +08:00
712 changed files with 22817 additions and 74000 deletions

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name: Bug report
description: Report a bug or unexpected behavior
type: Bug
body:
- type: markdown
attributes:
value: |
## Bug Report
Thank you for taking the time to fill out this bug report.
- type: markdown
attributes:
value: |
### Environment
- type: input
id: pipecat-version
attributes:
label: pipecat version
description: Which version are you using?
placeholder: e.g., 0.0.63
validations:
required: true
- type: input
id: python-version
attributes:
label: Python version
description: Which Python version are you using?
placeholder: e.g., 3.12.8
validations:
required: true
- type: input
id: os
attributes:
label: Operating System
description: Which OS are you using?
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validations:
required: true
- type: textarea
id: description
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description: Provide a clear description of the issue.
validations:
required: true
- type: textarea
id: repro
attributes:
label: Reproduction steps
description: List the steps to reproduce the issue.
placeholder: |
1. Do this...
2. Then do that...
3. Observe the error...
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required: true
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id: expected
attributes:
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required: true
- type: textarea
id: actual
attributes:
label: Actual behavior
description: What actually happened?
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required: true
- type: textarea
id: logs
attributes:
label: Logs
description: If applicable, include any relevant logs or error messages
render: shell
validations:
required: false

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name: Question
description: Ask a question or get help
type: Question
body:
- type: markdown
attributes:
value: |
## Question
Use this form to ask a question about pipecat.
- type: markdown
attributes:
value: |
### Environment (if applicable)
- type: input
id: pipecat-version
attributes:
label: pipecat version
description: Which version are you using? (if applicable)
placeholder: e.g., 0.0.63
validations:
required: false
- type: input
id: python-version
attributes:
label: Python version
description: Which Python version are you using? (if applicable)
placeholder: e.g., 3.12.8
validations:
required: false
- type: input
id: os
attributes:
label: Operating System
description: Which OS are you using? (if applicable)
placeholder: e.g., Ubuntu 24.04, Windows 11, macOS 12.5
validations:
required: false
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description: Provide your question in detail here.
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name: Feature request
description: Suggest an enhancement or new feature
type: Enhancement
body:
- type: markdown
attributes:
value: |
## Feature Request
Thank you for suggesting an enhancement to pipecat.
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placeholder: You can drag and drop images here to include them.
validations:
required: false
- type: checkboxes
id: contribution
attributes:
label: Would you be willing to help implement this feature?
options:
- label: Yes, I'd like to contribute
- label: No, I'm just suggesting

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name: Service Issue
description: An issue with a third-party service
type: Service Issue
body:
- type: markdown
attributes:
value: |
## Service Issue
Use this form to report an issue with a third-party service integration.
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attributes:
label: pipecat version
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placeholder: e.g., 0.0.63
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placeholder: e.g., OpenAI, ElevenLabs, Anthropic
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required: true
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id: service-version
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validations:
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attributes:
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description: Provide steps to reproduce the issue.
placeholder: |
1. Configure service X
2. Call method Y
3. See error Z
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required: true
- type: textarea
id: expected
attributes:
label: Expected Behavior
description: What did you expect to happen?
validations:
required: true
- type: textarea
id: actual
attributes:
label: Actual Behavior
description: What actually happened?
validations:
required: true
- type: textarea
id: logs
attributes:
label: Error Logs
description: If available, include any error messages or logs.
render: shell
validations:
required: false

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name: New Service
description: Request to support a new third-party service
type: New Service
body:
- type: markdown
attributes:
value: |
## New Service Request
Use this form to request support for a new third-party service in pipecat.
- type: input
id: service-name
attributes:
label: Service Name
description: What is the name of the third-party service?
placeholder: e.g., NewAPI, SomeService
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attributes:
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placeholder: |
- API documentation link
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validations:
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- type: checkboxes
id: contribution
attributes:
label: Would you be willing to help implement this service?
options:
- label: Yes, I'd like to contribute
- label: No, I'm just suggesting

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name: Dependency Issue
description: An issue with a Pipecat dependency (not a third-party service)
type: Dependency Issue
body:
- type: markdown
attributes:
value: |
## Dependency Issue
Use this form to report an issue with a Pipecat dependency.
- type: input
id: pipecat-version
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description: Which version are you using?
placeholder: e.g., 0.0.63
validations:
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required: true
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attributes:
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- type: textarea
id: reproduction
attributes:
label: Reproduction Steps
description: If applicable, provide steps to reproduce the issue.
placeholder: |
1. Install dependency X
2. Run command Y
3. See error Z
validations:
required: false
- type: textarea
id: logs
attributes:
label: Error Logs
description: If applicable, include any relevant error messages or logs.
render: shell
validations:
required: false

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name: Troubleshooting
description: Help with a specific use case
type: Troubleshooting
body:
- type: markdown
attributes:
value: |
## Troubleshooting Request
Use this form to get help with a specific use case or implementation.
- type: input
id: pipecat-version
attributes:
label: pipecat version
description: Which version are you using?
placeholder: e.g., 0.0.63
validations:
required: true
- type: input
id: python-version
attributes:
label: Python version
description: Which version of Python are you using?
placeholder: e.g., 3.12.8
validations:
required: true
- type: input
id: os
attributes:
label: Operating System
description: Which OS are you using?
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validations:
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render: python
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attributes:
label: Additional Context
description: Any other information that might help us understand your situation.
validations:
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blank_issues_enabled: false

30
.gitignore vendored
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@@ -7,7 +7,7 @@ venv
/.idea
#*#
# Distribution / Packaging
# Distribution / packaging
.Python
build/
develop-eggs/
@@ -30,24 +30,24 @@ MANIFEST
.env
fly.toml
# Examples
examples/telnyx-chatbot/templates/streams.xml
examples/twilio-chatbot/templates/streams.xml
examples/**/node_modules/
examples/**/.expo/
examples/**/dist/
examples/**/npm-debug.*
examples/**/*.jks
examples/**/*.p8
examples/**/*.p12
examples/**/*.key
examples/**/*.mobileprovision
examples/**/*.orig.*
examples/**/web-build/
# Example files
pipecat/examples/twilio-chatbot/templates/streams.xml
pipecat/examples/bot-ready-signalling/client/react-native/node_modules/
pipecat/examples/bot-ready-signalling/client/react-native/.expo/
pipecat/examples/bot-ready-signalling/client/react-native/dist/
pipecat/examples/bot-ready-signalling/client/react-native/npm-debug.*
pipecat/examples/bot-ready-signalling/client/react-native/*.jks
pipecat/examples/bot-ready-signalling/client/react-native/*.p8
pipecat/examples/bot-ready-signalling/client/react-native/*.p12
pipecat/examples/bot-ready-signalling/client/react-native/*.key
pipecat/examples/bot-ready-signalling/client/react-native/*.mobileprovision
pipecat/examples/bot-ready-signalling/client/react-native/*.orig.*
pipecat/examples/bot-ready-signalling/client/react-native/web-build/
# macOS
.DS_Store
# Documentation
docs/api/_build/
docs/api/api

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repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.9.7
- repo: local
hooks:
- id: ruff
language_version: python3
args: [ --select, I, ]
- id: ruff-format
- id: ruff-format-hook
name: Check ruff formatting
entry: sh scripts/pre-commit.sh
language: system

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@@ -5,922 +5,10 @@ All notable changes to **Pipecat** will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.0.67] - 2025-05-07
## [Unreleased]
### Added
- Added `DebugLogObserver` for detailed frame logging with configurable
filtering by frame type and endpoint. This observer automatically extracts
and formats all frame data fields for debug logging.
- `UserImageRequestFrame.video_source` field has been added to request an image
from the desired video source.
- Added support for the AWS Nova Sonic speech-to-speech model with the new
`AWSNovaSonicLLMService`.
See https://docs.aws.amazon.com/nova/latest/userguide/speech.html.
Note that it requires Python >= 3.12 and `pip install pipecat-ai[aws-nova-sonic]`.
- Added new AWS services `AWSBedrockLLMService` and `AWSTranscribeSTTService`.
- Added `on_active_speaker_changed` event handler to the `DailyTransport` class.
- Added `enable_ssml_parsing` and `enable_logging` to `InputParams` in
`ElevenLabsTTSService`.
- Added support to `RimeHttpTTSService` for the `arcana` model.
### Changed
- Updated `ElevenLabsTTSService` to use the beta websocket API
(multi-stream-input). This new API supports context_ids and cancelling those
contexts, which greatly improves interruption handling.
- Observers `on_push_frame()` now take a single argument `FramePushed` instead
of multiple arguments.
- Updated the default voice for `DeepgramTTSService` to `aura-2-helena-en`.
### Deprecated
- `PollyTTSService` is now deprecated, use `AWSPollyTTSService` instead.
- Observer `on_push_frame(src, dst, frame, direction, timestamp)` is now
deprecated, use `on_push_frame(data: FramePushed)` instead.
### Fixed
- Fixed a `DailyTransport` issue that was causing issues when multiple audio or
video sources where being captured.
- Fixed a `UltravoxSTTService` issue that would cause the service to generate
all tokens as one word.
- Fixed a `PipelineTask` issue that would cause tasks to not be cancelled if
task was cancelled from outside of Pipecat.
- Fixed a `TaskManager` that was causing dangling tasks to be reported.
- Fixed an issue that could cause data to be sent to the transports when they
were still not ready.
- Remove custom audio tracks from `DailyTransport` before leaving.
### Removed
- Removed `CanonicalMetricsService` as it's no longer maintained.
## [0.0.66] - 2025-05-02
### Added
- Added two new input parameters to `RimeTTSService`: `pause_between_brackets`
and `phonemize_between_brackets`.
- Added support for cross-platform local smart turn detection. You can use
`LocalSmartTurnAnalyzer` for on-device inference using Torch.
- `BaseOutputTransport` now allows multiple destinations if the transport
implementation supports it (e.g. Daily's custom tracks). With multiple
destinations it is possible to send different audio or video tracks with a
single transport simultaneously. To do that, you need to set the new
`Frame.transport_destination` field with your desired transport destination
(e.g. custom track name), tell the transport you want a new destination with
`TransportParams.audio_out_destinations` or
`TransportParams.video_out_destinations` and the transport should take care of
the rest.
- Similar to the new `Frame.transport_destination`, there's a new
`Frame.transport_source` field which is set by the `BaseInputTransport` if the
incoming data comes from a non-default source (e.g. custom tracks).
- `TTSService` has a new `transport_destination` constructor parameter. This
parameter will be used to update the `Frame.transport_destination` field for
each generated `TTSAudioRawFrame`. This allows sending multiple bots' audio to
multiple destinations in the same pipeline.
- Added `DailyTransportParams.camera_out_enabled` and
`DailyTransportParams.microphone_out_enabled` which allows you to
enable/disable the main output camera or microphone tracks. This is useful if
you only want to use custom tracks and not send the main tracks. Note that you
still need `audio_out_enabled=True` or `video_out_enabled`.
- Added `DailyTransport.capture_participant_audio()` which allows you to capture
an audio source (e.g. "microphone", "screenAudio" or a custom track name) from
a remote participant.
- Added `DailyTransport.update_publishing()` which allows you to update the call
video and audio publishing settings (e.g. audio and video quality).
- Added `RTVIObserverParams` which allows you to configure what RTVI messages
are sent to the clients.
- Added a `context_window_compression` InputParam to
`GeminiMultimodalLiveLLMService` which allows you to enable a sliding context
window for the session as well as set the token limit of the sliding window.
- Updated `SmallWebRTCConnection` to support `ice_servers` with credentials.
- Added `VADUserStartedSpeakingFrame` and `VADUserStoppedSpeakingFrame`,
indicating when the VAD detected the user to start and stop speaking. These
events are helpful when using smart turn detection, as the user's stop time
can differ from when their turn ends (signified by UserStoppedSpeakingFrame).
- Added `TranslationFrame`, a new frame type that contains a translated
transcription.
- Added `TransportParams.audio_in_passthrough`. If set (the default), incoming
audio will be pushed downstream.
- Added `MCPClient`; a way to connect to MCP servers and use the MCP servers'
tools.
- Added `Mem0 OSS`, along with Mem0 cloud support now the OSS version is also
available.
### Changed
- `TransportParams.audio_mixer` now supports a string and also a dictionary to
provide a mixer per destination. For example:
```python
audio_out_mixer={
"track-1": SoundfileMixer(...),
"track-2": SoundfileMixer(...),
"track-N": SoundfileMixer(...),
},
```
- The `STTMuteFilter` now mutes `InterimTranscriptionFrame` and
`TranscriptionFrame` which allows the `STTMuteFilter` to be used in
conjunction with transports that generate transcripts, e.g. `DailyTransport`.
- Function calls now receive a single parameter `FunctionCallParams` instead of
`(function_name, tool_call_id, args, llm, context, result_callback)` which is
now deprecated.
- Changed the user aggregator timeout for late transcriptions from 1.0s to 0.5s
(`LLMUserAggregatorParams.aggregation_timeout`). Sometimes, the STT services
might give us more than one transcription which could come after the user
stopped speaking. We still want to include these additional transcriptions
with the first one because it's part of the user turn. This is what this
timeout is helpful with.
- Short utterances not detected by VAD while the bot is speaking are now
ignored. This reduces the amount of bot interruptions significantly providing
a more natural conversation experience.
- Updated `GladiaSTTService` to output a `TranslationFrame` when specifying a
`translation` and `translation_config`.
- STT services now passthrough audio frames by default. This allows you to add
audio recording without worrying about what's wrong in your pipeline when it
doesn't work the first time.
- Input transports now always push audio downstream unless disabled with
`TransportParams.audio_in_passthrough`. After many Pipecat releases, we
realized this is the common use case. There are use cases where the input
transport already provides STT and you also don't want recordings, in which
case there's no need to push audio to the rest of the pipeline, but this is
not a very common case.
- Added `RivaSegmentedSTTService`, which allows Riva offline/batch models, such
as to be "canary-1b-asr" used in Pipecat.
### Deprecated
- Function calls with parameters
`(function_name, tool_call_id, args, llm, context, result_callback)` are
deprectated, use a single `FunctionCallParams` parameter instead.
- `TransportParams.camera_*` parameters are now deprecated, use
`TransportParams.video_*` instead.
- `TransportParams.vad_enabled` parameter is now deprecated, use
`TransportParams.audio_in_enabled` and `TransportParams.vad_analyzer` instead.
- `TransportParams.vad_audio_passthrough` parameter is now deprecated, use
`TransportParams.audio_in_passthrough` instead.
- `ParakeetSTTService` is now deprecated, use `RivaSTTService` instead, which uses
the model "parakeet-ctc-1.1b-asr" by default.
- `FastPitchTTSService` is now deprecated, use `RivaTTSService` instead, which uses
the model "magpie-tts-multilingual" by default.
### Fixed
- Fixed an issue with `SimliVideoService` where the bot was continuously outputting
audio, which prevents the `BotStoppedSpeakingFrame` from being emitted.
- Fixed an issue where `OpenAIRealtimeBetaLLMService` would add two assistant
messages to the context.
- Fixed an issue with `GeminiMultimodalLiveLLMService` where the context
contained tokens instead of words.
- Fixed an issue with HTTP Smart Turn handling, where the service returns a 500
error. Previously, this would cause an unhandled exception. Now, a 500 error
is treated as an incomplete response.
- Fixed a TTS services issue that could cause assistant output not to be
aggregated to the context when also using `TTSSpeakFrame`s.
- Fixed an issue where the `SmartTurnMetricsData` was reporting 0ms for
inference and processing time when using the `FalSmartTurnAnalyzer`.
### Other
- Added `examples/daily-custom-tracks` to show how to send and receive Daily
custom tracks.
- Added `examples/daily-multi-translation` to showcase how to send multiple
simulataneous translations with the same transport.
- Added 04 foundational examples for client/server transports. Also, renamed
`29-livekit-audio-chat.py` to `04b-transports-livekit.py`.
- Added foundational example `13c-gladia-translation.py` showing how to use
`TranscriptionFrame` and `TranslationFrame`.
## [0.0.65] - 2025-04-23 "Sant Jordi's release" 🌹📕
https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
### Added
- Added automatic hangup logic to the Telnyx serializer. This feature hangs up
the Telnyx call when an `EndFrame` or `CancelFrame` is received. It is
enabled by default and is configurable via the `auto_hang_up` `InputParam`.
- Added a keepalive task to `GladiaSTTService` to prevent the websocket from
disconnecting after 30 seconds of no audio input.
### Changed
- The `InputParams` for `ElevenLabsTTSService` and `ElevenLabsHttpTTSService`
no longer require that `stability` and `similarity_boost` be set. You can
individually set each param.
- In `TwilioFrameSerializer`, `call_sid` is Optional so as to avoid a breaking
changed. `call_sid` is required to automatically hang up.
### Fixed
- Fixed an issue where `TwilioFrameSerializer` would send two hang up commands:
one for the `EndFrame` and one for the `CancelFrame`.
## [0.0.64] - 2025-04-22
### Added
- Added automatic hangup logic to the Twilio serializer. This feature hangs up
the Twilio call when an `EndFrame` or `CancelFrame` is received. It is
enabled by default and is configurable via the `auto_hang_up` `InputParam`.
- Added `SmartTurnMetricsData`, which contains end-of-turn prediction metrics,
to the `MetricsFrame`. Using `MetricsFrame`, you can now retrieve prediction
confidence scores and processing time metrics from the smart turn analyzers.
- Added support for Application Default Credentials in Google services,
`GoogleSTTService`, `GoogleTTSService`, and `GoogleVertexLLMService`.
- Added support for Smart Turn Detection via the `turn_analyzer` transport
parameter. You can now choose between `HttpSmartTurnAnalyzer()` or
`FalSmartTurnAnalyzer()` for remote inference or
`LocalCoreMLSmartTurnAnalyzer()` for on-device inference using Core ML.
- `DeepgramTTSService` accepts `base_url` argument again, allowing you to
connect to an on-prem service.
- Added `LLMUserAggregatorParams` and `LLMAssistantAggregatorParams` which allow
you to control aggregator settings. You can now pass these arguments when
creating aggregator pairs with `create_context_aggregator()`.
- Added `previous_text` context support to ElevenLabsHttpTTSService, improving
speech consistency across sentences within an LLM response.
- Added word/timestamp pairs to `ElevenLabsHttpTTSService`.
- It is now possible to disable `SoundfileMixer` when created. You can then use
`MixerEnableFrame` to dynamically enable it when necessary.
- Added `on_client_connected` and `on_client_disconnected` event handlers to
the `DailyTransport` class. These handlers map to the same underlying Daily
events as `on_participant_joined` and `on_participant_left`, respectively.
This makes it easier to write a single bot pipeline that can also use other
transports like `SmallWebRTCTransport` and `FastAPIWebsocketTransport`.
### Changed
- `GrokLLMService` now uses `grok-3-beta` as its default model.
- Daily's REST helpers now include an `eject_at_token_exp` param, which ejects
the user when their token expires. This new parameter defaults to False.
Also, the default value for `enable_prejoin_ui` changed to False and
`eject_at_room_exp` changed to False.
- `OpenAILLMService` and `OpenPipeLLMService` now use `gpt-4.1` as their
default model.
- `SoundfileMixer` constructor arguments need to be keywords.
### Deprecated
- `DeepgramSTTService` parameter `url` is now deprecated, use `base_url`
instead.
### Removed
- Parameters `user_kwargs` and `assistant_kwargs` when creating a context
aggregator pair using `create_context_aggregator()` have been removed. Use
`user_params` and `assistant_params` instead.
### Fixed
- Fixed an issue that would cause TTS websocket-based services to not cleanup
resources properly when disconnecting.
- Fixed a `TavusVideoService` issue that was causing audio choppiness.
- Fixed an issue in `SmallWebRTCTransport` where an error was thrown if the
client did not create a video transceiver.
- Fixed an issue where LLM input parameters were not working and applied
correctly in `GoogleVertexLLMService`, causing unexpected behavior during
inference.
### Other
- Updated the `twilio-chatbot` example to use the auto-hangup feature.
## [0.0.63] - 2025-04-11
### Added
- Added media resolution control to `GeminiMultimodalLiveLLMService` with
`GeminiMediaResolution` enum, allowing configuration of token usage for
image processing (LOW: 64 tokens, MEDIUM: 256 tokens, HIGH: zoomed reframing
with 256 tokens).
- Added Gemini's Voice Activity Detection (VAD) configuration to
`GeminiMultimodalLiveLLMService` with `GeminiVADParams`, allowing fine
control over speech detection sensitivity and timing, including:
- Start sensitivity (how quickly speech is detected)
- End sensitivity (how quickly turns end after pauses)
- Prefix padding (milliseconds of audio to keep before speech is detected)
- Silence duration (milliseconds of silence required to end a turn)
- Added comprehensive language support to `GeminiMultimodalLiveLLMService`,
supporting over 30 languages via the `language` parameter, with proper
mapping between Pipecat's `Language` enum and Gemini's language codes.
- Added support in `SmallWebRTCTransport` to detect when remote tracks are
muted.
- Added support for image capture from a video stream to the
`SmallWebRTCTransport`.
- Added a new iOS client option to the `SmallWebRTCTransport`
**video-transform** example.
- Added new processors `ProducerProcessor` and `ConsumerProcessor`. The
producer processor processes frames from the pipeline and decides whether the
consumers should consume it or not. If so, the same frame that is received by
the producer is sent to the consumer. There can be multiple consumers per
producer. These processors can be useful to push frames from one part of a
pipeline to a different one (e.g. when using `ParallelPipeline`).
- Improvements for the `SmallWebRTCTransport`:
- Wait until the pipeline is ready before triggering the `connected` event.
- Queue messages if the data channel is not ready.
- Update the aiortc dependency to fix an issue where the 'video/rtx' MIME
type was incorrectly handled as a codec retransmission.
- Avoid initial video delays.
### Changed
- In `GeminiMultimodalLiveLLMService`, removed the `transcribe_model_audio`
parameter in favor of Gemini Live's native output transcription support. Now
text transcriptions are produced directly by the model. No configuration is
required.
- Updated `GeminiMultimodalLiveLLMService`s default `model` to
`models/gemini-2.0-flash-live-001` and `base_url` to the `v1beta` websocket
URL.
### Fixed
- Updated `daily-python` to 0.17.0 to fix an issue that was preventing to run on
older platforms.
- Fixed an issue where `CartesiaTTSService`'s spell feature would result in
the spelled word in the context appearing as "F,O,O,B,A,R" instead of
"FOOBAR".
- Fixed an issue in the Azure TTS services where the language was being set
incorrectly.
- Fixed `SmallWebRTCTransport` to support dynamic values for
`TransportParams.audio_out_10ms_chunks`. Previously, it only worked with 20ms
chunks.
- Fixed an issue with `GeminiMultimodalLiveLLMService` where the assistant
context messages had no space between words.
- Fixed an issue where `LLMAssistantContextAggregator` would prevent a
`BotStoppedSpeakingFrame` from moving through the pipeline.
## [0.0.62] - 2025-04-01 "An April Fools' release"
### Added
- Added `TransportParams.audio_out_10ms_chunks` parameter to allow controlling
the amount of audio being sent by the output transport. It defaults to 4, so
40ms audio chunks are sent.
- Added `QwenLLMService` for Qwen integration with an OpenAI-compatible
interface. Added foundational example `14q-function-calling-qwen.py`.
- Added `Mem0MemoryService`. Mem0 is a self-improving memory layer for LLM
applications. Learn more at: https://mem0.ai/.
- Added `WhisperSTTServiceMLX` for Whisper transcription on Apple Silicon.
See example in `examples/foundational/13e-whisper-mlx.py`. Latency of
completed transcription using Whisper large-v3-turbo on an M4 macbook is
~500ms.
- Added `SmallWebRTCTransport`, a new P2P WebRTC transport.
- Created two examples in `p2p-webrtc`:
- **video-transform**: Demonstrates sending and receiving audio/video with
`SmallWebRTCTransport` using `TypeScript`. Includes video frame
processing with OpenCV.
- **voice-agent**: A minimal example of creating a voice agent with
`SmallWebRTCTransport`.
- `GladiaSTTService` now have comprehensive support for the latest API config
options, including model, language detection, preprocessing, custom
vocabulary, custom spelling, translation, and message filtering options.
- Added `SmallWebRTCTransport`, a new P2P WebRTC transport.
- Created two examples in `p2p-webrtc`:
- **video-transform**: Demonstrates sending and receiving audio/video with
`SmallWebRTCTransport` using `TypeScript`. Includes video frame
processing with OpenCV.
- **voice-agent**: A minimal example of creating a voice agent with
`SmallWebRTCTransport`.
- Added support to `ProtobufFrameSerializer` to send the messages from
`TransportMessageFrame` and `TransportMessageUrgentFrame`.
- Added support for a new TTS service, `PiperTTSService`.
(see https://github.com/rhasspy/piper/)
- It is now possible to tell whether `UserStartedSpeakingFrame` or
`UserStoppedSpeakingFrame` have been generated because of emulation frames.
### Changed
- `FunctionCallResultFrame`a are now system frames. This is to prevent function
call results to be discarded during interruptions.
- Pipecat services have been reorganized into packages. Each package can have
one or more of the following modules (in the future new module names might be
needed) depending on the services implemented:
- image: for image generation services
- llm: for LLM services
- memory: for memory services
- stt: for Speech-To-Text services
- tts: for Text-To-Speech services
- video: for video generation services
- vision: for video recognition services
- Base classes for AI services have been reorganized into modules. They can now
be found in
`pipecat.services.[ai_service,image_service,llm_service,stt_service,vision_service]`.
- `GladiaSTTService` now uses the `solaria-1` model by default. Other params
use Gladia's default values. Added support for more language codes.
### Deprecated
- All Pipecat services imports have been deprecated and a warning will be shown
when using the old import. The new import should be
`pipecat.services.[service].[image,llm,memory,stt,tts,video,vision]`. For
example, `from pipecat.services.openai.llm import OpenAILLMService`.
- Import for AI services base classes from `pipecat.services.ai_services` is now
deprecated, use one of
`pipecat.services.[ai_service,image_service,llm_service,stt_service,vision_service]`.
- Deprecated the `language` parameter in `GladiaSTTService.InputParams` in
favor of `language_config`, which better aligns with Gladia's API.
- Deprecated using `GladiaSTTService.InputParams` directly. Use the new
`GladiaInputParams` class instead.
### Fixed
- Fixed a `FastAPIWebsocketTransport` and `WebsocketClientTransport` issue that
would cause the transport to be closed prematurely, preventing the internally
queued audio to be sent. The same issue could also cause an infinite loop
while using an output mixer and when sending an `EndFrame`, preventing the bot
to finish.
- Fixed an issue that could cause the `TranscriptionUpdateFrame` being pushed
because of an interruption to be discarded.
- Fixed an issue that would cause `SegmentedSTTService` based services
(e.g. `OpenAISTTService`) to try to transcribe non-spoken audio, causing
invalid transcriptions.
- Fixed an issue where `GoogleTTSService` was emitting two `TTSStoppedFrames`.
### Performance
- Output transports now send 40ms audio chunks instead of 20ms. This should
improve performance.
- `BotSpeakingFrame`s are now sent every 200ms. If the output transport audio chunks
are higher than 200ms then they will be sent at every audio chunk.
### Other
- Added foundational example `37-mem0.py` demonstrating how to use the
`Mem0MemoryService`.
- Added foundational example `13e-whisper-mlx.py` demonstrating how to use the
`WhisperSTTServiceMLX`.
## [0.0.61] - 2025-03-26
### Added
- Added a new frame, `LLMSetToolChoiceFrame`, which provides a mechanism
for modifying the `tool_choice` in the context.
- Added `GroqTTSService` which provides text-to-speech functionality using
Groq's API.
- Added support in `DailyTransport` for updating remote participants'
`canReceive` permission via the `update_remote_participants()` method, by
bumping the daily-python dependency to >= 0.16.0.
- ElevenLabs TTS services now support a sample rate of 8000.
- Added support for `instructions` in `OpenAITTSService`.
- Added support for `base_url` in `OpenAIImageGenService` and
`OpenAITTSService`.
### Fixed
- Fixed an issue in `RTVIObserver` that prevented handling of Google LLM
context messages. The observer now processes both OpenAI-style and
Google-style contexts.
- Fixed an issue in Daily involving switching virtual devices, by bumping the
daily-python dependency to >= 0.16.1.
- Fixed a `GoogleAssistantContextAggregator` issue where function calls
placeholders where not being updated when then function call result was
different from a string.
- Fixed an issue that would cause `LLMAssistantContextAggregator` to block
processing more frames while processing a function call result.
- Fixed an issue where the `RTVIObserver` would report two bot started and
stopped speaking events for each bot turn.
- Fixed an issue in `UltravoxSTTService` that caused improper audio processing
and incorrect LLM frame output.
### Other
- Added `examples/foundational/07x-interruptible-local.py` to show how a local
transport can be used.
## [0.0.60] - 2025-03-20
### Added
- Added `default_headers` parameter to `BaseOpenAILLMService` constructor.
### Changed
- Rollback to `deepgram-sdk` 3.8.0 since 3.10.1 was causing connections issues.
- Changed the default `InputAudioTranscription` model to `gpt-4o-transcribe`
for `OpenAIRealtimeBetaLLMService`.
### Other
- Update the `19-openai-realtime-beta.py` and `19a-azure-realtime-beta.py`
examples to use the FunctionSchema format.
## [0.0.59] - 2025-03-20
### Added
- When registering a function call it is now possible to indicate if you want
the function call to be cancelled if there's a user interruption via
`cancel_on_interruption` (defaults to False). This is now possible because
function calls are executed concurrently.
- Added support for detecting idle pipelines. By default, if no activity has
been detected during 5 minutes, the `PipelineTask` will be automatically
cancelled. It is possible to override this behavior by passing
`cancel_on_idle_timeout=False`. It is also possible to change the default
timeout with `idle_timeout_secs` or the frames that prevent the pipeline from
being idle with `idle_timeout_frames`. Finally, an `on_idle_timeout` event
handler will be triggered if the idle timeout is reached (whether the pipeline
task is cancelled or not).
- Added `FalSTTService`, which provides STT for Fal's Wizper API.
- Added a `reconnect_on_error` parameter to websocket-based TTS services as well
as a `on_connection_error` event handler. The `reconnect_on_error` indicates
whether the TTS service should reconnect on error. The `on_connection_error`
will always get called if there's any error no matter the value of
`reconnect_on_error`. This allows, for example, to fallback to a different TTS
provider if something goes wrong with the current one.
- Added new `SkipTagsAggregator` that extends `BaseTextAggregator` to aggregate
text and skips end of sentence matching if aggregated text is between
start/end tags.
- Added new `PatternPairAggregator` that extends `BaseTextAggregator` to
identify content between matching pattern pairs in streamed text. This allows
for detection and processing of structured content like XML-style tags that
may span across multiple text chunks or sentence boundaries.
- Added new `BaseTextAggregator`. Text aggregators are used by the TTS service
to aggregate LLM tokens and decide when the aggregated text should be pushed
to the TTS service. They also allow for the text to be manipulated while it's
being aggregated. A text aggregator can be passed via `text_aggregator` to the
TTS service.
- Added new `sample_rate` constructor parameter to `TavusVideoService` to allow
changing the output sample rate.
- Added new `NeuphonicTTSService`.
(see https://neuphonic.com)
- Added new `UltravoxSTTService`.
(see https://github.com/fixie-ai/ultravox)
- Added `on_frame_reached_upstream` and `on_frame_reached_downstream` event
handlers to `PipelineTask`. Those events will be called when a frame reaches
the beginning or end of the pipeline respectively. Note that by default, the
event handlers will not be called unless a filter is set with
`PipelineTask.set_reached_upstream_filter()` or
`PipelineTask.set_reached_downstream_filter()`.
- Added support for Chirp voices in `GoogleTTSService`.
- Added a `flush_audio()` method to `FishTTSService` and `LmntTTSService`.
- Added a `set_language` convenience method for `GoogleSTTService`, allowing
you to set a single language. This is in addition to the `set_languages`
method which allows you to set a list of languages.
- Added `on_user_turn_audio_data` and `on_bot_turn_audio_data` to
`AudioBufferProcessor`. This gives the ability to grab the audio of only that
turn for both the user and the bot.
- Added new base class `BaseObject` which is now the base class of
`FrameProcessor`, `PipelineRunner`, `PipelineTask` and `BaseTransport`. The
new `BaseObject` adds supports for event handlers.
- Added support for a unified format for specifying function calling across all
LLM services.
```python
weather_function = FunctionSchema(
name="get_current_weather",
description="Get the current weather",
properties={
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the user's location.",
},
},
required=["location"],
)
tools = ToolsSchema(standard_tools=[weather_function])
```
- Added `speech_threshold` parameter to `GladiaSTTService`.
- Allow passing user (`user_kwargs`) and assistant (`assistant_kwargs`) context
aggregator parameters when using `create_context_aggregator()`. The values are
passed as a mapping that will then be converted to arguments.
- Added `speed` as an `InputParam` for both `ElevenLabsTTSService` and
`ElevenLabsHttpTTSService`.
- Added new `LLMFullResponseAggregator` to aggregate full LLM completions. At
every completion the `on_completion` event handler is triggered.
- Added a new frame, `RTVIServerMessageFrame`, and RTVI message
`RTVIServerMessage` which provides a generic mechanism for sending custom
messages from server to client. The `RTVIServerMessageFrame` is processed by
the `RTVIObserver` and will be delivered to the client's `onServerMessage`
callback or `ServerMessage` event.
- Added `GoogleLLMOpenAIBetaService` for Google LLM integration with an
OpenAI-compatible interface. Added foundational example
`14o-function-calling-gemini-openai-format.py`.
- Added `AzureRealtimeBetaLLMService` to support Azure's OpeanAI Realtime API. Added
foundational example `19a-azure-realtime-beta.py`.
- Introduced `GoogleVertexLLMService`, a new class for integrating with Vertex AI
Gemini models. Added foundational example
`14p-function-calling-gemini-vertex-ai.py`.
- Added support in `OpenAIRealtimeBetaLLMService` for a slate of new features:
- The `'gpt-4o-transcribe'` input audio transcription model, along
with new `language` and `prompt` options specific to that model.
- The `input_audio_noise_reduction` session property.
```python
session_properties = SessionProperties(
# ...
input_audio_noise_reduction=InputAudioNoiseReduction(
type="near_field" # also supported: "far_field"
)
# ...
)
```
- The `'semantic_vad'` `turn_detection` session property value, a more
sophisticated model for detecting when the user has stopped speaking.
- `on_conversation_item_created` and `on_conversation_item_updated`
events to `OpenAIRealtimeBetaLLMService`.
```python
@llm.event_handler("on_conversation_item_created")
async def on_conversation_item_created(llm, item_id, item):
# ...
@llm.event_handler("on_conversation_item_updated")
async def on_conversation_item_updated(llm, item_id, item):
# `item` may not always be available here
# ...
```
- The `retrieve_conversation_item(item_id)` method for introspecting a
conversation item on the server.
```python
item = await llm.retrieve_conversation_item(item_id)
```
### Changed
- Updated `OpenAISTTService` to use `gpt-4o-transcribe` as the default
transcription model.
- Updated `OpenAITTSService` to use `gpt-4o-mini-tts` as the default TTS model.
- Function calls are now executed in tasks. This means that the pipeline will
not be blocked while the function call is being executed.
- ⚠️ `PipelineTask` will now be automatically cancelled if no bot activity is
happening in the pipeline. There are a few settings to configure this
behavior, see `PipelineTask` documentation for more details.
- All event handlers are now executed in separate tasks in order to prevent
blocking the pipeline. It is possible that event handlers take some time to
execute in which case the pipeline would be blocked waiting for the event
handler to complete.
- Updated `TranscriptProcessor` to support text output from
`OpenAIRealtimeBetaLLMService`.
- `OpenAIRealtimeBetaLLMService` and `GeminiMultimodalLiveLLMService` now push
a `TTSTextFrame`.
- Updated the default mode for `CartesiaTTSService` and
`CartesiaHttpTTSService` to `sonic-2`.
### Deprecated
- Passing a `start_callback` to `LLMService.register_function()` is now
deprecated, simply move the code from the start callback to the function call.
- `TTSService` parameter `text_filter` is now deprecated, use `text_filters`
instead which is now a list. This allows passing multiple filters that will be
executed in order.
### Removed
- Removed deprecated `audio.resample_audio()`, use `create_default_resampler()`
instead.
- Removed deprecated`stt_service` parameter from `STTMuteFilter`.
- Removed deprecated RTVI processors, use an `RTVIObserver` instead.
- Removed deprecated `AWSTTSService`, use `PollyTTSService` instead.
- Removed deprecated field `tier` from `DailyTranscriptionSettings`, use `model`
instead.
- Removed deprecated `pipecat.vad` package, use `pipecat.audio.vad` instead.
### Fixed
- Fixed an assistant aggregator issue that could cause assistant text to be
split into multiple chunks during function calls.
- Fixed an assistant aggregator issue that was causing assistant text to not be
added to the context during function calls. This could lead to duplications.
- Fixed a `SegmentedSTTService` issue that was causing audio to be sent
prematurely to the STT service. Instead of analyzing the volume in this
service we rely on VAD events which use both VAD and volume.
- Fixed a `GeminiMultimodalLiveLLMService` issue that was causing messages to be
duplicated in the context when pushing `LLMMessagesAppendFrame` frames.
- Fixed an issue with `SegmentedSTTService` based services
(e.g. `GroqSTTService`) that was not allow audio to pass-through downstream.
- Fixed a `CartesiaTTSService` and `RimeTTSService` issue that would consider
text between spelling out tags end of sentence.
- Fixed a `match_endofsentence` issue that would result in floating point
numbers to be considered an end of sentence.
- Fixed a `match_endofsentence` issue that would result in emails to be
considered an end of sentence.
- Fixed an issue where the RTVI message `disconnect-bot` was pushing an
`EndFrame`, resulting in the pipeline not shutting down. It now pushes an
`EndTaskFrame` upstream to shutdown the pipeline.
- Fixed an issue with the `GoogleSTTService` where stream timeouts during
periods of inactivity were causing connection failures. The service now
properly detects timeout errors and handles reconnection gracefully,
ensuring continuous operation even after periods of silence or when using an
`STTMuteFilter`.
- Fixed an issue in `RimeTTSService` where the last line of text sent didn't
result in an audio output being generated.
- Fixed `OpenAIRealtimeBetaLLMService` by adding proper handling for:
- The `conversation.item.input_audio_transcription.delta` server message,
which was added server-side at some point and not handled client-side.
- Errors reported by the `response.done` server message.
### Other
- Add foundational example `07w-interruptible-fal.py`, showing `FalSTTService`.
- Added a new Ultravox example
`examples/foundational/07u-interruptible-ultravox.py`.
- Added new Neuphonic examples
`examples/foundational/07v-interruptible-neuphonic.py` and
`examples/foundational/07v-interruptible-neuphonic-http.py`.
- Added a new example `examples/foundational/36-user-email-gathering.py` to show
how to gather user emails. The example uses's Cartesia's `<spell></spell>`
tags and Rime `spell()` function to spell out the emails for confirmation.
- Update the `34-audio-recording.py` example to include an STT processor.
- Added foundational example `35-voice-switching.py` showing how to use the new
`PatternPairAggregator`. This example shows how to encode information for the
LLM to instruct TTS voice changes, but this can be used to encode any
information into the LLM response, which you want to parse and use in other
parts of your application.
- Added a Pipecat Cloud deployment example to the `examples` directory.
- Removed foundational examples 28b and 28c as the TranscriptProcessor no
longer has an LLM depedency. Renamed foundational example 28a to
`28-transcript-processor.py`.
## [0.0.58] - 2025-02-26
### Added
- Added track-specific audio event `on_track_audio_data` to
`AudioBufferProcessor` for accessing separate input and output audio tracks.
- Pipecat version will now be logged on every application startup. This will
help us identify what version we are running in case of any issues.
@@ -957,10 +45,6 @@ https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
- ⚠️ `PipelineTask` now requires keyword arguments (except for the first one for
the pipeline).
- Updated `PlayHTHttpTTSService` to take a `voice_engine` and `protocol` input
in the constructor. The previous method of providing a `voice_engine` input
that contains the engine and protocol is deprecated by PlayHT.
- The base `TTSService` class now strips leading newlines before sending text
to the TTS provider. This change is to solve issues where some TTS providers,
like Azure, would not output text due to newlines.
@@ -994,12 +78,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
### Fixed
- Fixed an issue that would cause undesired interruptions via
`EmulateUserStartedSpeakingFrame`.
- Fixed a `GoogleLLMService` that was causing an exception when sending inline
audio in some cases.
- Fixed an `AudioContextWordTTSService` issue that would cause an `EndFrame` to
disconnect from the TTS service before audio from all the contexts was
received. This affected services like Cartesia and Rime.
@@ -1013,6 +91,10 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
- Fixed `match_endofsentence` support for ellipses.
- Fixed an issue that would cause undesired interruptions via
`EmulateUserStartedSpeakingFrame` when only interim transcriptions (i.e. no
final transcriptions) where received.
- Fixed an issue where `EndTaskFrame` was not triggering
`on_client_disconnected` or closing the WebSocket in FastAPI.
@@ -1042,9 +124,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
- Added Gemini support to `examples/phone-chatbot`.
- Added foundational example `34-audio-recording.py` showing how to use the
AudioBufferProcessor callbacks to save merged and track recordings.
## [0.0.57] - 2025-02-14
### Added
@@ -2677,7 +1756,7 @@ async def on_connected(processor):
completed. If a task is never ran `has_finished()` will return False.
- `PipelineRunner` now supports SIGTERM. If received, the runner will be
cancelled.
canceled.
### Fixed

View File

@@ -26,52 +26,11 @@ git commit -m "Description of your changes"
git push origin your-branch-name
```
8. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
> Important: Describe the changes you've made clearly!
9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
> Important: Describe the changes you've made clearly!
Our maintainers will review your PR, and once everything is good, your contributions will be merged!
## Code Style and Documentation
### Python Code Style
We use Ruff for code linting and formatting. Please ensure your code passes all linting checks before submitting a PR.
### Docstring Conventions
We follow Google-style docstrings with these specific conventions:
- Class docstrings should fully document all parameters used in `__init__`
- We don't require separate docstrings for `__init__` methods when parameters are documented in the class docstring
- Property methods should have docstrings explaining their purpose and return value
Example of correctly documented class:
```python
class MyClass:
"""Class description.
Additional details about the class.
Args:
param1: Description of first parameter.
param2: Description of second parameter.
"""
def __init__(self, param1, param2):
# No docstring required here as parameters are documented above
self.param1 = param1
self.param2 = param2
@property
def some_property(self) -> str:
"""Get the formatted property value.
Returns:
A string representation of the property.
"""
return f"Property: {self.param1}"
```
# Contributor Covenant Code of Conduct
@@ -92,23 +51,23 @@ diverse, inclusive, and healthy community.
Examples of behavior that contributes to a positive environment for our
community include:
- Demonstrating empathy and kindness toward other people
- Being respectful of differing opinions, viewpoints, and experiences
- Giving and gracefully accepting constructive feedback
- Accepting responsibility and apologizing to those affected by our mistakes,
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
- Focusing on what is best not just for us as individuals, but for the overall
* Focusing on what is best not just for us as individuals, but for the overall
community
Examples of unacceptable behavior include:
- The use of sexualized language or imagery, and sexual attention or advances of
* The use of sexualized language or imagery, and sexual attention or advances of
any kind
- Trolling, insulting or derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information, such as a physical or email address,
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email address,
without their explicit permission
- Other conduct which could reasonably be considered inappropriate in a
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
@@ -203,4 +162,4 @@ For answers to common questions about this code of conduct, see the FAQ at
[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
[Mozilla CoC]: https://github.com/mozilla/diversity
[FAQ]: https://www.contributor-covenant.org/faq
[translations]: https://www.contributor-covenant.org/translations
[translations]: https://www.contributor-covenant.org/translations

236
README.md
View File

@@ -1,72 +1,43 @@
<h1><div align="center">
<img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
 <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
</div></h1>
[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![codecov](https://codecov.io/gh/pipecat-ai/pipecat/graph/badge.svg?token=LNVUIVO4Y9)](https://codecov.io/gh/pipecat-ai/pipecat) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat)
# 🎙️ Pipecat: Real-Time Voice & Multimodal AI Agents
Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences.
**Pipecat** is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
## What you can build
## 🚀 What You Can Build
- **Voice Assistants**: [Natural, real-time conversations with AI](https://demo.dailybots.ai/)
- **Interactive Agents**: Personal coaches and meeting assistants
- **Multimodal Apps**: Combine voice, video, images, and text
- **Creative Tools**: [Story-telling experiences](https://storytelling-chatbot.fly.dev/) and social companions
- **Business Solutions**: [Customer intake flows](https://www.youtube.com/watch?v=lDevgsp9vn0) and support bots
- **Complex conversational flows**: [Refer to Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) to learn more
- **Voice Assistants** natural, streaming conversations with AI
- **AI Companions** coaches, meeting assistants, characters
- **Multimodal Interfaces** voice, video, images, and more
- **Interactive Storytelling** creative tools with generative media
- **Business Agents** customer intake, support bots, guided flows
- **Complex Dialog Systems** design logic with structured conversations
🧭 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
## 🧠 Why Pipecat?
- **Voice-first**: Integrates speech recognition, text-to-speech, and conversation handling
- **Pluggable**: Supports many AI services and tools
- **Composable Pipelines**: Build complex behavior from modular components
- **Real-Time**: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
## 🎬 See it in action
## See it in action
<p float="left">
<a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/simple-chatbot/image.png" width="400" /></a>&nbsp;
<a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/storytelling-chatbot/image.png" width="400" /></a>
<a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/simple-chatbot/image.png" width="280" /></a>&nbsp;
<a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/storytelling-chatbot/image.png" width="280" /></a>
<br/>
<a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/translation-chatbot/image.png" width="400" /></a>&nbsp;
<a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/moondream-chatbot/image.png" width="400" /></a>
<a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/translation-chatbot/image.png" width="280" /></a>&nbsp;
<a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/moondream-chatbot/image.png" width="280" /></a>
</p>
## 📱 Client SDKs
## Key features
You can connect to Pipecat from any platform using our official SDKs:
- **Voice-first Design**: Built-in speech recognition, TTS, and conversation handling
- **Flexible Integration**: Works with popular AI services (OpenAI, ElevenLabs, etc.)
- **Pipeline Architecture**: Build complex apps from simple, reusable components
- **Real-time Processing**: Frame-based pipeline architecture for fluid interactions
- **Production Ready**: Enterprise-grade WebRTC and Websocket support
| Platform | SDK Repo | Description |
| -------- | ------------------------------------------------------------------------------ | -------------------------------- |
| Web | [pipecat-client-web](https://github.com/pipecat-ai/pipecat-client-web) | JavaScript and React client SDKs |
| iOS | [pipecat-client-ios](https://github.com/pipecat-ai/pipecat-client-ios) | Swift SDK for iOS |
| Android | [pipecat-client-android](https://github.com/pipecat-ai/pipecat-client-android) | Kotlin SDK for Android |
| C++ | [pipecat-client-cxx](https://github.com/pipecat-ai/pipecat-client-cxx) | C++ client SDK |
💡 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
## 🧩 Available services
## Getting started
| Category | Services |
|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
| Text-to-Speech | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
| Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) |
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
| Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) |
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter) |
| Analytics & Metrics | [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
## ⚡ Getting started
You can get started with Pipecat running on your local machine, then move your agent processes to the cloud when youre ready.
You can get started with Pipecat running on your local machine, then move your agent processes to the cloud when youre ready. You can also add a 📞 telephone number, 🖼️ image output, 📺 video input, use different LLMs, and more.
```shell
# Install the module
@@ -82,71 +53,154 @@ To keep things lightweight, only the core framework is included by default. If y
pip install "pipecat-ai[option,...]"
```
## 🧪 Code examples
### Available services
| Category | Services | Install Command Example |
| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) | `pip install "pipecat-ai[deepgram]"` |
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"` |
| Text-to-Speech | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"` |
| Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) | `pip install "pipecat-ai[google]"` |
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local | `pip install "pipecat-ai[daily]"` |
| Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) | `pip install "pipecat-ai[tavus,simli]"` |
| Vision & Image | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal) | `pip install "pipecat-ai[moondream]"` |
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter) | `pip install "pipecat-ai[silero]"` |
| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) | `pip install "pipecat-ai[canonical]"` |
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
## Code examples
- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
- [Example apps](https://github.com/pipecat-ai/pipecat/tree/main/examples/) — complete applications that you can use as starting points for development
## 🛠️ Hacking on the framework itself
## A simple voice agent running locally
1. Set up a virtual environment before following these instructions. From the root of the repo:
Here is a very basic Pipecat bot that greets a user when they join a real-time session. We'll use [Daily](https://daily.co) for real-time media transport, and [Cartesia](https://cartesia.ai/) for text-to-speech.
```shell
python3 -m venv venv
source venv/bin/activate
```
```python
import asyncio
2. Install the development dependencies:
from pipecat.frames.frames import TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.task import PipelineTask
from pipecat.pipeline.runner import PipelineRunner
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport
```shell
pip install -r dev-requirements.txt
```
async def main():
# Use Daily as a real-time media transport (WebRTC)
transport = DailyTransport(
room_url=...,
token="", # leave empty. Note: token is _not_ your api key
bot_name="Bot Name",
params=DailyParams(audio_out_enabled=True))
3. Install the git pre-commit hooks (these help ensure your code follows project rules):
# Use Cartesia for Text-to-Speech
tts = CartesiaTTSService(
api_key=...,
voice_id=...
)
```shell
pre-commit install
```
# Simple pipeline that will process text to speech and output the result
pipeline = Pipeline([tts, transport.output()])
4. Install the `pipecat-ai` package locally in editable mode:
# Create Pipecat processor that can run one or more pipelines tasks
runner = PipelineRunner()
```shell
pip install -e .
```
# Assign the task callable to run the pipeline
task = PipelineTask(pipeline)
> The `-e` or `--editable` option allows you to modify the code without reinstalling.
# Register an event handler to play audio when a
# participant joins the transport WebRTC session
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
participant_name = participant.get("info", {}).get("userName", "")
# Queue a TextFrame that will get spoken by the TTS service (Cartesia)
await task.queue_frame(TextFrame(f"Hello there, {participant_name}!"))
5. Include optional dependencies as needed. For example:
# Register an event handler to exit the application when the user leaves.
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.cancel()
```shell
pip install -e ".[daily,deepgram,cartesia,openai,silero]"
```
# Run the pipeline task
await runner.run(task)
6. (Optional) If you want to use this package from another directory:
if __name__ == "__main__":
asyncio.run(main())
```
```shell
pip install "path_to_this_repo[option,...]"
```
### Running tests
Install the test dependencies:
Run it with:
```shell
pip install -r test-requirements.txt
python app.py
```
Daily provides a prebuilt WebRTC user interface. While the app is running, you can visit at `https://<yourdomain>.daily.co/<room_url>` and listen to the bot say hello!
## WebRTC for production use
WebSockets are fine for server-to-server communication or for initial development. But for production use, youll need client-server audio to use a protocol designed for real-time media transport. (For an explanation of the difference between WebSockets and WebRTC, see [this post.](https://www.daily.co/blog/how-to-talk-to-an-llm-with-your-voice/#webrtc))
One way to get up and running quickly with WebRTC is to sign up for a Daily developer account. Daily gives you SDKs and global infrastructure for audio (and video) routing. Every account gets 10,000 audio/video/transcription minutes free each month.
Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https://docs.daily.co/reference/rest-api/rooms) in the developer Dashboard.
## Hacking on the framework itself
_Note: You may need to set up a virtual environment before following these instructions. From the root of the repo:_
```shell
python3 -m venv venv
source venv/bin/activate
```
Install the development dependencies:
```shell
pip install -r dev-requirements.txt
```
Install the git pre-commit hooks (these help ensure your code follows project rules):
```shell
pre-commit install
```
Install the `pipecat-ai` package locally in editable mode:
```shell
pip install -e .
```
The `-e` or `--editable` option allows you to modify the code without reinstalling.
To include optional dependencies, add them to the install command. For example:
```shell
pip install -e ".[daily,deepgram,cartesia,openai,silero]" # Updated for the services you're using
```
If you want to use this package from another directory:
```shell
pip install "path_to_this_repo[option,...]"
```
### Running tests
From the root directory, run:
```shell
pytest
```
### Setting up your editor
## Setting up your editor
This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting via [Ruff](https://github.com/astral-sh/ruff).
#### Emacs
### Emacs
You can use [use-package](https://github.com/jwiegley/use-package) to install [emacs-lazy-ruff](https://github.com/christophermadsen/emacs-lazy-ruff) package and configure `ruff` arguments:
@@ -168,7 +222,7 @@ You can use [use-package](https://github.com/jwiegley/use-package) to install [e
:hook ((python-mode . pyvenv-auto-run)))
```
#### Visual Studio Code
### Visual Studio Code
Install the
[Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, and enable formatting on save:
@@ -180,7 +234,7 @@ Install the
}
```
#### PyCharm
### PyCharm
`ruff` was installed in the `venv` environment described before, now to enable autoformatting on save, go to `File` -> `Settings` -> `Tools` -> `File Watchers` and add a new watcher with the following settings:
@@ -190,7 +244,7 @@ Install the
4. **Arguments**: `format $FilePath$`
5. **Program**: `$PyInterpreterDirectory$/ruff`
## 🤝 Contributing
## Contributing
We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:
@@ -203,7 +257,7 @@ Before submitting a pull request, please check existing issues and PRs to avoid
We aim to review all contributions promptly and provide constructive feedback to help get your changes merged.
## 🛟 Getting help
## Getting help
➡️ [Join our Discord](https://discord.gg/pipecat)

View File

@@ -3,11 +3,10 @@ coverage~=7.6.12
grpcio-tools~=1.67.1
pip-tools~=7.4.1
pre-commit~=4.0.1
pyright~=1.1.397
pyright~=1.1.394
pytest~=8.3.4
pytest-asyncio~=0.25.3
pytest-aiohttp==1.1.0
ruff~=0.11.1
ruff~=0.9.7
setuptools~=70.0.0
setuptools_scm~=8.1.0
python-dotenv~=1.0.1

22
docs/ISSUE_TEMPLATE.md Normal file
View File

@@ -0,0 +1,22 @@
# Description
Is this reporting a bug or feature request?
If reporting a bug, please fill out the following:
### Environment
- pipecat-ai version:
- python version:
- OS:
### Issue description
Provide a clear description of the issue.
### Repro steps
List the steps to reproduce the issue.
### Expected behavior
### Actual behavior
### Logs

View File

@@ -50,13 +50,6 @@ autodoc_mock_imports = [
"pyht.protos",
"pyht.protos.api_pb2",
"pipecat_ai_playht", # PlayHT wrapper
"aiortc",
"aiortc.mediastreams",
"cv2",
"av",
"pyneuphonic",
"mem0",
"mlx_whisper",
"anthropic",
"assemblyai",
"boto3",
@@ -75,6 +68,7 @@ autodoc_mock_imports = [
"openpipe",
"simli",
"soundfile",
# Existing mocks
"pipecat_ai_krisp",
"pyaudio",
"_tkinter",
@@ -85,66 +79,6 @@ autodoc_mock_imports = [
"pydantic.Field",
"pydantic._internal._model_construction",
"pydantic._internal._fields",
# Moondream dependencies
"torch",
"transformers",
"intel_extension_for_pytorch",
# Ultravox dependencies
"huggingface_hub",
"vllm",
"vllm.engine.arg_utils",
"transformers.AutoTokenizer",
# Langchain dependencies
"langchain_core",
"langchain_core.messages",
"langchain_core.runnables",
"langchain_core.messages.AIMessageChunk",
"langchain_core.runnables.Runnable",
# LiveKit dependencies
"livekit",
"livekit.rtc",
"livekit_api",
"livekit_protocol",
"tenacity",
"tenacity.retry",
"tenacity.stop_after_attempt",
"tenacity.wait_exponential",
"rtc",
"rtc.Room",
"rtc.RoomOptions",
"rtc.AudioSource",
"rtc.LocalAudioTrack",
"rtc.TrackPublishOptions",
"rtc.TrackSource",
"rtc.AudioStream",
"rtc.AudioFrameEvent",
"rtc.AudioFrame",
"rtc.Track",
"rtc.TrackKind",
"rtc.RemoteParticipant",
"rtc.RemoteTrackPublication",
"rtc.DataPacket",
# Riva dependencies
"riva",
"riva.client",
"riva.client.Auth",
"riva.client.ASRService",
"riva.client.StreamingRecognitionConfig",
"riva.client.RecognitionConfig",
"riva.client.AudioEncoding",
"riva.client.proto.riva_tts_pb2",
"riva.client.SpeechSynthesisService",
# Local CoreML Smart Turn dependencies
"coremltools",
"coremltools.models",
"coremltools.models.MLModel",
"torch",
"torch.nn",
"torch.nn.functional",
"transformers",
"transformers.AutoFeatureExtractor",
# Also add specific classes that are imported
"AutoFeatureExtractor",
]
# HTML output settings
@@ -176,25 +110,12 @@ def verify_modules():
},
}
# Skip importing modules that are in autodoc_mock_imports
skipped_modules = set(autodoc_mock_imports)
missing = []
for category, modules in required_modules.items():
if isinstance(modules, dict):
# Handle nested structure
for subcategory, submodules in modules.items():
for module in submodules:
# Check if module is in autodoc_mock_imports
if (
f"pipecat.{category}.{subcategory}.{module}" in skipped_modules
or module in skipped_modules
):
logger.info(
f"Skipping import of mocked module: pipecat.{category}.{subcategory}.{module}"
)
continue
try:
__import__(f"pipecat.{category}.{subcategory}.{module}")
logger.info(
@@ -208,11 +129,6 @@ def verify_modules():
else:
# Handle flat structure
for module in modules:
# Check if module is in autodoc_mock_imports
if f"pipecat.{category}.{module}" in skipped_modules or module in skipped_modules:
logger.info(f"Skipping import of mocked module: pipecat.{category}.{module}")
continue
try:
__import__(f"pipecat.{category}.{module}")
logger.info(f"Successfully imported pipecat.{category}.{module}")

View File

@@ -45,10 +45,8 @@ Transport & Serialization
Utilities
~~~~~~~~~
* :mod:`Adapters <pipecat.adapters>`
* :mod:`Clocks <pipecat.clocks>`
* :mod:`Metrics <pipecat.metrics>`
* :mod:`Observers <pipecat.observers>`
* :mod:`Sync <pipecat.sync>`
* :mod:`Transcriptions <pipecat.transcriptions>`
* :mod:`Utils <pipecat.utils>`
@@ -58,12 +56,10 @@ Utilities
:caption: API Reference
:hidden:
Adapters <api/pipecat.adapters>
Audio <api/pipecat.audio>
Clocks <api/pipecat.clocks>
Frames <api/pipecat.frames>
Metrics <api/pipecat.metrics>
Observers <api/pipecat.observers>
Pipeline <api/pipecat.pipeline>
Processors <api/pipecat.processors>
Serializers <api/pipecat.serializers>

View File

@@ -10,44 +10,31 @@ pipecat-ai[anthropic]
pipecat-ai[assemblyai]
pipecat-ai[aws]
pipecat-ai[azure]
pipecat-ai[canonical]
pipecat-ai[cartesia]
pipecat-ai[cerebras]
pipecat-ai[deepseek]
pipecat-ai[daily]
pipecat-ai[deepgram]
pipecat-ai[elevenlabs]
pipecat-ai[fal]
pipecat-ai[fireworks]
pipecat-ai[fish]
pipecat-ai[gladia]
pipecat-ai[google]
pipecat-ai[grok]
pipecat-ai[groq]
# pipecat-ai[krisp] # Mocked
pipecat-ai[koala]
# pipecat-ai[langchain] # Mocked
# pipecat-ai[livekit] # Mocked
# pipecat-ai[krisp] # Mocked instead
pipecat-ai[langchain]
pipecat-ai[livekit]
pipecat-ai[lmnt]
pipecat-ai[local]
# pipecat-ai[local-smart-turn] # Mocked
# pipecat-ai[mem0] # Mocked
# pipecat-ai[mlx-whisper] # Mocked
# pipecat-ai[moondream] # Mocked
pipecat-ai[moondream]
pipecat-ai[nim]
# pipecat-ai[neuphonic] # Mocked
pipecat-ai[noisereduce]
pipecat-ai[openai]
# pipecat-ai[openpipe]
# pipecat-ai[playht] # Mocked due to grpcio conflict with riva
pipecat-ai[qwen]
pipecat-ai[remote-smart-turn]
# pipecat-ai[riva] # Mocked
pipecat-ai[riva]
pipecat-ai[silero]
pipecat-ai[simli]
pipecat-ai[soundfile]
pipecat-ai[tavus]
pipecat-ai[together]
# pipecat-ai[ultravox] # Mocked
# pipecat-ai[webrtc] # Mocked
pipecat-ai[websocket]
pipecat-ai[whisper]

View File

@@ -29,9 +29,6 @@ DAILY_SAMPLE_ROOM_URL=https://...
ELEVENLABS_API_KEY=...
ELEVENLABS_VOICE_ID=...
# Neuphonic
NEUPHONIC_API_KEY=...
# Fal
FAL_KEY=...
@@ -90,14 +87,3 @@ ASSEMBLYAI_API_KEY=...
# OpenRouter
OPENROUTER_API_KEY=...
# Piper
PIPER_BASE_URL=...
# Smart turn
LOCAL_SMART_TURN_MODEL_PATH=
FAL_SMART_TURN_API_KEY=...
# Twilio
TWILIO_ACCOUNT_SID=
TWILIO_AUTH_TOKEN=

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

View File

@@ -18,7 +18,7 @@ from pipecat.frames.frames import AudioRawFrame, EndFrame, OutputAudioRawFrame,
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
@@ -64,7 +64,7 @@ async def main():
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
)
runner = PipelineRunner()

161
examples/canonical-metrics/.gitignore vendored Normal file
View File

@@ -0,0 +1,161 @@
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
recordings/
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
runpod.toml

View File

@@ -1,12 +1,7 @@
FROM python:3.10-bullseye
RUN mkdir /app
RUN mkdir /app/assets
RUN mkdir /app/utils
COPY *.py /app/
COPY requirements.txt /app/
WORKDIR /app
RUN pip3 install -r requirements.txt

View File

@@ -0,0 +1,66 @@
# Chatbot with canonical-metrics
This project implements a chatbot using a pipeline architecture that integrates audio processing, transcription, and a language model for conversational interactions. The chatbot operates within a daily communication environment, utilizing various services for text-to-speech and language model responses.
## Features
- **Audio Input and Output**: Captures microphone input and plays back audio responses.
- **Voice Activity Detection**: Utilizes Silero VAD to manage audio input intelligently.
- **Text-to-Speech**: Integrates ElevenLabs TTS service to convert text responses into audio.
- **Language Model Interaction**: Uses OpenAI's GPT-4 model to generate responses based on user input.
- **Transcription Services**: Captures and transcribes participant speech for analytics.
- **Metrics Collection**: Sends audio data for analysis via Canonical Metrics Service.
## Requirements
- Python 3.10+
- `python-dotenv`
- Additional libraries from the `pipecat` package.
## Setup
1. Clone the repository.
2. Install the required packages.
3. Set up environment variables for API keys:
- `OPENAI_API_KEY`
- `ELEVENLABS_API_KEY`
- `CANONICAL_API_KEY`
- `CANONICAL_API_URL`
4. Run the script.
## Usage
The chatbot introduces itself and engages in conversations, providing brief and creative responses. Designed for flexibility, it can support multiple languages with appropriate configuration.
## Events
- Participants joining or leaving the call are handled dynamically, adjusting the chatbot's behavior accordingly.
The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
## Get started
```python
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cp env.example .env # and add your credentials
```
## Run the server
```bash
python server.py
```
Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
## Build and test the Docker image
```
docker build -t chatbot .
docker run --env-file .env -p 7860:7860 chatbot
```

View File

@@ -0,0 +1,148 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import uuid
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.canonical import CanonicalMetricsService
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Chatbot",
DailyParams(
audio_out_enabled=True,
audio_in_enabled=True,
camera_out_enabled=False,
vad_enabled=True,
vad_audio_passthrough=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
# Spanish
#
# transcription_settings=DailyTranscriptionSettings(
# language="es",
# tier="nova",
# model="2-general"
# )
),
)
tts = ElevenLabsTTSService(
api_key=os.getenv("ELEVENLABS_API_KEY"),
#
# English
#
voice_id="cgSgspJ2msm6clMCkdW9",
#
# Spanish
#
# model="eleven_multilingual_v2",
# voice_id="gD1IexrzCvsXPHUuT0s3",
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
messages = [
{
"role": "system",
#
# English
#
"content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your responses to 12 words or fewer.",
#
# Spanish
#
# "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
"""
CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
call completion, CanonicalMetrics will send the audio buffer to Canonical for
analysis. Visit https://voice.canonical.chat to learn more.
"""
audio_buffer_processor = AudioBufferProcessor(num_channels=2)
canonical = CanonicalMetricsService(
audio_buffer_processor=audio_buffer_processor,
aiohttp_session=session,
api_key=os.getenv("CANONICAL_API_KEY"),
call_id=str(uuid.uuid4()),
assistant="pipecat-chatbot",
assistant_speaks_first=True,
context=context,
)
pipeline = Pipeline(
[
transport.input(), # microphone
context_aggregator.user(),
llm,
tts,
transport.output(),
audio_buffer_processor, # captures audio into a buffer
canonical, # uploads audio buffer to Canonical AI for metrics
context_aggregator.assistant(),
]
)
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await audio_buffer_processor.start_recording()
await transport.capture_participant_transcription(participant["id"])
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.cancel()
@transport.event_handler("on_call_state_updated")
async def on_call_state_updated(transport, state):
if state == "left":
# Here we don't want to cancel, we just want to finish sending
# whatever is queued, so we use an EndFrame().
await task.queue_frame(EndFrame())
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -1,5 +1,6 @@
DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
DAILY_API_KEY=7df...
OPENAI_API_KEY=sk-PL...
DEEPGRAM_API_KEY=efb...
CARTESIA_API_KEY=aeb...
ELEVENLABS_API_KEY=aeb...
CANONICAL_API_KEY=can...
CANONICAL_API_URL=

View File

@@ -0,0 +1,5 @@
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,openai,silero,elevenlabs,canonical]

View File

@@ -23,8 +23,8 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
@@ -32,16 +32,10 @@ load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
# Create the recordings directory if it doesn't exist
os.makedirs("recordings", exist_ok=True)
async def save_audio(audio: bytes, sample_rate: int, num_channels: int, name: str):
async def save_audio(audio: bytes, sample_rate: int, num_channels: int):
if len(audio) > 0:
filename = os.path.join(
"recordings",
f"{name}_conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav",
)
filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
with io.BytesIO() as buffer:
with wave.open(buffer, "wb") as wf:
wf.setsampwidth(2)
@@ -66,7 +60,9 @@ async def main():
DailyParams(
audio_out_enabled=True,
audio_in_enabled=True,
video_out_enabled=False,
camera_out_enabled=False,
vad_enabled=True,
vad_audio_passthrough=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
@@ -93,7 +89,7 @@ async def main():
# voice_id="gD1IexrzCvsXPHUuT0s3",
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
messages = [
{
@@ -114,7 +110,7 @@ async def main():
# NOTE: Watch out! This will save all the conversation in memory. You
# can pass `buffer_size` to get periodic callbacks.
audiobuffer = AudioBufferProcessor(enable_turn_audio=True)
audiobuffer = AudioBufferProcessor()
pipeline = Pipeline(
[
@@ -132,15 +128,7 @@ async def main():
@audiobuffer.event_handler("on_audio_data")
async def on_audio_data(buffer, audio, sample_rate, num_channels):
await save_audio(audio, sample_rate, num_channels, "full")
@audiobuffer.event_handler("on_user_turn_audio_data")
async def on_user_turn_audio_data(buffer, audio, sample_rate, num_channels):
await save_audio(audio, sample_rate, num_channels, "user")
@audiobuffer.event_handler("on_bot_turn_audio_data")
async def on_bot_turn_audio_data(buffer, audio, sample_rate, num_channels):
await save_audio(audio, sample_rate, num_channels, "bot")
await save_audio(audio, sample_rate, num_channels)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):

View File

@@ -53,3 +53,4 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
token = await daily_rest_helper.get_token(url, expiry_time)
return (url, token)
return (url, token)

View File

@@ -1,39 +0,0 @@
# Daily Custom Tracks
This example shows how to send and receive Daily custom tracks. We will run a simple `daily-python` application to send an audio file with a custom track (named "pipecat") to a room. Then, the Pipecat bot will mirror that custom track into another custom track (named "pipecat-mirror") in the same room.
## Get started
```python
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
## Run the bot
Start the bot by giving it a Daily room URL.
```bash
python bot.py -u ROOM_URL
```
The bot will wait for the first participant to join. Then, it will mirror a custom track named "pipecat" into a new custom track named "pipecat-mirror".
## Run the sender
Now, run the custom track sender. This is a simple `daily-python` application that opens and audio file and sends it as a custom track to the same Daily room.
```bash
python custom_track_sender.py -u ROOM_URL -i office-ambience-mono-16000.mp3
```
## Open client
Finally, open the client so you can hear both custom tracks.
```bash
open index.html
```
Once the client is opened, copy the URL of the Daily room and join it. You should be able to select which custom track you want to hear.

View File

@@ -1,87 +0,0 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import sys
import aiohttp
from loguru import logger
from runner import configure
from pipecat.frames.frames import Frame, InputAudioRawFrame, OutputAudioRawFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.transports.services.daily import DailyParams, DailyTransport
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
class CustomTrackMirrorProcessor(FrameProcessor):
def __init__(self, transport_destination: str, **kwargs):
super().__init__(**kwargs)
self._transport_destination = transport_destination
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, InputAudioRawFrame) and frame.transport_source:
output_frame = OutputAudioRawFrame(
audio=frame.audio,
sample_rate=frame.sample_rate,
num_channels=frame.num_channels,
)
output_frame.transport_destination = self._transport_destination
await self.push_frame(output_frame)
else:
await self.push_frame(frame, direction)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, _) = await configure(session)
transport = DailyTransport(
room_url,
None,
"Custom tracks mirror",
DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
microphone_out_enabled=False, # Disable since we just use custom tracks
audio_out_destinations=["pipecat-mirror"],
),
)
pipeline = Pipeline(
[
transport.input(), # Transport user input
CustomTrackMirrorProcessor("pipecat-mirror"),
transport.output(), # Transport bot output
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
audio_in_sample_rate=16000,
audio_out_sample_rate=16000,
),
)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await transport.capture_participant_audio(participant["id"], audio_source="pipecat")
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -1,74 +0,0 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import time
from daily import CallClient, CustomAudioSource, Daily
from pydub import AudioSegment
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
parser.add_argument("-u", "--url", type=str, required=True, help="URL of the Daily room to join")
parser.add_argument(
"-i", "--input", type=str, required=True, help="Input audio file (needs 16000 sample rate)"
)
args, _ = parser.parse_known_args()
audio = AudioSegment.from_mp3(args.input)
raw_bytes = audio.raw_data
sample_rate = audio.frame_rate
channels = audio.channels
print(f"Length: {len(raw_bytes)} bytes")
print(f"Sample rate: {sample_rate}, Channels: {channels}")
# Initialize the Daily context & create call client
Daily.init()
client = CallClient()
# Join the room and indicate we have a custom track named "pipecat".
client.join(
args.url,
client_settings={
"publishing": {
"camera": False,
"microphone": False,
"customAudio": {"pipecat": True},
},
},
)
# Just sleep for a couple of seconds. To do this well we should really use
# completions.
time.sleep(2)
# Create the custom audio source. This is where we will write our audio.
audio_source = CustomAudioSource(sample_rate, channels)
# Create an audio track and assign it our audio source.
client.add_custom_audio_track("pipecat", audio_source)
# Just sleep for a second. To do this well we should really use completions.
time.sleep(1)
try:
# Just write one second of audio until we have read all the file.
chunk_size = sample_rate * channels * 2
while len(raw_bytes) > 0:
chunk = raw_bytes[:chunk_size]
raw_bytes = raw_bytes[chunk_size:]
audio_source.write_frames(chunk)
except KeyboardInterrupt:
client.leave()
# Just sleep for a second. To do this well we should really use completions.
time.sleep(1)
client.release()

View File

@@ -1,173 +0,0 @@
<html>
<head>
<title>daily custom tracks</title>
</head>
<script crossorigin src="https://unpkg.com/@daily-co/daily-js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/fomantic-ui/2.8.6/semantic.min.js"></script>
<link
rel="stylesheet"
type="text/css"
href="https://cdnjs.cloudflare.com/ajax/libs/fomantic-ui/2.8.6/semantic.min.css"
/>
<script>
function enableButton(buttonId, enable) {
const button = document.getElementById(buttonId);
button.disabled = !enable;
}
function enableJoinButton(enable) {
enableButton("join-button", enable);
}
function enableLeaveButton(enable) {
enableButton("leave-button", enable);
}
function destroyPlayers(query) {
const items = document.querySelectorAll(query);
if (items) {
for (const item of items) {
item.remove();
}
}
}
function destroyParticipantPlayers(participantId) {
destroyPlayers(`audio[data-participant-id="${participantId}"]`);
destroyPlayers(`button[data-participant-id="${participantId}"]`);
}
async function startPlayer(player, track) {
player.muted = false;
player.autoplay = true;
if (track != null) {
player.srcObject = new MediaStream([track]);
}
}
async function buildAudioPlayer(track, participantId) {
const audioContainer = document.getElementById("audio-container");
const player = document.createElement("audio");
player.dataset.participantId = participantId;
// Create a new button for controlling audio
const audioControlButton = document.createElement("button");
audioControlButton.className = "ui primary green button"
audioControlButton.innerText = track._mediaTag == "cam-audio" ? "english" : track._mediaTag;
audioControlButton.dataset.participantId = participantId;
audioControlButton.onclick = () => {
if (player.paused) {
player.play();
audioControlButton.className = "ui primary red button"
} else {
player.pause();
audioControlButton.className = "ui primary green button"
}
};
audioContainer.appendChild(player);
audioContainer.appendChild(audioControlButton);
await startPlayer(player, track);
player.pause()
return player;
}
function subscribeToTracks(participantId) {
console.log(`subscribing to track`);
if (participantId === "local") {
return;
}
callObject.updateParticipant(participantId, {
setSubscribedTracks: {
audio: true,
video: false,
custom: true,
},
});
}
function startDaily() {
enableJoinButton(true);
enableLeaveButton(false);
window.callObject = window.DailyIframe.createCallObject({});
callObject.on("participant-joined", (e) => {
if (!e.participant.local) {
console.log("participant-joined", e.participant);
subscribeToTracks(e.participant.session_id);
}
});
callObject.on("participant-left", (e) => {
console.log("participant-left", e.participant.session_id);
destroyParticipantPlayers(e.participant.session_id);
});
callObject.on("track-started", async (e) => {
console.log("track-started", e.track);
if (e.track.kind === "audio") {
await buildAudioPlayer(e.track, e.participant.session_id);
}
});
}
async function joinRoom() {
enableJoinButton(false);
enableLeaveButton(true);
const meetingUrl = document.getElementById("meeting-url").value;
callObject.join({
url: meetingUrl,
startVideoOff: true,
startAudioOff: true,
subscribeToTracksAutomatically: false,
receiveSettings: {
base: { video: { layer: 0 } },
},
});
}
async function leaveRoom() {
enableJoinButton(true);
enableLeaveButton(false);
callObject.leave();
const audioContainer = document.getElementById("audio-container");
audioContainer.replaceChildren();
}
</script>
<body onload="startDaily()">
<div class="ui centered page grid" style="margin-top: 30px">
<div class="ten wide column">
<div class="ui form" style="margin-top: 30px">
<div class="field">
<label>Meeting URL</label>
<input id="meeting-url" value="" />
</div>
</div>
</div>
</div>
<div class="ui centered aligned header" style="margin-top: 30px">
<button id="join-button" class="ui primary button" onclick="joinRoom()">
Join
</button>
<button id="leave-button" class="ui button" onclick="leaveRoom()">
Leave
</button>
</div>
<div id="tile" class="ui container" style="margin-top: 30px">
<div id="tile" class="ui center aligned grid">
<div id="audio-container"></div><br/>
</div>
</div>
</body>
</html>

View File

@@ -1,2 +0,0 @@
pydub
pipecat-ai[daily]

View File

@@ -1,39 +0,0 @@
# Daily Multi Translation
This example shows how to use Daily to stream multiple simultaneous translations using a single transport. Daily provides custom tracks and in this example we will simultaneously translate incoming audio in English to Spanish, French and German, each of them being sent to a custom track.
## Get started
```python
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cp env.example .env # and add your credentials
```
## Run the server
```bash
python server.py
```
Then, visit `http://localhost:7860/` in your browser. This will open a Daily Prebuilt room where you will speak in English (make sure you are not muted).
## Open client
Next, you need to open the client that will listen to the translations.
```bash
open index.html
```
Once the client is opened, copy the URL of the Daily room created above and join it. You should be able to select which translation you want to hear.
## Build and test the Docker image
```
docker build -t daily-multi-translation .
docker run --env-file .env -p 7860:7860 daily-multi-translation
```

View File

@@ -1,165 +0,0 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.audio.mixers.soundfile_mixer import SoundfileMixer
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
BACKGROUND_SOUND_FILE = "office-ambience-mono-16000.mp3"
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Multi translation bot",
DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
audio_out_mixer={
"spanish": SoundfileMixer(
sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
),
"french": SoundfileMixer(
sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
),
"german": SoundfileMixer(
sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
),
},
audio_out_destinations=["spanish", "french", "german"],
microphone_out_enabled=False, # Disable since we just use custom tracks
vad_analyzer=SileroVADAnalyzer(),
),
)
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts_spanish = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="cefcb124-080b-4655-b31f-932f3ee743de",
transport_destination="spanish",
)
tts_french = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="8832a0b5-47b2-4751-bb22-6a8e2149303d",
transport_destination="french",
)
tts_german = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="38aabb6a-f52b-4fb0-a3d1-988518f4dc06",
transport_destination="german",
)
messages_spanish = [
{
"role": "system",
"content": "You will be provided with a sentence in English, and your task is to only translate it into Spanish.",
},
]
messages_french = [
{
"role": "system",
"content": "You will be provided with a sentence in English, and your task is to only translate it into French.",
},
]
messages_german = [
{
"role": "system",
"content": "You will be provided with a sentence in English, and your task is to only translate it into German.",
},
]
llm_spanish = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
llm_french = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
llm_german = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
context_spanish = OpenAILLMContext(messages_spanish)
context_aggregator_spanish = llm_spanish.create_context_aggregator(context_spanish)
context_french = OpenAILLMContext(messages_french)
context_aggregator_french = llm_french.create_context_aggregator(context_french)
context_german = OpenAILLMContext(messages_german)
context_aggregator_german = llm_german.create_context_aggregator(context_german)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt,
ParallelPipeline(
# Spanish pipeline.
[
context_aggregator_spanish.user(),
llm_spanish,
tts_spanish,
context_aggregator_spanish.assistant(),
],
# French pipeline.
[
context_aggregator_french.user(),
llm_french,
tts_french,
context_aggregator_french.assistant(),
],
# German pipeline.
[
context_aggregator_german.user(),
llm_german,
tts_german,
context_aggregator_german.assistant(),
],
),
transport.output(), # Transport bot output
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
audio_in_sample_rate=16000,
audio_out_sample_rate=16000,
allow_interruptions=True,
enable_metrics=True,
enable_usage_metrics=True,
report_only_initial_ttfb=True,
),
observers=[TranscriptionLogObserver()],
)
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -1,202 +0,0 @@
<html>
<head>
<title>daily multi translation</title>
</head>
<script crossorigin src="https://unpkg.com/@daily-co/daily-js"></script>
<script
src="https://code.jquery.com/jquery-3.1.1.min.js"
integrity="sha256-hVVnYaiADRTO2PzUGmuLJr8BLUSjGIZsDYGmIJLv2b8="
crossorigin="anonymous"
></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/fomantic-ui/2.8.6/semantic.min.js"></script>
<link
rel="stylesheet"
type="text/css"
href="https://cdnjs.cloudflare.com/ajax/libs/fomantic-ui/2.8.6/semantic.min.css"
/>
<script>
function enableButton(buttonId, enable) {
const button = document.getElementById(buttonId);
button.disabled = !enable;
}
function enableJoinButton(enable) {
enableButton("join-button", enable);
}
function enableLeaveButton(enable) {
enableButton("leave-button", enable);
}
function destroyPlayers(query) {
const items = document.querySelectorAll(query);
if (items) {
for (const item of items) {
item.remove();
}
}
}
function destroyParticipantPlayers(participantId) {
destroyPlayers(`video[data-participant-id="${participantId}"]`);
destroyPlayers(`audio[data-participant-id="${participantId}"]`);
destroyPlayers(`button[data-participant-id="${participantId}"]`);
}
async function startPlayer(player, track) {
player.muted = false;
player.autoplay = true;
if (track != null) {
player.srcObject = new MediaStream([track]);
}
}
async function buildVideoPlayer(track, participantId) {
const videoContainer = document.getElementById("video-container");
const player = document.createElement("video");
player.dataset.participantId = participantId;
videoContainer.appendChild(player);
await startPlayer(player, track);
await player.play();
return player;
}
async function buildAudioPlayer(track, participantId) {
const audioContainer = document.getElementById("audio-container");
const player = document.createElement("audio");
player.dataset.participantId = participantId;
// Create a new button for controlling audio
const audioControlButton = document.createElement("button");
audioControlButton.className = "ui primary green button"
audioControlButton.innerText = track._mediaTag == "cam-audio" ? "english" : track._mediaTag;
audioControlButton.dataset.participantId = participantId;
audioControlButton.onclick = () => {
if (player.paused) {
player.play();
audioControlButton.className = "ui primary red button"
} else {
player.pause();
audioControlButton.className = "ui primary green button"
}
};
audioContainer.appendChild(player);
audioContainer.appendChild(audioControlButton);
await startPlayer(player, track);
player.pause()
return player;
}
function subscribeToTracks(participantId) {
console.log(`subscribing to track`);
if (participantId === "local") {
return;
}
callObject.updateParticipant(participantId, {
setSubscribedTracks: {
audio: true,
video: true,
custom: true,
},
});
}
function startDaily() {
enableJoinButton(true);
enableLeaveButton(false);
window.callObject = window.DailyIframe.createCallObject({});
callObject.on("participant-joined", (e) => {
if (!e.participant.local) {
console.log("participant-joined", e.participant);
subscribeToTracks(e.participant.session_id);
}
});
callObject.on("participant-left", (e) => {
console.log("participant-left", e.participant.session_id);
destroyParticipantPlayers(e.participant.session_id);
});
callObject.on("track-started", async (e) => {
console.log("track-started", e.track);
if (e.track.kind === "video") {
await buildVideoPlayer(e.track, e.participant.session_id);
} else if (e.track.kind === "audio") {
await buildAudioPlayer(e.track, e.participant.session_id);
}
});
}
async function joinRoom() {
enableJoinButton(false);
enableLeaveButton(true);
const meetingUrl = document.getElementById("meeting-url").value;
callObject.join({
url: meetingUrl,
startVideoOff: true,
startAudioOff: true,
subscribeToTracksAutomatically: false,
receiveSettings: {
base: { video: { layer: 0 } },
},
});
}
async function leaveRoom() {
enableJoinButton(true);
enableLeaveButton(false);
callObject.leave();
const videoContainer = document.getElementById("video-container");
videoContainer.replaceChildren();
const audioContainer = document.getElementById("audio-container");
audioContainer.replaceChildren();
}
</script>
<body onload="startDaily()">
<div class="ui centered page grid" style="margin-top: 30px">
<div class="ten wide column">
<div class="ui form" style="margin-top: 30px">
<div class="field">
<label>Meeting URL</label>
<input id="meeting-url" value="" />
</div>
</div>
</div>
</div>
<div class="ui centered aligned header" style="margin-top: 30px">
<button id="join-button" class="ui primary button" onclick="joinRoom()">
Join
</button>
<button id="leave-button" class="ui button" onclick="leaveRoom()">
Leave
</button>
</div>
<div id="tile" class="ui container" style="margin-top: 30px">
<div id="tile" class="ui center aligned grid">
<div id="audio-container"></div><br/>
</div>
</div>
<div id="tile" class="ui container" style="margin-top: 30px">
<div id="tile" class="ui center aligned grid">
<div id="video-container" class="ui segment"></div>
</div>
</div>
</body>
</html>

View File

@@ -1,5 +0,0 @@
aiofiles
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,deepgram,openai,silero,cartesia]

View File

@@ -1,55 +0,0 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
import aiohttp
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
async def configure(aiohttp_session: aiohttp.ClientSession):
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
parser.add_argument(
"-u", "--url", type=str, required=False, help="URL of the Daily room to join"
)
parser.add_argument(
"-k",
"--apikey",
type=str,
required=False,
help="Daily API Key (needed to create an owner token for the room)",
)
args, unknown = parser.parse_known_args()
url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
key = args.apikey or os.getenv("DAILY_API_KEY")
if not url:
raise Exception(
"No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
)
if not key:
raise Exception(
"No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
)
daily_rest_helper = DailyRESTHelper(
daily_api_key=key,
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
# Create a meeting token for the given room with an expiration 1 hour in
# the future.
expiry_time: float = 60 * 60
token = await daily_rest_helper.get_token(url, expiry_time)
return (url, token)

View File

@@ -1,9 +1,3 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import asyncio
import os
@@ -18,8 +12,8 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
@@ -41,7 +35,8 @@ async def main(room_url: str, token: str):
api_key=daily_api_key,
audio_in_enabled=True,
audio_out_enabled=True,
video_out_enabled=False,
camera_out_enabled=False,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
),
@@ -52,7 +47,7 @@ async def main(room_url: str, token: str):
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
messages = [
{

View File

@@ -1,9 +1,3 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
import aiohttp

View File

@@ -1,9 +1,3 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
@@ -16,8 +10,8 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
@@ -32,18 +26,18 @@ async def main(room_url: str, token: str):
token,
"bot",
DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
transcription_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
messages = [
{

View File

@@ -1,4 +1,5 @@
python-dotenv==1.0.1
modal==0.71.3
pipecat-ai[daily,silero,cartesia,openai]
pipecat-ai[daily,silero,cartesia,openai]==0.0.52
fastapi==0.115.6
aiohttp==3.11.11

View File

@@ -1,178 +0,0 @@
# Handling PSTN/SIP Dial-in on Pipecat Cloud
This repository contains two server implementations for handling
the pinless dial-in workflow in Pipecat Cloud. This is the companion to the
Pipecat Cloud [pstn_sip starter image](https://github.com/daily-co/pipecat-cloud-images/tree/main/pipecat-starters/pstn_sip).
In addition you can use `/api/dial` to trigger dial-out, and
eventually, call-transfers.
1. [FastAPI Server](fastapi-webhook-server/README.md) -
A FastAPI implementation that handles PSTN (Public Switched Telephone
Network) and SIP (Session Initiation Protocol) calls using the Daily API.
2. [Next.js Serverless](nextjs-webhook-server/README.md) -
A Next.js API implementation designed for deployment on Vercel's
serverless platform.
Both implementations provide:
- HMAC signature validation for pinless webhook
- Structured logging
- Support for dial-in and dial-out settings
- Voicemail detection and call transfer functionality (coming soon)
- Test request handling
## Choosing an Implementation
- Use the **FastAPI Server** if you:
- Need a standalone server
- Prefer Python and FastAPI
- Want to deploy to traditional hosting platforms
- Use the **Next.js Serverless** implementation if you:
- Want serverless deployment
- Prefer JavaScript/TypeScript
- Already use Next.js and Vercel for other projects
- Need quick scaling and zero maintenance
## Prerequisites
### Environment Variables
Both implementations require similar environment variables:
- `PIPECAT_CLOUD_API_KEY`: Pipecat Cloud API Key, begins with pk\_\*
- `AGENT_NAME`: Your Daily agent name
- `PINLESS_HMAC_SECRET`: Your HMAC secret for request verification
- `LOG_LEVEL`: (Optional) Logging level (defaults to 'info')
See the individual README files in each implementation directory for
specific setup instructions.
### Phone number setup
You can buy a phone number through the Pipecat Cloud Dashboard:
1. Go to `Settings` > `Telephony`
2. Follow the UI to purchase a phone number
3. Configure the webhook URL to receive incoming calls (e.g. `https://my-webhook-url.com/api/dial`)
Or purchase the number using Daily's
[PhoneNumbers API](https://docs.daily.co/reference/rest-api/phone-numbers).
```bash
curl --request POST \
--url https://api.daily.co/v1/domain-dialin-config \
--header 'Authorization: Bearer $TOKEN' \
--header 'Content-Type: application/json' \
--data-raw '{
"type": "pinless_dialin",
"name_prefix": "Customer1",
"phone_number": "+1PURCHASED_NUM",
"room_creation_api": "https://example.com/api/dial",
"hold_music_url": "https://example.com/static/ringtone.mp3",
"timeout_config": {
"message": "No agent is available right now"
}
}'
```
The API will return a static SIP URI (`sip_uri`) that can be called
from other SIP services.
### `room_creation_api`
To make and receive calls currently you have to host a server that
handles incoming calls. In the coming weeks, incoming calls will be
directly handled within Daily and we will expose an endpoint similar
to `{service}/start` that will manage this for you.
In the meantime, the server described below serves as the webhook
handler for the `room_creation_api`. Configure your pinless phone
number or SIP interconnect to the `ngrok` tunnel or
the actual server URL, append `/api/dial` to the webhook URL.
## Example curl commands
Note: Replace `http://localhost:3000` with your actual server URL and
phone numbers with valid values for your use case.
### Dialin Request
The server will receive a request when a call is received from Daily.
### Dialout Request
Dial a number, will use any purchased number
```bash
curl -X POST http://localhost:3000/api/dial \
-H "Content-Type: application/json" \
-d '{
"dialout_settings": [
{
"phoneNumber": "+1234567890",
}
]
}'
```
Dial a number with callerId, which is the UUID of a purchased number.
```bash
curl -X POST http://localhost:3000/api/dial \
-H "Content-Type: application/json" \
-d '{
"dialout_settings": [
{
"phoneNumber": "+1234567890",
"callerId": "purchased_phone_uuid"
}
]
}'
```
Dial a number
```bash
curl -X POST http://localhost:3000/api/dial \
-H "Content-Type: application/json" \
-d '{
"dialout_settings": [
{
"phoneNumber": "+1234567890",
"callerId": "purchased_phone_uuid"
}
]
}'
```
### Advanced Request with Voicemail Detection
```bash
curl -X POST http://localhost:3000/api/dial \
-H "Content-Type: application/json" \
-d '{
"To": "+1234567890",
"From": "+1987654321",
"callId": "call-uuid-123",
"callDomain": "domain-uuid-456",
"dialout_settings": [
{
"phoneNumber": "+1234567890",
"callerId": "purchased_phone_uuid"
}
],
"voicemail_detection": {
"testInPrebuilt": true
},
"call_transfer": {
"mode": "dialout",
"speakSummary": true,
"storeSummary": true,
"operatorNumber": "+1234567890",
"testInPrebuilt": true
}
}'
```

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@@ -1,98 +0,0 @@
# FastAPI server for handling Daily PSTN/SIP Webhook
A FastAPI server that handles PSTN (Public Switched Telephone Network) and SIP (Session Initiation Protocol) calls using the Daily API.
## Setup
1. Clone the repository
2. Navigate to the `fastapi-webhook-server` directory:
```bash
cd fastapi-webhook-server
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Copy `env.example` to `.env`:
```bash
cp env.example .env
```
5. Update `.env` with your credentials:
- `AGENT_NAME`: Your Daily agent name
- `PIPECAT_CLOUD_API_KEY`: Your Daily API key
- `PINLESS_HMAC_SECRET`: Your HMAC secret for request verification
## Running the Server
Start the server:
```bash
python server.py
```
The server will run on `http://localhost:7860` and you can expose it via ngrok for testing:
```bash
`ngrok http 7860`
```
> Tip: Use a subdomain for a consistent URL (e.g. `ngrok http -subdomain=mydomain http://localhost:7860`)
## API Endpoints
### GET /
Health check endpoint that returns a "Hello, World!" message.
### POST /api/dial
Initiates a PSTN/SIP call with the following request body format:
```json
{
"To": "+14152251493",
"From": "+14158483432",
"callId": "string-contains-uuid",
"callDomain": "string-contains-uuid",
"dialout_settings": [
{
"phoneNumber": "+14158483432",
"callerId": "+14152251493"
}
],
"voicemail_detection": {
"testInPrebuilt": true
},
"call_transfer": {
"mode": "dialout",
"speakSummary": true,
"storeSummary": true,
"operatorNumber": "+14152250006",
"testInPrebuilt": true
}
}
```
#### Response
Returns a JSON object containing:
- `status`: Success/failure status
- `data`: Response from Daily API
- `room_properties`: Properties of the created Daily room
## Error Handling
- 401: Invalid signature
- 400: Invalid authorization header (e.g. missing Daily API key in bot.py)
- 405: Method not allowed (e.g. incorrect route on the webhook URL)
- 500: Server errors (missing API key, network issues)
- Other status codes are passed through from the Daily API

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@@ -1,3 +0,0 @@
AGENT_NAME="your-agent-name"
PIPECAT_CLOUD_API_KEY="your-daily-api-key"
PINLESS_HMAC_SECRET="hmac-secret-pinless-dialin"

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@@ -1,6 +0,0 @@
fastapi
uvicorn
python-dotenv
requests
pydantic
loguru

View File

@@ -1,202 +0,0 @@
#
# Copyright (c) 2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
# server.py
import base64 # for calculating hmac signature
import hmac
import os # for accessing environment variables
import time # for setting expiration time
from typing import Any, Dict, List, Optional
import requests
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from loguru import logger
from pydantic import BaseModel, Field
load_dotenv(override=True)
app = FastAPI()
class RoomRequest(BaseModel):
test: Optional[str] = Field(None, alias="Test", description="Test field")
To: Optional[str] = Field(None, alias="to", description="Destination phone number")
From: Optional[str] = Field(None, alias="from", description="Source phone number")
callId: Optional[str] = Field(None, alias="call_id", description="Unique call identifier")
callDomain: Optional[str] = Field(
None, alias="call_domain", description="Call domain identifier"
)
dialout_settings: Optional[List[Dict[str, Any]]] = Field(
None, description="An array of phone numbers or SIP URIs to dialout to"
)
voicemail_detection: Optional[Dict[str, Any]] = Field(
None, description="A flag to perform voicemail or answeing-machine detection"
)
call_transfer: Optional[Dict[str, Any]] = Field(None, description="to initiate a call transfer")
class Config:
populate_by_name = True
alias_generator = None
"""
body can contain any fields, but for handling PSTN/SIP,
we recommend sending the following custom values:
dialin, dialout, voicemail detection, and call transfer
"To": "+14152251493",
"From": "+14158483432",
"callId": "string-contains-uuid",
"callDomain": "string-contains-uuid"
These need to be remapped to dialin_settings
"dialout_settings": [
{"phoneNumber": "+14158483432", "callerId": "+14152251493"},
{"sipUri": "sip:username@sip.hostname"}
],
},
voicemail_detection:{
testInPrebuilt: true
},
"call_transfer": {
"mode": "dialout",
"speakSummary": true,
"storeSummary": true,
"operatorNumber": "+14152250006",
"testInPrebuilt": true
}
"""
@app.get("/")
async def read_root():
return {"message": "Hello, World!"}
@app.post("/api/dial")
async def dial(request: RoomRequest, raw_request: Request):
logger.info("Incoming request to /dial:")
logger.info(f"Headers: {dict(raw_request.headers)}")
raw_body = await raw_request.body()
raw_body_str = raw_body.decode()
logger.info(f"Raw body: {raw_body_str}")
logger.info(f"Parsed body: {request.dict()}")
# calculate signature and compare/verify
hmac_secret = os.getenv("PINLESS_HMAC_SECRET")
timestamp = raw_request.headers.get("x-pinless-timestamp")
signature = raw_request.headers.get("x-pinless-signature")
if not hmac_secret:
logger.debug("Skipping HMAC validation - PINLESS_HMAC_SECRET not set")
elif timestamp and signature:
message = timestamp + "." + raw_body_str
base64_decoded_secret = base64.b64decode(hmac_secret)
computed_signature = base64.b64encode(
hmac.new(base64_decoded_secret, message.encode(), "sha256").digest()
).decode()
if computed_signature != signature:
logger.error(f"Invalid signature. Expected {signature}, got {computed_signature}")
raise HTTPException(status_code=401, detail="Invalid signature")
else:
logger.debug("Skipping HMAC validation - no signature headers present")
if request.test == "test":
logger.debug("Test request received")
return {"status": "success", "message": "Test request received"}
dialin_settings = None
# these fields are camelCase in the request
required_fields = ["To", "From", "callId", "callDomain"]
if all(
field in request.dict() and request.dict()[field] is not None for field in required_fields
):
# transform from camelCase to snake_case because daily-python expects snake_case
dialin_settings = {
"From": request.From,
"To": request.To,
"call_id": request.callId,
"call_domain": request.callDomain,
# transform from camelCase to snake_case
}
logger.debug(f"Populated dialin_settings from request: {dialin_settings}")
daily_room_properties = {
"enable_dialout": request.dialout_settings is not None,
}
if dialin_settings is not None:
sip_config = {
"display_name": request.From,
"sip_mode": "dial-in",
"num_endpoints": 2 if request.call_transfer is not None else 1,
"codecs": {"audio": ["OPUS"]},
}
daily_room_properties["sip"] = sip_config
# Setting default expiry to 5 minutes from now
daily_room_properties["exp"] = int(time.time()) + (5 * 60)
logger.debug(f"Daily room properties: {daily_room_properties}")
payload = {
"createDailyRoom": True,
"dailyRoomProperties": daily_room_properties,
"body": {
"dialin_settings": dialin_settings,
"dialout_settings": request.dialout_settings,
"voicemail_detection": request.voicemail_detection,
"call_transfer": request.call_transfer,
},
}
pcc_api_key = os.getenv("PIPECAT_CLOUD_API_KEY")
agent_name = os.getenv("AGENT_NAME", "my-first-agent")
if not pcc_api_key:
raise HTTPException(status_code=500, detail="DAILY_API_KEY environment variable is not set")
headers = {"Authorization": f"Bearer {pcc_api_key}", "Content-Type": "application/json"}
url = f"https://api.pipecat.daily.co/v1/public/{agent_name}/start"
logger.debug(f"Making API call to Daily: {url} {headers} {payload}")
try:
response = requests.post(url, json=payload, headers=headers)
response.raise_for_status()
response_data = response.json()
logger.debug(f"Response: {response_data}")
return {
"status": "success",
"data": response_data,
"room_properties": daily_room_properties,
}
except requests.exceptions.HTTPError as e:
# Pass through the status code and error details from the Daily API
status_code = e.response.status_code
error_detail = e.response.json() if e.response.content else str(e)
logger.error(f"HTTP error: {error_detail}")
raise HTTPException(status_code=status_code, detail=error_detail)
except requests.exceptions.RequestException as e:
logger.error(f"Request error: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
try:
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)
except KeyboardInterrupt:
logger.info("Server stopped manually")

View File

@@ -1,53 +0,0 @@
# dependencies
/node_modules
/.pnp
.pnp.js
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
.pnpm-debug.log*
# local env files
.env*.local
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts
# IDE specific files
.idea/
.vscode/
*.swp
*.swo
# Logs
logs
*.log
# OS generated files
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db

View File

@@ -1,115 +0,0 @@
# Next.js server for handling Daily PSTN/SIP Webhook
Next.js API routes for handling Daily PSTN/SIP Pipecat requests.
## Features
- API endpoint for handling Daily PSTN/SIP Pipecat requests
- HMAC signature validation
- Structured logging with Pino
- Support for dial-in and dial-out settings
- Voicemail detection and call transfer functionality
- Test request handling
## Setup
1. Clone the repository
2. Navigate to the `nextjs-webhook-server` directory:
```bash
cd nextjs-webhook-server
```
3. Install dependencies:
```bash
npm install
```
4. Create `.env.local` file with your credentials:
```bash
cp env.local.example .env.local
```
5. Update your `.env` with your secrets:
```bash
PIPECAT_CLOUD_API_KEY=pk_*
AGENT_NAME=my-first-agent
PINLESS_HMAC_SECRET=your_hmac_secret
LOG_LEVEL=info
```
### Running the server
Run the development server:
```bash
npm run dev
```
The server will run on `http://localhost:7860` and you can expose it via ngrok for testing:
```bash
`ngrok http 7860`
```
> Tip: Use a subdomain for a consistent URL (e.g. `ngrok http -subdomain=mydomain http://localhost:7860`)
## API Endpoints
### GET /api
Returns a simple "Hello, World!" message with a cute cat emoji to verify the server is running.
### POST /api/dial
Handles dial-in and dial-out requests for Pipecat Cloud.
#### Test Requests
The endpoint handles test requests when a webhook is configured. Send a request with `"Test": "test"` to verify your setup:
```json
{
"Test": "test"
}
```
#### Production Request Format
```json
{
// for dial-in from webhook
"To": "+14152251493",
"From": "+14158483432",
"callId": "string-contains-uuid",
"callDomain": "string-contains-uuid",
// for making a dial out to a phone or SIP
"dialout_settings": [
{ "phoneNumber": "+14158483432", "callerId": "purchased_phone_uuid" },
{ "sipUri": "sip:username@sip.hostname.com" }
]
}
```
## Deployment
The application is configured for Vercel deployment:
1. Push your code to a Git repository
2. Import your project in Vercel dashboard
3. Configure environment variables:
- `PIPECAT_CLOUD_API_KEY`
- `AGENT_NAME`
- `PINLESS_HMAC_SECRET`
- `LOG_LEVEL` (optional, defaults to 'info')
4. Deploy!
## Security
- HMAC signature validation for request authentication
- Environment variables for sensitive credentials
- Method validation (POST only for /dial)

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@@ -1,4 +0,0 @@
AGENT_NAME=my-first-agent
PIPECAT_CLOUD_API_KEY=your_daily_api_key
PINLESS_HMAC_SECRET=your_hmac_secret
LOG_LEVEL="info"

View File

@@ -1,22 +0,0 @@
{
"name": "my-daily-app",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev -p 7860",
"build": "next build",
"start": "next start -p 7860",
"lint": "next lint"
},
"dependencies": {
"axios": "^1.6.0",
"next": "^14.0.0",
"pino": "^8.15.0",
"react": "^18.2.0",
"react-dom": "^18.2.0"
},
"devDependencies": {
"eslint": "^8.46.0",
"eslint-config-next": "^14.0.0"
}
}

View File

@@ -1,176 +0,0 @@
import { logger } from '../../lib/utils';
import axios from 'axios';
import crypto from 'crypto';
const validateSignature = (body, signature, timestamp, secret) => {
// Skip if any required fields are missing
if (!signature || !timestamp || !secret) {
logger.warn('Missing required fields for HMAC validation');
return true;
}
try {
const decodedSecret = Buffer.from(secret, 'base64');
const hmac = crypto.createHmac('sha256', decodedSecret);
const signatureData = `${timestamp}.${body}`;
const computedSignature = hmac.update(signatureData).digest('base64');
logger.debug('Signature validation:', {
timestamp,
signatureData: signatureData.substring(0, 50) + '...',
computedSignature,
receivedSignature: signature
});
return computedSignature === signature;
} catch (error) {
logger.error('Error validating signature:', error);
return true; // Allow request to proceed on error
}
};
export default async function handler(req, res) {
// Only allow POST requests
if (req.method !== 'POST') {
return res.status(405).json({ error: 'Method not allowed' });
}
try {
logger.info('Incoming request to /api/dial:');
logger.info(`Headers: ${JSON.stringify(req.headers)}`);
const rawBody = JSON.stringify(req.body);
logger.info(`Raw body: ${rawBody}`);
const signature = req.headers['x-pinless-signature'];
const timestamp = req.headers['x-pinless-timestamp'];
if (signature && timestamp) {
logger.info('Validating HMAC signature');
if (!validateSignature(rawBody, signature, timestamp, process.env.PINLESS_HMAC_SECRET)) {
logger.error('Invalid HMAC signature', { signature, timestamp });
return res.status(401).json({
error: 'Invalid signature',
message: 'Invalid HMAC signature'
});
}
} else {
logger.info('Skipping HMAC validation - no signature headers present');
}
// Extract request data
const {
Test: test,
To,
From,
callId,
callDomain,
dialout_settings,
voicemail_detection,
call_transfer
} = req.body;
// Handle test requests when a webhook is configured
if (test === 'test') {
logger.debug('Test request received');
return res.status(200).json({ status: 'success', message: 'Test request received' });
}
// Process dialin settings
let dialin_settings = null;
const requiredFields = ['To', 'From', 'callId', 'callDomain'];
if (requiredFields.every(field => req.body[field] !== undefined && req.body[field] !== null)) {
dialin_settings = {
// snake_case because pipecat expects this format
From,
To,
call_id: callId,
call_domain: callDomain,
};
logger.debug(`Populated dialin_settings from request: ${JSON.stringify(dialin_settings)}`);
}
// Set up Daily room properties
const daily_room_properties = {
enable_dialout: dialout_settings !== undefined && dialout_settings !== null,
exp: Math.floor(Date.now() / 1000) + (5 * 60), // 5 minutes from now
};
// Configure SIP if dialin settings are provided
if (dialin_settings !== null) {
const sip_config = {
display_name: From,
sip_mode: 'dial-in',
num_endpoints: call_transfer !== null ? 2 : 1,
codecs: {"audio": ["OPUS"]},
};
daily_room_properties.sip = sip_config;
}
// Prepare payload for {service}/start API call
const payload = {
createDailyRoom: true,
dailyRoomProperties: daily_room_properties,
body: {
dialin_settings,
dialout_settings,
voicemail_detection,
call_transfer,
},
};
logger.debug(`Daily room properties: ${JSON.stringify(daily_room_properties)}`);
// Get Daily API key and agent name from environment variables
const pccApiKey = process.env.PIPECAT_CLOUD_API_KEY;
const agentName = process.env.AGENT_NAME || 'my-first-agent';
if (!pccApiKey) {
throw new Error('PIPECAT_CLOUD_API_KEY environment variable is not set');
}
// Set up headers for Daily API call
const headers = {
'Authorization': `Bearer ${pccApiKey}`,
'Content-Type': 'application/json',
};
const url = `https://api.pipecat.daily.co/v1/public/${agentName}/start`;
logger.debug(`Making API call to Daily: ${url} ${JSON.stringify(headers)} ${JSON.stringify(payload)}`);
try {
const response = await axios.post(url, payload, { headers });
logger.debug(`Response: ${JSON.stringify(response.data)}`);
return res.status(200).json({
status: 'success',
data: response.data,
room_properties: daily_room_properties,
});
} catch (error) {
if (error.response) {
// Pass through status code and error details from the Daily API
const statusCode = error.response.status;
const errorDetail = error.response.data || error.message;
logger.error(`HTTP error: ${JSON.stringify(errorDetail)}`);
return res.status(statusCode).json(errorDetail);
} else {
logger.error(`Request error: ${error.message}`);
return res.status(500).json({ error: error.message });
}
}
} catch (error) {
logger.error(`Unexpected error: ${error.message}`);
return res.status(500).json({ error: 'Internal server error', message: error.message });
}
}
// Configure body parser to preserve raw body text
export const config = {
api: {
bodyParser: {
sizeLimit: '1mb',
},
},
};

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@@ -1,6 +0,0 @@
import { logger } from '../../lib/utils';
export default function handler(req, res) {
logger.info('Received request to /api');
res.status(200).json({ message: 'Hello, World! from ᓚᘏᗢ' });
}

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@@ -1,6 +0,0 @@
module.exports = {
version: 2,
buildCommand: "next build",
outputDirectory: ".next",
cleanUrls: true
};

View File

@@ -1,94 +0,0 @@
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
dist/
*.egg-info/
*.egg
.installed.cfg
.eggs/
downloads/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
MANIFEST
# Virtual Environments
venv/
env/
.env
.venv/
ENV/
env.bak/
venv.bak/
# IDE
.idea/
.vscode/
.spyderproject
.spyproject
.ropeproject
# Testing and Coverage
.coverage
.coverage.*
htmlcov/
.pytest_cache/
.tox/
.nox/
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
cover/
# Logs and Databases
*.log
*.db
db.sqlite3
db.sqlite3-journal
pip-log.txt
# System Files
.DS_Store
Thumbs.db
desktop.ini
*.swp
*.swo
*.bak
*.tmp
*~
# Build and Documentation
docs/_build/
.pybuilder/
target/
instance/
.webassets-cache
.pdm.toml
.pdm-python
.pdm-build/
__pypackages__/
# Other
*.mo
*.pot
*.sage.py
.mypy_cache/
.dmypy.json
dmypy.json
.pyre/
.pytype/
cython_debug/
.ipynb_checkpoints
# Pipecat cloud
.pcc-deploy.toml

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@@ -1,7 +0,0 @@
FROM dailyco/pipecat-base:latest
COPY ./requirements.txt requirements.txt
RUN pip install --no-cache-dir --upgrade -r requirements.txt
COPY ./bot.py bot.py

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@@ -1,196 +0,0 @@
# Pipecat Cloud Starter Project
[![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.daily.co) [![Discord](https://img.shields.io/discord/1217145424381743145)](https://discord.gg/dailyco)
A template voice agent for [Pipecat Cloud](https://www.daily.co/products/pipecat-cloud/) that demonstrates building and deploying a conversational AI agent.
> **For a detailed step-by-step guide, see our [Quickstart Documentation](https://docs.pipecat.daily.co/quickstart).**
## Prerequisites
- Python 3.10+
- Linux, MacOS, or Windows Subsystem for Linux (WSL)
- [Docker](https://www.docker.com) and a Docker repository (e.g., [Docker Hub](https://hub.docker.com))
- A Docker Hub account (or other container registry account)
- [Pipecat Cloud](https://pipecat.daily.co) account
> **Note**: If you haven't installed Docker yet, follow the official installation guides for your platform ([Linux](https://docs.docker.com/engine/install/), [Mac](https://docs.docker.com/desktop/setup/install/mac-install/), [Windows](https://docs.docker.com/desktop/setup/install/windows-install/)). For Docker Hub, [create a free account](https://hub.docker.com/signup) and log in via terminal with `docker login`.
## Get Started
### 1. Get the starter project
Clone the starter project from GitHub:
```bash
git clone https://github.com/daily-co/pipecat-cloud-starter
cd pipecat-cloud-starter
```
### 2. Set up your Python environment
We recommend using a virtual environment to manage your Python dependencies.
```bash
# Create a virtual environment
python -m venv .venv
# Activate it
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install the Pipecat Cloud CLI
pip install pipecatcloud
```
### 3. Authenticate with Pipecat Cloud
```bash
pcc auth login
```
### 4. Acquire required API keys
This starter requires the following API keys:
- **OpenAI API Key**: Get from [platform.openai.com/api-keys](https://platform.openai.com/api-keys)
- **Cartesia API Key**: Get from [play.cartesia.ai/keys](https://play.cartesia.ai/keys)
- **Daily API Key**: Automatically provided through your Pipecat Cloud account
### 5. Configure to run locally (optional)
You can test your agent locally before deploying to Pipecat Cloud:
```bash
# Set environment variables with your API keys
export CARTESIA_API_KEY="your_cartesia_key"
export DAILY_API_KEY="your_daily_key"
export OPENAI_API_KEY="your_openai_key"
```
> Your `DAILY_API_KEY` can be found at [https://pipecat.daily.co](https://pipecat.daily.co) under the `Settings` in the `Daily (WebRTC)` tab.
First install requirements:
```bash
pip install -r requirements.txt
```
Then, launch the bot.py script locally:
```bash
LOCAL_RUN=1 python bot.py
```
## Deploy & Run
### 1. Build and push your Docker image
```bash
# Build the image (targeting ARM architecture for cloud deployment)
docker build --platform=linux/arm64 -t my-first-agent:latest .
# Tag with your Docker username and version
docker tag my-first-agent:latest your-username/my-first-agent:0.1
# Push to Docker Hub
docker push your-username/my-first-agent:0.1
```
### 2. Create a secret set for your API keys
The starter project requires API keys for OpenAI and Cartesia:
```bash
# Copy the example env file
cp env.example .env
# Edit .env to add your API keys:
# CARTESIA_API_KEY=your_cartesia_key
# OPENAI_API_KEY=your_openai_key
# Create a secret set from your .env file
pcc secrets set my-first-agent-secrets --file .env
```
Alternatively, you can create secrets directly via CLI:
```bash
pcc secrets set my-first-agent-secrets \
CARTESIA_API_KEY=your_cartesia_key \
OPENAI_API_KEY=your_openai_key
```
### 3. Deploy to Pipecat Cloud
```bash
pcc deploy my-first-agent your-username/my-first-agent:0.1 --secrets my-first-agent-secrets
```
> **Note (Optional)**: For a more maintainable approach, you can use the included `pcc-deploy.toml` file:
>
> ```toml
> agent_name = "my-first-agent"
> image = "your-username/my-first-agent:0.1"
> secret_set = "my-first-agent-secrets"
>
> [scaling]
> min_instances = 0
> ```
>
> Then simply run `pcc deploy` without additional arguments.
> **Note**: If your repository is private, you'll need to add credentials:
>
> ```bash
> # Create pull secret (youll be prompted for credentials)
> pcc secrets image-pull-secret pull-secret https://index.docker.io/v1/
>
> # Deploy with credentials
> pcc deploy my-first-agent your-username/my-first-agent:0.1 --credentials pull-secret
> ```
### 4. Check deployment and scaling (optional)
By default, your agent will use "scale-to-zero" configuration, which means it may have a cold start of around 10 seconds when first used. By default, idle instances are maintained for 5 minutes before being terminated when using scale-to-zero.
For more responsive testing, you can scale your deployment to keep a minimum of one instance warm:
```bash
# Ensure at least one warm instance is always available
pcc deploy my-first-agent your-username/my-first-agent:0.1 --min-instances 1
# Check the status of your deployment
pcc agent status my-first-agent
```
By default, idle instances are maintained for 5 minutes before being terminated when using scale-to-zero.
### 5. Create an API key
```bash
# Create a public API key for accessing your agent
pcc organizations keys create
# Set it as the default key to use with your agent
pcc organizations keys use
```
### 6. Start your agent
```bash
# Start a session with your agent in a Daily room
pcc agent start my-first-agent --use-daily
```
This will return a URL, which you can use to connect to your running agent.
## Documentation
For more details on Pipecat Cloud and its capabilities:
- [Pipecat Cloud Documentation](https://docs.pipecat.daily.co)
- [Pipecat Project Documentation](https://docs.pipecat.ai)
## Support
Join our [Discord community](https://discord.gg/dailyco) for help and discussions.

View File

@@ -1,165 +0,0 @@
#
# Copyright (c) 2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from pipecatcloud.agent import DailySessionArguments
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
# Load environment variables
load_dotenv(override=True)
# Check if we're in local development mode
LOCAL_RUN = os.getenv("LOCAL_RUN")
async def main(transport: DailyTransport):
"""Main pipeline setup and execution function.
Args:
transport: The DailyTransport object for the bot
"""
logger.debug("Starting bot")
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
allow_interruptions=True,
enable_metrics=True,
enable_usage_metrics=True,
report_only_initial_ttfb=True,
),
)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
logger.info("First participant joined: {}", participant["id"])
await transport.capture_participant_transcription(participant["id"])
# Kick off the conversation.
messages.append(
{
"role": "system",
"content": "Please start with 'Hello World' and introduce yourself to the user.",
}
)
await task.queue_frames([LLMMessagesFrame(messages)])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
logger.info("Participant left: {}", participant)
await task.cancel()
runner = PipelineRunner()
await runner.run(task)
async def bot(args: DailySessionArguments):
"""Main bot entry point compatible with the FastAPI route handler.
Args:
room_url: The Daily room URL
token: The Daily room token
body: The configuration object from the request body
session_id: The session ID for logging
"""
from pipecat.audio.filters.krisp_filter import KrispFilter
logger.info(f"Bot process initialized {args.room_url} {args.token}")
transport = DailyTransport(
args.room_url,
args.token,
"Pipecat Bot",
DailyParams(
audio_in_enabled=True,
audio_in_filter=None if LOCAL_RUN else KrispFilter(),
audio_out_enabled=True,
transcription_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
try:
await main(transport)
logger.info("Bot process completed")
except Exception as e:
logger.exception(f"Error in bot process: {str(e)}")
raise
# Local development functions
async def local_daily():
"""Function for local development testing."""
from local_runner import configure
try:
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Pipecat Bot",
DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
transcription_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
await main(transport)
except Exception as e:
logger.exception(f"Error in local development mode: {e}")
# Local development entry point
if LOCAL_RUN and __name__ == "__main__":
try:
asyncio.run(local_daily())
except Exception as e:
logger.exception(f"Failed to run in local mode: {e}")

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@@ -1,4 +0,0 @@
CARTESIA_API_KEY=
OPENAI_API_KEY=
# Local dev only
DAILY_API_KEY=

View File

@@ -1,47 +0,0 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
import aiohttp
from fastapi import HTTPException
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
async def configure(aiohttp_session: aiohttp.ClientSession):
(url, token) = await configure_with_args(aiohttp_session)
return (url, token)
async def configure_with_args(aiohttp_session: aiohttp.ClientSession = None):
key = os.getenv("DAILY_API_KEY")
if not key:
raise Exception(
"No Daily API key specified. set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
)
daily_rest_helper = DailyRESTHelper(
daily_api_key=key,
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
room = await daily_rest_helper.create_room(
DailyRoomParams(properties={"enable_prejoin_ui": False})
)
if not room.url:
raise HTTPException(status_code=500, detail="Failed to create room")
url = room.url
# Create a meeting token for the given room with an expiration 1 hour in
# the future.
expiry_time: float = 60 * 60
token = await daily_rest_helper.get_token(url, expiry_time)
return (url, token)

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@@ -1,8 +0,0 @@
agent_name = "my-first-agent"
image = "your-username/my-first-agent:0.1"
image_credentials = "your-dockerhub-creds"
secret_set = "my-first-agent-secrets"
enable_krisp = true
[scaling]
min_instances = 0

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@@ -1,3 +0,0 @@
pipecatcloud
pipecat-ai[cartesia,daily,openai,silero]>=0.0.58
python-dotenv~=1.0.1

View File

@@ -1,51 +0,0 @@
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
dist/
*.egg-info/
.installed.cfg
*.egg
.pytest_cache/
.coverage
.coverage.*
.env
.venv
env/
venv/
ENV/
.mypy_cache/
.dmypy.json
dmypy.json
# JavaScript/Node.js
node_modules/
dist/
dist-ssr/
*.local
.env.local
.env.development.local
.env.test.local
.env.production.local
# Logs
logs/
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
# Editor/IDE
.vscode/*
!.vscode/extensions.json
.idea/
*.swp
*.swo
.DS_Store
# Project specific
runpod.toml

View File

@@ -1,152 +0,0 @@
# Smart Turn Detection Demo
This demo showcases Pipecat's Smart Turn Detection feature - an advanced conversational turn detection system that uses machine learning to identify when a speaker has finished their turn in a conversation. Unlike basic Voice Activity Detection (VAD) which only detects speech vs. silence, Smart Turn detects natural conversational cues like intonation patterns, pacing, and linguistic signals.
This demo uses the [pipecat-ai/smart-turn](https://huggingface.co/pipecat-ai/smart-turn) model - an open-source, community-driven conversational turn detection model designed to provide more natural turn-taking in voice interactions. The model is being hosted on Fal's infrastructure for GPU acceleration, offering inference times between 50-60ms.
In the client UI, you can see the transcription messages along with the smart-turn model's prediction results in real-time.
## Try the demo
Try the hosted version of the demo here: https://pcc-smart-turn.vercel.app/.
## Run the demo locally
### Run the Server
1. Set up and activate your virtual environment:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Create your .env file and set your env vars:
```bash
cp env.example .env
```
Keys to provide:
- GOOGLE_API_KEY
- CARTESIA_API_KEY
- DEEPGRAM_API_KEY
- DAILY_API_KEY
- FAL_SMART_TURN_API_KEY
4. Run the server:
```bash
LOCAL=1 python server.py
```
### Run the client
1. Open a new terminal and navigate to the client directory:
```bash
cd client
```
2. Install dependencies:
```bash
npm install
```
3. Create your .env.local file:
```bash
cp env.local.example .env.local
```
> Note: No keys need to be modified. `NEXT_PUBLIC_API_BASE_URL` is already configured for local use.
4. Start the development server:
```bash
npm run dev
```
5. Open [http://localhost:3000](http://localhost:3000) in your browser.
## Deploy the app
### Deploy the server to Pipecat Cloud
1. Navigate to server
```bash
cd server
```
2. You should already have a .env set up from running locally. If not, do that now.
3. Update your build and deploy scripts.
- In build.sh, set `DOCKER_USERNAME` and `AGENT_NAME`.
- In pcc-deploy.toml, set `image`, which specifies where your Docker image is stored.
4. Build your Docker image by running the build script:
```bash
./build.sh
```
> Note: This builds, tags and pushes your docker image and assumes Docker Hub is the container registry.
5. Make sure you have the Pipecat Cloud CLI installed:
```bash
pip install pipecatcloud
```
6. Login via the Pipecat Cloud CLI:
```bash
pcc auth login
```
> Note: If you don't have an account, sign up at https://pipecat.daily.co.
7. Add a secrets set:
```bash
pcc secrets set pcc-smart-turn-secrets --file .env
```
8. Deploy your agent:
```bash
pcc deploy
```
> Note: This uses your pcc-deploy.toml settings. Modify as needed.
### Deploy the client to Vercel
This project uses TypeScript, React, and Next.js, making it a perfect fit for [Vercel](https://vercel.com/).
- In your client directory, install Vercel's CLI tool: `npm install -g vercel`
- Verify it's installed using `vercel --version`
- Log in your Vercel account using `vercel login`
- Deploy your client to Vercel using `vercel`
Follow the vercel prompts to deploy your project.
### Test your deployed app
Now with the client and server deployed, you can join the call using your Vercel URL.
See the debug information for the Smart Turn data. It prints a log line for each smart-turn inference:
```
Smart Turn: COMPLETE, Probability: 95.3%, Model inference: 65.23ms, Server processing: 82.09ms, End-to-end: 245.43ms
```

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@@ -1,41 +0,0 @@
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.*
.yarn/*
!.yarn/patches
!.yarn/plugins
!.yarn/releases
!.yarn/versions
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
.pnpm-debug.log*
# env files (can opt-in for committing if needed)
.env*
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts

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@@ -1,3 +0,0 @@
NEXT_PUBLIC_API_BASE_URL=http://localhost:7860
PIPECAT_CLOUD_API_KEY=
AGENT_NAME=pcc-smart-turn

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@@ -1,16 +0,0 @@
import { dirname } from "path";
import { fileURLToPath } from "url";
import { FlatCompat } from "@eslint/eslintrc";
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
const compat = new FlatCompat({
baseDirectory: __dirname,
});
const eslintConfig = [
...compat.extends("next/core-web-vitals", "next/typescript"),
];
export default eslintConfig;

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@@ -1,7 +0,0 @@
import type { NextConfig } from "next";
const nextConfig: NextConfig = {
/* config options here */
};
export default nextConfig;

File diff suppressed because it is too large Load Diff

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@@ -1,28 +0,0 @@
{
"name": "my-nextjs-app",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "next lint"
},
"dependencies": {
"@pipecat-ai/client-js": "^0.3.5",
"@pipecat-ai/client-react": "^0.3.5",
"@pipecat-ai/daily-transport": "^0.3.10",
"next": "15.3.1",
"react": "^19.0.0",
"react-dom": "^19.0.0"
},
"devDependencies": {
"@eslint/eslintrc": "^3",
"@types/node": "^20",
"@types/react": "^19",
"@types/react-dom": "^19",
"eslint": "^9",
"eslint-config-next": "15.2.3",
"typescript": "^5"
}
}

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@@ -1,7 +0,0 @@
<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M3.3088 5.05615C3.64682 4.92779 4.02833 5.02411 4.26653 5.29797L7.36884 8.86461H16.6312L19.7335 5.29797C19.9717 5.02411 20.3532 4.92779 20.6912 5.05615C21.0292 5.18452 21.253 5.51072 21.253 5.87504V13.75H24V15.5H19.5181V8.19909L17.6762 10.3167C17.5115 10.506 17.2738 10.6146 17.0241 10.6146H6.9759C6.72616 10.6146 6.48854 10.506 6.32383 10.3167L4.48193 8.19909V15.5H0V13.75H2.74699V5.87504C2.74699 5.51072 2.97078 5.18452 3.3088 5.05615Z" fill="black"/>
<path d="M19.5181 17.25H24V19H19.5181V17.25Z" fill="black"/>
<path d="M0 17.25H4.48193V19H0V17.25Z" fill="black"/>
<path d="M9.25301 14.3333C9.25301 14.9777 8.73517 15.5 8.09639 15.5C7.4576 15.5 6.93976 14.9777 6.93976 14.3333C6.93976 13.689 7.4576 13.1667 8.09639 13.1667C8.73517 13.1667 9.25301 13.689 9.25301 14.3333Z" fill="black"/>
<path d="M17.0602 14.3333C17.0602 14.9777 16.5424 15.5 15.9036 15.5C15.2648 15.5 14.747 14.9777 14.747 14.3333C14.747 13.689 15.2648 13.1667 15.9036 13.1667C16.5424 13.1667 17.0602 13.689 17.0602 14.3333Z" fill="black"/>
</svg>

Before

Width:  |  Height:  |  Size: 1.1 KiB

View File

@@ -1,44 +0,0 @@
import { NextResponse, NextRequest } from 'next/server';
export async function POST(request: NextRequest) {
const { MY_CUSTOM_DATA } = await request.json();
try {
const response = await fetch(
`https://api.pipecat.daily.co/v1/public/${process.env.AGENT_NAME}/start`,
{
method: 'POST',
headers: {
Authorization: `Bearer ${process.env.PIPECAT_CLOUD_API_KEY}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({
// Create Daily room
createDailyRoom: true,
// Optionally set Daily room properties
dailyRoomProperties: { start_video_off: true },
// Optionally pass custom data to the bot
body: { MY_CUSTOM_DATA },
}),
}
);
if (!response.ok) {
throw new Error(`API responded with status: ${response.status}`);
}
const data = await response.json();
// Transform the response to match what RTVI client expects
return NextResponse.json({
room_url: data.dailyRoom,
token: data.dailyToken,
});
} catch (error) {
console.error('API error:', error);
return NextResponse.json(
{ error: 'Failed to start agent' },
{ status: 500 }
);
}
}

View File

@@ -1,82 +0,0 @@
body {
margin: 0;
padding: 20px;
font-family: Arial, sans-serif;
background-color: #f0f0f0;
}
.app {
max-width: 1200px;
margin: 0 auto;
}
.status-bar {
display: flex;
justify-content: space-between;
align-items: center;
padding: 10px;
background-color: #fff;
border-radius: 8px;
margin-bottom: 20px;
}
.controls button {
padding: 8px 16px;
margin-left: 10px;
border: none;
border-radius: 4px;
cursor: pointer;
}
button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.connect-btn {
background-color: #4caf50;
color: white;
}
.disconnect-btn {
background-color: #f44336;
color: white;
}
.main-content {
background-color: #fff;
border-radius: 8px;
padding: 20px;
margin-bottom: 20px;
}
.bot-container {
display: flex;
flex-direction: column;
align-items: center;
}
.video-container {
width: 640px;
height: 360px;
background-color: #ddd;
margin-bottom: 20px;
border-radius: 8px;
overflow: hidden;
}
.video-container video {
width: 100%;
height: 100%;
object-fit: cover;
}
.mic-enabled {
background-color: #4caf50;
color: white;
}
.mic-disabled {
background-color: #f44336;
color: white;
}

View File

@@ -1,27 +0,0 @@
import './globals.css';
import { RTVIProvider } from '@/providers/RTVIProvider';
export const metadata = {
title: 'Pipecat React Client',
description: 'Pipecat RTVI Client using Next.js',
icons: {
icon: [{ url: '/favicon.svg', type: 'image/svg+xml' }],
},
};
export default function RootLayout({
children,
}: {
children: React.ReactNode;
}) {
return (
<html lang="en">
<head>
<link rel="icon" href="/favicon.svg" type="image/svg+xml" />
</head>
<body>
<RTVIProvider>{children}</RTVIProvider>
</body>
</html>
);
}

View File

@@ -1,41 +0,0 @@
'use client';
import {
RTVIClientAudio,
RTVIClientVideo,
useRTVIClientTransportState,
} from '@pipecat-ai/client-react';
import { ConnectButton } from '../components/ConnectButton';
import { StatusDisplay } from '../components/StatusDisplay';
import { DebugDisplay } from '../components/DebugDisplay';
function BotVideo() {
const transportState = useRTVIClientTransportState();
const isConnected = transportState !== 'disconnected';
return (
<div className="bot-container">
<div className="video-container">
{isConnected && <RTVIClientVideo participant="bot" fit="cover" />}
</div>
</div>
);
}
export default function Home() {
return (
<div className="app">
<div className="status-bar">
<StatusDisplay />
<ConnectButton />
</div>
<div className="main-content">
<BotVideo />
</div>
<DebugDisplay />
<RTVIClientAudio />
</div>
);
}

View File

@@ -1,40 +0,0 @@
import {
useRTVIClient,
useRTVIClientTransportState,
} from '@pipecat-ai/client-react';
export function ConnectButton() {
const client = useRTVIClient();
const transportState = useRTVIClientTransportState();
const isConnected = ['connected', 'ready'].includes(transportState);
const handleClick = async () => {
if (!client) {
console.error('RTVI client is not initialized');
return;
}
try {
if (isConnected) {
await client.disconnect();
} else {
await client.connect();
}
} catch (error) {
console.error('Connection error:', error);
}
};
return (
<div className="controls">
<button
className={isConnected ? 'disconnect-btn' : 'connect-btn'}
onClick={handleClick}
disabled={
!client || ['connecting', 'disconnecting'].includes(transportState)
}>
{isConnected ? 'Disconnect' : 'Connect'}
</button>
</div>
);
}

View File

@@ -1,26 +0,0 @@
.debug-panel {
background-color: #fff;
border-radius: 8px;
padding: 20px;
}
.debug-panel h3 {
margin: 0 0 10px 0;
font-size: 16px;
font-weight: bold;
}
.debug-log {
height: 200px;
overflow-y: auto;
background-color: #f8f8f8;
padding: 10px;
border-radius: 4px;
font-family: monospace;
font-size: 12px;
line-height: 1.4;
}
.debug-log div {
margin-bottom: 4px;
}

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@@ -1,171 +0,0 @@
import { useRef, useCallback } from 'react';
import {
Participant,
RTVIEvent,
TransportState,
TranscriptData,
BotLLMTextData,
} from '@pipecat-ai/client-js';
import { useRTVIClient, useRTVIClientEvent } from '@pipecat-ai/client-react';
import './DebugDisplay.css';
interface SmartTurnResultData {
type: 'smart_turn_result';
is_complete: boolean;
probability: number;
inference_time_ms: number; // Pure model inference time
server_total_time_ms: number; // Server processing time
e2e_processing_time_ms: number; // Complete end-to-end time
}
export function DebugDisplay() {
const debugLogRef = useRef<HTMLDivElement>(null);
const client = useRTVIClient();
const log = useCallback((message: string) => {
if (!debugLogRef.current) return;
const entry = document.createElement('div');
entry.textContent = `${new Date().toISOString()} - ${message}`;
// Add styling based on message type
if (message.startsWith('User: ')) {
entry.style.color = '#2196F3'; // blue for user
} else if (message.startsWith('Bot: ')) {
entry.style.color = '#4CAF50'; // green for bot
} else if (message.includes('Smart Turn:')) {
entry.style.color = '#9C27B0'; // purple for smart turn
}
debugLogRef.current.appendChild(entry);
debugLogRef.current.scrollTop = debugLogRef.current.scrollHeight;
}, []);
// Log transport state changes
useRTVIClientEvent(
RTVIEvent.TransportStateChanged,
useCallback(
(state: TransportState) => {
log(`Transport state changed: ${state}`);
},
[log]
)
);
// Log bot connection events
useRTVIClientEvent(
RTVIEvent.BotConnected,
useCallback(
(participant?: Participant) => {
log(`Bot connected: ${JSON.stringify(participant)}`);
},
[log]
)
);
useRTVIClientEvent(
RTVIEvent.BotDisconnected,
useCallback(
(participant?: Participant) => {
log(`Bot disconnected: ${JSON.stringify(participant)}`);
},
[log]
)
);
// Log track events
useRTVIClientEvent(
RTVIEvent.TrackStarted,
useCallback(
(track: MediaStreamTrack, participant?: Participant) => {
log(
`Track started: ${track.kind} from ${participant?.name || 'unknown'}`
);
},
[log]
)
);
useRTVIClientEvent(
RTVIEvent.TrackStopped,
useCallback(
(track: MediaStreamTrack, participant?: Participant) => {
log(
`Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`
);
},
[log]
)
);
// Log bot ready state and check tracks
useRTVIClientEvent(
RTVIEvent.BotReady,
useCallback(() => {
log(`Bot ready`);
if (!client) return;
const tracks = client.tracks();
log(
`Available tracks: ${JSON.stringify({
local: {
audio: !!tracks.local.audio,
video: !!tracks.local.video,
},
bot: {
audio: !!tracks.bot?.audio,
video: !!tracks.bot?.video,
},
})}`
);
}, [client, log])
);
// Log transcripts
useRTVIClientEvent(
RTVIEvent.UserTranscript,
useCallback(
(data: TranscriptData) => {
// Only log final transcripts
if (data.final) {
log(`User: ${data.text}`);
}
},
[log]
)
);
useRTVIClientEvent(
RTVIEvent.BotTranscript,
useCallback(
(data: BotLLMTextData) => {
log(`Bot: ${data.text}`);
},
[log]
)
);
useRTVIClientEvent(
RTVIEvent.ServerMessage,
useCallback(
(data: SmartTurnResultData) => {
log(
`Smart Turn:
${data.is_complete ? 'COMPLETE' : 'INCOMPLETE'},
Probability: ${(data.probability * 100).toFixed(1)}%,
Model inference: ${data.inference_time_ms?.toFixed(2) || 'N/A'}ms,
Server processing: ${data.server_total_time_ms?.toFixed(2) || 'N/A'}ms,
End-to-end: ${data.e2e_processing_time_ms?.toFixed(2) || 'N/A'}ms`
);
},
[log]
)
);
return (
<div className="debug-panel">
<h3>Debug Info</h3>
<div ref={debugLogRef} className="debug-log" />
</div>
);
}

View File

@@ -1,11 +0,0 @@
import { useRTVIClientTransportState } from '@pipecat-ai/client-react';
export function StatusDisplay() {
const transportState = useRTVIClientTransportState();
return (
<div className="status">
Status: <span>{transportState}</span>
</div>
);
}

View File

@@ -1,43 +0,0 @@
'use client';
import { RTVIClient } from '@pipecat-ai/client-js';
import { DailyTransport } from '@pipecat-ai/daily-transport';
import { RTVIClientProvider } from '@pipecat-ai/client-react';
import { PropsWithChildren, useEffect, useState } from 'react';
// Get the API base URL from environment variables
// Default to "/api" if not specified
// "/api" is the default for Next.js API routes and used
// for the Pipecat Cloud deployed agent
const API_BASE_URL = process.env.NEXT_PUBLIC_API_BASE_URL || '/api';
console.log('Using API base URL:', API_BASE_URL);
export function RTVIProvider({ children }: PropsWithChildren) {
const [client, setClient] = useState<RTVIClient | null>(null);
useEffect(() => {
const transport = new DailyTransport();
const rtviClient = new RTVIClient({
transport,
params: {
baseUrl: API_BASE_URL,
endpoints: {
connect: '/connect',
},
requestData: { foo: 'bar' },
},
enableMic: true,
enableCam: false,
});
setClient(rtviClient);
}, []);
if (!client) {
return null;
}
return <RTVIClientProvider client={client}>{children}</RTVIClientProvider>;
}

View File

@@ -1,28 +0,0 @@
{
"compilerOptions": {
"target": "ES2017",
"lib": ["dom", "dom.iterable", "esnext"],
"allowJs": true,
"skipLibCheck": true,
"strict": true,
"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "bundler",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "preserve",
"incremental": true,
"plugins": [
{
"name": "next"
}
],
"paths": {
"@/components/*": ["./src/components/*"],
"@/providers/*": ["./src/providers/*"]
}
},
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
"exclude": ["node_modules"]
}

View File

@@ -1,8 +0,0 @@
FROM dailyco/pipecat-base:latest
COPY ./requirements.txt requirements.txt
RUN pip install --no-cache-dir --upgrade -r requirements.txt
COPY ./assets assets
COPY ./bot.py bot.py

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