Remove extra

.github/workflows/android.yaml

@@ -6,13 +6,11 @@ on:
       - main
     paths:
       - "examples/simple-chatbot/client/android/**"
-      - "examples/p2p-webrtc/video-transform/client/android/**"
   pull_request:
     branches:
       - "**"
     paths:
       - "examples/simple-chatbot/client/android/**"
-      - "examples/p2p-webrtc/video-transform/client/android/**"
   workflow_dispatch:
     inputs:
       sdk_git_ref:
@@ -25,7 +23,7 @@ concurrency:
 
 jobs:
   sdk:
-    name: "Demo apps"
+    name: "Simple chatbot demo"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
@@ -39,22 +37,12 @@ jobs:
           distribution: 'temurin'
           java-version: '17'
 
-      - name: "Example app: Simple Chatbot"
+      - name: Build demo app
         working-directory: examples/simple-chatbot/client/android
         run: ./gradlew :simple-chatbot-client:assembleDebug
 
-      - name: Upload Simple Chatbot APK
+      - name: Upload demo APK
         uses: actions/upload-artifact@v4
         with:
           name: Simple Chatbot Android Client
           path: examples/simple-chatbot/client/android/simple-chatbot-client/build/outputs/apk/debug/simple-chatbot-client-debug.apk
-
-      - name: "Example app: Small WebRTC Client"
-        working-directory: examples/p2p-webrtc/video-transform/client/android
-        run: ./gradlew :small-webrtc-client:assembleDebug
-
-      - name: Upload Small WebRTC APK
-        uses: actions/upload-artifact@v4
-        with:
-          name: Small WebRTC Android Client
-          path: examples/p2p-webrtc/video-transform/client/android/small-webrtc-client/build/outputs/apk/debug/small-webrtc-client-debug.apk

CHANGELOG.md

@@ -5,416 +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).
 
-## [0.0.69] - 2025-06-02 "AI Engineer World's Fair release" ✨
+## [Unreleased]
 
 ### Added
 
-- Added a new frame `FunctionCallsStartedFrame`. This frame is pushed both
-  upstream and downstream from the LLM service to indicate that one or more
-  function calls are going to be executed.
-
-- Added LLM services `on_function_calls_started` event. This event will be
-  triggered when the LLM service receives function calls from the model and is
-  going to start executing them.
-
-- Function calls can now be executed sequentially (in the order received in the
-  completion) by passing `run_in_parallel=False` when creating your LLM
-  service. By default, if the LLM completion returns 2 or more function calls
-  they run concurrently. In both cases, concurrently and sequentially, a new LLM
-  completion will run when the last function call finishes.
-
-- Added OpenTelemetry tracing for `GeminiMultimodalLiveLLMService` and
-  `OpenAIRealtimeBetaLLMService`.
-
-- Added initial support for interruption strategies, which determine if the user
-  should interrupt the bot while the bot is speaking. Interruption strategies
-  can be based on factors such as audio volume or the number of words spoken by
-  the user. These can be specified via the new `interruption_strategies` field
-  in `PipelineParams`. A new `MinWordsInterruptionStrategy` strategy has been
-  introduced which triggers an interruption if the user has spoken a minimum
-  number of words. If no interruption strategies are specified, the normal
-  interruption behavior applies. If multiple strategies are provided, the first
-  one that evaluates to true will trigger the interruption.
-
-- `BaseInputTransport` now handles `StopFrame`. When a `StopFrame` is received
-  the transport will pause sending frames downstream until a new `StartFrame` is
-  received. This allows the transport to be reused (keeping the same connection)
-  in a different pipeline.
-
-- Updated AssemblyAI STT service to support their latest streaming
-  speech-to-text model with improved transcription latency and endpointing.
-
-- You can now access STT service results through the new
-  `TranscriptionFrame.result` and `InterimTranscriptionFrame.result` field. This
-  is useful in case you use some specific settings for the STT and you want to
-  access the STT results.
-
-- The examples runner is now public from the `pipecat.examples` package. This
-  allows everyone to build their own examples and run them easily.
-
-- It is now possible to push `OutputDTMFFrame` or `OutputDTMFUrgentFrame` with
-  `DailyTransport`. This will be sent properly if a Daily dial-out connection
-  has been established.
-
-- Added `OutputDTMFUrgentFrame` to send a DTMF keypress quickly. The previous
-  `OutputDTMFFrame` queues the keypress with the rest of data frames.
-
-- Added `DTMFAggregator`, which aggregates keypad presses into
-  `TranscriptionFrame`s. Aggregation occurs after a timeout, termination key
-  press, or user interruption. You can specify the prefix of the
-  `TranscriptionFrame`.
-
-- Added new functions `DailyTransport.start_transcription()` and
-  `DailyTransport.stop_transcription()` to be able to start and stop Daily
-  transcription dynamically (maybe with different settings).
-
-### Changed
-
-- Reverted the default model for `GeminiMultimodalLiveLLMService` back to
-  `models/gemini-2.0-flash-live-001`.
-  `gemini-2.5-flash-preview-native-audio-dialog` has inconsistent performance.
-  You can opt in to using this model by setting the `model` arg.
-
-- Function calls are now cancelled by default if there's an interruption. To
-  disable this behavior you can set `cancel_on_interruption=False` when
-  registering the function call. Since function calls are executed as tasks you
-  can tell if a function call has been cancelled by catching the
-  `asyncio.CancelledError` exception (and don't forget to raise it again!).
-
-- Updated OpenTelemetry tracing attribute `metrics.ttfb_ms` to `metrics.ttfb`.
-  The attribute reports TTFB in seconds.
-
-### Deprecated
-
-- `DailyTransport.send_dtmf()` is deprecated, push an `OutputDTMFFrame` or an
-  `OutputDTMFUrgentFrame` instead.
-
-### Fixed
-
-- Fixed an issue with `ElevenLabsTTSService` where long responses would
-  continue generating output even after an interruption.
-
-- Fixed an issue with the `OpenAILLMContext` where non-Roman characters were
-  being incorrectly encoded as Unicode escape sequences. This was a logging
-  issue and did not impact the actual conversation.
-
-- In `AWSBedrockLLMService`, worked around a possible bug in AWS Bedrock where
-  a `toolConfig` is required if there has been previous tool use in the
-  messages array. This workaround includes a no_op factory function call is
-  used to satisfy the requirement.
-
-- Fixed `WebsocketClientTransport` to use `FrameProcessorSetup.task_manager`
-  instead of `StartFrame.task_manager`.
-
-### Performance
-
-- Use `uvloop` as the new event loop on Linux and macOS systems.
-
-## [0.0.68] - 2025-05-28
-
-### Added
-
-- Added `GoogleHttpTTSService` which uses Google's HTTP TTS API.
-
-- Added `TavusTransport`, a new transport implementation compatible with any
-  Pipecat pipeline. When using the `TavusTransport`the Pipecat bot will
-  connect in the same room as the Tavus Avatar and the user.
-
-- Added `PlivoFrameSerializer` to support Plivo calls. A full running example
-  has also been added to `examples/plivo-chatbot`.
-
-- Added `UserBotLatencyLogObserver`. This is an observer that logs the latency
-  between when the user stops speaking and when the bot starts speaking. This
-  gives you an initial idea on how quickly the AI services respond.
-
-- Added `SarvamTTSService`, which implements Sarvam AI's TTS API:
-  https://docs.sarvam.ai/api-reference-docs/text-to-speech/convert.
-
-- Added `PipelineTask.add_observer()` and `PipelineTask.remove_observer()` to
-  allow mangaging observers at runtime. This is useful for cases where the task
-  is passed around to other code components that might want to observe the
-  pipeline dynamically.
-
-- Added `user_id` field to `TranscriptionMessage`. This allows identifying the
-  user in a multi-user scenario. Note that this requires that
-  `TranscriptionFrame` has the `user_id` properly set.
-
-- Added new `PipelineTask` event handlers `on_pipeline_started`,
-  `on_pipeline_stopped`, `on_pipeline_ended` and `on_pipeline_cancelled`, which
-  correspond to the `StartFrame`, `StopFrame`, `EndFrame` and `CancelFrame`
-  respectively.
-
-- Added additional languages to `LmntTTSService`. Languages include: `hi`,
-  `id`, `it`, `ja`, `nl`, `pl`, `ru`, `sv`, `th`, `tr`, `uk`, `vi`.
-
-- Added a `model` parameter to the `LmntTTSService` constructor, allowing
-  switching between LMNT models.
-
-- Added `MiniMaxHttpTTSService`, which implements MiniMax's T2A API for TTS.
-  Learn more: https://www.minimax.io/platform_overview
-
-- A new function `FrameProcessor.setup()` has been added to allow setting up
-  frame processors before receiving a `StartFrame`. This is what's happening
-  internally: `FrameProcessor.setup()` is called, `StartFrame` is pushed from
-  the beginning of the pipeline, your regular pipeline operations, `EndFrame`
-  or `CancelFrame` are pushed from the beginning of the pipeline and finally
-  `FrameProcessor.cleanup()` is called.
-
-- Added support for OpenTelemetry tracing in Pipecat. This initial
-  implementation includes:
-
-  - A `setup_tracing` method where you can specify your OpenTelemetry exporter
-  - Service decorators for STT (`@traced_stt`), LLM (`@traced_llm`), and TTS
-    (`@traced_tts`) which trace the execution and collect properties and
-    metrics (TTFB, token usage, character counts, etc.)
-  - Class decorators that provide execution tracking; these are generic and can
-    be used for service tracking as needed
-  - Spans that help track traces on a per conversations and turn basis:
-
-  ```
-  conversation-uuid
-  ├── turn-1
-  │   ├── stt_deepgramsttservice
-  │   ├── llm_openaillmservice
-  │   └── tts_cartesiattsservice
-  ...
-  └── turn-n
-      └── ...
-  ```
-
-  By default, Pipecat has implemented service decorators to trace execution of
-  STT, LLM, and TTS services. You can enable tracing by setting
-  `enable_tracing` to `True` in the PipelineTask.
-
-- Added `TurnTrackingObserver`, which tracks the start and end of a user/bot
-  turn pair and emits events `on_turn_started` and `on_turn_stopped`
-  corresponding to the start and end of a turn, respectively.
-
-- Allow passing observers to `run_test()` while running unit tests.
-
-### Changed
-
-- Upgraded `daily-python` to 0.19.1.
-
-- ⚠️ Updated `SmallWebRTCTransport` to align with how other transports handle
-  `on_client_disconnected`. Now, when the connection is closed and no reconnection
-  is attempted, `on_client_disconnected` is called instead of `on_client_close`. The
-  `on_client_close` callback is no longer used, use `on_client_disconnected` instead.
-
-- Check if `PipelineTask` has already been cancelled.
-
-- Don't raise an exception if event handler is not registered.
-
-- Upgraded `deepgram-sdk` to 4.1.0.
-
-- Updated `GoogleTTSService` to use Google's streaming TTS API. The default
-  voice also updated to `en-US-Chirp3-HD-Charon`.
-
-- ⚠️ Refactored the `TavusVideoService`, so it acts like a proxy, sending audio
-  to Tavus and receiving both audio and video. This will make
-  `TavusVideoService` usable with any Pipecat pipeline and with any transport.
-  This is a **breaking change**, check the
-  `examples/foundational/21a-tavus-layer-small-webrtc.py` to see how to use it.
-
-- `DailyTransport` now uses custom microphone audio tracks instead of virtual
-  microphones. Now, multiple Daily transports can be used in the same process.
-
-- `DailyTransport` now captures audio from individual participants instead of
-  the whole room. This allows identifying audio frames per participant.
-
-- Updated the default model for `AnthropicLLMService` to
-  `claude-sonnet-4-20250514`.
-
-- Updated the default model for `GeminiMultimodalLiveLLMService` to
-  `models/gemini-2.5-flash-preview-native-audio-dialog`.
-
-- `BaseTextFilter` methods `filter()`, `update_settings()`,
-  `handle_interruption()` and `reset_interruption()` are now async.
-
-- `BaseTextAggregator` methods `aggregate()`, `handle_interruption()` and
-  `reset()` are now async.
-
-- The API version for `CartesiaTTSService` and `CartesiaHttpTTSService` has
-  been updated. Also, the `cartesia` dependency has been updated to 2.x.
-
-- `CartesiaTTSService` and `CartesiaHttpTTSService` now support Cartesia's new
-  `speed` parameter which accepts values of `slow`, `normal`, and `fast`.
-
-- `GeminiMultimodalLiveLLMService` now uses the user transcription and usage
-  metrics provided by Gemini Live.
-
-- `GoogleLLMService` has been updated to use `google-genai` instead of the
-  deprecated `google-generativeai`.
-
-### Deprecated
-
-- In `CartesiaTTSService` and `CartesiaHttpTTSService`, `emotion` has been
-  deprecated by Cartesia. Pipecat is following suit and deprecating `emotion`
-  as well.
-
-### Removed
-
-- Since `GeminiMultimodalLiveLLMService` now transcribes it's own audio, the
-  `transcribe_user_audio` arg has been removed. Audio is now transcribed
-  automatically.
-
-- Removed `SileroVAD` frame processor, just use `SileroVADAnalyzer`
-  instead. Also removed, `07a-interruptible-vad.py` example.
-
-### Fixed
-
-- Fixed a `DailyTransport` issue that was not allow capturing video frames if
-  framerate was greater than zero.
-
-- Fixed a `DeegramSTTService` connection issue when the user provided their own
-  `LiveOptions`.
-
-- Fixed a `DailyTransport` issue that would cause images needing resize to block
-  the event loop.
-
-- Fixed an issue with `ElevenLabsTTSService` where changing the model or voice
-  while the service is running wasn't working.
-
-- Fixed an issue that would cause multiple instances of the same class to behave
-  incorrectly if any of the given constructor arguments defaulted to a mutable
-  value (e.g. lists, dictionaries, objects).
-
-- Fixed an issue with `CartesiaTTSService` where `TTSTextFrame` messages weren't
-  being emitted when the model was set to `sonic`. This resulted in the
-  assistant context not being updated with assistant messages.
-
-### Performance
-
-- `DailyTransport`: process audio, video and events in separate tasks.
-
-- Don't create event handler tasks if no user event handlers have been
-  registered.
-
-### Other
-
-- It is now possible to run all (or most) foundational example with multiple
-  transports. By default, they run with P2P (Peer-To-Peer) WebRTC so you can try
-  everything locally. You can also run them with Daily or even with a Twilio
-  phone number.
-
-- Added foundation examples `07y-interruptible-minimax.py` and
-  `07z-interruptible-sarvam.py`to show how to use the `MiniMaxHttpTTSService`
-  and `SarvamTTSService`, respectively.
-
-- Added an `open-telemetry-tracing` example, showing how to setup tracing. The
-  example also includes Jaeger as an open source OpenTelemetry client to review
-  traces from the example runs.
-
-- Added foundational example `29-turn-tracking-observer.py` to show how to use
-  the `TurnTrackingObserver`.
-
-## [0.0.67] - 2025-05-07
-
-### 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.
 
@@ -443,17 +37,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### 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`.
@@ -487,9 +70,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   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
@@ -505,20 +85,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `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.
 
@@ -534,12 +102,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### 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`.
 

MANIFEST.in

@@ -1,4 +0,0 @@
-prune docs
-prune examples
-prune scripts
-prune tests

README.md

@@ -8,8 +8,6 @@
 
 **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.
 
-> Want to dive right in? [Install Pipecat](https://docs.pipecat.ai/getting-started/installation) then try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
-
 ## 🚀 What You Can Build
 
 - **Voice Assistants** – natural, streaming conversations with AI
@@ -51,19 +49,18 @@ You can connect to Pipecat from any platform using our official SDKs:
 
 ## 🧩 Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                            |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                 |
-| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [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), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
-| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
-| Video               | [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 | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                            |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
+| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                       |
+| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 

docs/api/requirements.txt

@@ -10,6 +10,7 @@ pipecat-ai[anthropic]
 pipecat-ai[assemblyai]
 pipecat-ai[aws]
 pipecat-ai[azure]
+pipecat-ai[canonical]
 pipecat-ai[cartesia]
 pipecat-ai[cerebras]
 pipecat-ai[deepseek]

dot-env.template

@@ -95,16 +95,9 @@ OPENROUTER_API_KEY=...
 PIPER_BASE_URL=...
 
 # Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=...
+LOCAL_SMART_TURN_MODEL_PATH=
 FAL_SMART_TURN_API_KEY=...
 
 # Twilio
-TWILIO_ACCOUNT_SID=...
-TWILIO_AUTH_TOKEN=...
-
-# MiniMax
-MINIMAX_API_KEY=...
-MINIMAX_GROUP_ID=...
-
-# Sarvam AI
-SARVAM_API_KEY=...
+TWILIO_ACCOUNT_SID=
+TWILIO_AUTH_TOKEN=

examples/bot-ready-signalling/client/javascript/package-lock.json

@@ -12,7 +12,7 @@
         "@daily-co/daily-js": "0.74.0"
       },
       "devDependencies": {
-        "vite": "^6.3.5"
+        "vite": "^6.0.9"
       }
     },
     "node_modules/@babel/runtime": {
@@ -999,9 +999,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.3.5",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.5.tgz",
-      "integrity": "sha512-cZn6NDFE7wdTpINgs++ZJ4N49W2vRp8LCKrn3Ob1kYNtOo21vfDoaV5GzBfLU4MovSAB8uNRm4jgzVQZ+mBzPQ==",
+      "version": "6.3.3",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.3.tgz",
+      "integrity": "sha512-5nXH+QsELbFKhsEfWLkHrvgRpTdGJzqOZ+utSdmPTvwHmvU6ITTm3xx+mRusihkcI8GeC7lCDyn3kDtiki9scw==",
       "dev": true,
       "dependencies": {
         "esbuild": "^0.25.0",

examples/bot-ready-signalling/client/javascript/package.json

@@ -12,7 +12,7 @@
   "license": "ISC",
   "description": "",
   "devDependencies": {
-    "vite": "^6.3.5"
+    "vite": "^6.0.9"
   },
   "dependencies": {
     "@daily-co/daily-js": "0.74.0"

examples/canonical-metrics/.gitignore

@@ -2,7 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
-
+recordings/
 # C extensions
 *.so
 

examples/canonical-metrics/Dockerfile

@@ -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
 

examples/canonical-metrics/README.md

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

examples/canonical-metrics/bot.py

@@ -0,0 +1,146 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+import uuid
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import EndFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
+from pipecat.services.canonical.metrics import CanonicalMetricsService
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Chatbot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_in_enabled=True,
+                video_out_enabled=False,
+                vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
+                #
+                # Spanish
+                #
+                # transcription_settings=DailyTranscriptionSettings(
+                #     language="es",
+                #     tier="nova",
+                #     model="2-general"
+                # )
+            ),
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            #
+            # English
+            #
+            voice_id="cgSgspJ2msm6clMCkdW9",
+            #
+            # Spanish
+            #
+            # model="eleven_multilingual_v2",
+            # voice_id="gD1IexrzCvsXPHUuT0s3",
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+        messages = [
+            {
+                "role": "system",
+                #
+                # English
+                #
+                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your responses to 12 words or fewer.",
+                #
+                # Spanish
+                #
+                # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        """
+        CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
+        call completion, CanonicalMetrics will send the audio buffer to Canonical for
+        analysis. Visit https://voice.canonical.chat to learn more.
+        """
+        audio_buffer_processor = AudioBufferProcessor(num_channels=2)
+        canonical = CanonicalMetricsService(
+            audio_buffer_processor=audio_buffer_processor,
+            aiohttp_session=session,
+            api_key=os.getenv("CANONICAL_API_KEY"),
+            call_id=str(uuid.uuid4()),
+            assistant="pipecat-chatbot",
+            assistant_speaks_first=True,
+            context=context,
+        )
+        pipeline = Pipeline(
+            [
+                transport.input(),  # microphone
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                canonical,  # uploads audio buffer to Canonical AI for metrics
+                audio_buffer_processor,  # captures audio into a buffer
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await audio_buffer_processor.start_recording()
+            await transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            print(f"Participant left: {participant}")
+            await task.cancel()
+
+        @transport.event_handler("on_call_state_updated")
+        async def on_call_state_updated(transport, state):
+            if state == "left":
+                # Here we don't want to cancel, we just want to finish sending
+                # whatever is queued, so we use an EndFrame().
+                await task.queue_frame(EndFrame())
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/canonical-metrics/env.example

@@ -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=

examples/canonical-metrics/requirements.txt

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

examples/chatbot-audio-recording/bot.py

@@ -128,14 +128,7 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=16000,
-                allow_interruptions=True,
-            ),
-        )
+        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
 
         @audiobuffer.event_handler("on_audio_data")
         async def on_audio_data(buffer, audio, sample_rate, num_channels):

examples/chatbot-audio-recording/runner.py

@@ -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)

examples/daily-custom-tracks/README.md

@@ -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.

examples/daily-custom-tracks/bot.py

@@ -1,87 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import sys
-
-import aiohttp
-from loguru import logger
-from runner import configure
-
-from pipecat.frames.frames import Frame, InputAudioRawFrame, OutputAudioRawFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-class CustomTrackMirrorProcessor(FrameProcessor):
-    def __init__(self, transport_destination: str, **kwargs):
-        super().__init__(**kwargs)
-        self._transport_destination = transport_destination
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, InputAudioRawFrame) and frame.transport_source:
-            output_frame = OutputAudioRawFrame(
-                audio=frame.audio,
-                sample_rate=frame.sample_rate,
-                num_channels=frame.num_channels,
-            )
-            output_frame.transport_destination = self._transport_destination
-            await self.push_frame(output_frame)
-        else:
-            await self.push_frame(frame, direction)
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, _) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            None,
-            "Custom tracks mirror",
-            DailyParams(
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                microphone_out_enabled=False,  # Disable since we just use custom tracks
-                audio_out_destinations=["pipecat-mirror"],
-            ),
-        )
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                CustomTrackMirrorProcessor("pipecat-mirror"),
-                transport.output(),  # Transport bot output
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=16000,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_audio(participant["id"], audio_source="pipecat")
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/daily-custom-tracks/custom_track_sender.py

@@ -1,74 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import argparse
-import time
-
-from daily import CallClient, CustomAudioSource, Daily
-from pydub import AudioSegment
-
-parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
-parser.add_argument("-u", "--url", type=str, required=True, help="URL of the Daily room to join")
-parser.add_argument(
-    "-i", "--input", type=str, required=True, help="Input audio file (needs 16000 sample rate)"
-)
-
-args, _ = parser.parse_known_args()
-
-audio = AudioSegment.from_mp3(args.input)
-
-raw_bytes = audio.raw_data
-sample_rate = audio.frame_rate
-channels = audio.channels
-
-print(f"Length: {len(raw_bytes)} bytes")
-print(f"Sample rate: {sample_rate}, Channels: {channels}")
-
-# Initialize the Daily context & create call client
-Daily.init()
-
-client = CallClient()
-
-# Join the room and indicate we have a custom track named "pipecat".
-client.join(
-    args.url,
-    client_settings={
-        "publishing": {
-            "camera": False,
-            "microphone": False,
-            "customAudio": {"pipecat": True},
-        },
-    },
-)
-
-# Just sleep for a couple of seconds. To do this well we should really use
-# completions.
-time.sleep(2)
-
-# Create the custom audio source. This is where we will write our audio.
-audio_source = CustomAudioSource(sample_rate, channels)
-
-# Create an audio track and assign it our audio source.
-client.add_custom_audio_track("pipecat", audio_source)
-
-# Just sleep for a second. To do this well we should really use completions.
-time.sleep(1)
-
-try:
-    # Just write one second of audio until we have read all the file.
-    chunk_size = sample_rate * channels * 2
-    while len(raw_bytes) > 0:
-        chunk = raw_bytes[:chunk_size]
-        raw_bytes = raw_bytes[chunk_size:]
-        audio_source.write_frames(chunk)
-
-except KeyboardInterrupt:
-    client.leave()
-
-# Just sleep for a second. To do this well we should really use completions.
-time.sleep(1)
-
-client.release()

examples/daily-custom-tracks/index.html

@@ -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>

examples/daily-custom-tracks/requirements.txt

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

examples/daily-multi-translation/README.md

@@ -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
-```

examples/daily-multi-translation/bot.py

@@ -1,165 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.mixers.soundfile_mixer import SoundfileMixer
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-BACKGROUND_SOUND_FILE = "office-ambience-mono-16000.mp3"
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Multi translation bot",
-            DailyParams(
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                audio_out_mixer={
-                    "spanish": SoundfileMixer(
-                        sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
-                    ),
-                    "french": SoundfileMixer(
-                        sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
-                    ),
-                    "german": SoundfileMixer(
-                        sound_files={"office": BACKGROUND_SOUND_FILE}, default_sound="office"
-                    ),
-                },
-                audio_out_destinations=["spanish", "french", "german"],
-                microphone_out_enabled=False,  # Disable since we just use custom tracks
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts_spanish = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="cefcb124-080b-4655-b31f-932f3ee743de",
-            transport_destination="spanish",
-        )
-        tts_french = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="8832a0b5-47b2-4751-bb22-6a8e2149303d",
-            transport_destination="french",
-        )
-        tts_german = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="38aabb6a-f52b-4fb0-a3d1-988518f4dc06",
-            transport_destination="german",
-        )
-
-        messages_spanish = [
-            {
-                "role": "system",
-                "content": "You will be provided with a sentence in English, and your task is to only translate it into Spanish.",
-            },
-        ]
-        messages_french = [
-            {
-                "role": "system",
-                "content": "You will be provided with a sentence in English, and your task is to only translate it into French.",
-            },
-        ]
-        messages_german = [
-            {
-                "role": "system",
-                "content": "You will be provided with a sentence in English, and your task is to only translate it into German.",
-            },
-        ]
-
-        llm_spanish = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-        llm_french = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-        llm_german = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-        context_spanish = OpenAILLMContext(messages_spanish)
-        context_aggregator_spanish = llm_spanish.create_context_aggregator(context_spanish)
-
-        context_french = OpenAILLMContext(messages_french)
-        context_aggregator_french = llm_french.create_context_aggregator(context_french)
-
-        context_german = OpenAILLMContext(messages_german)
-        context_aggregator_german = llm_german.create_context_aggregator(context_german)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                ParallelPipeline(
-                    # Spanish pipeline.
-                    [
-                        context_aggregator_spanish.user(),
-                        llm_spanish,
-                        tts_spanish,
-                        context_aggregator_spanish.assistant(),
-                    ],
-                    # French pipeline.
-                    [
-                        context_aggregator_french.user(),
-                        llm_french,
-                        tts_french,
-                        context_aggregator_french.assistant(),
-                    ],
-                    # German pipeline.
-                    [
-                        context_aggregator_german.user(),
-                        llm_german,
-                        tts_german,
-                        context_aggregator_german.assistant(),
-                    ],
-                ),
-                transport.output(),  # Transport bot output
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=16000,
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-            observers=[TranscriptionLogObserver()],
-        )
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/daily-multi-translation/index.html

@@ -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>

examples/daily-multi-translation/requirements.txt

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

examples/daily-multi-translation/runner.py

@@ -1,55 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import argparse
-import os
-
-import aiohttp
-
-from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
-
-
-async def configure(aiohttp_session: aiohttp.ClientSession):
-    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
-    parser.add_argument(
-        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=False,
-        help="Daily API Key (needed to create an owner token for the room)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
-    key = args.apikey or os.getenv("DAILY_API_KEY")
-
-    if not url:
-        raise Exception(
-            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
-        )
-
-    if not key:
-        raise Exception(
-            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
-        )
-
-    daily_rest_helper = DailyRESTHelper(
-        daily_api_key=key,
-        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-        aiohttp_session=aiohttp_session,
-    )
-
-    # Create a meeting token for the given room with an expiration 1 hour in
-    # the future.
-    expiry_time: float = 60 * 60
-
-    token = await daily_rest_helper.get_token(url, expiry_time)
-
-    return (url, token)

examples/deployment/modal-example/.gitignore

@@ -1,6 +1,3 @@
-# Modal clone
-modal-examples
-
 # Python
 __pycache__/
 *.py[cod]

examples/deployment/modal-example/README.md

@@ -1,91 +1,37 @@
 # Deploying Pipecat to Modal.com
 
-Deployment example for [modal.com](https://www.modal.com). This example demonstrates how to deploy a FastAPI webapp to Modal with an RTVI compatible `/connect` endpoint that launches a Pipecat pipeline in a separate Modal container and returns a room/token for the client to join. This example also supports providing a parameter to the `/connect` endpoint for specifying which Pipecat pipeline to launch; openai, gemini, or vllm. The vllm pipeline points to a self-hosted OpenAI compatible LLM, using a llama model (neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16), deployed to Modal.
+Barebones deployment example for [modal.com](https://www.modal.com)
 
-![](diagram.jpg)
+1. Install dependencies
 
-# Running this Example
+```bash
+python -m venv venv
+source venv/bin/active # or OS equivalent
+pip install -r requirements.txt
+```
 
-## Install the Modal CLI
+2. Setup .env
 
-Setup a Modal account and install it on your machine if you have not already, following their easy 3 steps in their [Getting Started Guide](https://modal.com/docs/guide#getting-started)
+```bash
+cp env.example .env
+```
 
-## Deploy a self-serve LLM
+Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
 
-1. Deploy Modal's OpenAI-compatible LLM service:
+3. Test the app locally
 
-   ```bash
-   git clone https://github.com/modal-labs/modal-examples
-   cd modal-examples
-   modal deploy 06_gpu_and_ml/llm-serving/vllm_inference.py
-   ```
-
-   Refer to Modal's guide and example for [Deploying an OpenAI-compatible LLM service with vLLM](https://modal.com/docs/examples/vllm_inference) for more details.
-
-2. Take note of the endpoint URL from the previous step, which will look like:
-   ```
-   https://{your-workspace}--example-vllm-openai-compatible-serve.modal.run
-   ```
-   You'll need this for the `bot_vllm.py` file in the next section.
-
-    **Note:**  The default Modal LLM example uses Llama-3.1 and will shut down after 15 minutes of inactivity. Cold starts take 5-10 minutes. To prepare the service, we recommend visiting the `/docs` endpoint (`https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run/docs`) for your deployed LLM and wait for it to fully load before connecting your client.
-
-## Deploy FastAPI App and Pipecat pipeline to Modal 
-
-1. Setup environment variables
-
-   ```bash
-   cd server
-   cp env.example .env
-   # Modify .env to provide your service API Keys
-   ```
-
-   Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
-
-2. Update the `modal_url` in `server/src/bot_vllm.py` to point to the url produced from the self-serve llm deploy, mentioned above.
-
-3. From within the `server` directory, test the app locally:
-
-   ```bash
-   modal serve app.py
-   ```
+```bash
+modal serve app.py
+```
 
 4. Deploy to production
 
-   ```bash
-   modal deploy app.py
-   ```
+```bash
+modal deploy app.py
+```
 
-5. Note the endpoint URL produced from this deployment. It will look like:
+## Configuration options
 
-   ```bash
-   https://{your-workspace}--pipecat-modal-fastapi-app.modal.run
-   ```
+This app sets some sensible defaults for reducing cold starts, such as `minkeep_warm=1`, which will keep at least 1 warm instance ready for your bot function.
 
-   You'll need this URL for the client's `app.js` configuration mentioned in its README.
-
-## Launch your bots on Modal
-
-### Option 1: Direct Link
-
-Simply click on the url displayed after running the server or deploy step to launch an agent and be redirected to a Daily room to talk with the launched bot. This will use the OpenAI pipeline.
-
-### Option 2: Connect via an RTVI Client
-
-Follow the instructions provided in the [client folder's README](client/javascript/README.md) for building and running a custom client that connects to your Modal endpoint. The provided client provides a dropdown for choosing which bot pipeline to run.
-
-# Navigating your llm, server, and Pipecat logs
-
-In your [Modal dashboard](https://modal.com/apps), you should have two Apps listed under Live Apps:
-
-1. `example-vllm-openai-compatible`: This App contains the containers and logs used to run your self-hosted LLM. There will be just one App Function listed: `serve`. Click on this function to view logs for your LLM.
-2. `pipecat-modal`: This App contains the containers and logs used to run your `connect` endpoints and Pipecat pipelines. It will list two App Functions:
-    1. `fastapi_app`: This function is running the endpoints that your client will interact with and initiate starting a new pipeline (`/`, `/connect`, `/status`). Click on this function to see logs for each endpoint hit.
-    2. `bot_runner`: This function handles launching and running a bot pipeline. Click on this function to get a list of all pipeline runs and access each run's logs.
-
-# Modal + Pipecat Tips
-
-- In most other Pipecat examples, we use `Popen` to launch the pipeline process from the `/connect` endpoint. In this example, we use a Modal function instead. This allows us to run the pipelines using a separately defined Modal image as well as run each pipeline in an isolated container.
-- For the FastAPI and most common Pipecat Pipeline containers, a default `debian_slim` CPU-only should be all that's required to run. GPU containers are needed for self-hosted services.
-- To minimize cold starts of the pipeline and reduce latency for users, set `min_containers=1` on the Modal Function that launches the pipeline to ensure at least one warm instance of your function is always available.
-- For next steps on running a self-hosted llm and reducing latency, check out all of [Modal's LLM examples](https://modal.com/docs/examples/vllm_inference).
+It has been configured to only allow a concurrency of 1 (`max_inputs=1`) as each user will require their own running function.

examples/deployment/modal-example/app.py

@@ -0,0 +1,80 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+
+import aiohttp
+import modal
+from bot import _voice_bot_process
+from fastapi import HTTPException
+from fastapi.responses import JSONResponse
+from loguru import logger
+
+MAX_SESSION_TIME = 15 * 60  # 15 minutes
+
+app = modal.App("pipecat-modal")
+
+
+image = modal.Image.debian_slim(python_version="3.12").pip_install_from_requirements(
+    "requirements.txt"
+)
+
+
+@app.function(
+    image=image,
+    cpu=1.0,
+    secrets=[modal.Secret.from_dotenv()],
+    keep_warm=1,
+    enable_memory_snapshot=True,
+    max_inputs=1,  # Do not reuse instances across requests
+    retries=0,
+)
+def launch_bot_process(room_url: str, token: str):
+    _voice_bot_process(room_url, token)
+
+
+@app.function(
+    image=image,
+    secrets=[modal.Secret.from_dotenv()],
+)
+@modal.web_endpoint(method="POST")
+async def start():
+    from pipecat.transports.services.helpers.daily_rest import (
+        DailyRESTHelper,
+        DailyRoomParams,
+    )
+
+    logger.info("Request received")
+
+    async with aiohttp.ClientSession() as session:
+        daily_rest_helper = DailyRESTHelper(
+            daily_api_key=os.getenv("DAILY_API_KEY", ""),
+            daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+            aiohttp_session=session,
+        )
+
+        # Create new Daily room
+        room = await daily_rest_helper.create_room(DailyRoomParams())
+        if not room.url:
+            raise HTTPException(
+                status_code=500,
+                detail="Unable to create room",
+            )
+        logger.info(f"Created room: {room.url}")
+
+        # Create bot token for room
+        token = await daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
+        if not token:
+            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+        logger.info(f"Bot token created: {token}")
+
+        # Spawn a new bot process
+        launch_bot_process.spawn(room_url=room.url, token=token)
+
+        # Return room URL to the user to join
+        # Note: in production, you would want to return a token to the user
+        return JSONResponse(content={"room_url": room.url, token: token})

examples/deployment/modal-example/bot.py

@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main(room_url: str, token: str):
+    transport = DailyTransport(
+        room_url,
+        token,
+        "bot",
+        DailyParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            transcription_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
+    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):
+        await transport.capture_participant_transcription(participant["id"])
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        await task.cancel()
+
+    runner = PipelineRunner()
+
+    await runner.run(task)
+
+
+def _voice_bot_process(room_url: str, token: str):
+    asyncio.run(main(room_url, token))

examples/deployment/modal-example/client/javascript/.gitignore

@@ -1 +0,0 @@
-node_modules

examples/deployment/modal-example/client/javascript/README.md

@@ -1,29 +0,0 @@
-# JavaScript Implementation
-
-Basic implementation using the [Pipecat JavaScript SDK](https://docs.pipecat.ai/client/js/introduction).
-
-## Setup
-
-1. Deploy the Modal server. See the main [README](../../README).
-
-2. Navigate to the `client/javascript` directory:
-
-```bash
-cd client/javascript
-```
-
-3. Modify the baseUrl in src/app.js to point to your deployed Modal endpoint
-
-4. Install dependencies:
-
-```bash
-npm install
-```
-
-5. Run the client app:
-
-```
-npm run dev
-```
-
-6. Visit http://localhost:5173 in your browser.

examples/deployment/modal-example/client/javascript/index.html

@@ -1,49 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>AI Chatbot</title>
-  </head>
-
-  <body>
-    <div class="container">
-      <div class="status-bar">
-        <div class="status">
-          Status: <span id="connection-status">Disconnected</span>
-        </div>
-        <div class="controls">
-          <select id="bot-selector">
-            <option value="openai">OpenAI</option>
-            <option value="gemini">Gemini</option>
-            <option value="vllm">Llama</option>
-          </select>
-          <button id="connect-btn">Connect</button>
-          <button id="disconnect-btn" disabled>Disconnect</button>
-        </div>
-      </div>
-
-      <div class="main-content">
-        <div class="bot-container">
-          <div id="bot-video-container"></div>
-          <audio id="bot-audio" autoplay></audio>
-        </div>
-      </div>
-
-      <div class="device-bar">
-        <div class="device-controls">
-          <select id="device-selector"></select>
-          <button id="mic-toggle-btn">Mute Mic</button>
-        </div>
-      </div>
-
-      <div class="debug-panel">
-        <h3>Debug Info</h3>
-        <div id="debug-log"></div>
-      </div>
-    </div>
-
-    <script type="module" src="/src/app.js"></script>
-    <link rel="stylesheet" href="/src/style.css" />
-  </body>
-</html>

examples/deployment/modal-example/client/javascript/package.json

@@ -1,21 +0,0 @@
-{
-  "name": "client",
-  "version": "1.0.0",
-  "main": "index.js",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview"
-  },
-  "keywords": [],
-  "author": "",
-  "license": "ISC",
-  "description": "",
-  "devDependencies": {
-    "vite": "^6.3.5"
-  },
-  "dependencies": {
-    "@pipecat-ai/client-js": "^0.3.5",
-    "@pipecat-ai/daily-transport": "^0.3.10"
-  }
-}

examples/deployment/modal-example/client/javascript/src/app.js

@@ -1,381 +0,0 @@
-/**
- * Copyright (c) 2024–2025, Daily
- *
- * SPDX-License-Identifier: BSD 2-Clause License
- */
-
-/**
- * RTVI Client Implementation
- *
- * This client connects to an RTVI-compatible bot server using WebRTC (via Daily).
- * It handles audio/video streaming and manages the connection lifecycle.
- *
- * Requirements:
- * - A running RTVI bot server (defaults to http://localhost:7860)
- * - The server must implement the /connect endpoint that returns Daily.co room credentials
- * - Browser with WebRTC support
- */
-
-import { RTVIClient, RTVIEvent } from '@pipecat-ai/client-js';
-import { DailyTransport } from '@pipecat-ai/daily-transport';
-
-/**
- * ChatbotClient handles the connection and media management for a real-time
- * voice and video interaction with an AI bot.
- */
-class ChatbotClient {
-  constructor() {
-    // Initialize client state
-    this.rtviClient = null;
-    this.setupDOMElements();
-    this.initializeClientAndTransport();
-    this.setupEventListeners();
-  }
-
-  /**
-   * Set up references to DOM elements and create necessary media elements
-   */
-  setupDOMElements() {
-    // Get references to UI control elements
-    this.connectBtn = document.getElementById('connect-btn');
-    this.disconnectBtn = document.getElementById('disconnect-btn');
-    this.statusSpan = document.getElementById('connection-status');
-    this.debugLog = document.getElementById('debug-log');
-    this.botVideoContainer = document.getElementById('bot-video-container');
-    this.deviceSelector = document.getElementById('device-selector');
-
-    // Create an audio element for bot's voice output
-    this.botAudio = document.createElement('audio');
-    this.botAudio.autoplay = true;
-    this.botAudio.playsInline = true;
-    document.body.appendChild(this.botAudio);
-  }
-
-  /**
-   * Set up event listeners for connect/disconnect buttons
-   */
-  setupEventListeners() {
-    this.connectBtn.addEventListener('click', () => this.connect());
-    this.disconnectBtn.addEventListener('click', () => this.disconnect());
-
-    // Populate device selector
-    this.rtviClient.getAllMics().then((mics) => {
-      console.log('Available mics:', mics);
-      mics.forEach((device) => {
-        const option = document.createElement('option');
-        option.value = device.deviceId;
-        option.textContent = device.label || `Microphone ${device.deviceId}`;
-        this.deviceSelector.appendChild(option);
-      });
-    });
-    this.deviceSelector.addEventListener('change', (event) => {
-      const selectedDeviceId = event.target.value;
-      console.log('Selected device ID:', selectedDeviceId);
-      this.rtviClient.updateMic(selectedDeviceId);
-    });
-
-    // Handle mic mute/unmute toggle
-    const micToggleBtn = document.getElementById('mic-toggle-btn');
-
-    micToggleBtn.addEventListener('click', () => {
-      let micEnabled = this.rtviClient.isMicEnabled;
-      micToggleBtn.textContent = micEnabled ? 'Unmute Mic' : 'Mute Mic';
-      this.rtviClient.enableMic(!micEnabled);
-      // Add logic to mute/unmute the mic
-      if (micEnabled) {
-        console.log('Mic muted');
-        // Add code to mute the mic
-      } else {
-        console.log('Mic unmuted');
-        // Add code to unmute the mic
-      }
-    });
-  }
-
-  /**
-   * Set up the RTVI client and Daily transport
-   */
-  async initializeClientAndTransport() {
-    // Initialize the RTVI client with a DailyTransport and our configuration
-    this.rtviClient = new RTVIClient({
-      transport: new DailyTransport(),
-      params: {
-        // REPLACE WITH YOUR MODAL URL ENDPOINT
-        baseUrl:
-          'https://<Modal workspace>--pipecat-modal-bot-launcher.modal.run',
-        endpoints: {
-          connect: '/connect',
-        },
-        requestData: {
-          bot_name: 'openai',
-        },
-      },
-      enableMic: true, // Enable microphone for user input
-      enableCam: false,
-      callbacks: {
-        // Handle connection state changes
-        onConnected: () => {
-          this.updateStatus('Connected');
-          this.connectBtn.disabled = true;
-          this.disconnectBtn.disabled = false;
-          this.log('Client connected');
-        },
-        onDisconnected: () => {
-          this.updateStatus('Disconnected');
-          this.connectBtn.disabled = false;
-          this.disconnectBtn.disabled = true;
-          this.log('Client disconnected');
-        },
-        // Handle transport state changes
-        onTransportStateChanged: (state) => {
-          this.updateStatus(`Transport: ${state}`);
-          this.log(`Transport state changed: ${state}`);
-          if (state === 'connecting') {
-            window.startTime = Date.now();
-          }
-          if (state === 'ready') {
-            this.setupMediaTracks();
-            console.warn('TIME TO BOT READY:', Date.now() - window.startTime);
-          }
-        },
-        // Handle bot connection events
-        onBotConnected: (participant) => {
-          this.log(`Bot connected: ${JSON.stringify(participant)}`);
-        },
-        onBotDisconnected: (participant) => {
-          this.log(`Bot disconnected: ${JSON.stringify(participant)}`);
-        },
-        onBotReady: (data) => {
-          this.log(`Bot ready: ${JSON.stringify(data)}`);
-          this.setupMediaTracks();
-        },
-        // Transcript events
-        onUserTranscript: (data) => {
-          // Only log final transcripts
-          if (data.final) {
-            this.log(`User: ${data.text}`);
-          }
-        },
-        onBotTranscript: (data) => {
-          this.log(`Bot: ${data.text}`);
-        },
-        // Error handling
-        onMessageError: (error) => {
-          console.log('Message error:', error);
-        },
-        onMicUpdated: (data) => {
-          console.log('Mic updated:', data);
-          this.deviceSelector.value = data.deviceId;
-        },
-        onError: (error) => {
-          console.log('Error:', JSON.stringify(error));
-        },
-      },
-    });
-
-    // Set up listeners for media track events
-    this.setupTrackListeners();
-
-    await this.rtviClient.initDevices();
-    window.client = this.rtviClient;
-  }
-
-  /**
-   * Add a timestamped message to the debug log
-   */
-  log(message) {
-    const entry = document.createElement('div');
-    entry.textContent = `${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
-    }
-
-    this.debugLog.appendChild(entry);
-    this.debugLog.scrollTop = this.debugLog.scrollHeight;
-    console.log(message);
-  }
-
-  /**
-   * Update the connection status display
-   */
-  updateStatus(status) {
-    this.statusSpan.textContent = status;
-    this.log(`Status: ${status}`);
-  }
-
-  /**
-   * Check for available media tracks and set them up if present
-   * This is called when the bot is ready or when the transport state changes to ready
-   */
-  setupMediaTracks() {
-    if (!this.rtviClient) return;
-
-    // Get current tracks from the client
-    const tracks = this.rtviClient.tracks();
-
-    // Set up any available bot tracks
-    if (tracks.bot?.audio) {
-      this.setupAudioTrack(tracks.bot.audio);
-    }
-    if (tracks.bot?.video) {
-      this.setupVideoTrack(tracks.bot.video);
-    }
-  }
-
-  /**
-   * Set up listeners for track events (start/stop)
-   * This handles new tracks being added during the session
-   */
-  setupTrackListeners() {
-    if (!this.rtviClient) return;
-
-    // Listen for new tracks starting
-    this.rtviClient.on(RTVIEvent.TrackStarted, (track, participant) => {
-      // Only handle non-local (bot) tracks
-      if (!participant?.local) {
-        if (track.kind === 'audio') {
-          this.setupAudioTrack(track);
-        } else if (track.kind === 'video') {
-          this.setupVideoTrack(track);
-        }
-        this.log(
-          `Track started event: ${track.kind} from ${
-            participant?.name || 'unknown'
-          }`
-        );
-      } else {
-        this.log('Local mic unmuted');
-      }
-    });
-
-    // Listen for tracks stopping
-    this.rtviClient.on(RTVIEvent.TrackStopped, (track, participant) => {
-      if (participant.local) {
-        this.log('Local mic muted');
-        return;
-      }
-      this.log(
-        `Track stopped event: ${track.kind} from ${
-          participant?.name || 'unknown'
-        }`
-      );
-    });
-  }
-
-  /**
-   * Set up an audio track for playback
-   * Handles both initial setup and track updates
-   */
-  setupAudioTrack(track) {
-    this.log('Setting up audio track');
-    // Check if we're already playing this track
-    if (this.botAudio.srcObject) {
-      const oldTrack = this.botAudio.srcObject.getAudioTracks()[0];
-      if (oldTrack?.id === track.id) return;
-    }
-    // Create a new MediaStream with the track and set it as the audio source
-    this.botAudio.srcObject = new MediaStream([track]);
-  }
-
-  /**
-   * Set up a video track for display
-   * Handles both initial setup and track updates
-   */
-  setupVideoTrack(track) {
-    this.log('Setting up video track');
-    const videoEl = document.createElement('video');
-    videoEl.autoplay = true;
-    videoEl.playsInline = true;
-    videoEl.muted = true;
-    videoEl.style.width = '100%';
-    videoEl.style.height = '100%';
-    videoEl.style.objectFit = 'cover';
-
-    // Check if we're already displaying this track
-    if (this.botVideoContainer.querySelector('video')?.srcObject) {
-      const oldTrack = this.botVideoContainer
-        .querySelector('video')
-        .srcObject.getVideoTracks()[0];
-      if (oldTrack?.id === track.id) return;
-    }
-
-    // Create a new MediaStream with the track and set it as the video source
-    videoEl.srcObject = new MediaStream([track]);
-    this.botVideoContainer.innerHTML = '';
-    this.botVideoContainer.appendChild(videoEl);
-  }
-
-  /**
-   * Initialize and connect to the bot
-   * This sets up the RTVI client, initializes devices, and establishes the connection
-   */
-  async connect() {
-    try {
-      const botSelector = document.getElementById('bot-selector');
-      const selectedBot = botSelector.value;
-      this.rtviClient.params.requestData.bot_name = selectedBot;
-
-      // Initialize audio/video devices
-      this.log('Initializing devices...');
-      await this.rtviClient.initDevices();
-
-      // Connect to the bot
-      this.log(`Connecting to bot: ${selectedBot}`);
-      await this.rtviClient.connect();
-
-      this.log('Connection complete');
-    } catch (error) {
-      // Handle any errors during connection
-      console.error('Connection error:', error);
-      this.log(`Error connecting: ${JSON.stringify(error.message)}`);
-      this.log(`Error stack: ${error.stack}`);
-      this.updateStatus('Error');
-
-      // Clean up if there's an error
-      if (this.rtviClient) {
-        try {
-          await this.rtviClient.disconnect();
-        } catch (disconnectError) {
-          this.log(`Error during disconnect: ${disconnectError.message}`);
-        }
-      }
-    }
-  }
-
-  /**
-   * Disconnect from the bot and clean up media resources
-   */
-  async disconnect() {
-    if (this.rtviClient) {
-      try {
-        // Disconnect the RTVI client
-        await this.rtviClient.disconnect();
-
-        // Clean up audio
-        if (this.botAudio.srcObject) {
-          this.botAudio.srcObject.getTracks().forEach((track) => track.stop());
-          this.botAudio.srcObject = null;
-        }
-
-        // Clean up video
-        if (this.botVideoContainer.querySelector('video')?.srcObject) {
-          const video = this.botVideoContainer.querySelector('video');
-          video.srcObject.getTracks().forEach((track) => track.stop());
-          video.srcObject = null;
-        }
-        this.botVideoContainer.innerHTML = '';
-      } catch (error) {
-        this.log(`Error disconnecting: ${error.message}`);
-      }
-    }
-  }
-}
-
-// Initialize the client when the page loads
-window.addEventListener('DOMContentLoaded', () => {
-  new ChatbotClient();
-});

examples/deployment/modal-example/client/javascript/src/style.css

@@ -1,135 +0,0 @@
-body {
-  margin: 0;
-  padding: 20px;
-  font-family: Arial, sans-serif;
-  background-color: #f0f0f0;
-}
-
-.container {
-  max-width: 1200px;
-  margin: 0 auto;
-}
-
-.status-bar,
-.device-bar {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  padding: 10px;
-  background-color: #fff;
-  border-radius: 8px;
-  margin-bottom: 20px;
-}
-
-.controls,
-.device-controls {
-  display: flex;
-  align-items: center;
-  gap: 10px; /* Adds spacing between elements */
-}
-
-.device-controls {
-  margin-left: auto;
-}
-
-.controls button,
-.device-controls button {
-  padding: 8px 16px;
-  margin-left: 10px;
-  border: none;
-  border-radius: 4px;
-  cursor: pointer;
-}
-
-#bot-selector,
-#device-selector {
-  padding: 8px 16px;
-  padding-right: 40px;
-  border: none;
-  border-radius: 4px;
-  background-color: #6c757d; /* Gray background */
-  color: white; /* White text */
-  cursor: pointer;
-  appearance: none; /* Removes default browser styling for dropdowns */
-  background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' fill='white'%3E%3Cpath d='M7 10l5 5 5-5z'/%3E%3C/svg%3E"); /* Custom arrow */
-  background-repeat: no-repeat;
-  background-position: right 8px center; /* Position the arrow */
-}
-
-#bot-selector:focus,
-#device-selector:focus {
-  outline: none;
-  box-shadow: 0 0 4px rgba(0, 0, 0, 0.3); /* Add a subtle focus effect */
-}
-
-#connect-btn {
-  background-color: #4caf50;
-  color: white;
-}
-
-#disconnect-btn {
-  background-color: #f44336;
-  color: white;
-}
-
-#mic-toggle-btn {
-}
-
-button:disabled {
-  opacity: 0.5;
-  cursor: not-allowed;
-}
-
-.main-content {
-  background-color: #fff;
-  border-radius: 8px;
-  padding: 20px;
-  margin-bottom: 20px;
-}
-
-.bot-container {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-}
-
-#bot-video-container {
-  width: 640px;
-  height: 360px;
-  background-color: #e0e0e0;
-  border-radius: 8px;
-  margin: 20px auto;
-  overflow: hidden;
-  display: flex;
-  align-items: center;
-  justify-content: center;
-}
-
-#bot-video-container video {
-  width: 100%;
-  height: 100%;
-  object-fit: cover;
-}
-
-.debug-panel {
-  background-color: #fff;
-  border-radius: 8px;
-  padding: 20px;
-}
-
-.debug-panel h3 {
-  margin: 0 0 10px 0;
-  font-size: 16px;
-  font-weight: bold;
-}
-
-#debug-log {
-  height: 200px;
-  overflow-y: auto;
-  background-color: #f8f8f8;
-  padding: 10px;
-  border-radius: 4px;
-  font-family: monospace;
-  font-size: 12px;
-  line-height: 1.4;
-}

examples/deployment/modal-example/env.example

@@ -0,0 +1,3 @@
+DAILY_API_KEY=
+OPENAI_API_KEY=
+CARTESIA_API_KEY=

examples/deployment/modal-example/requirements.txt

@@ -0,0 +1,4 @@
+python-dotenv==1.0.1
+modal==0.71.3
+pipecat-ai[daily,silero,cartesia,openai]
+fastapi==0.115.6

examples/deployment/modal-example/server/app.py

@@ -1,307 +0,0 @@
-"""modal_example.
-
-This module shows a simple example of how to deploy a bot using Modal and FastAPI.
-
-It includes:
-- FastAPI endpoints for starting agents and checking bot statuses.
-- Dynamic loading of bot implementations.
-- Use of a Daily transport for bot communication.
-"""
-
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import importlib
-import os
-from contextlib import asynccontextmanager
-from typing import Any, Dict, Literal
-
-import aiohttp
-import modal
-from fastapi import APIRouter, FastAPI, HTTPException
-from fastapi.responses import JSONResponse, RedirectResponse
-from pydantic import BaseModel
-
-# container specifications for the FastAPI web server
-web_image = (
-    modal.Image.debian_slim(python_version="3.13")
-    .pip_install_from_requirements("requirements.txt")
-    .pip_install("pipecat-ai[daily]")
-    .add_local_dir("src", remote_path="/root/src")
-)
-
-# container specifications for the Pipecat pipeline
-bot_image = (
-    modal.Image.debian_slim(python_version="3.13")
-    .apt_install("ffmpeg")
-    .pip_install_from_requirements("requirements.txt")
-    .pip_install("pipecat-ai[daily,elevenlabs,openai,silero,google]")
-    .add_local_dir("src", remote_path="/root/src")
-)
-
-app = modal.App("pipecat-modal", secrets=[modal.Secret.from_dotenv()])
-
-router = APIRouter()
-
-bot_jobs = {}
-daily_helpers = {}
-
-# Names of all supported bot implementations
-# These correspond to the bot files in the src directory
-BotName = Literal["openai", "gemini", "vllm"]
-
-
-def cleanup():
-    """Cleanup function to terminate all bot processes.
-
-    Called during server shutdown.
-    """
-    for entry in bot_jobs.values():
-        func = modal.FunctionCall.from_id(entry[0])
-        if func:
-            func.cancel()
-
-
-def get_bot_file(bot_name: BotName) -> str:
-    """Retrieve the bot file name corresponding to the provided bot_name.
-
-    Args:
-        bot_name (BotName): The name of the bot (e.g., 'openai', 'gemini', 'vllm').
-
-    Returns:
-        str: The file name corresponding to the bot implementation.
-
-    Raises:
-        ValueError: If the bot name is invalid or not supported.
-    """
-    # bot_implementation = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
-    bot_implementation = bot_name.lower().strip()
-    if not bot_implementation:
-        bot_implementation = "openai"
-    if bot_implementation not in ["openai", "gemini", "vllm"]:
-        raise ValueError(
-            f"Invalid BOT_IMPLEMENTATION: {bot_implementation}. Must be 'openai' or 'gemini' or 'vllm'"
-        )
-
-    return f"bot_{bot_implementation}"
-
-
-def get_runner(path: str, bot_file: str) -> callable:
-    """Dynamically import the run_bot function based on the bot name.
-
-    Args:
-        path (str): The path to the bot files (e.g., 'src').
-        bot_file (str): The file name of the bot implementation (e.g., 'openai', 'gemini', 'vllm').
-
-    Returns:
-        function: The run_bot function from the specified bot module.
-
-    Raises:
-        ImportError: If the specified bot module or run_bot function is not found.
-    """
-    try:
-        # Dynamically construct the module name
-        module_name = f"{path}.{bot_file}"
-        # Import the module
-        module = importlib.import_module(module_name)
-        # Get the run_bot function from the module
-        return getattr(module, "run_bot")
-    except (ImportError, AttributeError) as e:
-        raise ImportError(f"Failed to import run_bot from {module_name}: {e}")
-
-
-async def create_room_and_token() -> tuple[str, str]:
-    """Create a Daily room and generate an authentication token.
-
-    This function checks for existing room URL and token in the environment variables.
-    If not found, it creates a new room using the Daily API and generates a token for it.
-
-    Returns:
-        tuple[str, str]: A tuple containing the room URL and the authentication token.
-
-    Raises:
-        HTTPException: If room creation or token generation fails.
-    """
-    from pipecat.transports.services.helpers.daily_rest import DailyRoomParams
-
-    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", None)
-    token = os.getenv("DAILY_SAMPLE_ROOM_TOKEN", None)
-    if not room_url:
-        room = await daily_helpers["rest"].create_room(DailyRoomParams())
-        if not room.url:
-            raise HTTPException(status_code=500, detail="Failed to create room")
-        room_url = room.url
-
-        token = await daily_helpers["rest"].get_token(room_url)
-        if not token:
-            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room_url}")
-
-    return room_url, token
-
-
-@app.function(image=bot_image, min_containers=1)
-async def bot_runner(room_url, token, bot_name: BotName = "openai"):
-    """Launch the provided bot process, providing the given room URL and token for the bot to join.
-
-    Args:
-        room_url (str): The URL of the Daily room where the bot and client will communicate.
-        token (str): The authentication token for the room.
-        bot_name (BotName): The name of the bot implementation to use. Defaults to "openai".
-
-    Raises:
-        HTTPException: If the bot pipeline fails to start.
-    """
-    try:
-        path = "src"
-        bot_file = get_bot_file(bot_name)
-        run_bot = get_runner(path, bot_file)
-
-        print(f"Starting bot process: {bot_file} -u {room_url} -t {token}")
-        await run_bot(room_url, token)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Failed to start bot pipeline: {e}")
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    """FastAPI lifespan manager that handles startup and shutdown tasks.
-
-    - Creates aiohttp session
-    - Initializes Daily API helper
-    - Cleans up resources on shutdown
-    """
-    from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
-
-    aiohttp_session = aiohttp.ClientSession()
-    daily_helpers["rest"] = DailyRESTHelper(
-        daily_api_key=os.getenv("DAILY_API_KEY", ""),
-        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-        aiohttp_session=aiohttp_session,
-    )
-    yield
-    await aiohttp_session.close()
-    cleanup()
-
-
-class ConnectData(BaseModel):
-    """Data provided by client to specify the bot pipeline.
-
-    Attributes:
-        bot_name (BotName): The name of the bot to connect to. Defaults to "openai".
-    """
-
-    bot_name: BotName = "openai"
-
-
-async def start(data: ConnectData):
-    """Internal method to start a bot agent and return the room URL and token.
-
-    Args:
-        data (ConnectData): The data containing the bot name to use.
-
-    Returns:
-        tuple[str, str]: A tuple containing the room URL and token.
-    """
-    room_url, token = await create_room_and_token()
-    launch_bot_func = modal.Function.from_name("pipecat-modal", "bot_runner")
-    function_id = launch_bot_func.spawn(room_url, token, data.bot_name)
-    bot_jobs[function_id] = (function_id, room_url)
-
-    return room_url, token
-
-
-@router.get("/")
-async def start_agent():
-    """A user endpoint for launching a bot agent and redirecting to the created room URL.
-
-    This function retrieves the bot implementation from the environment,
-    starts the bot agent, and redirects the user to the room URL to
-    interact with the bot through a Daily Prebuilt Interface.
-
-    Returns:
-        RedirectResponse: A response that redirects to the room URL.
-    """
-    bot_name = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
-    print(f"Starting bot: {bot_name}")
-    room_url, token = await start(ConnectData(bot_name=bot_name))
-
-    return RedirectResponse(room_url)
-
-
-@router.post("/connect")
-async def rtvi_connect(data: ConnectData) -> Dict[Any, Any]:
-    """A user endpoint for launching a bot agent and retrieving the room/token credentials.
-
-    This function retrieves the bot implementation from the request, if provided,
-    starts the bot agent, and returns the room URL and token for the bot. This allows the
-    client to then connect to the bot using their own RTVI interface.
-
-    Args:
-        data (ConnectData): Optional. The data containing the bot name to use.
-
-    Returns:
-        Dict[Any, Any]: A dictionary containing the room URL and token.
-    """
-    print(f"Starting bot: {data.bot_name}")
-    if data is None or not data.bot_name:
-        data.bot_name = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
-    room_url, token = await start(data)
-
-    return {"room_url": room_url, "token": token}
-
-
-@router.get("/status/{fid}")
-def get_status(fid: str):
-    """Retrieve the status of a bot process by its function ID.
-
-    Args:
-        fid (str): The function ID of the bot process.
-
-    Returns:
-        JSONResponse: A JSON response containing the bot's status and result code.
-
-    Raises:
-        HTTPException: If the bot process with the given ID is not found.
-    """
-    func = modal.FunctionCall.from_id(fid)
-    if not func:
-        raise HTTPException(status_code=404, detail=f"Bot with process id: {fid} not found")
-
-    try:
-        result = func.get(timeout=0)
-        return JSONResponse({"bot_id": fid, "status": "finished", "code": result})
-    except modal.exception.OutputExpiredError:
-        return JSONResponse({"bot_id": fid, "status": "finished", "code": 404})
-    except TimeoutError:
-        return JSONResponse({"bot_id": fid, "status": "running", "code": 202})
-
-
-@app.function(image=web_image, min_containers=1)
-@modal.concurrent(max_inputs=1)
-@modal.asgi_app()
-def fastapi_app():
-    """Create and configure the FastAPI application.
-
-    This function initializes the FastAPI app with middleware, routes, and lifespan management.
-    It is decorated to be used as a Modal ASGI app.
-    """
-    from fastapi.middleware.cors import CORSMiddleware
-
-    # Initialize FastAPI app
-    web_app = FastAPI(lifespan=lifespan)
-
-    web_app.add_middleware(
-        CORSMiddleware,
-        allow_origins=["*"],
-        allow_credentials=True,
-        allow_methods=["*"],
-        allow_headers=["*"],
-    )
-
-    # Include the endpoints from endpoints.py
-    web_app.include_router(router)
-
-    return web_app

examples/deployment/modal-example/server/env.example

@@ -1,14 +0,0 @@
-DAILY_API_KEY=
-
-# determines which bot file to default to: 'openai', 'gemini', or 'vllm'
-BOT_IMPLEMENTATION=openai
-
-# needed for the openai bot pipeline
-OPENAI_API_KEY=
-ELEVENLABS_API_KEY=
-
-# needed for the gemini live bot pipeline
-GOOGLE_API_KEY=
-
-# needed if you modified the API Key for your self-hosted LLM
-VLLM_API_KEY=

examples/deployment/modal-example/server/requirements.txt

@@ -1,2 +0,0 @@
-python-dotenv==1.0.1
-modal==0.71.3

examples/deployment/modal-example/server/src/bot_gemini.py

@@ -1,198 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Gemini Bot Implementation.
-
-This module implements a chatbot using Google's Gemini Multimodal Live model.
-It includes:
-- Real-time audio/video interaction through Daily
-- Animated robot avatar
-- Speech-to-speech model
-
-The bot runs as part of a pipeline that processes audio/video frames and manages
-the conversation flow using Gemini's streaming capabilities.
-"""
-
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import (
-    BotStartedSpeakingFrame,
-    BotStoppedSpeakingFrame,
-    Frame,
-    OutputImageRawFrame,
-    SpriteFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
-from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-try:
-    logger.remove(0)
-    logger.add(sys.stderr, level="DEBUG")
-except ValueError:
-    # Handle the case where logger is already initialized
-    pass
-
-sprites = []
-script_dir = os.path.dirname(__file__)
-
-for i in range(1, 26):
-    # Build the full path to the image file
-    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
-    # Get the filename without the extension to use as the dictionary key
-    # Open the image and convert it to bytes
-    with Image.open(full_path) as img:
-        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
-
-# Create a smooth animation by adding reversed frames
-flipped = sprites[::-1]
-sprites.extend(flipped)
-
-# Define static and animated states
-quiet_frame = sprites[0]  # Static frame for when bot is listening
-talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
-
-
-class TalkingAnimation(FrameProcessor):
-    """Manages the bot's visual animation states.
-
-    Switches between static (listening) and animated (talking) states based on
-    the bot's current speaking status.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self._is_talking = False
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process incoming frames and update animation state.
-
-        Args:
-            frame: The incoming frame to process
-            direction: The direction of frame flow in the pipeline
-        """
-        await super().process_frame(frame, direction)
-
-        # Switch to talking animation when bot starts speaking
-        if isinstance(frame, BotStartedSpeakingFrame):
-            if not self._is_talking:
-                await self.push_frame(talking_frame)
-                self._is_talking = True
-        # Return to static frame when bot stops speaking
-        elif isinstance(frame, BotStoppedSpeakingFrame):
-            await self.push_frame(quiet_frame)
-            self._is_talking = False
-
-        await self.push_frame(frame, direction)
-
-
-async def run_bot(room_url: str, token: str):
-    """Main bot execution function.
-
-    Sets up and runs the bot pipeline including:
-    - Daily video transport with specific audio parameters
-    - Gemini Live multimodal model integration
-    - Voice activity detection
-    - Animation processing
-    - RTVI event handling
-    """
-    # Set up Daily transport with specific audio/video parameters for Gemini
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Chatbot",
-        DailyParams(
-            audio_out_enabled=True,
-            camera_out_enabled=True,
-            camera_out_width=1024,
-            camera_out_height=576,
-            vad_enabled=True,
-            vad_audio_passthrough=True,
-            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
-        ),
-    )
-
-    # Initialize the Gemini Multimodal Live model
-    llm = GeminiMultimodalLiveLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
-        transcribe_user_audio=True,
-    )
-
-    messages = [
-        {
-            "role": "user",
-            "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.",
-        },
-    ]
-
-    # Set up conversation context and management
-    # The context_aggregator will automatically collect conversation context
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    ta = TalkingAnimation()
-
-    #
-    # RTVI events for Pipecat client UI
-    #
-    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            rtvi,
-            context_aggregator.user(),
-            llm,
-            ta,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        observers=[RTVIObserver(rtvi)],
-    )
-    await task.queue_frame(quiet_frame)
-
-    @rtvi.event_handler("on_client_ready")
-    async def on_client_ready(rtvi):
-        await rtvi.set_bot_ready()
-        # Kick off the conversation
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        await transport.capture_participant_transcription(participant["id"])
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        print(f"Participant left: {participant}")
-        await task.cancel()
-
-    runner = PipelineRunner()
-
-    await runner.run(task)

examples/deployment/modal-example/server/src/bot_openai.py

@@ -1,226 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""OpenAI Bot Implementation.
-
-This module implements a chatbot using OpenAI's GPT-4 model for natural language
-processing. It includes:
-- Real-time audio/video interaction through Daily
-- Animated robot avatar
-- Text-to-speech using ElevenLabs
-- Support for both English and Spanish
-
-The bot runs as part of a pipeline that processes audio/video frames and manages
-the conversation flow.
-"""
-
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import (
-    BotStartedSpeakingFrame,
-    BotStoppedSpeakingFrame,
-    Frame,
-    OutputImageRawFrame,
-    SpriteFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-try:
-    logger.remove(0)
-    logger.add(sys.stderr, level="DEBUG")
-except ValueError:
-    # Handle the case where logger is already initialized
-    pass
-
-sprites = []
-script_dir = os.path.dirname(__file__)
-
-# Load sequential animation frames
-for i in range(1, 26):
-    # Build the full path to the image file
-    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
-    # Get the filename without the extension to use as the dictionary key
-    # Open the image and convert it to bytes
-    with Image.open(full_path) as img:
-        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
-
-# Create a smooth animation by adding reversed frames
-flipped = sprites[::-1]
-sprites.extend(flipped)
-
-# Define static and animated states
-quiet_frame = sprites[0]  # Static frame for when bot is listening
-talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
-
-
-class TalkingAnimation(FrameProcessor):
-    """Manages the bot's visual animation states.
-
-    Switches between static (listening) and animated (talking) states based on
-    the bot's current speaking status.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self._is_talking = False
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process incoming frames and update animation state.
-
-        Args:
-            frame: The incoming frame to process
-            direction: The direction of frame flow in the pipeline
-        """
-        await super().process_frame(frame, direction)
-
-        # Switch to talking animation when bot starts speaking
-        if isinstance(frame, BotStartedSpeakingFrame):
-            if not self._is_talking:
-                await self.push_frame(talking_frame)
-                self._is_talking = True
-        # Return to static frame when bot stops speaking
-        elif isinstance(frame, BotStoppedSpeakingFrame):
-            await self.push_frame(quiet_frame)
-            self._is_talking = False
-
-        await self.push_frame(frame, direction)
-
-
-async def run_bot(room_url: str, token: str):
-    """Main bot execution function.
-
-    Sets up and runs the bot pipeline including:
-    - Daily video transport
-    - Speech-to-text and text-to-speech services
-    - Language model integration
-    - Animation processing
-    - RTVI event handling
-    """
-    # Set up Daily transport with video/audio parameters
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Chatbot",
-        DailyParams(
-            audio_out_enabled=True,
-            camera_out_enabled=True,
-            camera_out_width=1024,
-            camera_out_height=576,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            transcription_enabled=True,
-            #
-            # Spanish
-            #
-            # transcription_settings=DailyTranscriptionSettings(
-            #     language="es",
-            #     tier="nova",
-            #     model="2-general"
-            # )
-        ),
-    )
-
-    # Initialize text-to-speech service
-    tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY"),
-        #
-        # English
-        #
-        voice_id="SAz9YHcvj6GT2YYXdXww",
-        #
-        # Spanish
-        #
-        # model="eleven_multilingual_v2",
-        # voice_id="gD1IexrzCvsXPHUuT0s3",
-    )
-
-    # Initialize LLM service
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            #
-            # English
-            #
-            "content": "You are an incessant one-upper. Start by asking the user how their day is going.",
-            #
-            # 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.",
-        },
-    ]
-
-    # Set up conversation context and management
-    # The context_aggregator will automatically collect conversation context
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    ta = TalkingAnimation()
-
-    #
-    # RTVI events for Pipecat client UI
-    #
-    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            rtvi,
-            context_aggregator.user(),
-            llm,
-            tts,
-            ta,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        observers=[RTVIObserver(rtvi)],
-    )
-    await task.queue_frame(quiet_frame)
-
-    @rtvi.event_handler("on_client_ready")
-    async def on_client_ready(rtvi):
-        await rtvi.set_bot_ready()
-        # Kick off the conversation
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        await transport.capture_participant_transcription(participant["id"])
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        print(f"Participant left: {participant}")
-        await task.cancel()
-
-    runner = PipelineRunner()
-
-    await runner.run(task)

examples/deployment/modal-example/server/src/bot_vllm.py

@@ -1,239 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""OpenAI Bot Implementation.
-
-This module implements a chatbot using OpenAI's GPT-4 model for natural language
-processing. It includes:
-- Real-time audio/video interaction through Daily
-- Animated robot avatar
-- Text-to-speech using ElevenLabs
-- Support for both English and Spanish
-
-The bot runs as part of a pipeline that processes audio/video frames and manages
-the conversation flow.
-"""
-
-import os
-import sys
-from typing import List
-
-from dotenv import load_dotenv
-from loguru import logger
-from openai.types.chat import ChatCompletionMessageParam
-from PIL import Image
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import (
-    BotStartedSpeakingFrame,
-    BotStoppedSpeakingFrame,
-    Frame,
-    OutputImageRawFrame,
-    SpriteFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-try:
-    logger.remove(0)
-    logger.add(sys.stderr, level="DEBUG")
-except ValueError:
-    # Handle the case where logger is already initialized
-    pass
-
-# REPLACE WITH YOUR MODAL URL ENDPOINT
-modal_url = "https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run"
-api_key = os.getenv("VLLM_API_KEY", "super-secret-key")
-
-
-sprites = []
-script_dir = os.path.dirname(__file__)
-
-# Load sequential animation frames
-for i in range(1, 26):
-    # Build the full path to the image file
-    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
-    # Get the filename without the extension to use as the dictionary key
-    # Open the image and convert it to bytes
-    with Image.open(full_path) as img:
-        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
-
-# Create a smooth animation by adding reversed frames
-flipped = sprites[::-1]
-sprites.extend(flipped)
-
-# Define static and animated states
-quiet_frame = sprites[0]  # Static frame for when bot is listening
-talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
-
-
-class TalkingAnimation(FrameProcessor):
-    """Manages the bot's visual animation states.
-
-    Switches between static (listening) and animated (talking) states based on
-    the bot's current speaking status.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self._is_talking = False
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process incoming frames and update animation state.
-
-        Args:
-            frame: The incoming frame to process
-            direction: The direction of frame flow in the pipeline
-        """
-        await super().process_frame(frame, direction)
-
-        # Switch to talking animation when bot starts speaking
-        if isinstance(frame, BotStartedSpeakingFrame):
-            if not self._is_talking:
-                await self.push_frame(talking_frame)
-                self._is_talking = True
-        # Return to static frame when bot stops speaking
-        elif isinstance(frame, BotStoppedSpeakingFrame):
-            await self.push_frame(quiet_frame)
-            self._is_talking = False
-
-        await self.push_frame(frame, direction)
-
-
-async def run_bot(room_url: str, token: str):
-    """Main bot execution function.
-
-    Sets up and runs the bot pipeline including:
-    - Daily video transport
-    - Speech-to-text and text-to-speech services
-    - Language model integration
-    - Animation processing
-    - RTVI event handling
-    """
-    # Set up Daily transport with video/audio parameters
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Chatbot",
-        DailyParams(
-            audio_out_enabled=True,
-            camera_out_enabled=True,
-            camera_out_width=1024,
-            camera_out_height=576,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            transcription_enabled=True,
-            #
-            # Spanish
-            #
-            # transcription_settings=DailyTranscriptionSettings(
-            #     language="es",
-            #     tier="nova",
-            #     model="2-general"
-            # )
-        ),
-    )
-
-    # Initialize text-to-speech service
-    tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY"),
-        #
-        # English
-        #
-        voice_id="D38z5RcWu1voky8WS1ja",
-        #
-        # Spanish
-        #
-        # model="eleven_multilingual_v2",
-        # voice_id="gD1IexrzCvsXPHUuT0s3",
-    )
-
-    # Initialize LLM service
-    llm = OpenAILLMService(
-        # To use OpenAI
-        api_key=api_key,
-        # Or, to use a local vLLM (or similar) api server
-        model="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",
-        base_url=f"{modal_url}/v1",
-    )
-
-    messages = [
-        {
-            "role": "system",
-            #
-            # English
-            #
-            "content": "You are a salesman for Modal, the cloud-native serverless Python computing platform.",
-            #
-            # 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.",
-        },
-    ]
-
-    # Set up conversation context and management
-    # The context_aggregator will automatically collect conversation context
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    ta = TalkingAnimation()
-
-    #
-    # RTVI events for Pipecat client UI
-    #
-    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            rtvi,
-            context_aggregator.user(),
-            llm,
-            tts,
-            ta,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        observers=[RTVIObserver(rtvi)],
-    )
-    await task.queue_frame(quiet_frame)
-
-    @rtvi.event_handler("on_client_ready")
-    async def on_client_ready(rtvi):
-        await rtvi.set_bot_ready()
-        # Kick off the conversation
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        await transport.capture_participant_transcription(participant["id"])
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        print(f"Participant left: {participant}")
-        await task.cancel()
-
-    runner = PipelineRunner()
-
-    await runner.run(task)

examples/deployment/modal-example/server/src/runner.py

@@ -1,84 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import argparse
-import asyncio
-import importlib
-import os
-
-
-def get_bot_file(arg_bot: str | None) -> str:
-    bot_implementation = arg_bot or os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
-    if not bot_implementation:
-        bot_implementation = "openai"
-    if bot_implementation not in ["openai", "gemini", "vllm"]:
-        raise ValueError(
-            f"Invalid BOT_IMPLEMENTATION: {bot_implementation}. Must be 'openai' or 'gemini'"
-        )
-    return f"bot_{bot_implementation}"
-
-
-def get_runner(bot_file: str):
-    """Dynamically import the run_bot function based on the bot name.
-
-    Args:
-        bot_name (str): The name of the bot implementation (e.g., 'openai', 'gemini').
-
-    Returns:
-        function: The run_bot function from the specified bot module.
-
-    Raises:
-        ImportError: If the specified bot module or run_bot function is not found.
-    """
-    try:
-        # Dynamically construct the module name
-        module_name = f"{bot_file}"
-        # Import the module
-        module = importlib.import_module(module_name)
-        # Get the run_bot function from the module
-        return getattr(module, "run_bot")
-    except (ImportError, AttributeError) as e:
-        raise ImportError(f"Failed to import run_bot from {module_name}: {e}")
-
-
-def main():
-    """Parse the args to launch the appropriate bot using the given room/token."""
-    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(
-        "-t",
-        "--token",
-        type=str,
-        required=False,
-        help="Daily room token",
-    )
-    parser.add_argument(
-        "-b",
-        "--bot",
-        type=str,
-        required=False,
-        help="Bot runner to use (e.g., openai, gemini)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
-    token = args.token or os.getenv("DAILY_SAMPLE_ROOM_TOKEN")
-    bot_file = get_bot_file(args.bot)
-
-    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."
-        )
-
-    run_bot = get_runner(bot_file)
-    asyncio.run(run_bot(url, token))
-
-
-if __name__ == "__main__":
-    main()

examples/deployment/pipecat-cloud-daily-pstn-server/README.md

@@ -100,28 +100,7 @@ phone numbers with valid values for your use case.
 
 ### Dialin Request
 
-The server will receive a request when a call is received from Daily. 
-The payload that the webhook received is as follows:
-```json
-{
-  // for dial-in from webhook
-  "To": "+14152251493",
-  "From": "+14158483432",
-  "callId": "string-contains-uuid",
-  "callDomain": "string-contains-uuid",
-  "sipHeaders": {
-    "X-My-Custom-Header": "value",
-    "x-caller": "+1234567890",
-    "x-called": "+1987654321", 
-   },
-}
-```
-The `To`, `From`, `callId`, `callDomain` fields are converted to 
-`snake_case` and mapped to `dialin_settings`. In addition, `sipHeader` 
-contains any custom SIP headers received by Daily on the SIP 
-interconnect address (`sip_uri`). These are headers sent from 
-Twilio or other external SIP platforms, for example, to send the 
-caller's phone number.
+The server will receive a request when a call is received from Daily.
 
 ### Dialout Request
 
@@ -179,7 +158,6 @@ curl -X POST http://localhost:3000/api/dial \
     "From": "+1987654321",
     "callId": "call-uuid-123",
     "callDomain": "domain-uuid-456",
-    "sipHeader": {},
     "dialout_settings": [
       {
         "phoneNumber": "+1234567890",

examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/server.py

@@ -39,11 +39,6 @@ class RoomRequest(BaseModel):
         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")
-    sipHeaders: Optional[Dict[str, Any]] = Field(
-        None,
-        alias="sip_headers",
-        description="Custom SIP headers received from the external SIP provider",
-    )
 
     class Config:
         populate_by_name = True
@@ -62,14 +57,6 @@ class RoomRequest(BaseModel):
     "callDomain": "string-contains-uuid"
     These need to be remapped to dialin_settings
 
-    In addition, we may receive in the body that can be 
-    sent to the bot as a custom field, sip_headers
-    "sipHeaders": {
-        "X-My-Custom-Header": "value",
-        "x-caller": "+14158483432",
-        "x-called": "+14152251493",
-    },
-
     "dialout_settings": [
         {"phoneNumber": "+14158483432", "callerId": "+14152251493"}, 
         {"sipUri": "sip:username@sip.hostname"}
@@ -170,7 +157,6 @@ async def dial(request: RoomRequest, raw_request: Request):
             "dialout_settings": request.dialout_settings,
             "voicemail_detection": request.voicemail_detection,
             "call_transfer": request.call_transfer,
-            "sip_headers": request.sipHeaders,  # passing the SIP headers to the bot
         },
     }
 

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/dial.js

@@ -65,7 +65,6 @@ export default async function handler(req, res) {
       From,
       callId,
       callDomain,
-      sipHeaders,
       dialout_settings,
       voicemail_detection,
       call_transfer
@@ -118,7 +117,6 @@ export default async function handler(req, res) {
         dialout_settings,
         voicemail_detection,
         call_transfer,
-        sip_headers: sipHeaders,
       },
     };
 

examples/deployment/pipecat-cloud-example/bot.py

@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import asyncio
 import os
 
 import aiohttp
@@ -22,23 +21,44 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+# Check if we're in local development mode
+LOCAL_RUN = os.getenv("LOCAL_RUN")
+if LOCAL_RUN:
+    import asyncio
+    import webbrowser
+
+    try:
+        from local_runner import configure
+    except ImportError:
+        logger.error("Could not import local_runner module. Local development mode may not work.")
+
 # Load environment variables
 load_dotenv(override=True)
 
-# Check if we're in local development mode
-LOCAL_RUN = os.getenv("LOCAL_RUN")
 
-
-async def main(transport: DailyTransport):
+async def main(room_url: str, token: str):
     """Main pipeline setup and execution function.
 
     Args:
-        transport: The DailyTransport object for the bot
+        room_url: The Daily room URL
+        token: The Daily room token
     """
-    logger.debug("Starting bot")
+    logger.debug("Starting bot in room: {}", room_url)
+
+    transport = DailyTransport(
+        room_url,
+        token,
+        "bot",
+        DailyParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            transcription_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
 
     tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
+        api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
     )
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
@@ -106,25 +126,10 @@ async def bot(args: DailySessionArguments):
         body: The configuration object from the request body
         session_id: The session ID for logging
     """
-    from pipecat.audio.filters.krisp_filter import KrispFilter
-
     logger.info(f"Bot process initialized {args.room_url} {args.token}")
 
-    transport = DailyTransport(
-        args.room_url,
-        args.token,
-        "Pipecat Bot",
-        DailyParams(
-            audio_in_enabled=True,
-            audio_in_filter=None if LOCAL_RUN else KrispFilter(),
-            audio_out_enabled=True,
-            transcription_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
     try:
-        await main(transport)
+        await main(args.room_url, args.token)
         logger.info("Bot process completed")
     except Exception as e:
         logger.exception(f"Error in bot process: {str(e)}")
@@ -132,27 +137,18 @@ async def bot(args: DailySessionArguments):
 
 
 # Local development functions
-async def local_daily():
+async def local_main():
     """Function for local development testing."""
-    from local_runner import configure
-
     try:
         async with aiohttp.ClientSession() as session:
             (room_url, token) = await configure(session)
-            transport = DailyTransport(
-                room_url,
-                token,
-                "Pipecat Bot",
-                DailyParams(
-                    audio_in_enabled=True,
-                    audio_out_enabled=True,
-                    transcription_enabled=True,
-                    vad_analyzer=SileroVADAnalyzer(),
-                ),
-            )
-
-            await main(transport)
-
+            logger.warning("_")
+            logger.warning("_")
+            logger.warning(f"Talk to your voice agent here: {room_url}")
+            logger.warning("_")
+            logger.warning("_")
+            webbrowser.open(room_url)
+            await main(room_url, token)
     except Exception as e:
         logger.exception(f"Error in local development mode: {e}")
 
@@ -160,6 +156,6 @@ async def local_daily():
 # Local development entry point
 if LOCAL_RUN and __name__ == "__main__":
     try:
-        asyncio.run(local_daily())
+        asyncio.run(local_main())
     except Exception as e:
         logger.exception(f"Failed to run in local mode: {e}")

examples/deployment/pipecat-cloud-example/env.example

@@ -1,4 +1,2 @@
 CARTESIA_API_KEY=
-OPENAI_API_KEY=
-# Local dev only
-DAILY_API_KEY=
+OPENAI_API_KEY=

examples/deployment/pipecat-cloud-example/local_runner.py

@@ -7,7 +7,6 @@
 import os
 
 import aiohttp
-from fastapi import HTTPException
 
 from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
 

examples/deployment/pipecat-cloud-example/pcc-deploy.toml

@@ -1,8 +1,6 @@
 agent_name = "my-first-agent"
 image = "your-username/my-first-agent:0.1"
-image_credentials = "your-dockerhub-creds"
 secret_set = "my-first-agent-secrets"
-enable_krisp = true
 
 [scaling]
 	min_instances = 0

examples/foundational/01-say-one-thing-piper.py

@@ -16,26 +16,24 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.piper.tts import PiperTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_out_enabled=True),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_out_enabled=True,
+        ),
+    )
+
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
         tts = PiperTTSService(
@@ -49,12 +47,12 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
         async def on_client_connected(transport, client):
             await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-        runner = PipelineRunner(handle_sigint=handle_sigint)
+        runner = PipelineRunner(handle_sigint=False)
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/01-say-one-thing-rime.py

@@ -16,25 +16,24 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.rime.tts import RimeHttpTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_out_enabled=True),
-}
 
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_out_enabled=True,
+        ),
+    )
+
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
         tts = RimeHttpTTSService(
@@ -50,12 +49,12 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
         async def on_client_connected(transport, client):
             await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-        runner = PipelineRunner(handle_sigint=handle_sigint)
+        runner = PipelineRunner(handle_sigint=False)
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/01-say-one-thing.py

@@ -15,26 +15,24 @@ 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.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_out_enabled=True),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_out_enabled=True,
+        ),
+    )
+
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
@@ -47,12 +45,12 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     async def on_client_connected(transport, client):
         await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/01c-fastpitch.py

@@ -15,26 +15,24 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.riva.tts import FastPitchTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_out_enabled=True),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_out_enabled=True,
+        ),
+    )
+
     tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
     task = PipelineTask(Pipeline([tts, transport.output()]))
@@ -44,12 +42,12 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     async def on_client_connected(transport, client):
         await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/02-llm-say-one-thing.py

@@ -16,26 +16,24 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_out_enabled=True),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_out_enabled=True,
+        ),
+    )
+
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
@@ -57,12 +55,12 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     async def on_client_connected(transport, client):
         await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/03-still-frame.py

@@ -16,32 +16,26 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.fal.image import FalImageGenService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-    "webrtc": lambda: TransportParams(
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            video_out_enabled=True,
+            video_out_width=1024,
+            video_out_height=1024,
+        ),
+    )
+
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
         imagegen = FalImageGenService(
@@ -60,14 +54,18 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
+
+        @transport.event_handler("on_client_closed")
+        async def on_client_closed(transport, client):
+            logger.info(f"Client closed connection")
             await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=handle_sigint)
+        runner = PipelineRunner(handle_sigint=False)
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/03b-still-frame-imagen.py

@@ -15,32 +15,26 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.services.google.image import GoogleImageGenService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-    "webrtc": lambda: TransportParams(
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            video_out_enabled=True,
+            video_out_width=1024,
+            video_out_height=1024,
+        ),
+    )
+
     imagegen = GoogleImageGenService(
         api_key=os.getenv("GOOGLE_API_KEY"),
     )
@@ -60,14 +54,18 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/04-transports-small-webrtc.py

@@ -5,17 +5,10 @@
 #
 
 import argparse
-import asyncio
 import os
-from contextlib import asynccontextmanager
-from typing import Dict
 
-import uvicorn
 from dotenv import load_dotenv
-from fastapi import BackgroundTasks, FastAPI
-from fastapi.responses import RedirectResponse
 from loguru import logger
-from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
@@ -27,29 +20,14 @@ from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import IceServer, SmallWebRTCConnection
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
-app = FastAPI()
 
-# Store connections by pc_id
-pcs_map: Dict[str, SmallWebRTCConnection] = {}
-
-ice_servers = [
-    IceServer(
-        urls="stun:stun.l.google.com:19302",
-    )
-]
-
-# Mount the frontend at /
-app.mount("/client", SmallWebRTCPrebuiltUI)
-
-
-async def run_example(webrtc_connection: SmallWebRTCConnection):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
-    # Create a transport using the WebRTC connection
     transport = SmallWebRTCTransport(
         webrtc_connection=webrtc_connection,
         params=TransportParams(
@@ -110,6 +88,10 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
         await task.cancel()
 
     runner = PipelineRunner(handle_sigint=False)
@@ -117,58 +99,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
     await runner.run(task)
 
 
-@app.get("/", include_in_schema=False)
-async def root_redirect():
-    return RedirectResponse(url="/client/")
-
-
-@app.post("/api/offer")
-async def offer(request: dict, background_tasks: BackgroundTasks):
-    pc_id = request.get("pc_id")
-
-    if pc_id and pc_id in pcs_map:
-        pipecat_connection = pcs_map[pc_id]
-        logger.info(f"Reusing existing connection for pc_id: {pc_id}")
-        await pipecat_connection.renegotiate(
-            sdp=request["sdp"],
-            type=request["type"],
-            restart_pc=request.get("restart_pc", False),
-        )
-    else:
-        pipecat_connection = SmallWebRTCConnection(ice_servers)
-        await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])
-
-        @pipecat_connection.event_handler("closed")
-        async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
-            logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
-            pcs_map.pop(webrtc_connection.pc_id, None)
-
-        # Run example function with SmallWebRTC transport arguments.
-        background_tasks.add_task(run_example, pipecat_connection)
-
-    answer = pipecat_connection.get_answer()
-    # Updating the peer connection inside the map
-    pcs_map[answer["pc_id"]] = pipecat_connection
-
-    return answer
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    yield  # Run app
-    coros = [pc.disconnect() for pc in pcs_map.values()]
-    await asyncio.gather(*coros)
-    pcs_map.clear()
-
-
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
-    parser.add_argument(
-        "--host", default="localhost", help="Host for HTTP server (default: localhost)"
-    )
-    parser.add_argument(
-        "--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
-    )
-    args = parser.parse_args()
+    from run import main
 
-    uvicorn.run(app, host=args.host, port=args.port)
+    main()

examples/foundational/04a-transports-daily.py

@@ -9,11 +9,11 @@ import os
 import sys
 
 import aiohttp
+from daily_runner import configure
 from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.examples.daily_runner import configure
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -37,9 +37,9 @@ async def main():
             token,
             "Respond bot",
             DailyParams(
-                audio_in_enabled=True,
                 audio_out_enabled=True,
                 transcription_enabled=True,
+                vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
             ),
         )

examples/foundational/04b-transports-livekit.py

@@ -10,6 +10,7 @@ import json
 import os
 import sys
 
+import aiohttp
 from deepgram import LiveOptions
 from dotenv import load_dotenv
 from livekit import api
@@ -103,100 +104,101 @@ async def configure_livekit():
 
 
 async def main():
-    (url, token, room_name) = await configure_livekit()
+    async with aiohttp.ClientSession() as session:
+        (url, token, room_name) = await configure_livekit()
 
-    transport = LiveKitTransport(
-        url=url,
-        token=token,
-        room_name=room_name,
-        params=LiveKitParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
+        transport = LiveKitTransport(
+            url=url,
+            token=token,
+            room_name=room_name,
+            params=LiveKitParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    stt = DeepgramSTTService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        live_options=LiveOptions(
-            vad_events=True,
-        ),
-    )
+        stt = DeepgramSTTService(
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
+            live_options=LiveOptions(
+                vad_events=True,
+            ),
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        )
 
-    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.",
-        },
-    ]
+        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)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-    runner = PipelineRunner()
+        runner = PipelineRunner()
 
-    task = PipelineTask(
-        Pipeline(
-            [
-                transport.input(),
-                stt,
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                context_aggregator.assistant(),
-            ],
-        ),
-        params=PipelineParams(
-            allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True
-        ),
-    )
+        task = PipelineTask(
+            Pipeline(
+                [
+                    transport.input(),
+                    stt,
+                    context_aggregator.user(),
+                    llm,
+                    tts,
+                    transport.output(),
+                    context_aggregator.assistant(),
+                ],
+            ),
+            params=PipelineParams(
+                allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True
+            ),
+        )
 
-    # Register an event handler so we can play the audio when the
-    # participant joins.
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant_id):
-        await asyncio.sleep(1)
-        await task.queue_frame(
-            TextFrame(
-                "Hello there! How are you doing today? Would you like to talk about the weather?"
+        # Register an event handler so we can play the audio when the
+        # participant joins.
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant_id):
+            await asyncio.sleep(1)
+            await task.queue_frame(
+                TextFrame(
+                    "Hello there! How are you doing today? Would you like to talk about the weather?"
+                )
             )
-        )
 
-    # Register an event handler to receive data from the participant via text chat
-    # in the LiveKit room. This will be used to as transcription frames and
-    # interrupt the bot and pass it to llm for processing and
-    # then pass back to the participant as audio output.
-    @transport.event_handler("on_data_received")
-    async def on_data_received(transport, data, participant_id):
-        logger.info(f"Received data from participant {participant_id}: {data}")
-        # convert data from bytes to string
-        json_data = json.loads(data)
+        # Register an event handler to receive data from the participant via text chat
+        # in the LiveKit room. This will be used to as transcription frames and
+        # interrupt the bot and pass it to llm for processing and
+        # then pass back to the participant as audio output.
+        @transport.event_handler("on_data_received")
+        async def on_data_received(transport, data, participant_id):
+            logger.info(f"Received data from participant {participant_id}: {data}")
+            # convert data from bytes to string
+            json_data = json.loads(data)
 
-        await task.queue_frames(
-            [
-                BotInterruptionFrame(),
-                UserStartedSpeakingFrame(),
-                TranscriptionFrame(
-                    user_id=participant_id,
-                    timestamp=json_data["timestamp"],
-                    text=json_data["message"],
-                ),
-                UserStoppedSpeakingFrame(),
-            ],
-        )
+            await task.queue_frames(
+                [
+                    BotInterruptionFrame(),
+                    UserStartedSpeakingFrame(),
+                    TranscriptionFrame(
+                        user_id=participant_id,
+                        timestamp=json_data["timestamp"],
+                        text=json_data["message"],
+                    ),
+                    UserStoppedSpeakingFrame(),
+                ],
+            )
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":

examples/foundational/05-sync-speech-and-image.py

@@ -28,8 +28,9 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
 from pipecat.services.fal.image import FalImageGenService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
@@ -63,26 +64,7 @@ class MonthPrepender(FrameProcessor):
             await self.push_frame(frame, direction)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     """Run the Calendar Month Narration bot using WebRTC transport.
 
     Args:
@@ -91,6 +73,17 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     """
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_out_enabled=True,
+            video_out_enabled=True,
+            video_out_width=1024,
+            video_out_height=1024,
+        ),
+    )
+
     # Create an HTTP session for API calls
     async with aiohttp.ClientSession() as session:
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
@@ -166,14 +159,18 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
+
+        @transport.event_handler("on_client_closed")
+        async def on_client_closed(transport, client):
+            logger.info(f"Client closed connection")
             await task.cancel()
 
         # Run the pipeline
-        runner = PipelineRunner(handle_sigint=handle_sigint)
+        runner = PipelineRunner(handle_sigint=False)
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/06-listen-and-respond.py

@@ -26,9 +26,9 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
@@ -53,31 +53,18 @@ class MetricsLogger(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaTTSService(
@@ -130,13 +117,17 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/06a-image-sync.py

@@ -26,8 +26,9 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
@@ -67,32 +68,21 @@ class ImageSyncAggregator(FrameProcessor):
         await self.push_frame(frame)
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            video_out_enabled=True,
+            video_out_width=1024,
+            video_out_height=1024,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaTTSService(
@@ -149,13 +139,17 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/07-interruptible.py

@@ -18,37 +18,25 @@ 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.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
 
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaTTSService(
@@ -100,14 +88,18 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/07a-interruptible-vad.py

@@ -10,49 +10,36 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import MinWordsInterruptionStrategy
 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.transcript_processor import TranscriptProcessor
+from pipecat.processors.audio.vad.silero import SileroVAD
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
 
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+        ),
+    )
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
+    vad = SileroVAD()
+
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
@@ -60,8 +47,6 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
-    transcript = TranscriptProcessor()
-
     messages = [
         {
             "role": "system",
@@ -74,14 +59,14 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
 
     pipeline = Pipeline(
         [
-            transport.input(),  # Transport user input
+            transport.input(),
             stt,
-            transcript.user(),  # User transcripts
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+            vad,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
         ]
     )
 
@@ -92,7 +77,6 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
             enable_metrics=True,
             enable_usage_metrics=True,
             report_only_initial_ttfb=True,
-            interruption_strategies=[MinWordsInterruptionStrategy(min_words=3)],
         ),
     )
 
@@ -106,20 +90,18 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
         await task.cancel()
 
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for message in frame.messages:
-            logger.info(f"Transcription [{message.role}]: {message.content}")
-
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()

examples/foundational/07b-interruptible-langchain.py

@@ -27,9 +27,9 @@ from pipecat.processors.aggregators.llm_response import (
 from pipecat.processors.frameworks.langchain import LangchainProcessor
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
-from pipecat.transports.services.daily import DailyParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
@@ -43,31 +43,18 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
     return message_store[session_id]
 
 
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
-
-
-async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaTTSService(
@@ -133,14 +120,18 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=handle_sigint)
+    runner = PipelineRunner(handle_sigint=False)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from pipecat.examples.run import main
+    from run import main
 
-    main(run_example, transport_params=transport_params)
+    main()