example: realtime with transcripts

.pre-commit-config.yaml

@@ -1,8 +1,7 @@
 repos:
-  - repo: https://github.com/astral-sh/ruff-pre-commit
+  - repo: local
-    rev: v0.9.7
     hooks:
-      - id: ruff
+      - id: ruff-format-hook
-        language_version: python3
+        name: Check ruff formatting
-        args: [ --select,  I, ]
+        entry: sh scripts/pre-commit.sh
-      - id: ruff-format
+        language: system

CHANGELOG.md

@@ -9,635 +9,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-- Added support for Smart Turn Detection via the `turn_analyzer` transport
-  parameter. You can now choose between `SmartTurnAnalyzer()` for remote 
-  inference or `LocalCoreMLSmartTurnAnalyzer()` for on-device inference using 
-  Core ML.
-
-- `DeepgramTTSService` accepts `base_url` argument again, allowing you to
-  connect to an on-prem service.
-
-- Added `LLMUserAggregatorParams` and `LLMAssistantAggregatorParams` which allow
-  you to control aggregator settings. You can now pass these arguments when
-  creating aggregator pairs with `create_context_aggregator()`.
-
-- Added `previous_text` context support to ElevenLabsHttpTTSService, improving
-  speech consistency across sentences within an LLM response.
-
-- Added word/timestamp pairs to `ElevenLabsHttpTTSService`.
-
-- It is now possible to disable `SoundfileMixer` when created. You can then use
-  `MixerEnableFrame` to dynamically enable it when necessary.
-
-- Added `on_client_connected` and `on_client_disconnected` event handlers to
-  the `DailyTransport` class. These handlers map to the same underlying Daily
-  events as `on_participant_joined` and `on_participant_left`, respectively.
-  This makes it easier to write a single bot pipeline that can also use other
-  transports like `SmallWebRTCTransport` and `FastAPIWebsocketTransport`.
-
-### Changed
-
-- Daily's REST helpers now include an `eject_at_token_exp` param, which ejects
-  the user when their token expires. This new parameter defaults to False.
-  Also, the default value for `enable_prejoin_ui` changed to False and
-  `eject_at_room_exp` changed to False.
-
-- `OpenAILLMService` and `OpenPipeLLMService` now use `gpt-4.1` as their
-  default model.
-
-- `SoundfileMixer` constructor arguments need to be keywords.
-
-### Deprecated
-
-- `DeepgramSTTService` parameter `url` is now deprecated, use `base_url`
-  instead.
-
-### Removed
-
-- Parameters `user_kwargs` and `assistant_kwargs` when creating a context
-  aggregator pair using `create_context_aggregator()` have been removed. Use
-  `user_params` and `assistant_params` instead.
-
-### Fixed
-
-- Fixed an issue that would cause TTS websocket-based services to not cleanup
-  resources properly when disconnecting.
-
-- Fixed a `TavusVideoService` issue that was causing audio choppiness.
-
-- Fixed an issue in `SmallWebRTCTransport` where an error was thrown if the
-  client did not create a video transceiver.
-
-- Fixed an issue where LLM input parameters were not working and applied correctly in `GoogleVertexLLMService`, causing
-  unexpected behavior during inference.
-
-## [0.0.63] - 2025-04-11
-
-### Added
-
-- Added media resolution control to `GeminiMultimodalLiveLLMService` with
-  `GeminiMediaResolution` enum, allowing configuration of token usage for
-  image processing (LOW: 64 tokens, MEDIUM: 256 tokens, HIGH: zoomed reframing
-  with 256 tokens).
-
-- Added Gemini's Voice Activity Detection (VAD) configuration to
-  `GeminiMultimodalLiveLLMService` with `GeminiVADParams`, allowing fine
-  control over speech detection sensitivity and timing, including:
-
-  - Start sensitivity (how quickly speech is detected)
-  - End sensitivity (how quickly turns end after pauses)
-  - Prefix padding (milliseconds of audio to keep before speech is detected)
-  - Silence duration (milliseconds of silence required to end a turn)
-
-- Added comprehensive language support to `GeminiMultimodalLiveLLMService`,
-  supporting over 30 languages via the `language` parameter, with proper
-  mapping between Pipecat's `Language` enum and Gemini's language codes.
-
-- Added support in `SmallWebRTCTransport` to detect when remote tracks are
-  muted.
-
-- Added support for image capture from a video stream to the
-  `SmallWebRTCTransport`.
-
-- Added a new iOS client option to the `SmallWebRTCTransport`
-  **video-transform** example.
-
-- Added new processors `ProducerProcessor` and `ConsumerProcessor`. The
-  producer processor processes frames from the pipeline and decides whether the
-  consumers should consume it or not. If so, the same frame that is received by
-  the producer is sent to the consumer. There can be multiple consumers per
-  producer. These processors can be useful to push frames from one part of a
-  pipeline to a different one (e.g. when using `ParallelPipeline`).
-
-- Improvements for the `SmallWebRTCTransport`:
-  - Wait until the pipeline is ready before triggering the `connected` event.
-  - Queue messages if the data channel is not ready.
-  - Update the aiortc dependency to fix an issue where the 'video/rtx' MIME
-    type was incorrectly handled as a codec retransmission.
-  - Avoid initial video delays.
-
-### Changed
-
-- In `GeminiMultimodalLiveLLMService`, removed the `transcribe_model_audio`
-  parameter in favor of Gemini Live's native output transcription support. Now
-  text transcriptions are produced directly by the model. No configuration is
-  required.
-
-- Updated `GeminiMultimodalLiveLLMService`’s default `model` to
-  `models/gemini-2.0-flash-live-001` and `base_url` to the `v1beta` websocket
-  URL.
-
-### Fixed
-
-- Updated `daily-python` to 0.17.0 to fix an issue that was preventing to run on
-  older platforms.
-
-- Fixed an issue where `CartesiaTTSService`'s spell feature would result in
-  the spelled word in the context appearing as "F,O,O,B,A,R" instead of
-  "FOOBAR".
-
-- Fixed an issue in the Azure TTS services where the language was being set
-  incorrectly.
-
-- Fixed `SmallWebRTCTransport` to support dynamic values for
-  `TransportParams.audio_out_10ms_chunks`. Previously, it only worked with 20ms
-  chunks.
-
-- Fixed an issue with `GeminiMultimodalLiveLLMService` where the assistant
-  context messages had no space between words.
-
-- Fixed an issue where `LLMAssistantContextAggregator` would prevent a
-  `BotStoppedSpeakingFrame` from moving through the pipeline.
-
-## [0.0.62] - 2025-04-01 "An April Fools' release"
-
-### Added
-
-- Added `TransportParams.audio_out_10ms_chunks` parameter to allow controlling
-  the amount of audio being sent by the output transport. It defaults to 4, so
-  40ms audio chunks are sent.
-
-- Added `QwenLLMService` for Qwen integration with an OpenAI-compatible
-  interface. Added foundational example `14q-function-calling-qwen.py`.
-
-- Added `Mem0MemoryService`. Mem0 is a self-improving memory layer for LLM
-  applications. Learn more at: https://mem0.ai/.
-
-- Added `WhisperSTTServiceMLX` for Whisper transcription on Apple Silicon.
-  See example in `examples/foundational/13e-whisper-mlx.py`. Latency of
-  completed transcription using Whisper large-v3-turbo on an M4 macbook is
-  ~500ms.
-
-- Added `SmallWebRTCTransport`, a new P2P WebRTC transport.
-
-  - Created two examples in `p2p-webrtc`:
-    - **video-transform**: Demonstrates sending and receiving audio/video with
-      `SmallWebRTCTransport` using `TypeScript`. Includes video frame
-      processing with OpenCV.
-    - **voice-agent**: A minimal example of creating a voice agent with
-      `SmallWebRTCTransport`.
-
-- `GladiaSTTService` now have comprehensive support for the latest API config
-  options, including model, language detection, preprocessing, custom
-  vocabulary, custom spelling, translation, and message filtering options.
-
-- Added `SmallWebRTCTransport`, a new P2P WebRTC transport.
-
-  - Created two examples in `p2p-webrtc`:
-    - **video-transform**: Demonstrates sending and receiving audio/video with
-      `SmallWebRTCTransport` using `TypeScript`. Includes video frame
-      processing with OpenCV.
-    - **voice-agent**: A minimal example of creating a voice agent with
-      `SmallWebRTCTransport`.
-
-- Added support to `ProtobufFrameSerializer` to send the messages from
-  `TransportMessageFrame` and `TransportMessageUrgentFrame`.
-
-- Added support for a new TTS service, `PiperTTSService`.
-  (see https://github.com/rhasspy/piper/)
-
-- It is now possible to tell whether `UserStartedSpeakingFrame` or
-  `UserStoppedSpeakingFrame` have been generated because of emulation frames.
-
-### Changed
-
-- `FunctionCallResultFrame`a are now system frames. This is to prevent function
-  call results to be discarded during interruptions.
-
-- Pipecat services have been reorganized into packages. Each package can have
-  one or more of the following modules (in the future new module names might be
-  needed) depending on the services implemented:
-
-  - image: for image generation services
-  - llm: for LLM services
-  - memory: for memory services
-  - stt: for Speech-To-Text services
-  - tts: for Text-To-Speech services
-  - video: for video generation services
-  - vision: for video recognition services
-
-- Base classes for AI services have been reorganized into modules. They can now
-  be found in
-  `pipecat.services.[ai_service,image_service,llm_service,stt_service,vision_service]`.
-
-- `GladiaSTTService` now uses the `solaria-1` model by default. Other params
-  use Gladia's default values. Added support for more language codes.
-
-### Deprecated
-
-- All Pipecat services imports have been deprecated and a warning will be shown
-  when using the old import. The new import should be
-  `pipecat.services.[service].[image,llm,memory,stt,tts,video,vision]`. For
-  example, `from pipecat.services.openai.llm import OpenAILLMService`.
-
-- Import for AI services base classes from `pipecat.services.ai_services` is now
-  deprecated, use one of
-  `pipecat.services.[ai_service,image_service,llm_service,stt_service,vision_service]`.
-
-- Deprecated the `language` parameter in `GladiaSTTService.InputParams` in
-  favor of `language_config`, which better aligns with Gladia's API.
-
-- Deprecated using `GladiaSTTService.InputParams` directly. Use the new
-  `GladiaInputParams` class instead.
-
-### Fixed
-
-- Fixed a `FastAPIWebsocketTransport` and `WebsocketClientTransport` issue that
-  would cause the transport to be closed prematurely, preventing the internally
-  queued audio to be sent. The same issue could also cause an infinite loop
-  while using an output mixer and when sending an `EndFrame`, preventing the bot
-  to finish.
-
-- Fixed an issue that could cause the `TranscriptionUpdateFrame` being pushed
-  because of an interruption to be discarded.
-
-- Fixed an issue that would cause `SegmentedSTTService` based services
-  (e.g. `OpenAISTTService`) to try to transcribe non-spoken audio, causing
-  invalid transcriptions.
-
-- Fixed an issue where `GoogleTTSService` was emitting two `TTSStoppedFrames`.
-
-### Performance
-
-- Output transports now send 40ms audio chunks instead of 20ms. This should
-  improve performance.
-
-- `BotSpeakingFrame`s are now sent every 200ms. If the output transport audio chunks
-  are higher than 200ms then they will be sent at every audio chunk.
-
-### Other
-
-- Added foundational example `37-mem0.py` demonstrating how to use the
-  `Mem0MemoryService`.
-
-- Added foundational example `13e-whisper-mlx.py` demonstrating how to use the
-  `WhisperSTTServiceMLX`.
-
-## [0.0.61] - 2025-03-26
-
-### Added
-
-- Added a new frame, `LLMSetToolChoiceFrame`, which provides a mechanism
-  for modifying the `tool_choice` in the context.
-
-- Added `GroqTTSService` which provides text-to-speech functionality using
-  Groq's API.
-
-- Added support in `DailyTransport` for updating remote participants'
-  `canReceive` permission via the `update_remote_participants()` method, by
-  bumping the daily-python dependency to >= 0.16.0.
-
-- ElevenLabs TTS services now support a sample rate of 8000.
-
-- Added support for `instructions` in `OpenAITTSService`.
-
-- Added support for `base_url` in `OpenAIImageGenService` and
-  `OpenAITTSService`.
-
-### Fixed
-
-- Fixed an issue in `RTVIObserver` that prevented handling of Google LLM
-  context messages. The observer now processes both OpenAI-style and
-  Google-style contexts.
-
-- Fixed an issue in Daily involving switching virtual devices, by bumping the
-  daily-python dependency to >= 0.16.1.
-
-- Fixed a `GoogleAssistantContextAggregator` issue where function calls
-  placeholders where not being updated when then function call result was
-  different from a string.
-
-- Fixed an issue that would cause `LLMAssistantContextAggregator` to block
-  processing more frames while processing a function call result.
-
-- Fixed an issue where the `RTVIObserver` would report two bot started and
-  stopped speaking events for each bot turn.
-
-- Fixed an issue in `UltravoxSTTService` that caused improper audio processing
-  and incorrect LLM frame output.
-
-### Other
-
-- Added `examples/foundational/07x-interruptible-local.py` to show how a local
-  transport can be used.
-
-## [0.0.60] - 2025-03-20
-
-### Added
-
-- Added `default_headers` parameter to `BaseOpenAILLMService` constructor.
-
-### Changed
-
-- Rollback to `deepgram-sdk` 3.8.0 since 3.10.1 was causing connections issues.
-
-- Changed the default `InputAudioTranscription` model to `gpt-4o-transcribe`
-  for `OpenAIRealtimeBetaLLMService`.
-
-### Other
-
-- Update the `19-openai-realtime-beta.py` and `19a-azure-realtime-beta.py`
-  examples to use the FunctionSchema format.
-
-## [0.0.59] - 2025-03-20
-
-### Added
-
-- When registering a function call it is now possible to indicate if you want
-  the function call to be cancelled if there's a user interruption via
-  `cancel_on_interruption` (defaults to False). This is now possible because
-  function calls are executed concurrently.
-
-- Added support for detecting idle pipelines. By default, if no activity has
-  been detected during 5 minutes, the `PipelineTask` will be automatically
-  cancelled. It is possible to override this behavior by passing
-  `cancel_on_idle_timeout=False`. It is also possible to change the default
-  timeout with `idle_timeout_secs` or the frames that prevent the pipeline from
-  being idle with `idle_timeout_frames`. Finally, an `on_idle_timeout` event
-  handler will be triggered if the idle timeout is reached (whether the pipeline
-  task is cancelled or not).
-
-- Added `FalSTTService`, which provides STT for Fal's Wizper API.
-
-- Added a `reconnect_on_error` parameter to websocket-based TTS services as well
-  as a `on_connection_error` event handler. The `reconnect_on_error` indicates
-  whether the TTS service should reconnect on error. The `on_connection_error`
-  will always get called if there's any error no matter the value of
-  `reconnect_on_error`. This allows, for example, to fallback to a different TTS
-  provider if something goes wrong with the current one.
-
-- Added new `SkipTagsAggregator` that extends `BaseTextAggregator` to aggregate
-  text and skips end of sentence matching if aggregated text is between
-  start/end tags.
-
-- Added new `PatternPairAggregator` that extends `BaseTextAggregator` to
-  identify content between matching pattern pairs in streamed text. This allows
-  for detection and processing of structured content like XML-style tags that
-  may span across multiple text chunks or sentence boundaries.
-
-- Added new `BaseTextAggregator`. Text aggregators are used by the TTS service
-  to aggregate LLM tokens and decide when the aggregated text should be pushed
-  to the TTS service. They also allow for the text to be manipulated while it's
-  being aggregated. A text aggregator can be passed via `text_aggregator` to the
-  TTS service.
-
-- Added new `sample_rate` constructor parameter to `TavusVideoService` to allow
-  changing the output sample rate.
-
-- Added new `NeuphonicTTSService`.
-  (see https://neuphonic.com)
-
-- Added new `UltravoxSTTService`.
-  (see https://github.com/fixie-ai/ultravox)
-
-- Added `on_frame_reached_upstream` and `on_frame_reached_downstream` event
-  handlers to `PipelineTask`. Those events will be called when a frame reaches
-  the beginning or end of the pipeline respectively. Note that by default, the
-  event handlers will not be called unless a filter is set with
-  `PipelineTask.set_reached_upstream_filter()` or
-  `PipelineTask.set_reached_downstream_filter()`.
-
-- Added support for Chirp voices in `GoogleTTSService`.
-
-- Added a `flush_audio()` method to `FishTTSService` and `LmntTTSService`.
-
-- Added a `set_language` convenience method for `GoogleSTTService`, allowing
-  you to set a single language. This is in addition to the `set_languages`
-  method which allows you to set a list of languages.
-
-- Added `on_user_turn_audio_data` and `on_bot_turn_audio_data` to
-  `AudioBufferProcessor`. This gives the ability to grab the audio of only that
-  turn for both the user and the bot.
-
-- Added new base class `BaseObject` which is now the base class of
-  `FrameProcessor`, `PipelineRunner`, `PipelineTask` and `BaseTransport`. The
-  new `BaseObject` adds supports for event handlers.
-
-- Added support for a unified format for specifying function calling across all
-  LLM services.
-
-```python
-  weather_function = FunctionSchema(
-      name="get_current_weather",
-      description="Get the current weather",
-      properties={
-          "location": {
-              "type": "string",
-              "description": "The city and state, e.g. San Francisco, CA",
-          },
-          "format": {
-              "type": "string",
-              "enum": ["celsius", "fahrenheit"],
-              "description": "The temperature unit to use. Infer this from the user's location.",
-          },
-      },
-      required=["location"],
-  )
-  tools = ToolsSchema(standard_tools=[weather_function])
-```
-
-- Added `speech_threshold` parameter to `GladiaSTTService`.
-
-- Allow passing user (`user_kwargs`) and assistant (`assistant_kwargs`) context
-  aggregator parameters when using `create_context_aggregator()`. The values are
-  passed as a mapping that will then be converted to arguments.
-
-- Added `speed` as an `InputParam` for both `ElevenLabsTTSService` and
-  `ElevenLabsHttpTTSService`.
-
-- Added new `LLMFullResponseAggregator` to aggregate full LLM completions. At
-  every completion the `on_completion` event handler is triggered.
-
-- Added a new frame, `RTVIServerMessageFrame`, and RTVI message
-  `RTVIServerMessage` which provides a generic mechanism for sending custom
-  messages from server to client. The `RTVIServerMessageFrame` is processed by
-  the `RTVIObserver` and will be delivered to the client's `onServerMessage`
-  callback or `ServerMessage` event.
-
-- Added `GoogleLLMOpenAIBetaService` for Google LLM integration with an
-  OpenAI-compatible interface. Added foundational example
-  `14o-function-calling-gemini-openai-format.py`.
-
-- Added `AzureRealtimeBetaLLMService` to support Azure's OpeanAI Realtime API. Added
-  foundational example `19a-azure-realtime-beta.py`.
-
-- Introduced `GoogleVertexLLMService`, a new class for integrating with Vertex AI
-  Gemini models. Added foundational example
-  `14p-function-calling-gemini-vertex-ai.py`.
-
-- Added support in `OpenAIRealtimeBetaLLMService` for a slate of new features:
-
-  - The `'gpt-4o-transcribe'` input audio transcription model, along
-    with new `language` and `prompt` options specific to that model.
-  - The `input_audio_noise_reduction` session property.
-
-    ```python
-    session_properties = SessionProperties(
-      # ...
-      input_audio_noise_reduction=InputAudioNoiseReduction(
-        type="near_field" # also supported: "far_field"
-      )
-      # ...
-    )
-    ```
-
-  - The `'semantic_vad'` `turn_detection` session property value, a more
-    sophisticated model for detecting when the user has stopped speaking.
-  - `on_conversation_item_created` and `on_conversation_item_updated`
-    events to `OpenAIRealtimeBetaLLMService`.
-
-    ```python
-    @llm.event_handler("on_conversation_item_created")
-    async def on_conversation_item_created(llm, item_id, item):
-      # ...
-
-    @llm.event_handler("on_conversation_item_updated")
-    async def on_conversation_item_updated(llm, item_id, item):
-      # `item` may not always be available here
-      # ...
-    ```
-
-  - The `retrieve_conversation_item(item_id)` method for introspecting a
-    conversation item on the server.
-
-    ```python
-    item = await llm.retrieve_conversation_item(item_id)
-    ```
-
-### Changed
-
-- Updated `OpenAISTTService` to use `gpt-4o-transcribe` as the default
-  transcription model.
-
-- Updated `OpenAITTSService` to use `gpt-4o-mini-tts` as the default TTS model.
-
-- Function calls are now executed in tasks. This means that the pipeline will
-  not be blocked while the function call is being executed.
-
-- ⚠️ `PipelineTask` will now be automatically cancelled if no bot activity is
-  happening in the pipeline. There are a few settings to configure this
-  behavior, see `PipelineTask` documentation for more details.
-
-- All event handlers are now executed in separate tasks in order to prevent
-  blocking the pipeline. It is possible that event handlers take some time to
-  execute in which case the pipeline would be blocked waiting for the event
-  handler to complete.
-
-- Updated `TranscriptProcessor` to support text output from
-  `OpenAIRealtimeBetaLLMService`.
-
-- `OpenAIRealtimeBetaLLMService` and `GeminiMultimodalLiveLLMService` now push
-  a `TTSTextFrame`.
-
-- Updated the default mode for `CartesiaTTSService` and
-  `CartesiaHttpTTSService` to `sonic-2`.
-
-### Deprecated
-
-- Passing a `start_callback` to `LLMService.register_function()` is now
-  deprecated, simply move the code from the start callback to the function call.
-
-- `TTSService` parameter `text_filter` is now deprecated, use `text_filters`
-  instead which is now a list. This allows passing multiple filters that will be
-  executed in order.
-
-### Removed
-
-- Removed deprecated `audio.resample_audio()`, use `create_default_resampler()`
-  instead.
-
-- Removed deprecated`stt_service` parameter from `STTMuteFilter`.
-
-- Removed deprecated RTVI processors, use an `RTVIObserver` instead.
-
-- Removed deprecated `AWSTTSService`, use `PollyTTSService` instead.
-
-- Removed deprecated field `tier` from `DailyTranscriptionSettings`, use `model`
-  instead.
-
-- Removed deprecated `pipecat.vad` package, use `pipecat.audio.vad` instead.
-
-### Fixed
-
-- Fixed an assistant aggregator issue that could cause assistant text to be
-  split into multiple chunks during function calls.
-
-- Fixed an assistant aggregator issue that was causing assistant text to not be
-  added to the context during function calls. This could lead to duplications.
-
-- Fixed a `SegmentedSTTService` issue that was causing audio to be sent
-  prematurely to the STT service. Instead of analyzing the volume in this
-  service we rely on VAD events which use both VAD and volume.
-
-- Fixed a `GeminiMultimodalLiveLLMService` issue that was causing messages to be
-  duplicated in the context when pushing `LLMMessagesAppendFrame` frames.
-
-- Fixed an issue with `SegmentedSTTService` based services
-  (e.g. `GroqSTTService`) that was not allow audio to pass-through downstream.
-
-- Fixed a `CartesiaTTSService` and `RimeTTSService` issue that would consider
-  text between spelling out tags end of sentence.
-
-- Fixed a `match_endofsentence` issue that would result in floating point
-  numbers to be considered an end of sentence.
-
-- Fixed a `match_endofsentence` issue that would result in emails to be
-  considered an end of sentence.
-
-- Fixed an issue where the RTVI message `disconnect-bot` was pushing an
-  `EndFrame`, resulting in the pipeline not shutting down. It now pushes an
-  `EndTaskFrame` upstream to shutdown the pipeline.
-
-- Fixed an issue with the `GoogleSTTService` where stream timeouts during
-  periods of inactivity were causing connection failures. The service now
-  properly detects timeout errors and handles reconnection gracefully,
-  ensuring continuous operation even after periods of silence or when using an
-  `STTMuteFilter`.
-
-- Fixed an issue in `RimeTTSService` where the last line of text sent didn't
-  result in an audio output being generated.
-
-- Fixed `OpenAIRealtimeBetaLLMService` by adding proper handling for:
-  - The `conversation.item.input_audio_transcription.delta` server message,
-    which was added server-side at some point and not handled client-side.
-  - Errors reported by the `response.done` server message.
-
-### Other
-
-- Add foundational example `07w-interruptible-fal.py`, showing `FalSTTService`.
-
-- Added a new Ultravox example
-  `examples/foundational/07u-interruptible-ultravox.py`.
-
-- Added new Neuphonic examples
-  `examples/foundational/07v-interruptible-neuphonic.py` and
-  `examples/foundational/07v-interruptible-neuphonic-http.py`.
-
-- Added a new example `examples/foundational/36-user-email-gathering.py` to show
-  how to gather user emails. The example uses's Cartesia's `<spell></spell>`
-  tags and Rime `spell()` function to spell out the emails for confirmation.
-
-- Update the `34-audio-recording.py` example to include an STT processor.
-
-- Added foundational example `35-voice-switching.py` showing how to use the new
-  `PatternPairAggregator`. This example shows how to encode information for the
-  LLM to instruct TTS voice changes, but this can be used to encode any
-  information into the LLM response, which you want to parse and use in other
-  parts of your application.
-
-- Added a Pipecat Cloud deployment example to the `examples` directory.
-
-- Removed foundational examples 28b and 28c as the TranscriptProcessor no
-  longer has an LLM depedency. Renamed foundational example 28a to
-  `28-transcript-processor.py`.
-
-## [0.0.58] - 2025-02-26
-
-### Added
-
-- Added track-specific audio event `on_track_audio_data` to
-  `AudioBufferProcessor` for accessing separate input and output audio tracks.
-
 - Pipecat version will now be logged on every application startup. This will
   help us identify what version we are running in case of any issues.
 
@@ -674,10 +45,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - ⚠️ `PipelineTask` now requires keyword arguments (except for the first one for
   the pipeline).
 
-- Updated `PlayHTHttpTTSService` to take a `voice_engine` and `protocol` input
-  in the constructor. The previous method of providing a `voice_engine` input
-  that contains the engine and protocol is deprecated by PlayHT.
-
 - The base `TTSService` class now strips leading newlines before sending text
   to the TTS provider. This change is to solve issues where some TTS providers,
   like Azure, would not output text due to newlines.
@@ -711,12 +78,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 
 ### Fixed
 
-- Fixed an issue that would cause undesired interruptions via
-  `EmulateUserStartedSpeakingFrame`.
-
-- Fixed a `GoogleLLMService` that was causing an exception when sending inline
-  audio in some cases.
-
 - Fixed an `AudioContextWordTTSService` issue that would cause an `EndFrame` to
   disconnect from the TTS service before audio from all the contexts was
   received. This affected services like Cartesia and Rime.
@@ -730,6 +91,10 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 
 - Fixed `match_endofsentence` support for ellipses.
 
+- Fixed an issue that would cause undesired interruptions via
+  `EmulateUserStartedSpeakingFrame` when only interim transcriptions (i.e. no
+  final transcriptions) where received.
+
 - Fixed an issue where `EndTaskFrame` was not triggering
   `on_client_disconnected` or closing the WebSocket in FastAPI.
 
@@ -759,9 +124,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 
 - Added Gemini support to `examples/phone-chatbot`.
 
-- Added foundational example `34-audio-recording.py` showing how to use the
-  AudioBufferProcessor callbacks to save merged and track recordings.
-
 ## [0.0.57] - 2025-02-14
 
 ### Added
@@ -2394,7 +1756,7 @@ async def on_connected(processor):
   completed. If a task is never ran `has_finished()` will return False.
 
 - `PipelineRunner` now supports SIGTERM. If received, the runner will be
-  cancelled.
+  canceled.
 
 ### Fixed
 

CONTRIBUTING.md

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

README.md

@@ -55,18 +55,17 @@ pip install "pipecat-ai[option,...]"
 
 ### Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | Install Command Example                 |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | Install Command Example                 |
-| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
+| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [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)                                                                                                                                                                                                                                            | `pip install "pipecat-ai[deepgram]"`    |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[deepgram]"`    |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"`      |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"`      |
-| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [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)                       | `pip install "pipecat-ai[cartesia]"`    |
+| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                                                                                                                                                                          | `pip install "pipecat-ai[cartesia]"`    |
-| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | `pip install "pipecat-ai[google]"`      |
+| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[google]"`      |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | `pip install "pipecat-ai[daily]"`       |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | `pip install "pipecat-ai[daily]"`       |
-| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | `pip install "pipecat-ai[tavus,simli]"` |
+| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[tavus,simli]"` |
-| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | `pip install "pipecat-ai[mem0]"`        |
+| Vision & Image      | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[moondream]"`   |
-| 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)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | `pip install "pipecat-ai[moondream]"`   |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | `pip install "pipecat-ai[silero]"`      |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | `pip install "pipecat-ai[silero]"`      |
+| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | `pip install "pipecat-ai[canonical]"`   |
-| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | `pip install "pipecat-ai[canonical]"`   |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 

dev-requirements.txt

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

docs/api/conf.py

@@ -50,14 +50,6 @@ autodoc_mock_imports = [
     "pyht.protos",
     "pyht.protos.api_pb2",
     "pipecat_ai_playht",  # PlayHT wrapper
-    "vllm",
-    "aiortc",
-    "aiortc.mediastreams",
-    "cv2",
-    "av",
-    "pyneuphonic",
-    "mem0",
-    "mlx_whisper",
     "anthropic",
     "assemblyai",
     "boto3",

docs/api/index.rst

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

docs/api/requirements.txt

@@ -12,29 +12,22 @@ pipecat-ai[aws]
 pipecat-ai[azure]
 pipecat-ai[canonical]
 pipecat-ai[cartesia]
-pipecat-ai[cerebras]
-pipecat-ai[deepseek]
 pipecat-ai[daily]
 pipecat-ai[deepgram]
 pipecat-ai[elevenlabs]
 pipecat-ai[fal]
 pipecat-ai[fireworks]
-pipecat-ai[fish]
 pipecat-ai[gladia]
 pipecat-ai[google]
 pipecat-ai[grok]
 pipecat-ai[groq]
-# pipecat-ai[krisp] # Mocked
+# pipecat-ai[krisp] # Mocked instead
-pipecat-ai[koala]
 pipecat-ai[langchain]
 pipecat-ai[livekit]
 pipecat-ai[lmnt]
 pipecat-ai[local]
-# pipecat-ai[mem0] # Mocked
-# pipecat-ai[mlx-whisper] # Mocked
 pipecat-ai[moondream]
 pipecat-ai[nim]
-# pipecat-ai[neuphonic] # Mocked
 pipecat-ai[noisereduce]
 pipecat-ai[openai]
 # pipecat-ai[openpipe]
@@ -43,9 +36,5 @@ pipecat-ai[riva]
 pipecat-ai[silero]
 pipecat-ai[simli]
 pipecat-ai[soundfile]
-pipecat-ai[tavus]
-pipecat-ai[together]
-# pipecat-ai[ultravox] # Mocked
-# pipecat-ai[webrtc] # Mocked
 pipecat-ai[websocket]
 pipecat-ai[whisper]

dot-env.template

@@ -29,9 +29,6 @@ DAILY_SAMPLE_ROOM_URL=https://...
 ELEVENLABS_API_KEY=...
 ELEVENLABS_VOICE_ID=...
 
-# Neuphonic
-NEUPHONIC_API_KEY=...
-
 # Fal
 FAL_KEY=...
 
@@ -90,10 +87,3 @@ ASSEMBLYAI_API_KEY=...
 
 # OpenRouter
 OPENROUTER_API_KEY=...
-
-# Piper
-PIPER_BASE_URL=...
-
-# Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=
-REMOTE_SMART_TURN_URL=

examples/bot-ready-signalling/server/signalling_bot.py

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

examples/canonical-metrics/bot.py

@@ -21,9 +21,9 @@ 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.canonical import CanonicalMetricsService
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -72,7 +72,7 @@ async def main():
             # voice_id="gD1IexrzCvsXPHUuT0s3",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -113,8 +113,8 @@ async def main():
                 llm,
                 tts,
                 transport.output(),
-                canonical,  # uploads audio buffer to Canonical AI for metrics
                 audio_buffer_processor,  # captures audio into a buffer
+                canonical,  # uploads audio buffer to Canonical AI for metrics
                 context_aggregator.assistant(),
             ]
         )

examples/chatbot-audio-recording/bot.py

@@ -23,8 +23,8 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -32,16 +32,10 @@ load_dotenv(override=True)
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
-# Create the recordings directory if it doesn't exist
-os.makedirs("recordings", exist_ok=True)
 
-
+async def save_audio(audio: bytes, sample_rate: int, num_channels: int):
-async def save_audio(audio: bytes, sample_rate: int, num_channels: int, name: str):
     if len(audio) > 0:
-        filename = os.path.join(
+        filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
-            "recordings",
-            f"{name}_conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav",
-        )
         with io.BytesIO() as buffer:
             with wave.open(buffer, "wb") as wf:
                 wf.setsampwidth(2)
@@ -95,7 +89,7 @@ async def main():
             # voice_id="gD1IexrzCvsXPHUuT0s3",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -116,7 +110,7 @@ async def main():
 
         # NOTE: Watch out! This will save all the conversation in memory. You
         # can pass `buffer_size` to get periodic callbacks.
-        audiobuffer = AudioBufferProcessor(enable_turn_audio=True)
+        audiobuffer = AudioBufferProcessor()
 
         pipeline = Pipeline(
             [
@@ -134,15 +128,7 @@ async def main():
 
         @audiobuffer.event_handler("on_audio_data")
         async def on_audio_data(buffer, audio, sample_rate, num_channels):
-            await save_audio(audio, sample_rate, num_channels, "full")
+            await save_audio(audio, sample_rate, num_channels)
-
-        @audiobuffer.event_handler("on_user_turn_audio_data")
-        async def on_user_turn_audio_data(buffer, audio, sample_rate, num_channels):
-            await save_audio(audio, sample_rate, num_channels, "user")
-
-        @audiobuffer.event_handler("on_bot_turn_audio_data")
-        async def on_bot_turn_audio_data(buffer, audio, sample_rate, num_channels):
-            await save_audio(audio, sample_rate, num_channels, "bot")
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):

examples/deployment/flyio-example/bot.py

@@ -1,9 +1,3 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
 import argparse
 import asyncio
 import os
@@ -18,8 +12,8 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -53,7 +47,7 @@ async def main(room_url: str, token: str):
         voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/deployment/modal-example/app.py

@@ -1,9 +1,3 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
 import os
 
 import aiohttp

examples/deployment/modal-example/bot.py

@@ -1,9 +1,3 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
 import asyncio
 import os
 import sys
@@ -16,8 +10,8 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -40,10 +34,10 @@ async def main(room_url: str, token: str):
     )
 
     tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
+        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

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

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

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

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

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

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

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

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

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

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

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/.gitignore

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

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

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

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/env.local.example

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

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/package.json

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

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

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

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

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

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/vercel.js

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

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

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

examples/deployment/pipecat-cloud-example/Dockerfile

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

examples/deployment/pipecat-cloud-example/README.md

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

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

@@ -1,161 +0,0 @@
-#
-# Copyright (c) 2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from pipecatcloud.agent import DailySessionArguments
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMMessagesFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-# 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)
-
-
-async def main(room_url: str, token: str):
-    """Main pipeline setup and execution function.
-
-    Args:
-        room_url: The Daily room URL
-        token: The Daily room token
-    """
-    logger.debug("Starting bot in room: {}", room_url)
-
-    transport = DailyTransport(
-        room_url,
-        token,
-        "bot",
-        DailyParams(
-            audio_out_enabled=True,
-            transcription_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
-
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        logger.info("First participant joined: {}", participant["id"])
-        await transport.capture_participant_transcription(participant["id"])
-        # Kick off the conversation.
-        messages.append(
-            {
-                "role": "system",
-                "content": "Please start with 'Hello World' and introduce yourself to the user.",
-            }
-        )
-        await task.queue_frames([LLMMessagesFrame(messages)])
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        logger.info("Participant left: {}", participant)
-        await task.cancel()
-
-    runner = PipelineRunner()
-
-    await runner.run(task)
-
-
-async def bot(args: DailySessionArguments):
-    """Main bot entry point compatible with the FastAPI route handler.
-
-    Args:
-        room_url: The Daily room URL
-        token: The Daily room token
-        body: The configuration object from the request body
-        session_id: The session ID for logging
-    """
-    logger.info(f"Bot process initialized {args.room_url} {args.token}")
-
-    try:
-        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)}")
-        raise
-
-
-# Local development functions
-async def local_main():
-    """Function for local development testing."""
-    try:
-        async with aiohttp.ClientSession() as session:
-            (room_url, token) = await configure(session)
-            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}")
-
-
-# Local development entry point
-if LOCAL_RUN and __name__ == "__main__":
-    try:
-        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

@@ -1,2 +0,0 @@
-CARTESIA_API_KEY=
-OPENAI_API_KEY=

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

@@ -1,46 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-
-from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
-
-
-async def configure(aiohttp_session: aiohttp.ClientSession):
-    (url, token) = await configure_with_args(aiohttp_session)
-    return (url, token)
-
-
-async def configure_with_args(aiohttp_session: aiohttp.ClientSession = None):
-    key = os.getenv("DAILY_API_KEY")
-    if not key:
-        raise Exception(
-            "No Daily API key specified. set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
-        )
-
-    daily_rest_helper = DailyRESTHelper(
-        daily_api_key=key,
-        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-        aiohttp_session=aiohttp_session,
-    )
-
-    room = await daily_rest_helper.create_room(
-        DailyRoomParams(properties={"enable_prejoin_ui": False})
-    )
-    if not room.url:
-        raise HTTPException(status_code=500, detail="Failed to create room")
-
-    url = room.url
-
-    # Create a meeting token for the given room with an expiration 1 hour in
-    # the future.
-    expiry_time: float = 60 * 60
-
-    token = await daily_rest_helper.get_token(url, expiry_time)
-
-    return (url, token)

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

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

examples/deployment/pipecat-cloud-example/requirements.txt

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

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

@@ -1,57 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import EndFrame, TTSSpeakFrame
-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 TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
-
-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        tts = PiperTTSService(
-            base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
-        )
-
-        task = PipelineTask(Pipeline([tts, transport.output()]))
-
-        # Register an event handler so we can play the audio when the client joins
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
-
-        runner = PipelineRunner(handle_sigint=False)
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

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

@@ -1,59 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import EndFrame, TTSSpeakFrame
-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 TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
-
-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        tts = RimeHttpTTSService(
-            api_key=os.getenv("RIME_API_KEY", ""),
-            voice_id="rex",
-            aiohttp_session=session,
-        )
-
-        task = PipelineTask(Pipeline([tts, transport.output()]))
-
-        # Register an event handler so we can play the audio when the client joins
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
-
-        runner = PipelineRunner(handle_sigint=False)
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

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

@@ -4,53 +4,56 @@
 # 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.frames.frames import EndFrame, TranscriptionFrame, TTSSpeakFrame
+from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.frame_processor import FrameProcessor
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    # Create a transport using the WebRTC connection
+async def main():
-    transport = SmallWebRTCTransport(
+    async with aiohttp.ClientSession() as session:
-        webrtc_connection=webrtc_connection,
+        (room_url, _) = await configure(session)
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
 
-    tts = CartesiaTTSService(
+        transport = DailyTransport(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        )
-    )
 
-    task = PipelineTask(Pipeline([tts, transport.output()]))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    # Register an event handler so we can play the audio when the client joins
+        runner = PipelineRunner()
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        task = PipelineTask(Pipeline([tts, transport.output()]))
 
-    await runner.run(task)
+        # 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):
+            participant_name = participant.get("info", {}).get("userName", "")
+            await task.queue_frames(
+                [TTSSpeakFrame(f"Hello there, {participant_name}!"), EndFrame()]
+            )
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/01a-local-audio.py

@@ -15,7 +15,7 @@ from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
 
 load_dotenv(override=True)
@@ -29,7 +29,7 @@ async def main():
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
     )
 
     pipeline = Pipeline([tts, transport.output()])

examples/foundational/01b-livekit-audio.py

@@ -1,9 +1,3 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
 import argparse
 import asyncio
 import os
@@ -18,7 +12,7 @@ from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.livekit import LiveKitParams, LiveKitTransport
 
 load_dotenv(override=True)
@@ -89,7 +83,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         runner = PipelineRunner()

examples/foundational/01c-fastpitch.py

@@ -4,49 +4,51 @@
 # 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.frames.frames import EndFrame, TTSSpeakFrame
 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.services.riva import FastPitchTTSService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    # Create a transport using the WebRTC connection
+async def main():
-    transport = SmallWebRTCTransport(
+    async with aiohttp.ClientSession() as session:
-        webrtc_connection=webrtc_connection,
+        (room_url, _) = await configure(session)
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
 
-    tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+        transport = DailyTransport(
+            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
+        )
 
-    task = PipelineTask(Pipeline([tts, transport.output()]))
+        tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
-    # Register an event handler so we can play the audio when the client joins
+        runner = PipelineRunner()
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        task = PipelineTask(Pipeline([tts, transport.output()]))
 
-    await runner.run(task)
+        # 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):
+            participant_name = participant.get("info", {}).get("userName", "")
+            await task.queue_frames([TTSSpeakFrame(f"Aloha, {participant_name}!"), EndFrame()])
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

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

@@ -4,62 +4,61 @@
 # 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.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    # Create a transport using the WebRTC connection
+async def main():
-    transport = SmallWebRTCTransport(
+    async with aiohttp.ClientSession() as session:
-        webrtc_connection=webrtc_connection,
+        (room_url, _) = await configure(session)
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
 
-    tts = CartesiaTTSService(
+        transport = DailyTransport(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            room_url, None, "Say One Thing From an LLM", DailyParams(audio_out_enabled=True)
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        )
-    )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    messages = [
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        {
-            "role": "system",
-            "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
-        }
-    ]
 
-    task = PipelineTask(Pipeline([llm, tts, transport.output()]))
+        messages = [
+            {
+                "role": "system",
+                "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
+            }
+        ]
 
-    # Register an event handler so we can play the audio when the client joins
+        runner = PipelineRunner()
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        task = PipelineTask(Pipeline([llm, tts, transport.output()]))
 
-    await runner.run(task)
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/03-still-frame.py

@@ -4,67 +4,59 @@
 # 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.frames.frames import TextFrame
 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.services.fal import FalImageGenService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    # Create a transport using the WebRTC connection
+async def main():
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            camera_out_enabled=True,
-            camera_out_width=1024,
-            camera_out_height=1024,
-        ),
-    )
-
-    # Create an HTTP session
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Show a still frame image",
+            DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
+        )
+
         imagegen = FalImageGenService(
             params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
 
+        runner = PipelineRunner()
+
         task = PipelineTask(Pipeline([imagegen, transport.output()]))
 
-        # Register an event handler so we can play the audio when the client joins
+        @transport.event_handler("on_first_participant_joined")
-        @transport.event_handler("on_client_connected")
+        async def on_first_participant_joined(transport, participant):
-        async def on_client_connected(transport, client):
             await task.queue_frame(TextFrame("a cat in the style of picasso"))
 
-        @transport.event_handler("on_client_disconnected")
+        @transport.event_handler("on_participant_left")
-        async def on_client_disconnected(transport, client):
+        async def on_participant_left(transport, participant, reason):
-            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)
-
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/03a-local-still-frame.py

@@ -17,7 +17,7 @@ from pipecat.frames.frames import TextFrame
 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.services.fal import FalImageGenService
 from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
 
 load_dotenv(override=True)

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

@@ -4,67 +4,62 @@
 # 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.frames.frames import TextFrame
+from pipecat.frames.frames import EndFrame, TextFrame
 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.services.google import GoogleImageGenService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    # Create a transport using the WebRTC connection
+async def main():
-    transport = SmallWebRTCTransport(
+    async with aiohttp.ClientSession() as session:
-        webrtc_connection=webrtc_connection,
+        (room_url, _) = await configure(session)
-        params=TransportParams(
-            camera_out_enabled=True,
-            camera_out_width=1024,
-            camera_out_height=1024,
-        ),
-    )
 
-    imagegen = GoogleImageGenService(
+        transport = DailyTransport(
-        api_key=os.getenv("GOOGLE_API_KEY"),
+            room_url,
-    )
+            None,
+            "Show a still frame image",
+            DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
+        )
 
-    task = PipelineTask(
+        imagegen = GoogleImageGenService(
-        Pipeline([imagegen, transport.output()]),
+            api_key=os.getenv("GOOGLE_API_KEY"),
-        params=PipelineParams(enable_metrics=True),
+        )
-    )
 
-    # Register an event handler so we can play the audio when the client joins
+        runner = PipelineRunner()
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        await task.queue_frame(TextFrame("a cat in the style of picasso"))
-        await task.queue_frame(TextFrame("a dog in the style of picasso"))
-        await task.queue_frame(TextFrame("a fish in the style of picasso"))
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            Pipeline([imagegen, transport.output()]),
-        logger.info(f"Client disconnected")
+            params=PipelineParams(enable_metrics=True),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await task.queue_frame(TextFrame("a cat in the style of picasso"))
-        await task.cancel()
+            await task.queue_frame(TextFrame("a dog in the style of picasso"))
+            await task.queue_frame(TextFrame("a fish in the style of picasso"))
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.queue_frame(EndFrame())
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/04-utterance-and-speech.py

@@ -13,9 +13,9 @@ import os
 import sys
 
 import aiohttp
-from daily_runner import configure
 from dotenv import load_dotenv
 from loguru import logger
+from runner import configure
 
 from pipecat.frames.frames import EndPipeFrame, LLMMessagesFrame, TextFrame
 from pipecat.pipeline.merge_pipeline import SequentialMergePipeline

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

@@ -4,12 +4,15 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 from dataclasses import dataclass
 
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from runner import configure
 
 from pipecat.frames.frames import (
     DataFrame,
@@ -24,15 +27,16 @@ from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia.tts import CartesiaHttpTTSService
+from pipecat.services.cartesia import CartesiaHttpTTSService
-from pipecat.services.fal.image import FalImageGenService
+from pipecat.services.fal import FalImageGenService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 @dataclass
 class MonthFrame(DataFrame):
@@ -63,33 +67,27 @@ class MonthPrepender(FrameProcessor):
             await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    """Run the Calendar Month Narration bot using WebRTC transport.
-
-    Args:
-        webrtc_connection: The WebRTC connection to use
-        room_name: Optional room name for display purposes
-    """
-    logger.info(f"Starting bot")
-
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_out_enabled=True,
-            camera_out_enabled=True,
-            camera_out_width=1024,
-            camera_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"))
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Month Narration Bot",
+            DailyParams(
+                audio_out_enabled=True,
+                camera_out_enabled=True,
+                camera_out_width=1024,
+                camera_out_height=1024,
+            ),
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         imagegen = FalImageGenService(
@@ -146,30 +144,14 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             frames.append(MonthFrame(month=month))
             frames.append(LLMMessagesFrame(messages))
 
+        runner = PipelineRunner()
+
         task = PipelineTask(pipeline)
 
-        # Set up transport event handlers
+        await task.queue_frames(frames)
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Start the month narration once connected
-            await task.queue_frames(frames)
 
-        @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=False)
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

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

@@ -27,9 +27,9 @@ from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia.tts import CartesiaHttpTTSService
+from pipecat.services.cartesia import CartesiaHttpTTSService
-from pipecat.services.fal.image import FalImageGenService
+from pipecat.services.fal import FalImageGenService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
 
 load_dotenv(override=True)
@@ -93,11 +93,11 @@ async def main():
                         self.frame = frame
                     await self.push_frame(frame, direction)
 
-            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
             tts = CartesiaHttpTTSService(
                 api_key=os.getenv("CARTESIA_API_KEY"),
-                voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
             )
 
             imagegen = FalImageGenService(

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

@@ -4,13 +4,17 @@
 # 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.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, MetricsFrame, TranscriptionFrame, TTSSpeakFrame
+from pipecat.frames.frames import Frame, MetricsFrame
 from pipecat.metrics.metrics import (
     LLMUsageMetricsData,
     ProcessingMetricsData,
@@ -22,40 +26,17 @@ 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.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
-
+logger.remove(0)
-# Custom processor that prints a message if it receives a TranscriptionFrame that says "banana"
+logger.add(sys.stderr, level="DEBUG")
-class BananaProcessor(FrameProcessor):
-    """A custom processor that listens for transcription frames containing the word 'banana'."""
-
-    def __init__(self):
-        super().__init__()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        # Ensure the super method is called first
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, TranscriptionFrame):
-            logger.debug(f"Received transcription frame: {frame.text}")
-            if "banana" in frame.text.lower():
-                logger.info("---- Received 'banana' in transcription frame")
-
-        # Push the frame after processing
-        await self.push_frame(frame)
 
 
 class MetricsLogger(FrameProcessor):
-    def __init__(self):
-        super().__init__()
-
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -75,86 +56,76 @@ class MetricsLogger(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url,
-        params=TransportParams(
+            token,
-            audio_in_enabled=True,
+            "Respond bot",
-            audio_out_enabled=True,
+            DailyParams(
-            vad_enabled=True,
+                audio_out_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
-            vad_audio_passthrough=True,
+                vad_enabled=True,
-        ),
+                vad_analyzer=SileroVADAnalyzer(),
-    )
+            ),
+        )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    tts = CartesiaTTSService(
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        ml = MetricsLogger()
 
-    ml = MetricsLogger()
+        messages = [
-
+            {
-    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.",
-            "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)
-
-    banana = BananaProcessor()
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            banana,
-            context_aggregator.user(),
-            llm,
-            tts,
-            ml,
-            transport.output(),
-            context_aggregator.assistant(),
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),
-        # Kick off the conversation.
+                context_aggregator.user(),
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,
+                ml,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
-    await runner.run(task)
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

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

@@ -4,11 +4,15 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 from PIL import Image
+from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
@@ -16,21 +20,22 @@ from pipecat.frames.frames import (
     BotStoppedSpeakingFrame,
     Frame,
     OutputImageRawFrame,
+    TextFrame,
 )
 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.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class ImageSyncAggregator(FrameProcessor):
     def __init__(self, speaking_path: str, waiting_path: str):
@@ -67,90 +72,83 @@ class ImageSyncAggregator(FrameProcessor):
         await self.push_frame(frame)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url,
-        params=TransportParams(
+            token,
-            audio_in_enabled=True,
+            "Respond bot",
-            audio_out_enabled=True,
+            DailyParams(
-            camera_out_enabled=True,
+                audio_out_enabled=True,
-            camera_out_width=1024,
+                camera_out_enabled=True,
-            camera_out_height=1024,
+                camera_out_width=1024,
-            vad_enabled=True,
+                camera_out_height=1024,
-            vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
-            vad_audio_passthrough=True,
+                vad_enabled=True,
-        ),
+                vad_analyzer=SileroVADAnalyzer(),
-    )
+            ),
+        )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    tts = CartesiaTTSService(
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        messages = [
-
+            {
-    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.",
-            "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)
-
-    image_sync_aggregator = ImageSyncAggregator(
-        os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
-        os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            tts,
-            image_sync_aggregator,
-            transport.output(),
-            context_aggregator.assistant(),
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        image_sync_aggregator = ImageSyncAggregator(
-    async def on_client_connected(transport, client):
+            os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
-        logger.info(f"Client connected")
+            os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
-        # Kick off the conversation.
+        )
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    @transport.event_handler("on_client_disconnected")
+        pipeline = Pipeline(
-    async def on_client_disconnected(transport, client):
+            [
-        logger.info(f"Client disconnected")
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                image_sync_aggregator,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
-    @transport.event_handler("on_client_closed")
+        task = PipelineTask(
-    async def on_client_closed(transport, client):
+            pipeline,
-        logger.info(f"Client closed connection")
+            params=PipelineParams(
-        await task.cancel()
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_first_participant_joined")
-    await runner.run(task)
+        async def on_first_participant_joined(transport, participant):
+            participant_name = participant.get("info", {}).get("userName", "")
+            await transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([TextFrame(f"Hi there {participant_name}!")])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07-interruptible-vad.py

@@ -0,0 +1,104 @@
+#
+# 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.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.vad.silero import SileroVAD
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                transcription_enabled=True,
+            ),
+        )
+
+        vad = SileroVAD()
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                vad,
+                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"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07-interruptible.py

@@ -4,103 +4,99 @@
 # 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.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.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = CartesiaTTSService(
+        tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-    )
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                context_aggregator.user(),  # User responses
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,  # LLM
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

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

@@ -4,57 +4,62 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.anthropic import AnthropicLLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.rime.tts import RimeHttpTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    # Create an HTTP session
     async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        (room_url, token) = await configure(session)
 
-        tts = RimeHttpTTSService(
+        transport = DailyTransport(
-            api_key=os.getenv("RIME_API_KEY", ""),
+            room_url,
-            voice_id="rex",
+            token,
-            aiohttp_session=session,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
+        llm = AnthropicLLMService(
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-opus-20240229"
+        )
+
+        # todo: think more about how to handle system prompts in a more general way. OpenAI,
+        # Google, and Anthropic all have slightly different approaches to providing a system
+        # prompt.
         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.",
+                "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, helpful, and brief way. Say hello.",
             },
         ]
 
@@ -64,7 +69,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
-                stt,
                 context_aggregator.user(),  # User responses
                 llm,  # LLM
                 tts,  # TTS
@@ -83,28 +87,20 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             ),
         )
 
-        @transport.event_handler("on_client_connected")
+        @transport.event_handler("on_first_participant_joined")
-        async def on_client_connected(transport, client):
+        async def on_first_participant_joined(transport, participant):
-            logger.info(f"Client connected")
+            await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-        @transport.event_handler("on_client_disconnected")
+        @transport.event_handler("on_participant_left")
-        async def on_client_disconnected(transport, client):
+        async def on_participant_left(transport, participant, reason):
-            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)
+        runner = PipelineRunner()
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

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

@@ -1,106 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.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.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 TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    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
-    )
-
-    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(),
-            stt,
-            vad,
-            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_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @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)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

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

@@ -4,8 +4,11 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 
+import aiohttp
 from dotenv import load_dotenv
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_community.chat_message_histories import ChatMessageHistory
@@ -13,6 +16,7 @@ from langchain_core.chat_history import BaseChatMessageHistory
 from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_openai import ChatOpenAI
 from loguru import logger
+from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
@@ -24,15 +28,15 @@ from pipecat.processors.aggregators.llm_response import (
     LLMUserResponseAggregator,
 )
 from pipecat.processors.frameworks.langchain import LangchainProcessor
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 message_store = {}
 
 
@@ -42,97 +46,90 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
     return message_store[session_id]
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url,
-        params=TransportParams(
+            token,
-            audio_in_enabled=True,
+            "Respond bot",
-            audio_out_enabled=True,
+            DailyParams(
-            vad_enabled=True,
+                audio_out_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
-            vad_audio_passthrough=True,
+                vad_enabled=True,
-        ),
+                vad_analyzer=SileroVADAnalyzer(),
-    )
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            (
-                "system",
-                "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
-                "Your response will be synthesized to voice and those characters will create unnatural sounds.",
             ),
-            MessagesPlaceholder("chat_history"),
+        )
-            ("human", "{input}"),
-        ]
-    )
-    chain = prompt | ChatOpenAI(model="gpt-4.1", temperature=0.7)
-    history_chain = RunnableWithMessageHistory(
-        chain,
-        get_session_history,
-        history_messages_key="chat_history",
-        input_messages_key="input",
-    )
-    lc = LangchainProcessor(history_chain)
 
-    tma_in = LLMUserResponseAggregator()
+        tts = CartesiaTTSService(
-    tma_out = LLMAssistantResponseAggregator()
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    pipeline = Pipeline(
+        prompt = ChatPromptTemplate.from_messages(
-        [
+            [
-            transport.input(),  # Transport user input
+                (
-            stt,
+                    "system",
-            tma_in,  # User responses
+                    "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
-            lc,  # Langchain
+                    "Your response will be synthesized to voice and those characters will create unnatural sounds.",
-            tts,  # TTS
+                ),
-            transport.output(),  # Transport bot output
+                MessagesPlaceholder("chat_history"),
-            tma_out,  # Assistant spoken responses
+                ("human", "{input}"),
-        ]
+            ]
-    )
+        )
+        chain = prompt | ChatOpenAI(model="gpt-4o", temperature=0.7)
+        history_chain = RunnableWithMessageHistory(
+            chain,
+            get_session_history,
+            history_messages_key="chat_history",
+            input_messages_key="input",
+        )
+        lc = LangchainProcessor(history_chain)
 
-    task = PipelineTask(
+        tma_in = LLMUserResponseAggregator()
-        pipeline,
+        tma_out = LLMAssistantResponseAggregator()
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                tma_in,  # User responses
-        # the `LLMMessagesFrame` will be picked up by the LangchainProcessor using
+                lc,  # Langchain
-        # only the content of the last message to inject it in the prompt defined
+                tts,  # TTS
-        # above. So no role is required here.
+                transport.output(),  # Transport bot output
-        messages = [({"content": "Please briefly introduce yourself to the user."})]
+                tma_out,  # Assistant spoken responses
-        await task.queue_frames([LLMMessagesFrame(messages)])
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            lc.set_participant_id(participant["id"])
+            # Kick off the conversation.
+            # the `LLMMessagesFrame` will be picked up by the LangchainProcessor using
+            # only the content of the last message to inject it in the prompt defined
+            # above. So no role is required here.
+            messages = [({"content": "Please briefly introduce yourself to the user."})]
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07c-interruptible-deepgram-vad.py

@@ -4,11 +4,15 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 
+import aiohttp
 from deepgram import LiveOptions
 from dotenv import load_dotenv
 from loguru import logger
+from runner import configure
 
 from pipecat.frames.frames import (
     BotInterruptionFrame,
@@ -20,98 +24,93 @@ 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.deepgram.stt import DeepgramSTTService
+from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
-from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, _) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(
+        transport = DailyTransport(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
+            room_url,
-        live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
+            None,
-    )
+            "Respond bot",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+            ),
+        )
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+        stt = DeepgramSTTService(
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
+            live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    messages = [
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        {
-            "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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @stt.event_handler("on_speech_started")
+        pipeline = Pipeline(
-    async def on_speech_started(stt, *args, **kwargs):
+            [
-        await task.queue_frames([BotInterruptionFrame(), UserStartedSpeakingFrame()])
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @stt.event_handler("on_utterance_end")
+        task = PipelineTask(
-    async def on_utterance_end(stt, *args, **kwargs):
+            pipeline,
-        await task.queue_frames([StopInterruptionFrame(), UserStoppedSpeakingFrame()])
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_connected")
+        @stt.event_handler("on_speech_started")
-    async def on_client_connected(transport, client):
+        async def on_speech_started(stt, *args, **kwargs):
-        logger.info(f"Client connected")
+            await task.queue_frames([BotInterruptionFrame(), UserStartedSpeakingFrame()])
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    @transport.event_handler("on_client_disconnected")
+        @stt.event_handler("on_utterance_end")
-    async def on_client_disconnected(transport, client):
+        async def on_utterance_end(stt, *args, **kwargs):
-        logger.info(f"Client disconnected")
+            await task.queue_frames([StopInterruptionFrame(), UserStoppedSpeakingFrame()])
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            # Kick off the conversation.
-        await task.cancel()
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07c-interruptible-deepgram.py

@@ -4,100 +4,98 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
-from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, _) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    messages = [
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        {
-            "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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            # Kick off the conversation.
-        await task.cancel()
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07d-interruptible-elevenlabs-http.py

@@ -1,110 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.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.deepgram.stt import DeepgramSTTService
-from pipecat.services.elevenlabs.tts import ElevenLabsHttpTTSService
-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 SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = ElevenLabsHttpTTSService(
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-            aiohttp_session=session,
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @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)
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/foundational/07d-interruptible-elevenlabs.py

@@ -4,103 +4,99 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = ElevenLabsTTSService(
+        tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-    )
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                context_aggregator.user(),  # User responses
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,  # LLM
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07e-interruptible-playht-http.py

@@ -4,104 +4,100 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.playht import PlayHTHttpTTSService
-from pipecat.services.playht.tts import PlayHTHttpTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = PlayHTHttpTTSService(
+        tts = PlayHTHttpTTSService(
-        user_id=os.getenv("PLAYHT_USER_ID"),
+            user_id=os.getenv("PLAYHT_USER_ID"),
-        api_key=os.getenv("PLAYHT_API_KEY"),
+            api_key=os.getenv("PLAYHT_API_KEY"),
-        voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
+            voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
-    )
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                context_aggregator.user(),  # User responses
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,  # LLM
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07e-interruptible-playht.py

@@ -4,106 +4,102 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.playht import PlayHTTTSService
-from pipecat.services.playht.tts import PlayHTTTSService
 from pipecat.transcriptions.language import Language
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = PlayHTTTSService(
+        tts = PlayHTTTSService(
-        user_id=os.getenv("PLAYHT_USER_ID"),
+            user_id=os.getenv("PLAYHT_USER_ID"),
-        api_key=os.getenv("PLAYHT_API_KEY"),
+            api_key=os.getenv("PLAYHT_API_KEY"),
-        voice_url="s3://voice-cloning-zero-shot/e46b4027-b38d-4d24-b292-38fbca2be0ef/original/manifest.json",
+            voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
-        params=PlayHTTTSService.InputParams(language=Language.EN),
+            params=PlayHTTTSService.InputParams(language=Language.EN),
-    )
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                context_aggregator.user(),  # User responses
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,  # LLM
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07f-interruptible-azure.py

@@ -4,110 +4,108 @@
 # 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.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.azure.llm import AzureLLMService
+from pipecat.services.azure import AzureLLMService, AzureSTTService, AzureTTSService
-from pipecat.services.azure.stt import AzureSTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.services.azure.tts import AzureTTSService
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = AzureSTTService(
+        transport = DailyTransport(
-        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            room_url,
-        region=os.getenv("AZURE_SPEECH_REGION"),
+            token,
-    )
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
 
-    tts = AzureTTSService(
+        stt = AzureSTTService(
-        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-        region=os.getenv("AZURE_SPEECH_REGION"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
-    )
+        )
 
-    llm = AzureLLMService(
+        tts = AzureTTSService(
-        api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-        endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
-        model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
-    )
 
-    messages = [
+        llm = AzureLLMService(
-        {
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
-            "role": "system",
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            "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.",
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
-        },
+        )
-    ]
 
-    context = OpenAILLMContext(messages)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07g-interruptible-openai.py

@@ -4,105 +4,106 @@
 # 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.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.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService, OpenAISTTService, OpenAITTSService
-from pipecat.services.openai.stt import OpenAISTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.services.openai.tts import OpenAITTSService
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = OpenAISTTService(
+        transport = DailyTransport(
-        api_key=os.getenv("OPENAI_API_KEY"),
+            room_url,
-        model="gpt-4o-transcribe-latest",
+            token,
-        prompt="Expect words related to dogs, such as breed names.",
+            "Respond bot",
-    )
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_sample_rate=24000,
+                transcription_enabled=False,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
 
-    tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
+        # You can use the OpenAI compatible API like Groq.
+        # stt = OpenAISTTService(
+        #     base_url="https://api.groq.com/openai/v1",
+        #     api_key="gsk_***",
+        #     model="whisper-large-v3",
+        # )
+        stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"), model="whisper-1")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
 
-    messages = [
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        {
-            "role": "system",
-            "content": "You are very knowledgable about dogs. 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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            audio_out_sample_rate=24000,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07h-interruptible-openpipe.py

@@ -4,109 +4,106 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 import time
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from runner import configure
 
 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.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openpipe import OpenPipeLLMService
-from pipecat.services.openpipe.llm import OpenPipeLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = CartesiaTTSService(
+        tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-    )
+        )
 
-    timestamp = int(time.time())
+        timestamp = int(time.time())
-    llm = OpenPipeLLMService(
+        llm = OpenPipeLLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
+            api_key=os.getenv("OPENAI_API_KEY"),
-        openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
+            openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
-        tags={"conversation_id": f"pipecat-{timestamp}"},
+            model="gpt-4o",
-    )
+            tags={"conversation_id": f"pipecat-{timestamp}"},
+        )
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                context_aggregator.user(),  # User responses
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,  # LLM
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07i-interruptible-xtts.py

@@ -4,44 +4,45 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.xtts import XTTSService
-from pipecat.services.xtts.tts import XTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    # Create an HTTP session
     async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
         tts = XTTSService(
             aiohttp_session=session,
@@ -49,7 +50,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             base_url="http://localhost:8000",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -64,7 +65,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
-                stt,
                 context_aggregator.user(),  # User responses
                 llm,  # LLM
                 tts,  # TTS
@@ -83,28 +83,21 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             ),
         )
 
-        @transport.event_handler("on_client_connected")
+        @transport.event_handler("on_first_participant_joined")
-        async def on_client_connected(transport, client):
+        async def on_first_participant_joined(transport, participant):
-            logger.info(f"Client connected")
+            await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-        @transport.event_handler("on_client_disconnected")
+        @transport.event_handler("on_participant_left")
-        async def on_client_disconnected(transport, client):
+        async def on_participant_left(transport, participant, reason):
-            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)
+        runner = PipelineRunner()
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07j-interruptible-gladia.py

@@ -4,111 +4,106 @@
 # 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.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.cartesia import CartesiaTTSService
-from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
+from pipecat.services.gladia import GladiaSTTService
-from pipecat.services.gladia.stt import GladiaSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transcriptions.language import Language
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = GladiaSTTService(
+        transport = DailyTransport(
-        api_key=os.getenv("GLADIA_API_KEY", ""),
+            room_url,
-        params=GladiaInputParams(
+            token,
-            language_config=LanguageConfig(
+            "Respond bot",
-                languages=[Language.EN],
+            DailyParams(
-            )
+                audio_out_enabled=True,
-        ),
+                vad_enabled=True,
-    )
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
 
-    tts = CartesiaTTSService(
+        stt = GladiaSTTService(
-        api_key=os.getenv("CARTESIA_API_KEY", ""),
+            api_key=os.getenv("GLADIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        )
-    )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    messages = [
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        {
-            "role": "system",
-            "content": f"You are a helpful LLM. 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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        # Register an event handler to exit the application when the user leaves.
-    await runner.run(task)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07k-interruptible-lmnt.py

@@ -4,100 +4,96 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.lmnt import LmntTTSService
-from pipecat.services.lmnt.tts import LmntTTSService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
+        tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User respones
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                context_aggregator.user(),  # User respones
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,  # LLM
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07l-interruptible-groq.py

@@ -1,102 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.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.groq.llm import GroqLLMService
-from pipecat.services.groq.stt import GroqSTTService
-from pipecat.services.groq.tts import GroqTTSService
-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)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"))
-
-    llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
-
-    tts = GroqTTSService(api_key=os.getenv("GROQ_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(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @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)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/foundational/07l-interruptible-together.py

@@ -0,0 +1,115 @@
+#
+# 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.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.services.ai_services import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.together import TogetherLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = TogetherLLMService(
+            api_key=os.getenv("TOGETHER_API_KEY"),
+            model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+            params=TogetherLLMService.InputParams(
+                temperature=1.0,
+                top_p=0.9,
+                top_k=40,
+                extra={
+                    "frequency_penalty": 2.0,
+                    "presence_penalty": 0.0,
+                },
+            ),
+        )
+
+        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 in plain language. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+        user_aggregator = context_aggregator.user()
+        assistant_aggregator = context_aggregator.assistant()
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                user_aggregator,  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                assistant_aggregator,  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07m-interruptible-polly.py

@@ -4,106 +4,106 @@
 # 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.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.aws.tts import PollyTTSService
+from pipecat.services.aws import PollyTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, _) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
 
-    tts = PollyTTSService(
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-        api_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
-        aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
-        region=os.getenv("AWS_REGION"),
-        voice_id="Amy",
-        params=PollyTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
-    )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = PollyTTSService(
+            api_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
+            aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
+            region=os.getenv("AWS_REGION"),
+            voice_id="Amy",
+            params=PollyTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
+        )
 
-    messages = [
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        {
-            "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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07n-interruptible-google.py

@@ -4,108 +4,104 @@
 # 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.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.google.llm import GoogleLLMService
+from pipecat.services.google import GoogleLLMService, GoogleSTTService, GoogleTTSService
-from pipecat.services.google.stt import GoogleSTTService
-from pipecat.services.google.tts import GoogleTTSService
 from pipecat.transcriptions.language import Language
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, _) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = GoogleSTTService(
+        transport = DailyTransport(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US),
+            room_url,
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
+            None,
-    )
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
 
-    tts = GoogleTTSService(
+        stt = GoogleSTTService(
-        voice_id="en-US-Chirp3-HD-Charon",
+            params=GoogleSTTService.InputParams(languages=Language.EN_US),
-        params=GoogleTTSService.InputParams(language=Language.EN_US),
+        )
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-    )
 
-    llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+        tts = GoogleTTSService(
+            voice_id="en-US-Journey-F",
+            params=GoogleTTSService.InputParams(language=Language.EN_US),
+        )
 
-    messages = [
+        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
-        {
-            "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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User respones
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User respones
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07o-interruptible-assemblyai.py

@@ -4,105 +4,105 @@
 # 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.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.assemblyai.stt import AssemblyAISTTService
+from pipecat.services.assemblyai import AssemblyAISTTService
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = AssemblyAISTTService(
+        transport = DailyTransport(
-        api_key=os.getenv("ASSEMBLYAI_API_KEY"),
+            room_url,
-    )
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
 
-    tts = CartesiaTTSService(
+        stt = AssemblyAISTTService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            api_key=os.getenv("ASSEMBLYAI_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        )
-    )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    messages = [
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        {
-            "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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07p-interruptible-krisp.py

@@ -4,102 +4,100 @@
 # 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.filters.krisp_filter import KrispFilter
-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.deepgram.stt import DeepgramSTTService
+from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
-from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.base_transport import TransportParams
+from pipecat.vad.silero import SileroVADAnalyzer
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-            audio_in_filter=KrispFilter(),
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+                audio_in_filter=KrispFilter(),
+            ),
+        )
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    messages = [
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        {
-            "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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            # Kick off the conversation.
-        await task.cancel()
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07q-interruptible-rime.py

@@ -4,103 +4,99 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.rime import RimeTTSService
-from pipecat.services.rime.tts import RimeTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = RimeTTSService(
+        tts = RimeTTSService(
-        api_key=os.getenv("RIME_API_KEY", ""),
+            api_key=os.getenv("RIME_API_KEY", ""),
-        voice_id="rex",
+            voice_id="rex",
-    )
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                context_aggregator.user(),  # User responses
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,  # LLM
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07r-interruptible-riva-nim.py

@@ -4,100 +4,92 @@
 # 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.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.nim.llm import NimLLMService
+from pipecat.services.nim import NimLLMService
-from pipecat.services.riva.stt import ParakeetSTTService
+from pipecat.services.riva import FastPitchTTSService, ParakeetSTTService
-from pipecat.services.riva.tts import FastPitchTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, _) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
 
-    llm = NimLLMService(api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct")
+        stt = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
 
-    tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+        llm = NimLLMService(
+            api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct"
+        )
 
-    messages = [
+        tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
-        {
-            "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)
+        messages = [
-    context_aggregator = llm.create_context_aggregator(context)
+            {
-
+                "role": "system",
-    pipeline = Pipeline(
+                "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.",
-        [
+            },
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                stt,  # STT
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            # Kick off the conversation.
-        await task.cancel()
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07s-interruptible-google-audio-in.py

@@ -4,12 +4,16 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 from dataclasses import dataclass
 
+import aiohttp
 import google.ai.generativelanguage as glm
 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 (
@@ -28,15 +32,14 @@ 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 FrameProcessor
-from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.google.tts import GoogleTTSService
+from pipecat.services.google import GoogleLLMService
-from pipecat.transcriptions.language import Language
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
 marker = "|----|"
 system_message = f"""
@@ -190,92 +193,85 @@ class TanscriptionContextFixup(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url,
-        params=TransportParams(
+            token,
-            audio_in_enabled=True,
+            "Respond bot",
-            audio_out_enabled=True,
+            DailyParams(
-            # No transcription at all. just audio input to Gemini!
+                audio_out_enabled=True,
-            # transcription_enabled=True,
+                # No transcription at all. just audio input to Gemini!
-            vad_enabled=True,
+                # transcription_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
+                vad_enabled=True,
-            vad_audio_passthrough=True,
+                vad_analyzer=SileroVADAnalyzer(),
-        ),
+                vad_audio_passthrough=True,
-    )
+            ),
+        )
 
-    llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    tts = GoogleTTSService(
+        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
-        voice_id="en-US-Chirp3-HD-Charon",
-        params=GoogleTTSService.InputParams(language=Language.EN_US),
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-    )
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "role": "system",
-            "content": system_message,
+                "content": system_message,
-        },
+            },
-        {
+            {
-            "role": "user",
+                "role": "user",
-            "content": "Start by saying hello.",
+                "content": "Start by saying hello.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-    audio_collector = UserAudioCollector(context, context_aggregator.user())
-    pull_transcript_out_of_llm_output = TranscriptExtractor(context)
-    fixup_context_messages = TanscriptionContextFixup(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            audio_collector,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            pull_transcript_out_of_llm_output,
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-            fixup_context_messages,
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
+        audio_collector = UserAudioCollector(context, context_aggregator.user())
-            allow_interruptions=True,
+        pull_transcript_out_of_llm_output = TranscriptExtractor(context)
-            enable_metrics=True,
+        fixup_context_messages = TanscriptionContextFixup(context)
-            enable_usage_metrics=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                audio_collector,
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                context_aggregator.user(),  # User responses
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,  # LLM
+                pull_transcript_out_of_llm_output,
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+                fixup_context_messages,
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07t-interruptible-fish.py

@@ -4,103 +4,99 @@
 # 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.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.deepgram.stt import DeepgramSTTService
+from pipecat.services.fish import FishAudioTTSService
-from pipecat.services.fish.tts import FishAudioTTSService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = FishAudioTTSService(
+        tts = FishAudioTTSService(
-        api_key=os.getenv("FISH_API_KEY"),
+            api_key=os.getenv("FISH_API_KEY"),
-        model="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
+            model="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
-    )
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
+            },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),  # Transport user input
-        # Kick off the conversation.
+                context_aggregator.user(),  # User responses
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+                llm,  # LLM
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/07u-interruptible-ultravox.py

@@ -1,95 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.ultravox.stt import UltravoxSTTService
-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)
-
-# NOTE: This example requires GPU resources to run efficiently.
-# The Ultravox model is compute-intensive and performs best with GPU acceleration.
-# This can be deployed on cloud GPU providers like Cerebrium.ai for optimal performance.
-
-
-# Want to initialize the ultravox processor since it takes time to load the model and dont
-# want to load it every time the pipeline is run
-ultravox_processor = UltravoxSTTService(
-    model_name="fixie-ai/ultravox-v0_5-llama-3_1-8b",
-    hf_token=os.getenv("HF_TOKEN"),
-)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.environ.get("CARTESIA_API_KEY"),
-        voice_id="97f4b8fb-f2fe-444b-bb9a-c109783a857a",
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            ultravox_processor,
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-        ),
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-
-    @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)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/foundational/07v-interruptible-neuphonic-http.py

@@ -1,106 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.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.deepgram.stt import DeepgramSTTService
-from pipecat.services.neuphonic.tts import NeuphonicHttpTTSService
-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 SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = NeuphonicHttpTTSService(
-        api_key=os.getenv("NEUPHONIC_API_KEY"),
-        voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
-    )
-
-    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(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @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)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/foundational/07v-interruptible-neuphonic.py

@@ -1,106 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.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.deepgram.stt import DeepgramSTTService
-from pipecat.services.neuphonic.tts import NeuphonicTTSService
-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 SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = NeuphonicTTSService(
-        api_key=os.getenv("NEUPHONIC_API_KEY"),
-        voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
-    )
-
-    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(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @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)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/foundational/07w-interruptible-fal.py

@@ -1,108 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.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.fal.stt import FalSTTService
-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 SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    stt = FalSTTService(
-        api_key=os.getenv("FAL_KEY"),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @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)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/foundational/07x-interruptible-local.py

@@ -1,91 +0,0 @@
-#
-# 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.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    transport = LocalAudioTransport(
-        LocalAudioTransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        )
-    )
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
-
-    messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-    await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    runner = PipelineRunner()
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/08-bots-arguing.py

@@ -4,8 +4,8 @@ import os
 from typing import Tuple
 
 import aiohttp
-from daily_runner import configure
 from dotenv import load_dotenv
+from runner import configure
 
 from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -72,8 +72,7 @@ async def main():
 
         async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
             """This function streams text from the LLM and uses the TTS service to convert
-            that text to speech as it's received.
+            that text to speech as it's received."""
-            """
             source_queue = asyncio.Queue()
             sink_queue = asyncio.Queue()
             sentence_aggregator = SentenceAggregator()

examples/foundational/09-mirror.py

@@ -4,9 +4,13 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
+import sys
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from runner import configure
 
 from pipecat.frames.frames import (
     Frame,
@@ -19,12 +23,13 @@ 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.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class MirrorProcessor(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -39,7 +44,6 @@ class MirrorProcessor(FrameProcessor):
                 )
             )
         elif isinstance(frame, InputImageRawFrame):
-            print(f"Received image frame: {frame.size} {frame.format}")
             await self.push_frame(
                 OutputImageRawFrame(image=frame.image, size=frame.size, format=frame.format)
             )
@@ -47,48 +51,42 @@ class MirrorProcessor(FrameProcessor):
             await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url,
-        params=TransportParams(
+            token,
-            audio_in_enabled=True,
+            "Test",
-            audio_out_enabled=True,
+            DailyParams(
-            camera_in_enabled=True,
+                audio_in_enabled=True,
-            camera_out_enabled=True,
+                audio_out_enabled=True,
-            camera_out_is_live=True,
+                camera_out_enabled=True,
-            camera_out_width=1280,
+                camera_out_is_live=True,
-            camera_out_height=720,
+                camera_out_width=1280,
-        ),
+                camera_out_height=720,
-    )
+            ),
+        )
 
-    pipeline = Pipeline([transport.input(), MirrorProcessor(), transport.output()])
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_video(participant["id"])
 
-    task = PipelineTask(
+        pipeline = Pipeline([transport.input(), MirrorProcessor(), transport.output()])
-        pipeline,
-        params=PipelineParams(),
-    )
 
-    @transport.event_handler("on_client_connected")
+        runner = PipelineRunner()
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                audio_in_sample_rate=24000,
+                audio_out_sample_rate=24000,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        await runner.run(task)
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/09a-local-mirror.py

@@ -5,10 +5,13 @@
 #
 
 import asyncio
+import sys
 import tkinter as tk
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from runner import configure
 
 from pipecat.frames.frames import (
     Frame,
@@ -21,13 +24,14 @@ 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.base_transport import TransportParams
 from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class MirrorProcessor(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -49,59 +53,52 @@ class MirrorProcessor(FrameProcessor):
             await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    p2p_transport = SmallWebRTCTransport(
+        tk_root = tk.Tk()
-        webrtc_connection=webrtc_connection,
+        tk_root.title("Local Mirror")
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_in_enabled=True,
-            camera_out_enabled=True,
-            camera_out_is_live=True,
-            camera_out_width=1280,
-            camera_out_height=720,
-        ),
-    )
 
-    tk_root = tk.Tk()
+        daily_transport = DailyTransport(
-    tk_root.title("Local Mirror")
+            room_url, token, "Test", DailyParams(audio_in_enabled=True)
+        )
 
-    tk_transport = TkLocalTransport(
+        tk_transport = TkLocalTransport(
-        tk_root,
+            tk_root,
-        TkTransportParams(
+            TkTransportParams(
-            audio_out_enabled=True,
+                audio_out_enabled=True,
-            camera_out_enabled=True,
+                camera_out_enabled=True,
-            camera_out_is_live=True,
+                camera_out_is_live=True,
-            camera_out_width=1280,
+                camera_out_width=1280,
-            camera_out_height=720,
+                camera_out_height=720,
-        ),
+            ),
-    )
+        )
 
-    @p2p_transport.event_handler("on_client_connected")
+        @daily_transport.event_handler("on_first_participant_joined")
-    async def on_client_connected(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client connected")
+            await transport.capture_participant_video(participant["id"])
 
-    pipeline = Pipeline([p2p_transport.input(), MirrorProcessor(), tk_transport.output()])
+        pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])
 
-    task = PipelineTask(
+        task = PipelineTask(
-        pipeline,
+            pipeline,
-        params=PipelineParams(),
+            params=PipelineParams(
-    )
+                audio_in_sample_rate=24000,
+                audio_out_sample_rate=24000,
+            ),
+        )
 
-    async def run_tk():
+        async def run_tk():
-        while not task.has_finished():
+            while not task.has_finished():
-            tk_root.update()
+                tk_root.update()
-            tk_root.update_idletasks()
+                tk_root.update_idletasks()
-            await asyncio.sleep(0.1)
+                await asyncio.sleep(0.1)
 
-    runner = PipelineRunner(handle_sigint=False)
+        runner = PipelineRunner()
 
-    await asyncio.gather(runner.run(task), run_tk())
+        await asyncio.gather(runner.run(task), run_tk())
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/10-wake-phrase.py

@@ -4,99 +4,89 @@
 # 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.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TTSSpeakFrame
 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.filters.wake_check_filter import WakeCheckFilter
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
 
-    transport = SmallWebRTCTransport(
+async def main():
-        webrtc_connection=webrtc_connection,
+    async with aiohttp.ClientSession() as session:
-        params=TransportParams(
+        (room_url, token) = await configure(session)
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Robot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    tts = CartesiaTTSService(
+        tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-    )
+        )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "role": "system",
-            "content": "You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
+                "content": "You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
-        },
+            },
-    ]
-
-    hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            hey_robot_filter,  # Filter out speech not directed at the robot
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
         ]
-    )
 
-    task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
 
-    @transport.event_handler("on_client_connected")
+        context = OpenAILLMContext(messages)
-    async def on_client_connected(transport, client):
+        context_aggregator = llm.create_context_aggregator(context)
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frame(TTSSpeakFrame("Hi! If you want to talk to me, just say 'Hey Robot'"))
 
-    @transport.event_handler("on_client_disconnected")
+        pipeline = Pipeline(
-    async def on_client_disconnected(transport, client):
+            [
-        logger.info(f"Client disconnected")
+                transport.input(),  # Transport user input
+                hey_robot_filter,  # Filter out speech not directed at the robot
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_closed")
+        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=False)
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            await tts.say("Hi! If you want to talk to me, just say 'Hey Robot'.")
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/11-sound-effects.py

@@ -4,18 +4,21 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 import wave
 
+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 (
     Frame,
     LLMFullResponseEndFrame,
     OutputAudioRawFrame,
-    TTSSpeakFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -26,15 +29,15 @@ from pipecat.processors.aggregators.openai_llm_context import (
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.logger import FrameLogger
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 sounds = {}
 sound_files = ["ding1.wav", "ding2.wav"]
@@ -77,83 +80,70 @@ class InboundSoundEffectWrapper(FrameProcessor):
             await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url,
-        params=TransportParams(
+            token,
-            audio_in_enabled=True,
+            "Respond bot",
-            audio_out_enabled=True,
+            DailyParams(
-            vad_enabled=True,
+                audio_out_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
-            vad_audio_passthrough=True,
+                vad_enabled=True,
-        ),
+                vad_analyzer=SileroVADAnalyzer(),
-    )
+            ),
+        )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    tts = CartesiaTTSService(
+        messages = [
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            {
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                "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. 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. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-    out_sound = OutboundSoundEffectWrapper()
-    in_sound = InboundSoundEffectWrapper()
-    fl = FrameLogger("LLM Out")
-    fl2 = FrameLogger("Transcription In")
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            in_sound,
-            fl2,
-            llm,
-            fl,
-            tts,
-            out_sound,
-            transport.output(),
-            context_aggregator.assistant(),
         ]
-    )
 
-    task = PipelineTask(pipeline)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+        out_sound = OutboundSoundEffectWrapper()
+        in_sound = InboundSoundEffectWrapper()
+        fl = FrameLogger("LLM Out")
+        fl2 = FrameLogger("Transcription In")
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),
-        # Kick off the conversation.
+                context_aggregator.user(),
-        await task.queue_frame(TTSSpeakFrame("Hi, I'm listening!"))
+                in_sound,
-        await transport.send_audio(sounds["ding1.wav"])
+                fl2,
+                llm,
+                fl,
+                tts,
+                out_sound,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_disconnected(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client disconnected")
+            await transport.capture_participant_transcription(participant["id"])
+            await tts.say("Hi, I'm listening!")
+            await transport.send_audio(sounds["ding1.wav"])
 
-    @transport.event_handler("on_client_closed")
+        runner = PipelineRunner()
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=False)
+        task = PipelineTask(pipeline)
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/12-describe-video.py

@@ -4,11 +4,15 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 from typing import Optional
 
+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 Frame, TextFrame, UserImageRequestFrame
@@ -18,15 +22,15 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.moondream import MoondreamService
-from pipecat.services.moondream.vision import MoondreamService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class UserImageRequester(FrameProcessor):
     def __init__(self, participant_id: Optional[str] = None):
@@ -46,81 +50,61 @@ class UserImageRequester(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    # Get WebRTC peer connection ID
+    async with aiohttp.ClientSession() as session:
-    webrtc_peer_id = webrtc_connection.pc_id
+        (room_url, token) = await configure(session)
 
-    logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Describe participant video",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    transport = SmallWebRTCTransport(
+        user_response = UserResponseAggregator()
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_in_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    user_response = UserResponseAggregator()
+        image_requester = UserImageRequester()
 
-    # Initialize the image requester without setting the participant ID yet
+        vision_aggregator = VisionImageFrameAggregator()
-    image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+        # If you run into weird description, try with use_cpu=True
+        moondream = MoondreamService()
 
-    # If you run into weird description, try with use_cpu=True
+        tts = CartesiaTTSService(
-    moondream = MoondreamService()
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await tts.say("Hi there! Feel free to ask me what I see.")
+            await transport.capture_participant_video(participant["id"], framerate=0)
+            await transport.capture_participant_transcription(participant["id"])
+            image_requester.set_participant_id(participant["id"])
 
-    tts = CartesiaTTSService(
+        pipeline = Pipeline(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            [
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                transport.input(),
-    )
+                user_response,
+                image_requester,
+                vision_aggregator,
+                moondream,
+                tts,
+                transport.output(),
+            ]
+        )
 
-    pipeline = Pipeline(
+        task = PipelineTask(pipeline)
-        [
-            transport.input(),
-            stt,
-            user_response,
-            image_requester,
-            vision_aggregator,
-            moondream,
-            tts,
-            transport.output(),
-        ]
-    )
 
-    task = PipelineTask(pipeline)
+        runner = PipelineRunner()
 
-    @transport.event_handler("on_client_connected")
+        await runner.run(task)
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected: {client}")
-
-        # Welcome message
-        await tts.say("Hi there! Feel free to ask me what I see.")
-
-        # Set the participant ID in the image requester
-        image_requester.set_participant_id(webrtc_peer_id)
-
-    @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)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/12a-describe-video-gemini-flash.py

@@ -4,29 +4,33 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 from typing import Optional
 
+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 Frame, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.google import GoogleLLMService
-from pipecat.services.google.llm import GoogleLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class UserImageRequester(FrameProcessor):
     def __init__(self, participant_id: Optional[str] = None):
@@ -46,84 +50,61 @@ class UserImageRequester(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    # Get WebRTC peer connection ID
+    async with aiohttp.ClientSession() as session:
-    webrtc_peer_id = webrtc_connection.pc_id
+        (room_url, token) = await configure(session)
 
-    logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Describe participant video",
+            DailyParams(
+                audio_in_enabled=True,  # This is so Silero VAD can get audio data
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    transport = SmallWebRTCTransport(
+        user_response = UserResponseAggregator()
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_in_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    user_response = UserResponseAggregator()
+        image_requester = UserImageRequester()
 
-    # Initialize the image requester without setting the participant ID yet
+        vision_aggregator = VisionImageFrameAggregator()
-    image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+        google = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    # Google Gemini model for vision analysis
+        @transport.event_handler("on_first_participant_joined")
-    google = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
+        async def on_first_participant_joined(transport, participant):
+            await tts.say("Hi there! Feel free to ask me what I see.")
+            await transport.capture_participant_video(participant["id"], framerate=0)
+            await transport.capture_participant_transcription(participant["id"])
+            image_requester.set_participant_id(participant["id"])
 
-    tts = CartesiaTTSService(
+        pipeline = Pipeline(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            [
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                transport.input(),
-    )
+                user_response,
+                image_requester,
+                vision_aggregator,
+                google,
+                tts,
+                transport.output(),
+            ]
+        )
 
-    pipeline = Pipeline(
+        task = PipelineTask(pipeline)
-        [
-            transport.input(),
-            stt,
-            user_response,
-            image_requester,
-            vision_aggregator,
-            google,
-            tts,
-            transport.output(),
-        ]
-    )
 
-    task = PipelineTask(
+        runner = PipelineRunner()
-        pipeline,
-        params=PipelineParams(allow_interruptions=True),
-    )
 
-    @transport.event_handler("on_client_connected")
+        await runner.run(task)
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected: {client}")
-
-        # Welcome message
-        await tts.say("Hi there! Feel free to ask me what I see.")
-
-        # Set the participant ID in the image requester
-        image_requester.set_participant_id(webrtc_peer_id)
-
-    @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)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/12b-describe-video-gpt-4o.py

@@ -4,29 +4,33 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 from typing import Optional
 
+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 Frame, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class UserImageRequester(FrameProcessor):
     def __init__(self, participant_id: Optional[str] = None):
@@ -46,84 +50,60 @@ class UserImageRequester(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    # Get WebRTC peer connection ID
+    async with aiohttp.ClientSession() as session:
-    webrtc_peer_id = webrtc_connection.pc_id
+        (room_url, token) = await configure(session)
 
-    logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Describe participant video",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    transport = SmallWebRTCTransport(
+        user_response = UserResponseAggregator()
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_in_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    user_response = UserResponseAggregator()
+        image_requester = UserImageRequester()
 
-    # Initialize the image requester without setting the participant ID yet
+        vision_aggregator = VisionImageFrameAggregator()
-    image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+        openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    # OpenAI GPT-4o for vision analysis
+        @transport.event_handler("on_first_participant_joined")
-    openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        async def on_first_participant_joined(transport, participant):
+            await tts.say("Hi there! Feel free to ask me what I see.")
+            await transport.capture_participant_video(participant["id"], framerate=0)
+            await transport.capture_participant_transcription(participant["id"])
+            image_requester.set_participant_id(participant["id"])
 
-    tts = CartesiaTTSService(
+        pipeline = Pipeline(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            [
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                transport.input(),
-    )
+                user_response,
+                image_requester,
+                vision_aggregator,
+                openai,
+                tts,
+                transport.output(),
+            ]
+        )
 
-    pipeline = Pipeline(
+        task = PipelineTask(pipeline)
-        [
-            transport.input(),
-            stt,
-            user_response,
-            image_requester,
-            vision_aggregator,
-            openai,
-            tts,
-            transport.output(),
-        ]
-    )
 
-    task = PipelineTask(
+        runner = PipelineRunner()
-        pipeline,
-        params=PipelineParams(allow_interruptions=True),
-    )
 
-    @transport.event_handler("on_client_connected")
+        await runner.run(task)
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected: {client}")
-
-        # Welcome message
-        await tts.say("Hi there! Feel free to ask me what I see.")
-
-        # Set the participant ID in the image requester
-        image_requester.set_participant_id(webrtc_peer_id)
-
-    @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)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/12c-describe-video-anthropic.py

@@ -4,29 +4,33 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
 import os
+import sys
 from typing import Optional
 
+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 Frame, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.anthropic import AnthropicLLMService
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class UserImageRequester(FrameProcessor):
     def __init__(self, participant_id: Optional[str] = None):
@@ -46,84 +50,60 @@ class UserImageRequester(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    # Get WebRTC peer connection ID
+    async with aiohttp.ClientSession() as session:
-    webrtc_peer_id = webrtc_connection.pc_id
+        (room_url, token) = await configure(session)
 
-    logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Describe participant video",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
 
-    transport = SmallWebRTCTransport(
+        user_response = UserResponseAggregator()
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_in_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    user_response = UserResponseAggregator()
+        image_requester = UserImageRequester()
 
-    # Initialize the image requester without setting the participant ID yet
+        vision_aggregator = VisionImageFrameAggregator()
-    image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+        anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    # Anthropic for vision analysis
+        @transport.event_handler("on_first_participant_joined")
-    anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
+        async def on_first_participant_joined(transport, participant):
+            await tts.say("Hi there! Feel free to ask me what I see.")
+            await transport.capture_participant_video(participant["id"], framerate=0)
+            await transport.capture_participant_transcription(participant["id"])
+            image_requester.set_participant_id(participant["id"])
 
-    tts = CartesiaTTSService(
+        pipeline = Pipeline(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            [
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                transport.input(),
-    )
+                user_response,
+                image_requester,
+                vision_aggregator,
+                anthropic,
+                tts,
+                transport.output(),
+            ]
+        )
 
-    pipeline = Pipeline(
+        task = PipelineTask(pipeline)
-        [
-            transport.input(),
-            stt,
-            user_response,
-            image_requester,
-            vision_aggregator,
-            anthropic,
-            tts,
-            transport.output(),
-        ]
-    )
 
-    task = PipelineTask(
+        runner = PipelineRunner()
-        pipeline,
-        params=PipelineParams(allow_interruptions=True),
-    )
 
-    @transport.event_handler("on_client_connected")
+        await runner.run(task)
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected: {client}")
-
-        # Welcome message
-        await tts.say("Hi there! Feel free to ask me what I see.")
-
-        # Set the participant ID in the image requester
-        image_requester.set_participant_id(webrtc_peer_id)
-
-    @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)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/13-whisper-transcription.py

@@ -4,23 +4,27 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import asyncio
+import sys
 
+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 Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.whisper.stt import WhisperSTTService
+from pipecat.services.whisper import WhisperSTTService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class TranscriptionLogger(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -30,42 +34,26 @@ class TranscriptionLogger(FrameProcessor):
             print(f"Transcription: {frame.text}")
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
-        params=TransportParams(
+        )
-            audio_in_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
 
-    stt = WhisperSTTService()
+        stt = WhisperSTTService()
 
-    tl = TranscriptionLogger()
+        tl = TranscriptionLogger()
 
-    pipeline = Pipeline([transport.input(), stt, tl])
+        pipeline = Pipeline([transport.input(), stt, tl])
 
-    task = PipelineTask(pipeline)
+        task = PipelineTask(pipeline)
 
-    @transport.event_handler("on_client_disconnected")
+        runner = PipelineRunner()
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
 
-    @transport.event_handler("on_client_closed")
+        await runner.run(task)
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/13a-whisper-local.py

@@ -10,13 +10,12 @@ import sys
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.whisper.stt import WhisperSTTService
+from pipecat.services.whisper import WhisperSTTService
 from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
 
 load_dotenv(override=True)
@@ -34,14 +33,7 @@ class TranscriptionLogger(FrameProcessor):
 
 
 async def main():
-    transport = LocalAudioTransport(
+    transport = LocalAudioTransport(LocalAudioTransportParams(audio_in_enabled=True))
-        LocalAudioTransportParams(
-            audio_in_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        )
-    )
 
     stt = WhisperSTTService()
 

examples/foundational/13b-deepgram-transcription.py

@@ -4,24 +4,28 @@
 # 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.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.deepgram.stt import DeepgramSTTService, Language, LiveOptions
+from pipecat.services.deepgram import DeepgramSTTService, Language, LiveOptions
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class TranscriptionLogger(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -31,40 +35,29 @@ class TranscriptionLogger(FrameProcessor):
             print(f"Transcription: {frame.text}")
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
-        params=TransportParams(audio_in_enabled=True),
+        )
-    )
 
-    stt = DeepgramSTTService(
+        stt = DeepgramSTTService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
-        live_options=LiveOptions(language=Language.EN),
+            # live_options=LiveOptions(language=Language.FR),
-    )
+        )
 
-    tl = TranscriptionLogger()
+        tl = TranscriptionLogger()
 
-    pipeline = Pipeline([transport.input(), stt, tl])
+        pipeline = Pipeline([transport.input(), stt, tl])
 
-    task = PipelineTask(pipeline)
+        task = PipelineTask(pipeline)
 
-    @transport.event_handler("on_client_disconnected")
+        runner = PipelineRunner()
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
 
-    @transport.event_handler("on_client_closed")
+        await runner.run(task)
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/13c-gladia-transcription.py

@@ -4,10 +4,14 @@
 # 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.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -15,12 +19,13 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.gladia import GladiaSTTService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class TranscriptionLogger(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -30,40 +35,29 @@ class TranscriptionLogger(FrameProcessor):
             print(f"Transcription: {frame.text}")
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
-        params=TransportParams(audio_in_enabled=True),
+        )
-    )
 
-    stt = GladiaSTTService(
+        stt = GladiaSTTService(
-        api_key=os.getenv("GLADIA_API_KEY"),
+            api_key=os.getenv("GLADIA_API_KEY"),
-        # live_options=LiveOptions(language=Language.FR),
+            # live_options=LiveOptions(language=Language.FR),
-    )
+        )
 
-    tl = TranscriptionLogger()
+        tl = TranscriptionLogger()
 
-    pipeline = Pipeline([transport.input(), stt, tl])
+        pipeline = Pipeline([transport.input(), stt, tl])
 
-    task = PipelineTask(pipeline)
+        task = PipelineTask(pipeline)
 
-    @transport.event_handler("on_client_disconnected")
+        runner = PipelineRunner()
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
 
-    @transport.event_handler("on_client_closed")
+        await runner.run(task)
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/13d-assemblyai-transcription.py

@@ -4,23 +4,28 @@
 # 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.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.assemblyai.stt import AssemblyAISTTService
+from pipecat.services.assemblyai import AssemblyAISTTService
-from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 class TranscriptionLogger(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -30,39 +35,28 @@ class TranscriptionLogger(FrameProcessor):
             print(f"Transcription: {frame.text}")
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
-        params=TransportParams(audio_in_enabled=True),
+        )
-    )
 
-    stt = AssemblyAISTTService(
+        stt = AssemblyAISTTService(
-        api_key=os.getenv("ASSEMBLYAI_API_KEY"),
+            api_key=os.getenv("ASSEMBLYAI_API_KEY"),
-    )
+        )
 
-    tl = TranscriptionLogger()
+        tl = TranscriptionLogger()
 
-    pipeline = Pipeline([transport.input(), stt, tl])
+        pipeline = Pipeline([transport.input(), stt, tl])
 
-    task = PipelineTask(pipeline)
+        task = PipelineTask(pipeline)
 
-    @transport.event_handler("on_client_disconnected")
+        runner = PipelineRunner()
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
 
-    @transport.event_handler("on_client_closed")
+        await runner.run(task)
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/13e-whisper-mlx.py

@@ -1,99 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import time
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import Frame, TranscriptionFrame, UserStoppedSpeakingFrame
-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.services.whisper.stt import MLXModel, WhisperSTTServiceMLX
-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)
-
-
-STOP_SECS = 2.0
-
-
-class TranscriptionLogger(FrameProcessor):
-    """Measures transcription latency.
-
-    Uses the (intentionally) long STOP_SECS parameter to give the transcription time to finish,
-    then outputs the timing between when the VAD first classified audio input as not-speech and
-    the delivery of the last transcription frame.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self._last_transcription_time = time.time()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, UserStoppedSpeakingFrame):
-            logger.debug(
-                f"Transcription latency: {(STOP_SECS - (time.time() - self._last_transcription_time)):.2f}"
-            )
-
-        if isinstance(frame, TranscriptionFrame):
-            self._last_transcription_time = time.time()
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    stt = WhisperSTTServiceMLX(model=MLXModel.LARGE_V3_TURBO)
-
-    tl = TranscriptionLogger()
-
-    pipeline = Pipeline([transport.input(), stt, tl])
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            report_only_initial_ttfb=False,
-        ),
-    )
-
-    @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)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/foundational/14-function-calling.py

@@ -4,132 +4,132 @@
 # 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 openai.types.chat import ChatCompletionToolParam
+from runner import configure
 
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 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 import CartesiaTTSService
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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 SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
+    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
 
 async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
     await result_callback({"conditions": "nice", "temperature": "75"})
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url,
-        params=TransportParams(
+            token,
-            audio_in_enabled=True,
+            "Respond bot",
-            audio_out_enabled=True,
+            DailyParams(
-            vad_enabled=True,
+                audio_out_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
-            vad_audio_passthrough=True,
+                vad_enabled=True,
-        ),
+                vad_analyzer=SileroVADAnalyzer(),
-    )
+            ),
+        )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    tts = CartesiaTTSService(
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        api_key=os.getenv("CARTESIA_API_KEY"),
+        # Register a function_name of None to get all functions
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        # sent to the same callback with an additional function_name parameter.
-    )
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        tools = [
-
+            ChatCompletionToolParam(
-    # You can also register a function_name of None to get all functions
+                type="function",
-    # sent to the same callback with an additional function_name parameter.
+                function={
-    llm.register_function("get_current_weather", fetch_weather_from_api)
+                    "name": "get_current_weather",
-
+                    "description": "Get the current weather",
-    weather_function = FunctionSchema(
+                    "parameters": {
-        name="get_current_weather",
+                        "type": "object",
-        description="Get the current weather",
+                        "properties": {
-        properties={
+                            "location": {
-            "location": {
+                                "type": "string",
-                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
-                "description": "The city and state, e.g. San Francisco, CA",
+                            },
-            },
+                            "format": {
-            "format": {
+                                "type": "string",
-                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
-                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
-                "description": "The temperature unit to use. Infer this from the user's location.",
+                            },
-            },
+                        },
-        },
+                        "required": ["location", "format"],
-        required=["location", "format"],
+                    },
-    )
+                },
-    tools = ToolsSchema(standard_tools=[weather_function])
+            )
-
+        ]
-    messages = [
+        messages = [
-        {
+            {
-            "role": "system",
+                "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.",
+                "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, tools)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
         ]
-    )
 
-    task = PipelineTask(
+        context = OpenAILLMContext(messages, tools)
-        pipeline,
+        context_aggregator = llm.create_context_aggregator(context)
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        pipeline = Pipeline(
-    async def on_client_connected(transport, client):
+            [
-        logger.info(f"Client connected")
+                transport.input(),
-        # Kick off the conversation.
+                context_aggregator.user(),
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
+        task = PipelineTask(
-    async def on_client_disconnected(transport, client):
+            pipeline,
-        logger.info(f"Client disconnected")
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_client_closed")
+        @transport.event_handler("on_first_participant_joined")
-    async def on_client_closed(transport, client):
+        async def on_first_participant_joined(transport, participant):
-        logger.info(f"Client closed connection")
+            await transport.capture_participant_transcription(participant["id"])
-        await task.cancel()
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+        runner = PipelineRunner()
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()

examples/foundational/14a-function-calling-anthropic.py

@@ -4,125 +4,119 @@
 # 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.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 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.anthropic.llm import AnthropicLLMService
+from pipecat.services.anthropic import AnthropicLLMService
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
-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)
 
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
 
 async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
     location = arguments["location"]
     await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def main():
-    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = SmallWebRTCTransport(
+        transport = DailyTransport(
-        webrtc_connection=webrtc_connection,
+            room_url,
-        params=TransportParams(
+            token,
-            audio_in_enabled=True,
+            "Respond bot",
-            audio_out_enabled=True,
+            DailyParams(
-            vad_enabled=True,
+                audio_out_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
-            vad_audio_passthrough=True,
+                vad_enabled=True,
-        ),
+                vad_analyzer=SileroVADAnalyzer(),
-    )
+            ),
+        )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    tts = CartesiaTTSService(
+        llm = AnthropicLLMService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        )
-    )
+        llm.register_function("get_weather", get_weather)
 
-    llm = AnthropicLLMService(
+        tools = [
-        api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
+            {
-    )
+                "name": "get_weather",
-    llm.register_function("get_weather", get_weather)
+                "description": "Get the current weather in a given location",
-
+                "input_schema": {
-    weather_function = FunctionSchema(
+                    "type": "object",
-        name="get_weather",
+                    "properties": {
-        description="Get the current weather",
+                        "location": {
-        properties={
+                            "type": "string",
-            "location": {
+                            "description": "The city and state, e.g. San Francisco, CA",
-                "type": "string",
+                        }
-                "description": "The city and state, e.g. San Francisco, CA",
+                    },
-            },
+                    "required": ["location"],
-        },
+                },
-        required=["location"],
+            }
-    )
-    tools = ToolsSchema(standard_tools=[weather_function])
-
-    # todo: test with very short initial user message
-
-    # messages = [{"role": "system",
-    #              "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."},
-    #             {"role": "user",
-    #              "content": " Start the conversation by introducing yourself."}]
-
-    messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]
-
-    context = OpenAILLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User spoken responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses and tool context
         ]
-    )
 
-    task = PipelineTask(
+        # todo: test with very short initial user message
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-        ),
-    )
 
-    @transport.event_handler("on_client_connected")
+        # messages = [{"role": "system",
-    async def on_client_connected(transport, client):
+        #              "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."},
-        logger.info(f"Client connected")
+        #             {"role": "user",
-        # Kick off the conversation.
+        #              "content": " Start the conversation by introducing yourself."}]
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    @transport.event_handler("on_client_disconnected")
+        messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
 
-    @transport.event_handler("on_client_closed")
+        context = OpenAILLMContext(messages, tools)
-    async def on_client_closed(transport, client):
+        context_aggregator = llm.create_context_aggregator(context)
-        logger.info(f"Client closed connection")
-        await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=False)
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User spoken responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses and tool context
+            ]
+        )
 
-    await runner.run(task)
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    asyncio.run(main())
-
-    main()