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DailyTransport

The DailyTransport is a full-featured transport that enables you to join DailyAI bots to Daily WebRTC video calls. It's built on top of daily-python, and it gives you access to a lot of advanced features—but it's also a great way to simply use WebRTC as the media backbone for building real-time bot interactions.

DailyTransport.new()​

DailyTransport.new(
    room_url: str,
    token: str | None,
    bot_name: str,
    min_others_count: int = 1,
    start_transcription: bool = False,
    **kwargs
)

Positional arguments (but it's probably better to call them by name):

  • room_url: The Daily room URL to connect to. It looks like https://YOURDOMAIN.daily.co/YOURROOM.
  • token: You'll usually want to connect the bot to the room with owner privileges. If so, you can Daily's REST API to create a meeting token, and include that token string here.
  • bot_name: You'll see the bot's name if you use Daily Prebuilt, for example.
  • min_others_count: After someone else joins, when the number of other participants in the room drops back below this number, the bot will exit. set this to 0 disable the bot automatically leaving the room.
  • start_transcription: If you want to receive TranscriptionFrames, you'll need to set this to True. This requires an owner token to be included in the token property.

Other available keyword arguments:

  • vad_enabled: Whether or not to use Voice Activity Detection (VAD). If True, the transport will emit UserStartedSpeaking and UserStoppedSpeaking frames. VAD is also necessary for interruptions support. Defaults to False.
  • vad_start_s: The amount of time a user needs to speak before the transport emits a UserStartedSpeaking frame. Defaults to 0.2, or 200ms.
  • vad_stop_s: The amount of time a user needs to stop speaking and remain silent before the transport emits a UserStoppedSpeaking frame. Defaults to 0.8, or 800ms. This value represents a good middle ground: It's short enough that conversation feels responsive, but long enough that Deepgram can usually return all the transcriptions before the UserStoppedSpeaking frame is emitted.

A bit more about VAD​

The DailyTransport can use the VAD support built into the WebRTC library with no additional dependencies. However, we recommend installing the optional silero dependency if your platform supports it. This is an AI VAD library powered by Torch, and it's generally a bit better at distinguising talking from background noise. Daily AI will automatically use Silero VAD if you've installed the dependencies.

Event Handlers​

add_event_handler

participant_joined first_other_participant_joined participant_left transcription_message app_message

Other transcription events

Send and receive behavior​

transport.run()​

transport.run(pipeline: Pipeline | None = None, override_pipeline_source_queue=True)

This method runs the transport. For the DailyTransport, that includes joining the Daily room and setting up audio/video send and receive, depending on what you've configured.

This method also accepts a pipeline argument. If you include a pipeline, the transport will run and manage that Pipeline for you. That includes connecting the pipeline's source and sink to the transport's send and receive queues. It will also start and stop your pipeline when the transport starts and stops.

Your app will almost always include some form of await transport.run(), usually await transport.run(pipeline).

transport.run_pipeline()​

async def run_pipeline(pipeline: Pipeline, override_pipeline_source_queue=True):

This method connects the pipeline's source and sink to the transport's send and receive queues, but it doesn't manage the pipeline's lifecycle. You'll need to await transport.run_pipeline(pipeline) separately from await transport.run().

The override_pipeline_source_queue property is used for a few things internally.

transport.run_interruptible_pipeline()​

transport.run_interruptible_pipeline(
    pipeline: Pipeline,
    pre_processor: FrameProcessor | None = None,
    post_processor: FrameProcessor | None = None,
)

This method runs the pipeline connected to the transport's queues, but it runs it inside a cancelable asyncio.task(). If the transport detects that the user starts speaking (which generates a UserStartedSpeaking frame), the transport will cancel the currently executing asyncio.task(), empty all the frames in the transport's send queue, and start a new task.

The end result of this is that you can run the pipeline, and anytime the user speaks, the bot will stop what it's doing and start listening to the user.

Typically, you'll want to create your pipeline such that it expects to receive and accumulate TranscriptionFrames from the user, and start generating a response as soon as it receives a UserStoppedSpeakingFrame.

This method also accepts two optional services as pre_processor and post_processor. As it turns out, pre_processor doesn't do anything special, so you can probably ignore it.

But post_processor is a bit different. As the transport runs, it consumes the frames coming out of the pipeline: Displaying ImageFrames as video, playing AudioFrames as audio, etc. But when running an interruptible pipeline, the transport will send each frame through the post_processor after it finishes doing whatever it's supposed to with that frame. More specifically, each AudioFrame goes to the post_processor after it has been successfully played.

If the pipeline gets interrupted, the contents of the transport's output queue get dumped, so none of those frames go through the post_processor.

By convention, immediately after sending AudioFrames with generated speech, text-to-speech services send a TextFrame with the text of that speech through the pipeline. So if you put an LLMContextAggregator in the post_processor of an interruptible pipeline, you can ensure that the bot's context will only contain sentences it actually said to the user. If a bot generates an 8-sentence response, but the user interrupts the bot in the middle of the 4th sentence, the context will only contain the first three sentences.

This method does not manage the pipeline lifecycle. You'll still need to do something like:

asyncio.gather(transport.run(), transport.run_interruptible_pipeline(pipeline))

Instance Methods​

(In order to keep the documentation a bit more readable, some of the functionality described here actually comes from a BaseTransport class.)

send_app_message​

Calls daily-python's send_app_message function.

dialout​

Calls start_dialout in daily-python, which can be used to call SIP or PSTN phone numbers. See the Daily docs for more information.

start_recording​

Starts a recording of the Daily room. See the Daily docs for more information.

say​

This is a convenience method for generating text-to-speech from a given sentence. It bypasses any running pipelines and just sends the sound directly to the transport.

stop and stop_when_done​

Functions for stopping a running transport. You probably don't need to call these directly; instead, sending an EndFrame though your pipeline should stop everything.

Frame Behaviors​

Here's a list of different kinds of frames, and how the DailyTransport handles them:

  • AudioFrame: The transport will break the audio data into ~0.5s chunks and play them using daily-python. The audio playback is synchronous in the transport's thread, which means that if the transport's queue contains several AudioFrames followed by an ImageFrame, the ImageFrame won't get handled until playback of the AudioFrames is completed.
  • ImageFrame: When the transport receives an ImageFrame, it will display that image in the bot's webcam video inside the Daily call. That image will stay set and appear on screen until another ImageFrame is received.
  • SpriteFrame: These frames contain a sequence of images. When the transport receives a SpriteFrame, it will loop those frames in the bot's webcam video at the configured frame rate of the transport until it receives another SpriteFrame or ImageFrame.
  • UserImageRequestFrame: If the transport's video_rendering_enabled property is set to True, when it receives a UserImageRequestFrame, it will grab a frame from one or all participants' cameras and put those frames into the pipeline as UserImageFrames
  • SendAppMessageFrame: If a DailyTransport receives this frame, it will use send_app_message() from daily-python to send a message to other call participants.