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Overview

The TranscriptProcessor is a factory class that creates and manages processors for handling conversation transcripts from both users and assistants. It provides unified access to transcript processors with shared event handling, making it easy to track and respond to conversation updates in real-time. The processor normalizes messages from various sources into a consistent TranscriptionMessage format and emits events when new messages are added to the conversation.

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Constructor

TranscriptProcessor()
Creates a new transcript processor factory with no parameters.

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Methods

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user()

def user(**kwargs) -> UserTranscriptProcessor
Get or create the user transcript processor instance. This processor handles TranscriptionFrames from STT services. Parameters:
  • **kwargs: Arguments passed to the UserTranscriptProcessor constructor
Returns: UserTranscriptProcessor instance for processing user messages.

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assistant()

def assistant(**kwargs) -> AssistantTranscriptProcessor
Get or create the assistant transcript processor instance. This processor handles TTSTextFrames from TTS services and aggregates them into complete utterances. Parameters:
  • **kwargs: Arguments passed to the AssistantTranscriptProcessor constructor
Returns: AssistantTranscriptProcessor instance for processing assistant messages.

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event_handler()

def event_handler(event_name: str)
Decorator that registers event handlers for both user and assistant processors. Parameters:
  • event_name: Name of the event to handle
Returns: Decorator function that registers the handler with both processors.

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Event Handlers

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on_transcript_update

Triggered when new messages are added to the conversation transcript. 
@transcript.event_handler("on_transcript_update")
async def handle_transcript_update(processor, frame):
    # Handle transcript updates
    pass
Parameters:
  • processor: The specific processor instance that emitted the event (UserTranscriptProcessor or AssistantTranscriptProcessor)
  • frame: TranscriptionUpdateFrame containing the new messages

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Data Structures

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TranscriptionMessage

@dataclass
class TranscriptionMessage:
    role: Literal["user", "assistant"]
    content: str
    timestamp: str | None = None
    user_id: str | None = None
Fields:
  • role: The message sender type (“user” or “assistant”)
  • content: The transcribed text content
  • timestamp: ISO 8601 timestamp when the message was created
  • user_id: Optional user identifier (for user messages only)

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TranscriptionUpdateFrame

Frame containing new transcript messages, emitted by the on_transcript_update event. Properties:
  • messages: List of TranscriptionMessage objects containing the new transcript content

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Frames

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UserTranscriptProcessor

  • Input: TranscriptionFrame from STT services
  • Output: TranscriptionMessage with role “user”

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AssistantTranscriptProcessor

  • Input: TTSTextFrame from TTS services
  • Output: TranscriptionMessage with role “assistant”

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Integration Notes

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Pipeline Placement

Place the processors at specific positions in your pipeline for accurate transcript collection: 
pipeline = Pipeline([
    transport.input(),
    stt,                        # Speech-to-text service
    transcript.user(),          # Place after STT
    context_aggregator.user(),
    llm,
    tts,                        # Text-to-speech service
    transport.output(),
    transcript.assistant(),     # Place after transport.output()
    context_aggregator.assistant(),
])

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Event Handler Registration

Event handlers are automatically applied to both user and assistant processors: 
transcript = TranscriptProcessor()

# This handler will receive events from both processors
@transcript.event_handler("on_transcript_update")
async def handle_update(processor, frame):
    for message in frame.messages:
        print(f"{message.role}: {message.content}")