Push the STTMuteFrame upstream and downstream
This commit is contained in:
Mark Backman
@@ -112,7 +112,7 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
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[
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transport.input(), # Transport user input
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stt, # STT
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stt_mute_processor, # Add the mute processor before STT
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stt_mute_processor, # Add the mute processor between STT and context aggregator
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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@@ -1,216 +0,0 @@
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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import sys
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from typing import List
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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Frame,
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LLMMessagesFrame,
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TranscriptionFrame,
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TranscriptionMessage,
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TranscriptionUpdateFrame,
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)
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.processors.frameworks.rtvi import RTVIObserver, RTVIProcessor
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from pipecat.processors.transcript_processor import TranscriptProcessor
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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"""
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This example looks a bit different than the chatbot example, because it isn't waiting on the user to stop talking to start translating.
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It also isn't saving what the user or bot says into the context object for use in subsequent interactions.
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"""
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# We need to use a custom service here to yield LLM frames without saving
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# any context
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class TranslationProcessor(FrameProcessor):
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"""A processor that translates text frames from a source language to a target language."""
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def __init__(self, in_language, out_language):
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"""Initialize the TranslationProcessor with source and target languages.
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Args:
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in_language (str): The language of the input text.
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out_language (str): The language to translate the text into.
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"""
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super().__init__()
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self._out_language = out_language
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self._in_language = in_language
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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"""Process a frame and translate text frames.
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Args:
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frame (Frame): The frame to process.
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direction (FrameDirection): The direction of the frame.
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"""
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await super().process_frame(frame, direction)
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if isinstance(frame, TranscriptionFrame):
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logger.debug(f"Translating {self._in_language}: {frame.text} to {self._out_language}")
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context = [
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{
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"role": "system",
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"content": f"You will be provided with a sentence in {self._in_language}, and your task is to only translate it into {self._out_language}.",
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},
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{"role": "user", "content": frame.text},
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]
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await self.push_frame(LLMMessagesFrame(context))
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else:
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await self.push_frame(frame)
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class TranscriptHandler:
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"""Simple handler to demonstrate transcript processing.
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Maintains a list of conversation messages and logs them with timestamps.
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"""
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def __init__(self, in_language="English", out_language="Spanish"):
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"""Initialize the TranscriptHandler with an empty list of messages."""
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self.messages: List[TranscriptionMessage] = []
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self.in_language = in_language
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self.out_language = out_language
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async def on_transcript_update(
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self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
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):
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"""Handle new transcript messages.
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Args:
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processor: The TranscriptProcessor that emitted the update
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frame: TranscriptionUpdateFrame containing new messages
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"""
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self.messages.extend(frame.messages)
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# Log the new messages
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logger.info("New transcript messages:")
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for msg in frame.messages:
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timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
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message = {
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"event": "translation",
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"timestamp": msg.timestamp,
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"role": msg.role,
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"language": self.out_language if msg.role == "assistant" else self.in_language,
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"text": msg.content,
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}
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logger.info(f"{timestamp}{msg.role}: {msg.content}")
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async def main():
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"""Main function to set up and run the translation chatbot pipeline."""
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Translator",
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DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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stt_mute_processor = STTMuteFilter(
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config=STTMuteConfig(
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strategies={
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STTMuteStrategy.ALWAYS,
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}
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),
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="34dbb662-8e98-413c-a1ef-1a3407675fe7", # Spanish Narrator Man
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model="sonic-2",
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)
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in_language = "English"
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out_language = "Spanish"
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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context = OpenAILLMContext()
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context_aggregator = llm.create_context_aggregator(context)
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tp = TranslationProcessor(in_language=in_language, out_language=out_language)
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transcript = TranscriptProcessor()
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transcript_handler = TranscriptHandler(in_language=in_language, out_language=out_language)
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# Register event handler for transcript updates
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@transcript.event_handler("on_transcript_update")
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async def on_transcript_update(processor, frame):
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await transcript_handler.on_transcript_update(processor, frame)
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rtvi = RTVIProcessor()
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pipeline = Pipeline(
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[
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transport.input(),
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rtvi,
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stt,
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stt_mute_processor, # We don't want to interrupt the translator bot
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transcript.user(), # User transcripts
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tp,
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llm,
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tts,
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transport.output(),
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transcript.assistant(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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observers=[RTVIObserver(rtvi)],
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)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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logger.info("First participant joined")
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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