Twilio Audio Output Causing Broken Audio Chunks

[saitharunsai]

Description

Bug Report: Malformed Twilio Audio Output Causing Broken Audio Chunks

Environment

• pipecat-ai version: 0.0.50
• python version: 3.11.10
• OS: M2

Issue description

The Twilio output context is being malformed, resulting in broken audio chunks during the pipeline processing. This appears to be happening in a WebSocket-based voice pipeline that integrates Twilio, Deepgram (for STT/TTS), and OpenAI's GPT-4 for conversation processing.

Repro steps

1. Initialize a WebSocket connection with Twilio stream_sid
2. Set up the voice pipeline with the following components:
   • FastAPIWebsocketTransport with TwilioFrameSerializer
   • SileroVADAnalyzer for voice activity detection
   • DeepgramSTTService for speech-to-text
   • OpenAILLMService for conversation processing
   • DeepgramTTSService for text-to-speech
3. Start the pipeline with an initial system message
4. Begin audio streaming

Expected behavior

• Audio chunks should be properly formed and maintained throughout the pipeline
• Smooth audio streaming without breaks or malformation
• Proper serialization of audio frames by TwilioFrameSerializer

Actual behavior

Audio chunks are breaking during processing, suggesting potential issues with either:

• Frame serialization in TwilioFrameSerializer
• Audio buffer handling in the WebSocket transport
• VAD passthrough configuration
• Sample rate or encoding mismatches (currently set to 8000Hz mulaw)

Code

`from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame, 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.serializers.twilio import TwilioFrameSerializer
from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.network.fastapi_websocket import (
    FastAPIWebsocketParams,
    FastAPIWebsocketTransport,
)

from app.core.config import settings


async def run_twilio_bot(websocket_client, stream_sid):
    transport = FastAPIWebsocketTransport(
        websocket=websocket_client,
        params=FastAPIWebsocketParams(
            audio_out_enabled=True,
            add_wav_header=False,
            vad_enabled=True,
            vad_analyzer=SileroVADAnalyzer(),
            vad_audio_passthrough=True,
            serializer=TwilioFrameSerializer(stream_sid),
        ),
    )

    llm = OpenAILLMService(api_key=settings.OPENAI_API_KEY, model="gpt-4o")

    stt = DeepgramSTTService(api_key=settings.DEEPGRAM_API_KEY)

    tts = DeepgramTTSService(
        api_key=settings.DEEPGRAM_API_KEY,
    )

    messages = [
        {
            "role": "system",
            "content": "You are a helpful LLM in an audio 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(),  # Websocket input from client
            stt,  # Speech-To-Text
            context_aggregator.user(),
            llm,  # LLM
            tts,  # Text-To-Speech
            transport.output(),  # Websocket output to client
            context_aggregator.assistant(),
        ]
    )

    task = PipelineTask(pipeline, params=PipelineParams(enable_metrics=True))

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        # Kick off the conversation.
        messages.append(
            {"role": "system", "content": "Please introduce yourself to the user."}
        )
        await task.queue_frames([LLMMessagesFrame(messages)])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        await task.queue_frames([EndFrame()])

    runner = PipelineRunner(handle_sigint=True)

    await runner.run(task)

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