Conector de vídeo Integración de Pipecat

from pipecat.transports.vonage.video_connector import (
    VonageVideoConnectorTransport,
    VonageVideoConnectorTransportParams,
)

# Initialize the transport
transport = VonageVideoConnectorTransport(
    application_id="your_application_id",
    session_id="your_session_id",
    token="your_participant_token",
    params=VonageVideoConnectorTransportParams()
)

from pipecat.audio.vad.silero import SileroVADAnalyzer

transport_params = VonageVideoConnectorTransportParams(
    audio_in_enabled=True,                          # Enable receiving audio
    audio_out_enabled=True,                         # Enable sending audio
    video_in_enabled=True,                          # Enable receiving video
    video_out_enabled=True,                         # Enable sending video
    publisher_name="AI Assistant",                  # Name for the published stream
    audio_in_sample_rate=16000,                     # Input sample rate (Hz)
    audio_in_channels=1,                            # Input channels
    audio_out_sample_rate=24000,                    # Output sample rate (Hz)
    audio_out_channels=1,                           # Output channels
    video_out_width=1280,                           # Output video width
    video_out_height=720,                           # Output video height
    video_out_framerate=30,                         # Output video framerate
    video_out_color_format="RGB",                   # Output video color format
    vad_analyzer=SileroVADAnalyzer(),               # Voice activity detection
    audio_in_auto_subscribe=True,                   # Auto-subscribe to audio streams
    video_in_auto_subscribe=False,                  # Auto-subscribe to video streams
    video_in_preferred_resolution=(640, 480),       # Preferred input resolution
    video_in_preferred_framerate=15,                # Preferred input framerate
    publisher_enable_opus_dtx=False,                # Enable Opus DTX
    session_enable_migration=False,                 # Enable session migration
    video_connector_log_level="INFO",               # Log level
    clear_buffers_on_interruption=True,             # Clear buffers on interruption
)

transport = VonageVideoConnectorTransport(
    application_id,
    session_id,
    token,
    params=transport_params
)

from pipecat.audio.vad.silero import SileroVADAnalyzer

# Configure VAD for better audio processing
vad = SileroVADAnalyzer()
transport_params = VonageVideoConnectorTransportParams(
    vad_analyzer=vad,
    # ... other parameters
)

transport_params = VonageVideoConnectorTransportParams(
    clear_buffers_on_interruption=True,  # Default: True
    # ... other parameters
)

# Audio and video input is handled automatically by the transport
# Incoming audio frames are converted to AudioRawFrame format
# Incoming video frames are converted to ImageRawFrame format
pipeline = Pipeline([
    transport.input(),     # Receives audio and video from Vonage session
    # ... your AI processing pipeline
])

# Audio and video output is sent automatically through the pipeline
pipeline = Pipeline([
    # ... your AI processing pipeline
    transport.output(),    # Sends audio and video to Vonage session
])

from pipecat.transports.vonage.video_connector import SubscribeSettings

# Disable auto-subscription
transport_params = VonageVideoConnectorTransportParams(
    audio_in_auto_subscribe=False,
    video_in_auto_subscribe=False,
    # ... other parameters
)

# Manually subscribe when a participant joins
@transport.event_handler("on_participant_joined")
async def on_participant_joined(transport, data):
    stream_id = data['streamId']
    logger.info(f"Participant joined with stream {stream_id}, subscribing...")
    await transport.subscribe_to_stream(
        stream_id,
        SubscribeSettings(
            subscribe_to_audio=True,
            subscribe_to_video=True,
            preferred_resolution=(640, 480),
            preferred_framerate=15
        )
    )

# Subscribe to a lower quality stream for bandwidth efficiency
await transport.subscribe_to_stream(
    stream_id,
    SubscribeSettings(
        subscribe_to_video=True,
        preferred_resolution=(320, 240),  # Request low spatial layer
        preferred_framerate=15            # Request lower temporal layer
    )
)

# Subscribe to a high quality stream for better visual fidelity
await transport.subscribe_to_stream(
    stream_id,
    SubscribeSettings(
        subscribe_to_video=True,
        preferred_resolution=(1280, 720),  # Request high spatial layer
        preferred_framerate=30             # Request higher temporal layer
    )
)

# Handle when the transport joins the session
@transport.event_handler("on_joined")
async def on_joined(transport, data):
    logger.info(f"Joined session {data['sessionId']}")
    # Initialize your application state
    await task.queue_frames([LLMMessagesFrame()])

# Handle when the transport leaves the session
@transport.event_handler("on_left")
async def on_left(transport):
    logger.info("Left session")
    # Clean up resources or save session data

# Handle session errors
@transport.event_handler("on_error")
async def on_error(transport, error):
    logger.error(f"Session error: {error}")

# Handle first participant joining (useful for triggering initial interactions)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, data):
    logger.info(f"First participant joined: stream {data['streamId']}")
    # Start recording, initialize conversation, etc.

# Handle any participant joining
@transport.event_handler("on_participant_joined")
async def on_participant_joined(transport, data):
    logger.info(f"Participant joined: stream {data['streamId']}")
    # Update participant list, send greeting, etc.

# Handle participant leaving
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, data):
    logger.info(f"Participant left: stream {data['streamId']}")
    # Update participant list, handle cleanup

# Handle when a subscriber successfully connects to a stream
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, data):
    logger.info(f"Client connected to stream {data['subscriberId']}")

# Handle when a subscriber disconnects from a stream
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, data):
    logger.info(f"Client disconnected from stream {data['subscriberId']}")

import asyncio
import os

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.aws_nova_sonic.aws import AWSNovaSonicLLMService
from pipecat.transports.vonage.video_connector import (
    VonageVideoConnectorTransport,
    VonageVideoConnectorTransportParams,
    SubscribeSettings,
)

async def main():
    # Configure transport
    transport = VonageVideoConnectorTransport(
        application_id=os.getenv("VONAGE_APPLICATION_ID"),
        session_id=os.getenv("VONAGE_SESSION_ID"),
        token=os.getenv("VONAGE_TOKEN"),
        params=VonageVideoConnectorTransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            publisher_name="AI Assistant",
            audio_in_sample_rate=16000,
            audio_out_sample_rate=24000,
            vad_analyzer=SileroVADAnalyzer(),
            audio_in_auto_subscribe=True,
        )
    )

    # Set up AI service
    llm = AWSNovaSonicLLMService(
        secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY", ""),
        access_key_id=os.getenv("AWS_ACCESS_KEY_ID", ""),
        region=os.getenv("AWS_REGION", ""),
        session_token=os.getenv("AWS_SESSION_TOKEN", ""),
    )

    # Create context for conversation
    context = OpenAILLMContext(
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},
        ]
    )
    context_aggregator = llm.create_context_aggregator(context)

    # Build pipeline
    pipeline = Pipeline([
        transport.input(),           # Audio from Vonage session
        context_aggregator.user(),   # Process user input
        llm,                        # AI processing
        transport.output(),         # Audio back to session
    ])

    task = PipelineTask(pipeline)

    # Register event handlers
    @transport.event_handler("on_joined")
    async def on_joined(transport, data):
        print(f"Joined session: {data['sessionId']}")

    @transport.event_handler("on_first_participant_joined")
    async def on_first_participant_joined(transport, data):
        print(f"First participant joined: {data['streamId']}")

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, data):
        await task.queue_frames([LLMRunFrame()])
        await llm.trigger_assistant_response()


    runner = PipelineRunner()
    await runner.run(task)

if __name__ == "__main__":
    asyncio.run(main())

from loguru import logger

# Pipecat uses loguru for logging
logger.enable("pipecat")

# Set Vonage Video Connector log level
transport_params = VonageVideoConnectorTransportParams(
    video_connector_log_level="DEBUG",  # DEBUG, INFO, WARNING, ERROR
    # ... other parameters
)