d3021b4590
Example files like openai.py shadow installed packages when Python adds the script directory to sys.path. Prepend the parent folder name to each example file (e.g. openai.py -> function-calling-openai.py). Also split thinking-and-mcp/ into separate mcp/ and thinking/ directories.
120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
#
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# Copyright (c) 2024-2026, 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 time
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import Frame, TranscriptionFrame, UserStoppedSpeakingFrame
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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.audio.vad_processor import VADProcessor
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.whisper.stt import MLXModel, WhisperSTTServiceMLX
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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STOP_SECS = 2.0
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class TranscriptionLogger(FrameProcessor):
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"""Measures transcription latency.
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Uses the (intentionally) long STOP_SECS parameter to give the transcription time to finish,
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then outputs the timing between when the VAD first classified audio input as not-speech and
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the delivery of the last transcription frame.
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"""
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def __init__(self):
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super().__init__()
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self._last_transcription_time = time.time()
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, UserStoppedSpeakingFrame):
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logger.debug(
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f"Transcription latency: {(STOP_SECS - (time.time() - self._last_transcription_time)):.2f}"
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)
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if isinstance(frame, TranscriptionFrame):
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self._last_transcription_time = time.time()
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# Push all frames through
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await self.push_frame(frame, direction)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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stt = WhisperSTTServiceMLX(
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settings=WhisperSTTServiceMLX.Settings(
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model=MLXModel.LARGE_V3_TURBO.value,
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),
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)
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tl = TranscriptionLogger()
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vad_processor = VADProcessor(
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS))
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)
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pipeline = Pipeline([transport.input(), vad_processor, stt, tl])
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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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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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