96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
#
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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 sys
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import time
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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.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.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.whisper.stt import MLXModel, WhisperSTTServiceMLX
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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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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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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(session)
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transport = DailyTransport(
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room_url,
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None,
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"Transcription bot",
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DailyParams(
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audio_in_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
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vad_audio_passthrough=True,
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),
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)
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stt = WhisperSTTServiceMLX(model=MLXModel.LARGE_V3_TURBO)
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tl = TranscriptionLogger()
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pipeline = Pipeline([transport.input(), 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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report_only_initial_ttfb=False,
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),
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)
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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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