mirror of
https://github.com/Monadical-SAS/reflector.git
synced 2025-12-20 20:29:06 +00:00
Mathieu Virbel
9265d201b5
This commit restore the original behavior with frame cutting. While silero is used on our gpu for files, look like it's not working great on the live pipeline. To be investigated, but at the moment, what we keep is: - refactored to extract the downscale for further processing in the pipeline - remove any downscale implementation from audio_chunker and audio_merge - removed batching from audio_merge too for now
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
"""
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Implementation using the GPU service from modal.com
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API will be a POST request to TRANSCRIPT_URL:
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```form
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"timestamp": 123.456
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"source_language": "en"
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"target_language": "en"
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"file": <audio file>
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```
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"""
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from openai import AsyncOpenAI
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from reflector.processors.audio_transcript import AudioTranscriptProcessor
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from reflector.processors.audio_transcript_auto import AudioTranscriptAutoProcessor
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from reflector.processors.types import AudioFile, Transcript, Word
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from reflector.settings import settings
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class AudioTranscriptModalProcessor(AudioTranscriptProcessor):
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def __init__(
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self,
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modal_api_key: str | None = None,
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**kwargs,
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):
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super().__init__()
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if not settings.TRANSCRIPT_URL:
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raise Exception(
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"TRANSCRIPT_URL required to use AudioTranscriptModalProcessor"
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)
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self.transcript_url = settings.TRANSCRIPT_URL + "/v1"
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self.timeout = settings.TRANSCRIPT_TIMEOUT
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self.modal_api_key = modal_api_key
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async def _transcript(self, data: AudioFile):
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async with AsyncOpenAI(
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base_url=self.transcript_url,
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api_key=self.modal_api_key,
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timeout=self.timeout,
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) as client:
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self.logger.debug(f"Try to transcribe audio {data.name}")
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audio_file = open(data.path, "rb")
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transcription = await client.audio.transcriptions.create(
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file=audio_file,
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model="whisper-1",
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response_format="verbose_json",
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language=self.get_pref("audio:source_language", "en"),
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timestamp_granularities=["word"],
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)
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self.logger.debug(f"Transcription: {transcription}")
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transcript = Transcript(
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words=[
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Word(
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text=word.word,
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start=word.start,
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end=word.end,
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)
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for word in transcription.words
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],
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)
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transcript.add_offset(data.timestamp)
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return transcript
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AudioTranscriptAutoProcessor.register("modal", AudioTranscriptModalProcessor)
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