73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
import os
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import uuid
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from app.tasks.celery_app import celery_app
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from app.config import settings
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from app.models.recording import Recording, RecordingStatus
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from app.models.transcription import Transcription
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from app.services.transcription import extract_audio, transcribe_audio
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from sqlalchemy import create_engine
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from sqlalchemy.orm import sessionmaker
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sync_db_url = settings.database_url.replace("+asyncpg", "+psycopg2").replace(
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"postgresql+psycopg2", "postgresql"
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)
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sync_engine = create_engine(sync_db_url)
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SyncSession = sessionmaker(sync_engine)
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@celery_app.task(bind=True, name="app.tasks.transcription.transcribe_recording")
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def transcribe_recording(self, recording_id: str):
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rec_uuid = uuid.UUID(recording_id)
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with SyncSession() as db:
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recording = db.execute(
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select(Recording).where(Recording.id == rec_uuid)
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).scalar_one_or_none()
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if not recording:
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return {"error": "Recording not found"}
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recording.status = RecordingStatus.processing
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db.commit()
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try:
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file_path = recording.file_path
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audio_path = file_path
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video_formats = {"mp4", "webm", "mkv", "avi", "mov"}
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if recording.format in video_formats:
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audio_path = extract_audio(file_path)
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result = transcribe_audio(audio_path)
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if audio_path != file_path and os.path.exists(audio_path):
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os.remove(audio_path)
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transcription = Transcription(
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recording_id=rec_uuid,
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text=result["text"],
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segments=result["segments"],
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language=result["language"],
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whisper_model=settings.whisper_model,
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processing_time=result["processing_time"],
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)
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db.add(transcription)
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recording.status = RecordingStatus.done
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db.commit()
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db.refresh(transcription)
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from app.tasks.protocol import generate_protocol
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generate_protocol.delay(str(transcription.id))
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return {"transcription_id": str(transcription.id), "status": "done"}
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except Exception as e:
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recording.status = RecordingStatus.error
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db.commit()
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raise self.retry(exc=e, max_retries=1, countdown=30)
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