69 lines
1.5 KiB
Python
69 lines
1.5 KiB
Python
import os
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import subprocess
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import time
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import uuid
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from app.config import settings
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_model = None
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def _get_model():
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global _model
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if _model is None:
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from faster_whisper import WhisperModel
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_model = WhisperModel(
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settings.whisper_model,
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device=settings.whisper_device,
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compute_type=settings.whisper_compute_type,
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)
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return _model
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def extract_audio(video_path: str) -> str:
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audio_path = os.path.join(
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settings.upload_dir, f"{uuid.uuid4().hex}.wav"
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)
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subprocess.run(
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[
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"ffmpeg", "-i", video_path,
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"-vn", "-acodec", "pcm_s16le",
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"-ar", "16000", "-ac", "1",
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audio_path, "-y",
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],
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check=True,
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capture_output=True,
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)
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return audio_path
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def transcribe_audio(audio_path: str) -> dict:
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model = _get_model()
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start_time = time.time()
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segments_iter, info = model.transcribe(
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audio_path,
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beam_size=5,
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language=None,
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vad_filter=True,
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)
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segments = []
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full_text_parts = []
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for segment in segments_iter:
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segments.append({
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"start": round(segment.start, 2),
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"end": round(segment.end, 2),
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"text": segment.text.strip(),
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})
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full_text_parts.append(segment.text.strip())
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processing_time = time.time() - start_time
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return {
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"text": " ".join(full_text_parts),
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"segments": segments,
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"language": info.language,
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"processing_time": round(processing_time, 2),
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}
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