87 lines
3.3 KiB
Python
87 lines
3.3 KiB
Python
"""CPU-friendly faster-whisper entry point used by the Go media worker.
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The process writes JSON only to stdout. Model/runtime diagnostics go to stderr so
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the Go caller never mistakes logs for transcript data.
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"""
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from __future__ import annotations
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import json
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import os
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import sys
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import tempfile
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def configure_model_cache() -> None:
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cache_root = os.getenv("JCF_ASR_CACHE_DIR") or os.path.join(tempfile.gettempdir(), "jcf-asr-cache")
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os.makedirs(cache_root, mode=0o700, exist_ok=True)
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hf_home = os.getenv("HF_HOME") or os.path.join(cache_root, "huggingface")
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os.makedirs(hf_home, mode=0o700, exist_ok=True)
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os.environ.setdefault("XDG_CACHE_HOME", cache_root)
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os.environ.setdefault("HF_HOME", hf_home)
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def main() -> int:
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if len(sys.argv) < 2:
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print(json.dumps({"error": "missing_audio_path", "segments": []}, ensure_ascii=False))
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return 2
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configure_model_cache()
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from faster_whisper import WhisperModel
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audio_path = sys.argv[1]
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language = (sys.argv[2] if len(sys.argv) > 2 else "").strip() or None
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model_name = os.getenv("FASTER_WHISPER_MODEL_PATH") or os.getenv("FASTER_WHISPER_MODEL_SIZE", "small")
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device = os.getenv("FASTER_WHISPER_DEVICE", "cpu")
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compute_type = os.getenv("FASTER_WHISPER_COMPUTE_TYPE", "int8")
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model = WhisperModel(model_name, device=device, compute_type=compute_type)
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segments, info = model.transcribe(
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audio_path,
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language=language,
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beam_size=5,
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best_of=5,
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temperature=0.0,
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condition_on_previous_text=True,
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vad_filter=True,
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vad_parameters={"min_silence_duration_ms": 700},
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word_timestamps=True,
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)
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result = []
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for segment in segments:
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text = (segment.text or "").strip()
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if not text:
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continue
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words = []
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probabilities = []
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for word in segment.words or []:
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if word.start is None or word.end is None or not word.word:
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continue
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probability = float(word.probability) if word.probability is not None else None
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if probability is not None:
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probabilities.append(probability)
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words.append({
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"start": round(float(word.start), 3),
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"end": round(float(word.end), 3),
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"text": str(word.word),
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"probability": round(probability, 4) if probability is not None else None,
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})
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mean_probability = sum(probabilities) / len(probabilities) if probabilities else None
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avg_logprob = float(getattr(segment, "avg_logprob", -1.0) or -1.0)
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no_speech = float(getattr(segment, "no_speech_prob", 0.0) or 0.0)
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confidence = max(0.0, min(1.0, (1.0 + avg_logprob) * 0.45 + (1.0 - no_speech) * 0.2 + (mean_probability if mean_probability is not None else max(0.0, 1.0 + avg_logprob)) * 0.35))
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result.append({
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"start": round(float(segment.start), 3),
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"end": round(float(segment.end), 3),
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"text": text,
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"words": words,
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"confidence": round(confidence, 3),
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"needs_review": confidence < 0.62 or (mean_probability is not None and mean_probability < 0.55),
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})
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print(json.dumps({"language": getattr(info, "language", language), "segments": result}, ensure_ascii=False))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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