8f05a7c188
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
120 lines
3.5 KiB
Python
Executable File
120 lines
3.5 KiB
Python
Executable File
#!/opt/homebrew/bin/python3.11
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import sys
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import json
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import os
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import argparse
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import signal
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import subprocess
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from faster_whisper import WhisperModel
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from redis_publisher import RedisPublisher
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def signal_handler(signum, frame):
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print(f"ASR: Received signal {signum}, exiting...")
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sys.exit(1)
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def has_audio_stream(video_path):
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"""Check if video file has audio stream using ffprobe."""
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try:
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cmd = [
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"ffprobe",
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"-v",
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"error",
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"-select_streams",
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"a",
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"-show_entries",
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"stream=codec_type",
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"-of",
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"csv=p=0",
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video_path,
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]
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result = subprocess.run(cmd, capture_output=True, text=True, check=True)
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return bool(result.stdout.strip())
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except subprocess.CalledProcessError:
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return False
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except FileNotFoundError:
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print("WARNING: ffprobe not found, assuming audio exists")
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return True
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def run_asr(video_path, output_path, uuid: str = ""):
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# Set up signal handlers
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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publisher = RedisPublisher(uuid) if uuid else None
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if publisher:
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publisher.info("asr", "ASR_START")
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# Check for audio stream
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if not has_audio_stream(video_path):
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if publisher:
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publisher.info("asr", "No audio stream detected, skipping transcription")
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output = {"language": "", "language_probability": 0.0, "segments": []}
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with open(output_path, "w") as f:
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json.dump(output, f, indent=2)
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if publisher:
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publisher.complete("asr", "0 segments (no audio)")
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sys.stderr.write("ASR: No audio stream, skipping transcription\n")
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sys.stderr.flush()
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sys.exit(0)
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if publisher:
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publisher.info("asr", "Loading Whisper model...")
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# Use small model with CPU (MPS not supported by faster_whisper)
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model = WhisperModel("small", device="cpu", compute_type="int8")
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if publisher:
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publisher.info("asr", f"Transcribing: {video_path}")
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segments, info = model.transcribe(video_path, beam_size=5)
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if publisher:
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publisher.info("asr", f"ASR_LANGUAGE:{info.language}")
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results = []
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total_segments = 0
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for segment in segments:
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results.append(
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{"start": segment.start, "end": segment.end, "text": segment.text.strip()}
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)
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total_segments += 1
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if total_segments % 100 == 0:
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if publisher:
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publisher.progress(
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"asr", total_segments, 0, f"Segment {total_segments}"
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)
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output = {
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"language": info.language,
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"language_probability": info.language_probability,
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"segments": results,
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}
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with open(output_path, "w") as f:
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json.dump(output, f, indent=2)
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if publisher:
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publisher.complete("asr", f"{len(results)} segments")
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sys.stderr.write(
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f"ASR: Transcription complete, {len(results)} segments written to {output_path}\n"
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)
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sys.stderr.flush()
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sys.exit(0)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="ASR Transcription (small model)")
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parser.add_argument("video_path", help="Path to video file")
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parser.add_argument("output_path", help="Output JSON path")
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parser.add_argument("--uuid", "-u", help="UUID for Redis progress", default="")
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args = parser.parse_args()
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run_asr(args.video_path, args.output_path, args.uuid)
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