48c3b13c37
- Re-ran identity_bind.py to restore identity_id on face_detections - Dedup cleanup had removed rows with identity_id, kept NULL rows - 70691 face_detections now have identity_id, 428 identities - Full package rebuild: 169MB sqlite, 1358MB tar.gz - identities.json: 428 identities + 5483 bindings + 5483 trace maps - TMDB matching complete: Audrey Hepburn 843 traces, Cary Grant 482
72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Minimal test: run faster-whisper on full video, output each segment's text.
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No VAD tuning, no speaker detection, no splitting. Just raw ASR output.
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"""
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import json, os, sys, time, subprocess, tempfile, shutil
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import torchaudio
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from faster_whisper import WhisperModel
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def extract_audio(video_path, tmp_dir, sr=16000):
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wav_path = os.path.join(tmp_dir, "audio.wav")
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subprocess.run(["ffmpeg", "-y", "-v", "quiet", "-i", video_path,
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"-ar", str(sr), "-ac", "1", "-sample_fmt", "s16", wav_path],
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check=True, capture_output=True, timeout=300)
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wav_data, sr_actual = torchaudio.load(wav_path)
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if wav_data.shape[0] > 1:
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wav_data = wav_data.mean(dim=0, keepdim=True)
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return wav_path, wav_data, sr_actual
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def main():
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video = "/Users/accusys/momentry/var/sftpgo/data/demo/Charade (1963) Cary Grant & Audrey Hepburn ｜ Comedy Mystery Romance Thriller ｜ Full Movie.mp4"
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output = "/Users/accusys/momentry/output_dev/segment_texts.txt"
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t0 = time.time()
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# Load model
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print("Loading faster-whisper small int8 CPU...")
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model = WhisperModel("small", device="cpu", compute_type="int8")
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print(f"Model loaded ({time.time()-t0:.0f}s)")
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# Extract audio
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print("Extracting audio...")
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tmp_dir = tempfile.mkdtemp(prefix="asr_test_")
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wav_path, wav_data, sr = extract_audio(video, tmp_dir)
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total_audio_s = wav_data.shape[1] / sr
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print(f"Audio: {total_audio_s:.0f}s, {sr}Hz ({time.time()-t0:.0f}s)")
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# Transcribe - NO VAD filter, let the model segment naturally
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print("Transcribing (vad_filter=False)...")
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segments, info = model.transcribe(wav_path, beam_size=5,
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vad_filter=False, word_timestamps=True)
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print(f" Detected language: {info.language} (prob: {info.language_probability:.2f})")
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print(f" Duration after VAD: {info.duration_after_vad:.1f}s")
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# Write each segment to file
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count = 0
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total_words = 0
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total_dur = 0
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with open(output, "w") as f:
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for seg in segments:
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text = seg.text.strip()
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dur = seg.end - seg.start
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words = len(seg.words) if seg.words else 0
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f.write(f"{seg.start:.2f}\t{seg.end:.2f}\t{dur:.2f}\t{words}\t{text}\n")
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count += 1
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total_words += words
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total_dur += dur
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elapsed = time.time() - t0
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print(f"\n=== Results ===")
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print(f"Segments: {count}")
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print(f"Words: {total_words}")
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print(f"Speech duration: {total_dur:.0f}s")
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print(f"Avg segment: {total_dur/count:.1f}s, {total_words/count:.1f} words")
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print(f"Elapsed: {elapsed:.0f}s ({elapsed/60:.1f}min)")
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print(f"Output: {output}")
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shutil.rmtree(tmp_dir, ignore_errors=True)
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if __name__ == "__main__":
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main()
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