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@ -1206,10 +1206,13 @@ def whisper_transcribe( file, language=None ):
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device = "cuda" if get_device_name() == "cuda" else "cpu"
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if whisper_vad:
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"""
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if args.whisper_batchsize > 1:
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result = whisperx.transcribe_with_vad_parallel(whisper_model, file, whisper_vad, batch_size=args.whisper_batchsize, language=language, task="transcribe")
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else:
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result = whisperx.transcribe_with_vad(whisper_model, file, whisper_vad)
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"""
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result = whisperx.transcribe_with_vad(whisper_model, file, whisper_vad)
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else:
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result = whisper_model.transcribe(file)
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@ -1282,30 +1285,17 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
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for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
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basename = os.path.basename(file)
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modified = False
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if basename in results and skip_existings:
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print(f"Skipping already parsed file: {basename}")
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else:
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try:
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result = whisper_transcribe(file, language=language)
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modified = True
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except Exception as e:
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print("Failed to transcribe:", file)
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continue
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results[basename] = result
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continue
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"""
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try:
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sanitized = whisper_sanitize(results[basename])
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if len(sanitized['segments']) > 0 and len(sanitized['segments']) != len(results[basename]['segments']):
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results[basename] = sanitized
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modified = True
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print("Segments sanizited: ", basename)
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result = whisper_transcribe(file, language=language)
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except Exception as e:
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print("Failed to sanitize:", basename, e)
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pass
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"""
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print("Failed to transcribe:", file)
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continue
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results[basename] = result
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waveform, sample_rate = torchaudio.load(file)
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# resample to the input rate, since it'll get resampled for training anyways
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# this should also "help" increase throughput a bit when filling the dataloaders
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@ -1314,12 +1304,28 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
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waveform = waveform[:1]
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torchaudio.save(f"{indir}/audio/{basename}", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
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if modified:
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with open(infile, 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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with open(infile, 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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do_gc()
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modified = False
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for basename in results:
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try:
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sanitized = whisper_sanitize(results[basename])
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if len(sanitized['segments']) > 0 and len(sanitized['segments']) != len(results[basename]['segments']):
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results[basename] = sanitized
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modified = True
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print("Segments sanizited: ", basename)
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except Exception as e:
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print("Failed to sanitize:", basename, e)
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pass
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if modified:
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os.rename(infile, infile.replace(".json", ".unsanitized.json"))
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with open(infile, 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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return f"Processed dataset to: {indir}"
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def slice_waveform( waveform, sample_rate, start, end, trim ):
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