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@ -519,7 +519,7 @@ def update_baseline_for_latents_chunks( voice ):
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continue
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metadata = torchaudio.info(f'{path}/{file}')
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duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
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duration = metadata.num_frames / metadata.sample_rate
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total_duration += duration
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total = total + 1
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@ -1079,7 +1079,7 @@ def validate_waveform( waveform, sample_rate, min_only=False ):
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return "Waveform is empty"
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num_channels, num_frames = waveform.shape
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duration = num_channels * num_frames / sample_rate
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duration = num_frames / sample_rate
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if duration < MIN_TRAINING_DURATION:
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return "Duration too short ({:.3f}s < {:.3f}s)".format(duration, MIN_TRAINING_DURATION)
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@ -1176,7 +1176,7 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
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result = results[filename]
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waveform, sample_rate = torchaudio.load(path)
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num_channels, num_frames = waveform.shape
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duration = num_channels * num_frames / sample_rate
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duration = num_frames / sample_rate
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for segment in result['segments']:
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file = filename.replace(".wav", f"_{pad(segment['id'], 4)}.wav")
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@ -1234,7 +1234,7 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=F
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continue
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metadata = torchaudio.info(path)
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duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
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duration = metadata.num_frames / metadata.sample_rate
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if duration >= MAX_TRAINING_DURATION:
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message = f"Audio too large, using segments: {filename}"
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print(message)
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@ -1285,7 +1285,7 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=F
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culled = len(text) < text_length
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if not culled and audio_length > 0:
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num_channels, num_frames = waveform.shape
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duration = num_channels * num_frames / sample_rate
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duration = num_frames / sample_rate
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culled = duration < audio_length
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# for when i add in a little treat ;), as it requires normalized text
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