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@@ -67,18 +67,28 @@ def get_model(
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def process_batch(files: list[Path], model) -> float:
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wavs = []
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audio_lengths = []
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+ new_files = []
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max_length = total_time = 0
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for file in files:
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- wav, sr = torchaudio.load(file)
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+ try:
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+ wav, sr = torchaudio.load(file)
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+ except Exception as e:
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+ logger.error(f"Error reading {file}: {e}")
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+ continue
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+
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if wav.shape[0] > 1:
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wav = wav.mean(dim=0, keepdim=True)
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wav = torchaudio.functional.resample(wav.cuda(), sr, model.sampling_rate)[0]
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- wavs.append(wav)
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total_time += len(wav) / model.sampling_rate
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max_length = max(max_length, len(wav))
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+
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+ wavs.append(wav)
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audio_lengths.append(len(wav))
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+ new_files.append(file)
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+
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+ files = new_files
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# Pad to max length
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for i, wav in enumerate(wavs):
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@@ -161,6 +171,8 @@ def main(
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else:
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files = list_files(folder, AUDIO_EXTENSIONS, recursive=True, sort=True)
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+ print(f"Found {len(files)} files")
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+ files = [Path(f) for f in files if not Path(f).with_suffix(".npy").exists()]
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Random(42).shuffle(files)
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total_files = len(files)
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Lengyue