42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
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import os
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import torch
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from tqdm import tqdm
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from scripts.audio.gen.speech_synthesis_utils import load_speech_dvae, wav_to_mel
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if __name__ == '__main__':
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input_folder = 'C:\\Users\\James\\Downloads\\lex2\\lexfridman_training_mp3'
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output_folder = 'C:\\Users\\James\\Downloads\\lex2\\quantized'
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params = {
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'mode': 'unsupervised_audio',
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'path': [input_folder],
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'cache_path': f'{input_folder}/cache.pth',
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'sampling_rate': 22050,
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'pad_to_samples': 441000,
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'resample_clip': False,
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'extra_samples': 0,
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'phase': 'train',
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'n_workers': 2,
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'batch_size': 64,
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}
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from data import create_dataset, create_dataloader
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os.makedirs(output_folder, exist_ok=True)
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ds = create_dataset(params)
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dl = create_dataloader(ds, params)
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dvae = load_speech_dvae().cuda()
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with torch.no_grad():
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for batch in tqdm(dl):
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audio = batch['clip'].cuda()
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mel = wav_to_mel(audio)
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codes = dvae.get_codebook_indices(mel)
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for i in range(audio.shape[0]):
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c = codes[i, :batch['clip_lengths'][i]//1024+4] # +4 seems empirically to be a good clipping point - it seems to preserve the termination codes.
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fn = batch['path'][i]
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outp = os.path.join(output_folder, os.path.relpath(fn, input_folder) + ".pth")
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os.makedirs(os.path.dirname(outp), exist_ok=True)
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torch.save(c.tolist(), outp)
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