65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
import os
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from random import shuffle
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import torchaudio
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from api import TextToSpeech
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from utils.audio import load_audio
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def permutations(args):
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res = []
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k = next(iter(args.keys()))
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vals = args[k]
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del args[k]
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if not args:
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return [{k: v} for v in vals]
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lower = permutations(args)
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for v in vals:
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for l in lower:
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lc = l.copy()
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lc[k] = v
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res.append(lc)
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return res
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if __name__ == '__main__':
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fname = 'Y:\\clips\\books2\\subset512-oco.tsv'
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stop_after = 512
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outpath_base = 'D:\\tmp\\tortoise-tts-eval\\sweep-2'
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outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
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arg_ranges = {
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'top_p': [.8,1],
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'temperature': [.8,.9,1],
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'diffusion_temperature': [.8,1],
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'cond_free_k': [1,2,5,10],
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}
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cfgs = permutations(arg_ranges)
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shuffle(cfgs)
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for cfg in cfgs:
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cfg_desc = '_'.join([f'{k}-{v}' for k,v in cfg.items()])
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outpath = os.path.join(outpath_base, f'{cfg_desc}')
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os.makedirs(outpath, exist_ok=True)
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os.makedirs(outpath_real, exist_ok=True)
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with open(fname, 'r', encoding='utf-8') as f:
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lines = [l.strip().split('\t') for l in f.readlines()]
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recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8')
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tts = TextToSpeech()
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for e, line in enumerate(lines):
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if e >= stop_after:
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break
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transcript = line[0]
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path = os.path.join(os.path.dirname(fname), line[1])
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cond_audio = load_audio(path, 22050)
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torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
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sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=32, repetition_penalty=2.0,
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k=1, diffusion_iterations=32, length_penalty=1.0, **cfg)
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down = torchaudio.functional.resample(sample, 24000, 22050)
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fout_path = os.path.join(outpath, os.path.basename(line[1]))
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torchaudio.save(fout_path, down.squeeze(0), 22050)
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recorder.write(f'{transcript}\t{fout_path}\n')
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recorder.flush()
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recorder.close() |