67 lines
2.9 KiB
Python
67 lines
2.9 KiB
Python
import argparse
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import os
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import torch
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import torch.nn.functional as F
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import torchaudio
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from api import TextToSpeech, load_conditioning
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from utils.audio import load_audio
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from utils.tokenizer import VoiceBpeTokenizer
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def split_and_recombine_text(texts, desired_length=200, max_len=300):
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# TODO: also split across '!' and '?'. Attempt to keep quotations together.
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texts = [s.strip() + "." for s in texts.split('.')]
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i = 0
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while i < len(texts):
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ltxt = texts[i]
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if len(ltxt) >= desired_length or i == len(texts)-1:
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i += 1
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continue
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if len(ltxt) + len(texts[i+1]) > max_len:
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i += 1
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continue
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texts[i] = f'{ltxt} {texts[i+1]}'
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texts.pop(i+1)
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return texts
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if __name__ == '__main__':
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# These are voices drawn randomly from the training set. You are free to substitute your own voices in, but testing
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# has shown that the model does not generalize to new voices very well.
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preselected_cond_voices = {
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'emma_stone': ['voices/emma_stone/1.wav','voices/emma_stone/2.wav','voices/emma_stone/3.wav'],
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'tom_hanks': ['voices/tom_hanks/1.wav','voices/tom_hanks/2.wav','voices/tom_hanks/3.wav'],
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}
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parser = argparse.ArgumentParser()
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parser.add_argument('-textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt")
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parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='emma_stone')
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parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=512)
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parser.add_argument('-batch_size', type=int, help='How many samples to process at once in the autoregressive model.', default=16)
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parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/longform/')
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parser.add_argument('-generation_preset', type=str, help='Preset to use for generation', default='intelligible')
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args = parser.parse_args()
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os.makedirs(args.output_path, exist_ok=True)
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with open(args.textfile, 'r', encoding='utf-8') as f:
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text = ''.join([l for l in f.readlines()])
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texts = split_and_recombine_text(text)
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tts = TextToSpeech(autoregressive_batch_size=args.batch_size)
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priors = []
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for j, text in enumerate(texts):
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cond_paths = preselected_cond_voices[args.voice]
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conds = priors.copy()
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for cond_path in cond_paths:
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c = load_audio(cond_path, 22050)
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conds.append(c)
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gen = tts.tts_with_preset(text, conds, preset=args.generation_preset, num_autoregressive_samples=args.num_samples)
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torchaudio.save(os.path.join(args.output_path, f'{j}.wav'), gen.squeeze(0).cpu(), 24000)
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priors.append(torchaudio.functional.resample(gen, 24000, 22050).squeeze(0))
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while len(priors) > 2:
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priors.pop(0)
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