forked from mrq/tortoise-tts
50 lines
2.5 KiB
Python
50 lines
2.5 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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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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# Male voices
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'dotrice': ['voices/dotrice/1.wav', 'voices/dotrice/2.wav'],
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'harris': ['voices/harris/1.wav', 'voices/harris/2.wav'],
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'lescault': ['voices/lescault/1.wav', 'voices/lescault/2.wav'],
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'otto': ['voices/otto/1.wav', 'voices/otto/2.wav'],
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'obama': ['voices/obama/1.wav', 'voices/obama/2.wav'],
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# Female voices
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'atkins': ['voices/atkins/1.wav', 'voices/atkins/2.wav'],
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'grace': ['voices/grace/1.wav', 'voices/grace/2.wav'],
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'kennard': ['voices/kennard/1.wav', 'voices/kennard/2.wav'],
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'mol': ['voices/mol/1.wav', 'voices/mol/2.wav'],
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}
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parser = argparse.ArgumentParser()
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parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
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parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='obama,dotrice,harris,lescault,otto,atkins,grace,kennard,mol')
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parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=128)
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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('-num_diffusion_samples', type=int, help='Number of outputs that progress to the diffusion stage.', default=16)
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parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/')
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args = parser.parse_args()
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os.makedirs(args.output_path, exist_ok=True)
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tts = TextToSpeech(autoregressive_batch_size=args.batch_size)
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for voice in args.voice.split(','):
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cond_paths = preselected_cond_voices[voice]
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conds = []
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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(args.text, conds, num_autoregressive_samples=args.num_samples)
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torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), gen.squeeze(0).cpu(), 24000)
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