forked from mrq/tortoise-tts
35 lines
1.7 KiB
Python
35 lines
1.7 KiB
Python
import argparse
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import os
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import torchaudio
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from api import TextToSpeech
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from utils.audio import load_audio, get_voices
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if __name__ == '__main__':
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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='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
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'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='patrick_stewart')
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parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
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parser.add_argument('--voice_diversity_intelligibility_slider', type=float,
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help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
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default=.5)
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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()
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voices = get_voices()
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selected_voices = args.voice.split(',')
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for voice in selected_voices:
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cond_paths = 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_with_preset(args.text, conds, preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider)
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torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), gen.squeeze(0).cpu(), 24000)
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