| import argparse | |
| import os | |
| import torchaudio | |
| from api import TextToSpeech | |
| from tortoise.utils.audio import load_audio, get_voices, load_voice | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.") | |
| parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) ' | |
| 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat') | |
| parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard') | |
| parser.add_argument('--voice_diversity_intelligibility_slider', type=float, | |
| 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', | |
| default=.5) | |
| parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/') | |
| args = parser.parse_args() | |
| os.makedirs(args.output_path, exist_ok=True) | |
| tts = TextToSpeech() | |
| selected_voices = args.voice.split(',') | |
| for voice in selected_voices: | |
| voice_samples, conditioning_latents = load_voice(voice) | |
| gen = tts.tts_with_preset(args.text, voice_samples=voice_samples, conditioning_latents=conditioning_latents, | |
| preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider) | |
| torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), gen.squeeze(0).cpu(), 24000) | |