| import argparse | |
| import os | |
| import torch | |
| from api import TextToSpeech | |
| from tortoise.utils.audio import load_audio, get_voices | |
| """ | |
| Dumps the conditioning latents for the specified voice to disk. These are expressive latents which can be used for | |
| other ML models, or can be augmented manually and fed back into Tortoise to affect vocal qualities. | |
| """ | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--voice', type=str, help='Selects the voice to convert to conditioning latents', default='pat2') | |
| parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='../results/conditioning_latents') | |
| args = parser.parse_args() | |
| os.makedirs(args.output_path, exist_ok=True) | |
| tts = TextToSpeech() | |
| voices = get_voices() | |
| selected_voices = args.voice.split(',') | |
| for voice in selected_voices: | |
| cond_paths = voices[voice] | |
| conds = [] | |
| for cond_path in cond_paths: | |
| c = load_audio(cond_path, 22050) | |
| conds.append(c) | |
| conditioning_latents = tts.get_conditioning_latents(conds) | |
| torch.save(conditioning_latents, os.path.join(args.output_path, f'{voice}.pth')) | |