| import argparse | |
| import os | |
| import torchaudio | |
| from api import TextToSpeech | |
| from utils.audio import load_audio, get_voices | |
| 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='patrick_stewart') | |
| parser.add_argument('--num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=256) | |
| parser.add_argument('--batch_size', type=int, help='How many samples to process at once in the autoregressive model.', default=16) | |
| parser.add_argument('--num_diffusion_samples', type=int, help='Number of outputs that progress to the diffusion stage.', default=16) | |
| 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(autoregressive_batch_size=args.batch_size) | |
| 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) | |
| gen = tts.tts(args.text, conds, num_autoregressive_samples=args.num_samples) | |
| torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), gen.squeeze(0).cpu(), 24000) | |