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/') parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this' 'should only be specified if you have custom checkpoints.', default='.models') args = parser.parse_args() os.makedirs(args.output_path, exist_ok=True) tts = TextToSpeech(models_dir=args.model_dir) 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)