import argparse import os import torch import torch.nn.functional as F import torchaudio from api_new_autoregressive import TextToSpeech, load_conditioning from utils.audio import load_audio from utils.tokenizer import VoiceBpeTokenizer if __name__ == '__main__': # These are voices drawn randomly from the training set. You are free to substitute your own voices in, but testing # has shown that the model does not generalize to new voices very well. preselected_cond_voices = { # Male voices 'dotrice': ['voices/dotrice/1.wav', 'voices/dotrice/2.wav'], 'harris': ['voices/harris/1.wav', 'voices/harris/2.wav'], 'lescault': ['voices/lescault/1.wav', 'voices/lescault/2.wav'], 'otto': ['voices/otto/1.wav', 'voices/otto/2.wav'], # Female voices 'atkins': ['voices/atkins/1.wav', 'voices/atkins/2.wav'], 'grace': ['voices/grace/1.wav', 'voices/grace/2.wav'], 'kennard': ['voices/kennard/1.wav', 'voices/kennard/2.wav'], 'mol': ['voices/mol/1.wav', 'voices/mol/2.wav'], } 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='Use a preset conditioning voice (defined above). Overrides cond_path.', default='dotrice,harris,lescault,otto,atkins,grace,kennard,mol') parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=32) 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) for voice in args.voice.split(','): tokenizer = VoiceBpeTokenizer() text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda() text = F.pad(text, (0,1)) # This may not be necessary. cond_paths = preselected_cond_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)