import argparse import os import torch import torch.nn.functional as F import torchaudio from api import TextToSpeech, load_conditioning from utils.audio import load_audio from utils.tokenizer import VoiceBpeTokenizer def split_and_recombine_text(texts, desired_length=200, max_len=300): # TODO: also split across '!' and '?'. Attempt to keep quotations together. texts = [s.strip() + "." for s in texts.split('.')] i = 0 while i < len(texts): ltxt = texts[i] if len(ltxt) >= desired_length or i == len(texts)-1: i += 1 continue if len(ltxt) + len(texts[i+1]) > max_len: i += 1 continue texts[i] = f'{ltxt} {texts[i+1]}' texts.pop(i+1) return texts 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 = { 'emma_stone': ['voices/emma_stone/1.wav','voices/emma_stone/2.wav','voices/emma_stone/3.wav'], 'tom_hanks': ['voices/tom_hanks/1.wav','voices/tom_hanks/2.wav','voices/tom_hanks/3.wav'], } parser = argparse.ArgumentParser() parser.add_argument('-textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt") parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='emma_stone') parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=512) parser.add_argument('-batch_size', type=int, help='How many samples to process at once in the autoregressive model.', default=16) parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/longform/') parser.add_argument('-generation_preset', type=str, help='Preset to use for generation', default='intelligible') args = parser.parse_args() os.makedirs(args.output_path, exist_ok=True) with open(args.textfile, 'r', encoding='utf-8') as f: text = ''.join([l for l in f.readlines()]) texts = split_and_recombine_text(text) tts = TextToSpeech(autoregressive_batch_size=args.batch_size) priors = [] for j, text in enumerate(texts): cond_paths = preselected_cond_voices[args.voice] conds = priors.copy() for cond_path in cond_paths: c = load_audio(cond_path, 22050) conds.append(c) gen = tts.tts_with_preset(text, conds, preset=args.generation_preset, num_autoregressive_samples=args.num_samples) torchaudio.save(os.path.join(args.output_path, f'{j}.wav'), gen.squeeze(0).cpu(), 24000) priors.append(torchaudio.functional.resample(gen, 24000, 22050).squeeze(0)) while len(priors) > 2: priors.pop(0)