Spaces:
Runtime error
Runtime error
| import numpy as np | |
| import os | |
| import torch | |
| import commons | |
| import models | |
| import utils | |
| from argparse import ArgumentParser | |
| from tqdm import tqdm | |
| from text import text_to_sequence | |
| if __name__ == "__main__": | |
| parser = ArgumentParser() | |
| parser.add_argument("-m", "--model_dir", required=True, type=str) | |
| parser.add_argument("-s", "--mels_dir", required=True, type=str) | |
| args = parser.parse_args() | |
| MODEL_DIR = args.model_dir # path to model dir | |
| SAVE_MELS_DIR = args.mels_dir # path to save generated mels | |
| if not os.path.exists(SAVE_MELS_DIR): | |
| os.makedirs(SAVE_MELS_DIR) | |
| hps = utils.get_hparams_from_dir(MODEL_DIR) | |
| symbols = list(hps.data.punc) + list(hps.data.chars) | |
| checkpoint_path = utils.latest_checkpoint_path(MODEL_DIR) | |
| cleaner = hps.data.text_cleaners | |
| model = models.FlowGenerator( | |
| len(symbols) + getattr(hps.data, "add_blank", False), | |
| out_channels=hps.data.n_mel_channels, | |
| **hps.model | |
| ).to("cuda") | |
| utils.load_checkpoint(checkpoint_path, model) | |
| model.decoder.store_inverse() # do not calcuate jacobians for fast decoding | |
| _ = model.eval() | |
| def get_mel(text, fpath): | |
| if getattr(hps.data, "add_blank", False): | |
| text_norm = text_to_sequence(text, symbols, cleaner) | |
| text_norm = commons.intersperse(text_norm, len(symbols)) | |
| else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality | |
| text = " " + text.strip() + " " | |
| text_norm = text_to_sequence(text, symbols, cleaner) | |
| sequence = np.array(text_norm)[None, :] | |
| x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long() | |
| x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda() | |
| with torch.no_grad(): | |
| noise_scale = 0.667 | |
| length_scale = 1.0 | |
| (y_gen_tst, *_), *_, (attn_gen, *_) = model( | |
| x_tst, | |
| x_tst_lengths, | |
| gen=True, | |
| noise_scale=noise_scale, | |
| length_scale=length_scale, | |
| ) | |
| np.save(os.path.join(SAVE_MELS_DIR, fpath), y_gen_tst.cpu().detach().numpy()) | |
| for f in [hps.data.training_files, hps.data.validation_files]: | |
| file_lines = open(f).read().splitlines() | |
| for line in tqdm(file_lines): | |
| fname, text = line.split("|") | |
| fname = os.path.basename(fname).replace(".wav", ".npy") | |
| get_mel(text, fname) | |