import os.path as osp import logging import random import argparse import torchvision import utils import utils.options as option import utils.util as util from models.tacotron2.text import sequence_to_text from trainer.ExtensibleTrainer import ExtensibleTrainer from data import create_dataset, create_dataloader from tqdm import tqdm import torch import numpy as np from scipy.io import wavfile def forward_pass(model, data, output_dir, opt, b): with torch.no_grad(): model.feed_data(data, 0) model.test() if 'real_text' in opt['eval'].keys(): real = data[opt['eval']['real_text']][0] print(f'{b} Real text: "{real}"') pred_seq = model.eval_state[opt['eval']['gen_text']][0] pred_text = [sequence_to_text(ts) for ts in pred_seq] audio = model.eval_state[opt['eval']['audio']][0].cpu().numpy() wavfile.write(osp.join(output_dir, f'{b}_clip.wav'), 22050, audio) for i, text in enumerate(pred_text): print(f'{b} Predicted text {i}: "{text}"') if __name__ == "__main__": # Set seeds torch.manual_seed(5555) random.seed(5555) np.random.seed(5555) #### options torch.backends.cudnn.benchmark = True want_metrics = False parser = argparse.ArgumentParser() parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_asr_mozcv.yml') opt = option.parse(parser.parse_args().opt, is_train=False) opt = option.dict_to_nonedict(opt) utils.util.loaded_options = opt util.mkdirs( (path for key, path in opt['path'].items() if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key)) util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO, screen=True, tofile=True) logger = logging.getLogger('base') logger.info(option.dict2str(opt)) test_loaders = [] for phase, dataset_opt in sorted(opt['datasets'].items()): test_set, collate_fn = create_dataset(dataset_opt, return_collate=True) test_loader = create_dataloader(test_set, dataset_opt, collate_fn=collate_fn) logger.info('Number of test texts in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set))) test_loaders.append(test_loader) model = ExtensibleTrainer(opt) batch = 0 for test_loader in test_loaders: dataset_dir = opt['path']['results_root'] util.mkdir(dataset_dir) tq = tqdm(test_loader) for data in tq: forward_pass(model, data, dataset_dir, opt, batch) batch += 1