DL-Art-School/codes/scripts/audio/test_audio_speech_recognition.py

79 lines
2.6 KiB
Python

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