forked from mrq/DL-Art-School
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
import os
|
|
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, macro_b, dataset):
|
|
with torch.no_grad():
|
|
model.feed_data(data, 0)
|
|
model.test()
|
|
|
|
gt_key = opt['eval']['gen_text']
|
|
txts = []
|
|
for b in range(model.eval_state[gt_key][0].shape[0]):
|
|
if 'real_text' in opt['eval'].keys():
|
|
real = data[opt['eval']['real_text']][b]
|
|
print(f'{macro_b} {b} Real text: "{real}"')
|
|
|
|
codes = model.eval_state[opt['eval']['gen_text']][0][b].cpu()
|
|
if hasattr(dataset, 'tokenizer'):
|
|
text = dataset.tokenizer.decode(codes.numpy())
|
|
text = text.replace(' $$$', '')
|
|
txts.append(text)
|
|
else:
|
|
txts.append(sequence_to_text(codes))
|
|
return txts
|
|
|
|
|
|
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_hf2.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))
|
|
|
|
dataset_opt = opt['datasets']['val']
|
|
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)))
|
|
|
|
model = ExtensibleTrainer(opt)
|
|
|
|
batch = 0
|
|
output = open('results.tsv', 'w')
|
|
dataset_dir = opt['path']['results_root']
|
|
util.mkdir(dataset_dir)
|
|
|
|
for data in tqdm(test_loader):
|
|
#if data['clip'].shape[-1] > opt['networks']['asr_gen']['kwargs']['max_mel_frames']*255:
|
|
# continue
|
|
preds = forward_pass(model, data, dataset_dir, opt, batch, test_set)
|
|
for b, pred in enumerate(preds):
|
|
pred = pred.replace('_', '')
|
|
output.write(f'{pred}\t{os.path.basename(data["filenames"][b])}\n')
|
|
print(pred)
|
|
batch += 1
|
|
output.flush()
|
|
|