DL-Art-School/codes/scripts/audio/test_audio_gen.py
2021-08-30 21:20:52 -06:00

91 lines
3.5 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.waveglow.denoiser import Denoiser
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, denoiser, data, output_dir, opt, b):
with torch.no_grad():
model.feed_data(data, 0)
model.test()
pred_waveforms = model.eval_state[opt['eval']['output_state']][0]
pred_waveforms = denoiser(pred_waveforms)
gt = 'ground_truth' in opt['eval'].keys()
if gt:
ground_truth_waveforms = model.eval_state[opt['eval']['ground_truth']][0]
ground_truth_waveforms = denoiser(ground_truth_waveforms)
for i in range(pred_waveforms.shape[0]):
# Output predicted mels and waveforms.
pred_mel = model.eval_state[opt['eval']['pred_mel']][0][i].unsqueeze(0)
pred_mel = ((pred_mel - pred_mel.mean()) / max(abs(pred_mel.min()), pred_mel.max())).unsqueeze(1)
torchvision.utils.save_image(pred_mel, osp.join(output_dir, f'{b}_{i}_pred_mel.png'))
audio = pred_waveforms[i][0].cpu().numpy()
wavfile.write(osp.join(output_dir, f'{b}_{i}.wav'), 22050, audio)
if gt:
gt_mel = model.eval_state[opt['eval']['ground_truth_mel']][0][i].unsqueeze(0)
gt_mel = ((gt_mel - gt_mel.mean()) / max(abs(gt_mel.min()), gt_mel.max())).unsqueeze(1)
torchvision.utils.save_image(gt_mel, osp.join(output_dir, f'{b}_{i}_gt_mel.png'))
audio = ground_truth_waveforms[i][0].cpu().numpy()
wavfile.write(osp.join(output_dir, f'{b}_{i}_ground_truth.wav'), 22050, audio)
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_lrdvae_audio_clips.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)
denoiser = Denoiser(model.networks['waveglow'].module) # Pretty hacky, need to figure out a better way to integrate this.
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, denoiser, data, dataset_dir, opt, batch)
batch += 1