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| import os | |
| import glob | |
| import tqdm | |
| import torch | |
| import argparse | |
| from scipy.io.wavfile import write | |
| import numpy as np | |
| from model.generator import ModifiedGenerator | |
| from utils.hparams import HParam, load_hparam_str | |
| from denoiser import Denoiser | |
| MAX_WAV_VALUE = 32768.0 | |
| def main(args): | |
| checkpoint = torch.load(args.checkpoint_path) | |
| if args.config is not None: | |
| hp = HParam(args.config) | |
| else: | |
| hp = load_hparam_str(checkpoint['hp_str']) | |
| model = ModifiedGenerator(hp.audio.n_mel_channels, hp.model.n_residual_layers, | |
| ratios=hp.model.generator_ratio, mult = hp.model.mult, | |
| out_band = hp.model.out_channels).cuda() | |
| model.load_state_dict(checkpoint['model_g']) | |
| model.eval(inference=True) | |
| with torch.no_grad(): | |
| mel = torch.from_numpy(np.load(args.input)) | |
| if len(mel.shape) == 2: | |
| mel = mel.unsqueeze(0) | |
| mel = mel.cuda() | |
| audio = model.inference(mel) | |
| audio = audio.squeeze(0) # collapse all dimension except time axis | |
| if args.d: | |
| denoiser = Denoiser(model).cuda() | |
| audio = denoiser(audio, 0.01) | |
| audio = audio.squeeze() | |
| audio = audio[:-(hp.audio.hop_length*10)] | |
| audio = MAX_WAV_VALUE * audio | |
| audio = audio.clamp(min=-MAX_WAV_VALUE, max=MAX_WAV_VALUE-1) | |
| audio = audio.short() | |
| audio = audio.cpu().detach().numpy() | |
| out_path = args.input.replace('.npy', '_reconstructed_epoch%04d.wav' % checkpoint['epoch']) | |
| write(out_path, hp.audio.sampling_rate, audio) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('-c', '--config', type=str, default=None, | |
| help="yaml file for config. will use hp_str from checkpoint if not given.") | |
| parser.add_argument('-p', '--checkpoint_path', type=str, required=True, | |
| help="path of checkpoint pt file for evaluation") | |
| parser.add_argument('-i', '--input', type=str, required=True, | |
| help="directory of mel-spectrograms to invert into raw audio. ") | |
| parser.add_argument('-d', action='store_true', help="denoising ") | |
| args = parser.parse_args() | |
| main(args) | |