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| import glob | |
| import json | |
| import os | |
| import re | |
| import librosa | |
| import torch | |
| import utils | |
| from modules.hifigan.hifigan import HifiGanGenerator | |
| from utils.hparams import hparams, set_hparams | |
| from vocoders.base_vocoder import register_vocoder | |
| from vocoders.pwg import PWG | |
| from vocoders.vocoder_utils import denoise | |
| def load_model(config_path, checkpoint_path): | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| ckpt_dict = torch.load(checkpoint_path, map_location="cpu") | |
| if '.yaml' in config_path: | |
| config = set_hparams(config_path, global_hparams=False) | |
| state = ckpt_dict["state_dict"]["model_gen"] | |
| elif '.json' in config_path: | |
| config = json.load(open(config_path, 'r')) | |
| state = ckpt_dict["generator"] | |
| model = HifiGanGenerator(config) | |
| model.load_state_dict(state, strict=True) | |
| model.remove_weight_norm() | |
| model = model.eval().to(device) | |
| print(f"| Loaded model parameters from {checkpoint_path}.") | |
| print(f"| HifiGAN device: {device}.") | |
| return model, config, device | |
| total_time = 0 | |
| class HifiGAN(PWG): | |
| def __init__(self): | |
| base_dir = hparams['vocoder_ckpt'] | |
| config_path = f'{base_dir}/config.yaml' | |
| if os.path.exists(config_path): | |
| ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= | |
| lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1] | |
| print('| load HifiGAN: ', ckpt) | |
| self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt) | |
| else: | |
| config_path = f'{base_dir}/config.json' | |
| ckpt = f'{base_dir}/generator_v1' | |
| if os.path.exists(config_path): | |
| self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt) | |
| def spec2wav(self, mel, **kwargs): | |
| device = self.device | |
| with torch.no_grad(): | |
| c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(device) | |
| f0 = kwargs.get('f0') | |
| if f0 is not None and hparams.get('use_nsf'): | |
| f0 = torch.FloatTensor(f0[None, :]).to(device) | |
| y = self.model(c, f0).view(-1) | |
| else: | |
| y = self.model(c).view(-1) | |
| wav_out = y.cpu().numpy() | |
| if hparams.get('vocoder_denoise_c', 0.0) > 0: | |
| wav_out = denoise(wav_out, v=hparams['vocoder_denoise_c']) | |
| return wav_out | |
| # @staticmethod | |
| # def wav2spec(wav_fn, **kwargs): | |
| # wav, _ = librosa.core.load(wav_fn, sr=hparams['audio_sample_rate']) | |
| # wav_torch = torch.FloatTensor(wav)[None, :] | |
| # mel = mel_spectrogram(wav_torch, hparams).numpy()[0] | |
| # return wav, mel.T | |