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| import os | |
| import torch | |
| from modules.nsf_hifigan.models import load_model, Generator | |
| from modules.nsf_hifigan.nvSTFT import load_wav_to_torch, STFT | |
| from utils.hparams import hparams | |
| from network.vocoders.base_vocoder import BaseVocoder, register_vocoder | |
| class NsfHifiGAN(BaseVocoder): | |
| def __init__(self, device=None): | |
| if device is None: | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| self.device = device | |
| model_path = hparams['vocoder_ckpt'] | |
| if os.path.exists(model_path): | |
| print('| Load HifiGAN: ', model_path) | |
| self.model, self.h = load_model(model_path, device=self.device) | |
| else: | |
| print('Error: HifiGAN model file is not found!') | |
| def spec2wav_torch(self, mel, **kwargs): # mel: [B, T, bins] | |
| if self.h.sampling_rate != hparams['audio_sample_rate']: | |
| print('Mismatch parameters: hparams[\'audio_sample_rate\']=',hparams['audio_sample_rate'],'!=',self.h.sampling_rate,'(vocoder)') | |
| if self.h.num_mels != hparams['audio_num_mel_bins']: | |
| print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=',hparams['audio_num_mel_bins'],'!=',self.h.num_mels,'(vocoder)') | |
| if self.h.n_fft != hparams['fft_size']: | |
| print('Mismatch parameters: hparams[\'fft_size\']=',hparams['fft_size'],'!=',self.h.n_fft,'(vocoder)') | |
| if self.h.win_size != hparams['win_size']: | |
| print('Mismatch parameters: hparams[\'win_size\']=',hparams['win_size'],'!=',self.h.win_size,'(vocoder)') | |
| if self.h.hop_size != hparams['hop_size']: | |
| print('Mismatch parameters: hparams[\'hop_size\']=',hparams['hop_size'],'!=',self.h.hop_size,'(vocoder)') | |
| if self.h.fmin != hparams['fmin']: | |
| print('Mismatch parameters: hparams[\'fmin\']=',hparams['fmin'],'!=',self.h.fmin,'(vocoder)') | |
| if self.h.fmax != hparams['fmax']: | |
| print('Mismatch parameters: hparams[\'fmax\']=',hparams['fmax'],'!=',self.h.fmax,'(vocoder)') | |
| with torch.no_grad(): | |
| c = mel.transpose(2, 1) #[B, T, bins] | |
| #log10 to log mel | |
| c = 2.30259 * c | |
| f0 = kwargs.get('f0') #[B, T] | |
| if f0 is not None and hparams.get('use_nsf'): | |
| y = self.model(c, f0).view(-1) | |
| else: | |
| y = self.model(c).view(-1) | |
| return y | |
| def spec2wav(self, mel, **kwargs): | |
| if self.h.sampling_rate != hparams['audio_sample_rate']: | |
| print('Mismatch parameters: hparams[\'audio_sample_rate\']=',hparams['audio_sample_rate'],'!=',self.h.sampling_rate,'(vocoder)') | |
| if self.h.num_mels != hparams['audio_num_mel_bins']: | |
| print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=',hparams['audio_num_mel_bins'],'!=',self.h.num_mels,'(vocoder)') | |
| if self.h.n_fft != hparams['fft_size']: | |
| print('Mismatch parameters: hparams[\'fft_size\']=',hparams['fft_size'],'!=',self.h.n_fft,'(vocoder)') | |
| if self.h.win_size != hparams['win_size']: | |
| print('Mismatch parameters: hparams[\'win_size\']=',hparams['win_size'],'!=',self.h.win_size,'(vocoder)') | |
| if self.h.hop_size != hparams['hop_size']: | |
| print('Mismatch parameters: hparams[\'hop_size\']=',hparams['hop_size'],'!=',self.h.hop_size,'(vocoder)') | |
| if self.h.fmin != hparams['fmin']: | |
| print('Mismatch parameters: hparams[\'fmin\']=',hparams['fmin'],'!=',self.h.fmin,'(vocoder)') | |
| if self.h.fmax != hparams['fmax']: | |
| print('Mismatch parameters: hparams[\'fmax\']=',hparams['fmax'],'!=',self.h.fmax,'(vocoder)') | |
| with torch.no_grad(): | |
| c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(self.device) | |
| #log10 to log mel | |
| c = 2.30259 * c | |
| f0 = kwargs.get('f0') | |
| if f0 is not None and hparams.get('use_nsf'): | |
| f0 = torch.FloatTensor(f0[None, :]).to(self.device) | |
| y = self.model(c, f0).view(-1) | |
| else: | |
| y = self.model(c).view(-1) | |
| wav_out = y.cpu().numpy() | |
| return wav_out | |
| def wav2spec(inp_path, device=None): | |
| if device is None: | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| sampling_rate = hparams['audio_sample_rate'] | |
| num_mels = hparams['audio_num_mel_bins'] | |
| n_fft = hparams['fft_size'] | |
| win_size =hparams['win_size'] | |
| hop_size = hparams['hop_size'] | |
| fmin = hparams['fmin'] | |
| fmax = hparams['fmax'] | |
| stft = STFT(sampling_rate, num_mels, n_fft, win_size, hop_size, fmin, fmax) | |
| with torch.no_grad(): | |
| wav_torch, _ = load_wav_to_torch(inp_path, target_sr=stft.target_sr) | |
| mel_torch = stft.get_mel(wav_torch.unsqueeze(0).to(device)).squeeze(0).T | |
| #log mel to log10 mel | |
| mel_torch = 0.434294 * mel_torch | |
| return wav_torch.cpu().numpy(), mel_torch.cpu().numpy() |