| import torch |
| import numpy as np |
| from scipy.io.wavfile import read |
| from scipy.io.wavfile import write |
|
|
| import audio.stft as stft |
| from audio.audio_processing import griffin_lim |
| import hparams |
|
|
| _stft = stft.TacotronSTFT( |
| hparams.filter_length, hparams.hop_length, hparams.win_length, |
| hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin, |
| hparams.mel_fmax) |
|
|
|
|
| def load_wav_to_torch(full_path): |
| sampling_rate, data = read(full_path) |
| return torch.FloatTensor(data.astype(np.float32)), sampling_rate |
|
|
|
|
| def get_mel(filename): |
| audio, sampling_rate = load_wav_to_torch(filename) |
| if sampling_rate != _stft.sampling_rate: |
| raise ValueError("{} {} SR doesn't match target {} SR".format( |
| sampling_rate, _stft.sampling_rate)) |
| audio_norm = audio / hparams.max_wav_value |
| audio_norm = audio_norm.unsqueeze(0) |
| audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False) |
| melspec, energy = _stft.mel_spectrogram(audio_norm) |
| melspec = torch.squeeze(melspec, 0) |
| energy = torch.squeeze(energy, 0) |
| |
|
|
| return melspec, energy |
|
|
|
|
| def get_mel_from_wav(audio): |
| sampling_rate = hparams.sampling_rate |
| if sampling_rate != _stft.sampling_rate: |
| raise ValueError("{} {} SR doesn't match target {} SR".format( |
| sampling_rate, _stft.sampling_rate)) |
| audio_norm = audio / hparams.max_wav_value |
| audio_norm = audio_norm.unsqueeze(0) |
| audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False) |
| melspec, energy = _stft.mel_spectrogram(audio_norm) |
| melspec = torch.squeeze(melspec, 0) |
| energy = torch.squeeze(energy, 0) |
|
|
| return melspec, energy |
|
|
|
|
| def inv_mel_spec(mel, out_filename, griffin_iters=60): |
| mel = torch.stack([mel]) |
| |
| mel_decompress = _stft.spectral_de_normalize(mel) |
| mel_decompress = mel_decompress.transpose(1, 2).data.cpu() |
| spec_from_mel_scaling = 1000 |
| spec_from_mel = torch.mm(mel_decompress[0], _stft.mel_basis) |
| spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0) |
| spec_from_mel = spec_from_mel * spec_from_mel_scaling |
|
|
| audio = griffin_lim(torch.autograd.Variable( |
| spec_from_mel[:, :, :-1]), _stft.stft_fn, griffin_iters) |
|
|
| audio = audio.squeeze() |
| audio = audio.cpu().numpy() |
| audio_path = out_filename |
| write(audio_path, hparams.sampling_rate, audio) |
|
|
