import torch import numpy as np def get_mel_from_wav(audio, _stft): audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1) audio = torch.autograd.Variable(audio, requires_grad=False) melspec, log_magnitudes_stft, energy = _stft.mel_spectrogram(audio) melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32) log_magnitudes_stft = ( torch.squeeze(log_magnitudes_stft, 0).numpy().astype(np.float32) ) energy = torch.squeeze(energy, 0).numpy().astype(np.float32) return melspec, log_magnitudes_stft, energy # def inv_mel_spec(mel, out_filename, _stft, 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, _stft.sampling_rate, audio)