| import torch | |
| import numpy as np | |
| from scipy.io.wavfile import write | |
| import torchaudio | |
| from qa_mdt.audioldm_train.utilities.audio.audio_processing import griffin_lim | |
| 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, magnitudes, phases, energy = _stft.mel_spectrogram(audio) | |
| melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32) | |
| magnitudes = torch.squeeze(magnitudes, 0).numpy().astype(np.float32) | |
| energy = torch.squeeze(energy, 0).numpy().astype(np.float32) | |
| return melspec, magnitudes, 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) | |