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)