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import torch
import numpy as np
from scipy.io.wavfile import write
from 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, energy = _stft.mel_spectrogram(audio)
melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32)
energy = torch.squeeze(energy, 0).numpy().astype(np.float32)
return melspec, 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)
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