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- import torch
- import numpy as np
- from scipy.io.wavfile import read
- from scipy.io.wavfile import write
-
- import audio.stft as stft
- import audio.hparams as hparams
- from audio.audio_processing import griffin_lim
-
- _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 = _stft.mel_spectrogram(audio_norm)
- melspec = torch.squeeze(melspec, 0)
- # melspec = torch.from_numpy(_normalize(melspec.numpy()))
-
- return melspec
-
-
- 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 = _stft.mel_spectrogram(audio_norm)
- melspec = torch.squeeze(melspec, 0)
-
- return melspec
-
-
- def inv_mel_spec(mel, out_filename, griffin_iters=60):
- mel = torch.stack([mel])
- # mel = torch.stack([torch.from_numpy(_denormalize(mel.numpy()))])
- 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)
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