Fork of https://github.com/alokprasad/fastspeech_squeezewave to also fix denoising in squeezewave
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66 lines
2.3 KiB
66 lines
2.3 KiB
import torch
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import numpy as np
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from scipy.io.wavfile import read
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from scipy.io.wavfile import write
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import audio.stft as stft
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import audio.hparams as hparams
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from audio.audio_processing import griffin_lim
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_stft = stft.TacotronSTFT(
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hparams.filter_length, hparams.hop_length, hparams.win_length,
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hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
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hparams.mel_fmax)
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def load_wav_to_torch(full_path):
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sampling_rate, data = read(full_path)
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return torch.FloatTensor(data.astype(np.float32)), sampling_rate
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def get_mel(filename):
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audio, sampling_rate = load_wav_to_torch(filename)
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if sampling_rate != _stft.sampling_rate:
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raise ValueError("{} {} SR doesn't match target {} SR".format(
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sampling_rate, _stft.sampling_rate))
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audio_norm = audio / hparams.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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melspec = _stft.mel_spectrogram(audio_norm)
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melspec = torch.squeeze(melspec, 0)
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# melspec = torch.from_numpy(_normalize(melspec.numpy()))
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return melspec
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def get_mel_from_wav(audio):
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sampling_rate = hparams.sampling_rate
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if sampling_rate != _stft.sampling_rate:
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raise ValueError("{} {} SR doesn't match target {} SR".format(
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sampling_rate, _stft.sampling_rate))
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audio_norm = audio / hparams.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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melspec = _stft.mel_spectrogram(audio_norm)
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melspec = torch.squeeze(melspec, 0)
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return melspec
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def inv_mel_spec(mel, out_filename, griffin_iters=60):
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mel = torch.stack([mel])
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# mel = torch.stack([torch.from_numpy(_denormalize(mel.numpy()))])
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mel_decompress = _stft.spectral_de_normalize(mel)
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mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
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spec_from_mel_scaling = 1000
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spec_from_mel = torch.mm(mel_decompress[0], _stft.mel_basis)
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spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
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spec_from_mel = spec_from_mel * spec_from_mel_scaling
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audio = griffin_lim(torch.autograd.Variable(
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spec_from_mel[:, :, :-1]), _stft.stft_fn, griffin_iters)
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audio = audio.squeeze()
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audio = audio.cpu().numpy()
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audio_path = out_filename
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write(audio_path, hparams.sampling_rate, audio)
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