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| import torch | |
| from librosa.filters import mel as librosa_mel_fn | |
| from .audio_processing import dynamic_range_compression | |
| from .audio_processing import dynamic_range_decompression | |
| from .stft import STFT | |
| from .utils import get_mask_from_lengths | |
| class LinearNorm(torch.nn.Module): | |
| def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): | |
| super(LinearNorm, self).__init__() | |
| self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) | |
| torch.nn.init.xavier_uniform_( | |
| self.linear_layer.weight, | |
| gain=torch.nn.init.calculate_gain(w_init_gain)) | |
| def forward(self, x): | |
| return self.linear_layer(x) | |
| class ConvNorm(torch.nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, | |
| padding=None, dilation=1, bias=True, w_init_gain='linear'): | |
| super(ConvNorm, self).__init__() | |
| if padding is None: | |
| assert(kernel_size % 2 == 1) | |
| padding = int(dilation * (kernel_size - 1) / 2) | |
| self.conv = torch.nn.Conv1d(in_channels, out_channels, | |
| kernel_size=kernel_size, stride=stride, | |
| padding=padding, dilation=dilation, | |
| bias=bias) | |
| torch.nn.init.xavier_uniform_( | |
| self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) | |
| def forward(self, signal): | |
| conv_signal = self.conv(signal) | |
| return conv_signal | |
| class GlobalAvgPool(torch.nn.Module): | |
| def __init__(self): | |
| super(GlobalAvgPool, self).__init__() | |
| def forward(self, x, lengths=None): | |
| """Average pooling across time steps (dim=1) with optionally lengths. | |
| Args: | |
| x: torch.Tensor of shape (N, T, ...) | |
| lengths: None or torch.Tensor of shape (N,) | |
| dim: dimension to pool | |
| """ | |
| if lengths is None: | |
| return x.mean(dim=1, keepdim=False) | |
| else: | |
| mask = get_mask_from_lengths(lengths).type(x.type()).to(x.device) | |
| mask_shape = list(mask.size()) + [1 for _ in range(x.ndimension()-2)] | |
| mask = mask.reshape(*mask_shape) | |
| numer = (x * mask).sum(dim=1, keepdim=False) | |
| denom = mask.sum(dim=1, keepdim=False) | |
| return numer / denom | |
| class TacotronSTFT(torch.nn.Module): | |
| def __init__(self, filter_length=1024, hop_length=256, win_length=1024, | |
| n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0, | |
| mel_fmax=8000.0): | |
| super(TacotronSTFT, self).__init__() | |
| self.n_mel_channels = n_mel_channels | |
| self.sampling_rate = sampling_rate | |
| self.stft_fn = STFT(filter_length, hop_length, win_length) | |
| mel_basis = librosa_mel_fn( | |
| sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax) | |
| mel_basis = torch.from_numpy(mel_basis).float() | |
| self.register_buffer('mel_basis', mel_basis) | |
| def spectral_normalize(self, magnitudes): | |
| output = dynamic_range_compression(magnitudes) | |
| return output | |
| def spectral_de_normalize(self, magnitudes): | |
| output = dynamic_range_decompression(magnitudes) | |
| return output | |
| def mel_spectrogram(self, y): | |
| """Computes mel-spectrograms from a batch of waves | |
| PARAMS | |
| ------ | |
| y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] | |
| RETURNS | |
| ------- | |
| mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) | |
| """ | |
| assert(torch.min(y.data) >= -1) | |
| assert(torch.max(y.data) <= 1) | |
| magnitudes, phases = self.stft_fn.transform(y) | |
| magnitudes = magnitudes.data | |
| mel_output = torch.matmul(self.mel_basis, magnitudes) | |
| mel_output = self.spectral_normalize(mel_output) | |
| return mel_output | |