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| import copy | |
| import torch | |
| from glow import Invertible1x1Conv, remove | |
| def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): | |
| n_channels_int = n_channels[0] | |
| in_act = input_a+input_b | |
| t_act = torch.tanh(in_act[:, :n_channels_int, :]) | |
| s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) | |
| acts = t_act * s_act | |
| return acts | |
| class WN(torch.nn.Module): | |
| """ | |
| This is the WaveNet like layer for the affine coupling. The primary difference | |
| from WaveNet is the convolutions need not be causal. There is also no dilation | |
| size reset. The dilation only doubles on each layer | |
| """ | |
| def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels, | |
| kernel_size): | |
| super(WN, self).__init__() | |
| assert(kernel_size % 2 == 1) | |
| assert(n_channels % 2 == 0) | |
| self.n_layers = n_layers | |
| self.n_channels = n_channels | |
| self.in_layers = torch.nn.ModuleList() | |
| self.res_skip_layers = torch.nn.ModuleList() | |
| self.cond_layers = torch.nn.ModuleList() | |
| start = torch.nn.Conv1d(n_in_channels, n_channels, 1) | |
| start = torch.nn.utils.weight_norm(start, name='weight') | |
| self.start = start | |
| # Initializing last layer to 0 makes the affine coupling layers | |
| # do nothing at first. This helps with training stability | |
| end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1) | |
| end.weight.data.zero_() | |
| end.bias.data.zero_() | |
| self.end = end | |
| for i in range(n_layers): | |
| dilation = 2 ** i | |
| padding = int((kernel_size*dilation - dilation)/2) | |
| in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size, | |
| dilation=dilation, padding=padding) | |
| in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') | |
| self.in_layers.append(in_layer) | |
| cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels, 1) | |
| cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') | |
| self.cond_layers.append(cond_layer) | |
| # last one is not necessary | |
| if i < n_layers - 1: | |
| res_skip_channels = 2*n_channels | |
| else: | |
| res_skip_channels = n_channels | |
| res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1) | |
| res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') | |
| self.res_skip_layers.append(res_skip_layer) | |
| def forward(self, forward_input): | |
| audio, spect = forward_input | |
| audio = self.start(audio) | |
| for i in range(self.n_layers): | |
| acts = fused_add_tanh_sigmoid_multiply( | |
| self.in_layers[i](audio), | |
| self.cond_layers[i](spect), | |
| torch.IntTensor([self.n_channels])) | |
| res_skip_acts = self.res_skip_layers[i](acts) | |
| if i < self.n_layers - 1: | |
| audio = res_skip_acts[:,:self.n_channels,:] + audio | |
| skip_acts = res_skip_acts[:,self.n_channels:,:] | |
| else: | |
| skip_acts = res_skip_acts | |
| if i == 0: | |
| output = skip_acts | |
| else: | |
| output = skip_acts + output | |
| return self.end(output) | |
| class WaveGlow(torch.nn.Module): | |
| def __init__(self, n_mel_channels, n_flows, n_group, n_early_every, | |
| n_early_size, WN_config): | |
| super(WaveGlow, self).__init__() | |
| self.upsample = torch.nn.ConvTranspose1d(n_mel_channels, | |
| n_mel_channels, | |
| 1024, stride=256) | |
| assert(n_group % 2 == 0) | |
| self.n_flows = n_flows | |
| self.n_group = n_group | |
| self.n_early_every = n_early_every | |
| self.n_early_size = n_early_size | |
| self.WN = torch.nn.ModuleList() | |
| self.convinv = torch.nn.ModuleList() | |
| n_half = int(n_group/2) | |
| # Set up layers with the right sizes based on how many dimensions | |
| # have been output already | |
| n_remaining_channels = n_group | |
| for k in range(n_flows): | |
| if k % self.n_early_every == 0 and k > 0: | |
| n_half = n_half - int(self.n_early_size/2) | |
| n_remaining_channels = n_remaining_channels - self.n_early_size | |
| self.convinv.append(Invertible1x1Conv(n_remaining_channels)) | |
| self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config)) | |
| self.n_remaining_channels = n_remaining_channels # Useful during inference | |
| def forward(self, forward_input): | |
| return None | |
| """ | |
| forward_input[0] = audio: batch x time | |
| forward_input[1] = upsamp_spectrogram: batch x n_cond_channels x time | |
| """ | |
| """ | |
| spect, audio = forward_input | |
| # Upsample spectrogram to size of audio | |
| spect = self.upsample(spect) | |
| assert(spect.size(2) >= audio.size(1)) | |
| if spect.size(2) > audio.size(1): | |
| spect = spect[:, :, :audio.size(1)] | |
| spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3) | |
| spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1) | |
| audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1) | |
| output_audio = [] | |
| s_list = [] | |
| s_conv_list = [] | |
| for k in range(self.n_flows): | |
| if k%4 == 0 and k > 0: | |
| output_audio.append(audio[:,:self.n_multi,:]) | |
| audio = audio[:,self.n_multi:,:] | |
| # project to new basis | |
| audio, s = self.convinv[k](audio) | |
| s_conv_list.append(s) | |
| n_half = int(audio.size(1)/2) | |
| if k%2 == 0: | |
| audio_0 = audio[:,:n_half,:] | |
| audio_1 = audio[:,n_half:,:] | |
| else: | |
| audio_1 = audio[:,:n_half,:] | |
| audio_0 = audio[:,n_half:,:] | |
| output = self.nn[k]((audio_0, spect)) | |
| s = output[:, n_half:, :] | |
| b = output[:, :n_half, :] | |
| audio_1 = torch.exp(s)*audio_1 + b | |
| s_list.append(s) | |
| if k%2 == 0: | |
| audio = torch.cat([audio[:,:n_half,:], audio_1],1) | |
| else: | |
| audio = torch.cat([audio_1, audio[:,n_half:,:]], 1) | |
| output_audio.append(audio) | |
| return torch.cat(output_audio,1), s_list, s_conv_list | |
| """ | |
| def infer(self, spect, sigma=1.0): | |
| spect = self.upsample(spect) | |
| # trim conv artifacts. maybe pad spec to kernel multiple | |
| time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0] | |
| spect = spect[:, :, :-time_cutoff] | |
| spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3) | |
| spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1) | |
| if spect.type() == 'torch.cuda.HalfTensor': | |
| audio = torch.cuda.HalfTensor(spect.size(0), | |
| self.n_remaining_channels, | |
| spect.size(2)).normal_() | |
| else: | |
| audio = torch.cuda.FloatTensor(spect.size(0), | |
| self.n_remaining_channels, | |
| spect.size(2)).normal_() | |
| audio = torch.autograd.Variable(sigma*audio) | |
| for k in reversed(range(self.n_flows)): | |
| n_half = int(audio.size(1)/2) | |
| if k%2 == 0: | |
| audio_0 = audio[:,:n_half,:] | |
| audio_1 = audio[:,n_half:,:] | |
| else: | |
| audio_1 = audio[:,:n_half,:] | |
| audio_0 = audio[:,n_half:,:] | |
| output = self.WN[k]((audio_0, spect)) | |
| s = output[:, n_half:, :] | |
| b = output[:, :n_half, :] | |
| audio_1 = (audio_1 - b)/torch.exp(s) | |
| if k%2 == 0: | |
| audio = torch.cat([audio[:,:n_half,:], audio_1],1) | |
| else: | |
| audio = torch.cat([audio_1, audio[:,n_half:,:]], 1) | |
| audio = self.convinv[k](audio, reverse=True) | |
| if k%4 == 0 and k > 0: | |
| if spect.type() == 'torch.cuda.HalfTensor': | |
| z = torch.cuda.HalfTensor(spect.size(0), | |
| self.n_early_size, | |
| spect.size(2)).normal_() | |
| else: | |
| z = torch.cuda.FloatTensor(spect.size(0), | |
| self.n_early_size, | |
| spect.size(2)).normal_() | |
| audio = torch.cat((sigma*z, audio),1) | |
| return audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data | |
| def remove_weightnorm(model): | |
| waveglow = model | |
| for WN in waveglow.WN: | |
| WN.start = torch.nn.utils.remove_weight_norm(WN.start) | |
| WN.in_layers = remove(WN.in_layers) | |
| WN.cond_layers = remove(WN.cond_layers) | |
| WN.res_skip_layers = remove(WN.res_skip_layers) | |
| return waveglow | |