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A10G
| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| from torch.autograd import Variable | |
| import torch.nn.functional as F | |
| 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 Invertible1x1Conv(torch.nn.Module): | |
| """ | |
| The layer outputs both the convolution, and the log determinant | |
| of its weight matrix. If reverse=True it does convolution with | |
| inverse | |
| """ | |
| def __init__(self, c): | |
| super(Invertible1x1Conv, self).__init__() | |
| self.conv = torch.nn.Conv1d( | |
| c, c, kernel_size=1, stride=1, padding=0, bias=False | |
| ) | |
| # Sample a random orthonormal matrix to initialize weights | |
| W = torch.linalg.qr(torch.FloatTensor(c, c).normal_())[0] | |
| # Ensure determinant is 1.0 not -1.0 | |
| if torch.det(W) < 0: | |
| W[:, 0] = -1 * W[:, 0] | |
| W = W.view(c, c, 1) | |
| self.conv.weight.data = W | |
| def forward(self, z, reverse=False): | |
| # shape | |
| batch_size, group_size, n_of_groups = z.size() | |
| W = self.conv.weight.squeeze() | |
| if reverse: | |
| if not hasattr(self, "W_inverse"): | |
| # Reverse computation | |
| W_inverse = W.float().inverse() | |
| W_inverse = Variable(W_inverse[..., None]) | |
| if z.type() == "torch.cuda.HalfTensor": | |
| W_inverse = W_inverse.half() | |
| self.W_inverse = W_inverse | |
| z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0) | |
| return z | |
| else: | |
| # Forward computation | |
| log_det_W = batch_size * n_of_groups * torch.logdet(W) | |
| z = self.conv(z) | |
| return z, log_det_W | |
| 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() | |
| 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 | |
| cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels * n_layers, 1) | |
| self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") | |
| 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) | |
| # 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) | |
| output = torch.zeros_like(audio) | |
| n_channels_tensor = torch.IntTensor([self.n_channels]) | |
| spect = self.cond_layer(spect) | |
| for i in range(self.n_layers): | |
| spect_offset = i * 2 * self.n_channels | |
| acts = fused_add_tanh_sigmoid_multiply( | |
| self.in_layers[i](audio), | |
| spect[:, spect_offset : spect_offset + 2 * self.n_channels, :], | |
| n_channels_tensor, | |
| ) | |
| res_skip_acts = self.res_skip_layers[i](acts) | |
| if i < self.n_layers - 1: | |
| audio = audio + res_skip_acts[:, : self.n_channels, :] | |
| output = output + res_skip_acts[:, self.n_channels :, :] | |
| else: | |
| output = output + res_skip_acts | |
| return self.end(output) | |
| class WaveGlow(torch.nn.Module): | |
| def __init__(self, cfg): | |
| super(WaveGlow, self).__init__() | |
| self.cfg = cfg | |
| self.upsample = torch.nn.ConvTranspose1d( | |
| self.cfg.VOCODER.INPUT_DIM, | |
| self.cfg.VOCODER.INPUT_DIM, | |
| 1024, | |
| stride=256, | |
| ) | |
| assert self.cfg.VOCODER.N_GROUP % 2 == 0 | |
| self.n_flows = self.cfg.VOCODER.N_FLOWS | |
| self.n_group = self.cfg.VOCODER.N_GROUP | |
| self.n_early_every = self.cfg.VOCODER.N_EARLY_EVERY | |
| self.n_early_size = self.cfg.VOCODER.N_EARLY_SIZE | |
| self.WN = torch.nn.ModuleList() | |
| self.convinv = torch.nn.ModuleList() | |
| n_half = int(self.cfg.VOCODER.N_GROUP / 2) | |
| # Set up layers with the right sizes based on how many dimensions | |
| # have been output already | |
| n_remaining_channels = self.cfg.VOCODER.N_GROUP | |
| for k in range(self.cfg.VOCODER.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, | |
| self.cfg.VOCODER.INPUT_DIM * self.cfg.VOCODER.N_GROUP, | |
| self.cfg.VOCODER.N_LAYERS, | |
| self.cfg.VOCODER.N_CHANNELS, | |
| self.cfg.VOCODER.KERNEL_SIZE, | |
| ) | |
| ) | |
| self.n_remaining_channels = n_remaining_channels # Useful during inference | |
| def forward(self, forward_input): | |
| """ | |
| forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames | |
| forward_input[1] = audio: batch 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 = [] | |
| log_s_list = [] | |
| log_det_W_list = [] | |
| for k in range(self.n_flows): | |
| if k % self.n_early_every == 0 and k > 0: | |
| output_audio.append(audio[:, : self.n_early_size, :]) | |
| audio = audio[:, self.n_early_size :, :] | |
| audio, log_det_W = self.convinv[k](audio) | |
| log_det_W_list.append(log_det_W) | |
| n_half = int(audio.size(1) / 2) | |
| audio_0 = audio[:, :n_half, :] | |
| audio_1 = audio[:, n_half:, :] | |
| output = self.WN[k]((audio_0, spect)) | |
| log_s = output[:, n_half:, :] | |
| b = output[:, :n_half, :] | |
| audio_1 = torch.exp(log_s) * audio_1 + b | |
| log_s_list.append(log_s) | |
| audio = torch.cat([audio_0, audio_1], 1) | |
| output_audio.append(audio) | |
| return torch.cat(output_audio, 1), log_s_list, log_det_W_list | |
| 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_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer) | |
| WN.res_skip_layers = remove(WN.res_skip_layers) | |
| return waveglow | |
| def remove(conv_list): | |
| new_conv_list = torch.nn.ModuleList() | |
| for old_conv in conv_list: | |
| old_conv = torch.nn.utils.remove_weight_norm(old_conv) | |
| new_conv_list.append(old_conv) | |
| return new_conv_list | |