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| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| import modules | |
| import attentions | |
| from torch.nn import Conv1d, ConvTranspose1d, Conv2d | |
| from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
| from commons import init_weights, get_padding | |
| import torchaudio | |
| from einops import rearrange | |
| import transformers | |
| import math | |
| from styleencoder import StyleEncoder | |
| import commons | |
| from alias_free_torch import * | |
| import activations | |
| class Wav2vec2(torch.nn.Module): | |
| def __init__(self, layer=7, w2v='mms'): | |
| """we use the intermediate features of mms-300m. | |
| More specifically, we used the output from the 7th layer of the 24-layer transformer encoder. | |
| """ | |
| super().__init__() | |
| if w2v == 'mms': | |
| self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/mms-300m") | |
| else: | |
| self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-xls-r-300m") | |
| for param in self.wav2vec2.parameters(): | |
| param.requires_grad = False | |
| param.grad = None | |
| self.wav2vec2.eval() | |
| self.feature_layer = layer | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x: torch.Tensor of shape (B x t) | |
| Returns: | |
| y: torch.Tensor of shape(B x C x t) | |
| """ | |
| outputs = self.wav2vec2(x.squeeze(1), output_hidden_states=True) | |
| y = outputs.hidden_states[self.feature_layer] # B x t x C(1024) | |
| y = y.permute((0, 2, 1)) # B x t x C -> B x C x t | |
| return y | |
| class ResidualCouplingBlock_Transformer(nn.Module): | |
| def __init__(self, | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers=3, | |
| n_flows=4, | |
| gin_channels=0): | |
| super().__init__() | |
| self.channels = channels | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.n_flows = n_flows | |
| self.gin_channels = gin_channels | |
| self.cond_block = torch.nn.Sequential(torch.nn.Linear(gin_channels, 4 * hidden_channels), | |
| nn.SiLU(), torch.nn.Linear(4 * hidden_channels, hidden_channels)) | |
| self.flows = nn.ModuleList() | |
| for i in range(n_flows): | |
| self.flows.append(modules.ResidualCouplingLayer_Transformer_simple(channels, hidden_channels, kernel_size, dilation_rate, n_layers, mean_only=True)) | |
| self.flows.append(modules.Flip()) | |
| def forward(self, x, x_mask, g=None, reverse=False): | |
| g = self.cond_block(g.squeeze(2)) | |
| if not reverse: | |
| for flow in self.flows: | |
| x, _ = flow(x, x_mask, g=g, reverse=reverse) | |
| else: | |
| for flow in reversed(self.flows): | |
| x = flow(x, x_mask, g=g, reverse=reverse) | |
| return x | |
| class PosteriorAudioEncoder(nn.Module): | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=0): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.gin_channels = gin_channels | |
| self.pre = nn.Conv1d(in_channels, hidden_channels, 1) | |
| self.down_pre = nn.Conv1d(1, 16, 7, 1, padding=3) | |
| self.resblocks = nn.ModuleList() | |
| downsample_rates = [8,5,4,2] | |
| downsample_kernel_sizes = [17, 10, 8, 4] | |
| ch = [16, 32, 64, 128, 192] | |
| resblock = AMPBlock1 | |
| resblock_kernel_sizes = [3,7,11] | |
| resblock_dilation_sizes = [[1,3,5], [1,3,5], [1,3,5]] | |
| self.num_kernels = 3 | |
| self.downs = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(downsample_rates, downsample_kernel_sizes)): | |
| self.downs.append(weight_norm( | |
| Conv1d(ch[i], ch[i+1], k, u, padding=(k-1)//2))) | |
| for i in range(4): | |
| for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): | |
| self.resblocks.append(resblock(ch[i+1], k, d, activation="snakebeta")) | |
| activation_post = activations.SnakeBeta(ch[i+1], alpha_logscale=True) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| self.conv_post = Conv1d(ch[i+1], hidden_channels, 7, 1, padding=3) | |
| self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) | |
| self.proj = nn.Conv1d(hidden_channels*2, out_channels * 2, 1) | |
| def forward(self, x, x_audio, x_mask, g=None): | |
| x_audio = self.down_pre(x_audio) | |
| for i in range(4): | |
| x_audio = self.downs[i](x_audio) | |
| xs = None | |
| for j in range(self.num_kernels): | |
| if xs is None: | |
| xs = self.resblocks[i*self.num_kernels+j](x_audio) | |
| else: | |
| xs += self.resblocks[i*self.num_kernels+j](x_audio) | |
| x_audio = xs / self.num_kernels | |
| x_audio = self.activation_post(x_audio) | |
| x_audio = self.conv_post(x_audio) | |
| x = self.pre(x) * x_mask | |
| x = self.enc(x, x_mask, g=g) | |
| x_audio = x_audio * x_mask | |
| x = torch.cat([x, x_audio], dim=1) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask | |
| return z, m, logs | |
| class PosteriorSFEncoder(nn.Module): | |
| def __init__(self, | |
| src_channels, | |
| out_channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=0): | |
| super().__init__() | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.gin_channels = gin_channels | |
| self.pre_source = nn.Conv1d(src_channels, hidden_channels, 1) | |
| self.pre_filter = nn.Conv1d(1, hidden_channels, kernel_size=9, stride=4, padding=4) | |
| self.source_enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels) | |
| self.filter_enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels) | |
| self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, x_src, x_ftr, x_mask, g=None): | |
| x_src = self.pre_source(x_src) * x_mask | |
| x_ftr = self.pre_filter(x_ftr) * x_mask | |
| x_src = self.source_enc(x_src, x_mask, g=g) | |
| x_ftr = self.filter_enc(x_ftr, x_mask, g=g) | |
| x = self.enc(x_src+x_ftr, x_mask, g=g) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask | |
| return z, m, logs | |
| class MelDecoder(nn.Module): | |
| def __init__(self, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| mel_size=20, | |
| gin_channels=0): | |
| super().__init__() | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.conv_pre = Conv1d(hidden_channels, hidden_channels, 3, 1, padding=1) | |
| self.encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout) | |
| self.proj= nn.Conv1d(hidden_channels, mel_size, 1, bias=False) | |
| if gin_channels != 0: | |
| self.cond = nn.Conv1d(gin_channels, hidden_channels, 1) | |
| def forward(self, x, x_mask, g=None): | |
| x = self.conv_pre(x*x_mask) | |
| if g is not None: | |
| x = x + self.cond(g) | |
| x = self.encoder(x * x_mask, x_mask) | |
| x = self.proj(x) * x_mask | |
| return x | |
| class SourceNetwork(nn.Module): | |
| def __init__(self, upsample_initial_channel=256): | |
| super().__init__() | |
| resblock_kernel_sizes = [3,5,7] | |
| upsample_rates = [2,2] | |
| initial_channel = 192 | |
| upsample_initial_channel = upsample_initial_channel | |
| upsample_kernel_sizes = [4,4] | |
| resblock_dilation_sizes = [[1,3,5], [1,3,5], [1,3,5]] | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)) | |
| resblock = AMPBlock1 | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
| self.ups.append(weight_norm( | |
| ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), | |
| k, u, padding=(k-u)//2))) | |
| self.resblocks = nn.ModuleList() | |
| for i in range(len(self.ups)): | |
| ch = upsample_initial_channel//(2**(i+1)) | |
| for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): | |
| self.resblocks.append(resblock(ch, k, d, activation="snakebeta")) | |
| activation_post = activations.SnakeBeta(ch, alpha_logscale=True) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
| self.cond = Conv1d(256, upsample_initial_channel, 1) | |
| self.ups.apply(init_weights) | |
| def forward(self, x, g): | |
| x = self.conv_pre(x) + self.cond(g) | |
| for i in range(self.num_upsamples): | |
| x = self.ups[i](x) | |
| xs = None | |
| for j in range(self.num_kernels): | |
| if xs is None: | |
| xs = self.resblocks[i*self.num_kernels+j](x) | |
| else: | |
| xs += self.resblocks[i*self.num_kernels+j](x) | |
| x = xs / self.num_kernels | |
| x = self.activation_post(x) | |
| ## Predictor | |
| x_ = self.conv_post(x) | |
| return x, x_ | |
| def remove_weight_norm(self): | |
| print('Removing weight norm...') | |
| for l in self.ups: | |
| remove_weight_norm(l) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| class DBlock(nn.Module): | |
| def __init__(self, input_size, hidden_size, factor): | |
| super().__init__() | |
| self.factor = factor | |
| self.residual_dense = weight_norm(Conv1d(input_size, hidden_size, 1)) | |
| self.conv = nn.ModuleList([ | |
| weight_norm(Conv1d(input_size, hidden_size, 3, dilation=1, padding=1)), | |
| weight_norm(Conv1d(hidden_size, hidden_size, 3, dilation=2, padding=2)), | |
| weight_norm(Conv1d(hidden_size, hidden_size, 3, dilation=4, padding=4)), | |
| ]) | |
| self.conv.apply(init_weights) | |
| def forward(self, x): | |
| size = x.shape[-1] // self.factor | |
| residual = self.residual_dense(x) | |
| residual = F.interpolate(residual, size=size) | |
| x = F.interpolate(x, size=size) | |
| for layer in self.conv: | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| x = layer(x) | |
| return x + residual | |
| def remove_weight_norm(self): | |
| for l in self.conv: | |
| remove_weight_norm(l) | |
| class AMPBlock1(torch.nn.Module): | |
| def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): | |
| super(AMPBlock1, self).__init__() | |
| self.convs1 = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], | |
| padding=get_padding(kernel_size, dilation[2]))) | |
| ]) | |
| self.convs1.apply(init_weights) | |
| self.convs2 = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))) | |
| ]) | |
| self.convs2.apply(init_weights) | |
| self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers | |
| self.activations = nn.ModuleList([ | |
| Activation1d( | |
| activation=activations.SnakeBeta(channels, alpha_logscale=True)) | |
| for _ in range(self.num_layers) | |
| ]) | |
| def forward(self, x): | |
| acts1, acts2 = self.activations[::2], self.activations[1::2] | |
| for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): | |
| xt = a1(x) | |
| xt = c1(xt) | |
| xt = a2(xt) | |
| xt = c2(xt) | |
| x = xt + x | |
| return x | |
| def remove_weight_norm(self): | |
| for l in self.convs1: | |
| remove_weight_norm(l) | |
| for l in self.convs2: | |
| remove_weight_norm(l) | |
| class Generator(torch.nn.Module): | |
| def __init__(self, initial_channel, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=256): | |
| super(Generator, self).__init__() | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)) | |
| resblock = AMPBlock1 | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
| self.ups.append(weight_norm( | |
| ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), | |
| k, u, padding=(k-u)//2))) | |
| self.resblocks = nn.ModuleList() | |
| for i in range(len(self.ups)): | |
| ch = upsample_initial_channel//(2**(i+1)) | |
| for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): | |
| self.resblocks.append(resblock(ch, k, d, activation="snakebeta")) | |
| activation_post = activations.SnakeBeta(ch, alpha_logscale=True) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
| self.ups.apply(init_weights) | |
| if gin_channels != 0: | |
| self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) | |
| self.downs = DBlock(upsample_initial_channel//8, upsample_initial_channel, 4) | |
| self.proj = Conv1d(upsample_initial_channel//8, upsample_initial_channel//2, 7, 1, padding=3) | |
| def forward(self, x, pitch, g=None): | |
| x = self.conv_pre(x) + self.downs(pitch) + self.cond(g) | |
| for i in range(self.num_upsamples): | |
| x = self.ups[i](x) | |
| if i == 0: | |
| pitch = self.proj(pitch) | |
| x = x + pitch | |
| xs = None | |
| for j in range(self.num_kernels): | |
| if xs is None: | |
| xs = self.resblocks[i*self.num_kernels+j](x) | |
| else: | |
| xs += self.resblocks[i*self.num_kernels+j](x) | |
| x = xs / self.num_kernels | |
| x = self.activation_post(x) | |
| x = self.conv_post(x) | |
| x = torch.tanh(x) | |
| return x | |
| def remove_weight_norm(self): | |
| print('Removing weight norm...') | |
| for l in self.ups: | |
| remove_weight_norm(l) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| for l in self.downs: | |
| l.remove_weight_norm() | |
| remove_weight_norm(self.conv_pre) | |
| class DiscriminatorP(torch.nn.Module): | |
| def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): | |
| super(DiscriminatorP, self).__init__() | |
| self.period = period | |
| self.use_spectral_norm = use_spectral_norm | |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
| self.convs = nn.ModuleList([ | |
| norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), | |
| ]) | |
| self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) | |
| def forward(self, x): | |
| fmap = [] | |
| # 1d to 2d | |
| b, c, t = x.shape | |
| if t % self.period != 0: # pad first | |
| n_pad = self.period - (t % self.period) | |
| x = F.pad(x, (0, n_pad), "reflect") | |
| t = t + n_pad | |
| x = x.view(b, c, t // self.period, self.period) | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| class DiscriminatorR(torch.nn.Module): | |
| def __init__(self, resolution, use_spectral_norm=False): | |
| super(DiscriminatorR, self).__init__() | |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
| n_fft, hop_length, win_length = resolution | |
| self.spec_transform = torchaudio.transforms.Spectrogram( | |
| n_fft=n_fft, hop_length=hop_length, win_length=win_length, window_fn=torch.hann_window, | |
| normalized=True, center=False, pad_mode=None, power=None) | |
| self.convs = nn.ModuleList([ | |
| norm_f(nn.Conv2d(2, 32, (3, 9), padding=(1, 4))), | |
| norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))), | |
| norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(2,1), padding=(2, 4))), | |
| norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(4,1), padding=(4, 4))), | |
| norm_f(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))), | |
| ]) | |
| self.conv_post = norm_f(nn.Conv2d(32, 1, (3, 3), padding=(1, 1))) | |
| def forward(self, y): | |
| fmap = [] | |
| x = self.spec_transform(y) # [B, 2, Freq, Frames, 2] | |
| x = torch.cat([x.real, x.imag], dim=1) | |
| x = rearrange(x, 'b c w t -> b c t w') | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| class MultiPeriodDiscriminator(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(MultiPeriodDiscriminator, self).__init__() | |
| periods = [2,3,5,7,11] | |
| resolutions = [[2048, 512, 2048], [1024, 256, 1024], [512, 128, 512], [256, 64, 256], [128, 32, 128]] | |
| discs = [DiscriminatorR(resolutions[i], use_spectral_norm=use_spectral_norm) for i in range(len(resolutions))] | |
| discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] | |
| self.discriminators = nn.ModuleList(discs) | |
| def forward(self, y, y_hat): | |
| y_d_rs = [] | |
| y_d_gs = [] | |
| fmap_rs = [] | |
| fmap_gs = [] | |
| for i, d in enumerate(self.discriminators): | |
| y_d_r, fmap_r = d(y) | |
| y_d_g, fmap_g = d(y_hat) | |
| y_d_rs.append(y_d_r) | |
| y_d_gs.append(y_d_g) | |
| fmap_rs.append(fmap_r) | |
| fmap_gs.append(fmap_g) | |
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
| class SynthesizerTrn(nn.Module): | |
| """ | |
| Synthesizer for Training | |
| """ | |
| def __init__(self, | |
| spec_channels, | |
| segment_size, | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| gin_channels=256, | |
| prosody_size=20, | |
| uncond_ratio=0., | |
| cfg=False, | |
| **kwargs): | |
| super().__init__() | |
| self.spec_channels = spec_channels | |
| self.inter_channels = inter_channels | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.resblock = resblock | |
| self.resblock_kernel_sizes = resblock_kernel_sizes | |
| self.resblock_dilation_sizes = resblock_dilation_sizes | |
| self.upsample_rates = upsample_rates | |
| self.upsample_initial_channel = upsample_initial_channel | |
| self.upsample_kernel_sizes = upsample_kernel_sizes | |
| self.segment_size = segment_size | |
| self.mel_size = prosody_size | |
| self.enc_p_l = PosteriorSFEncoder(1024, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
| self.flow_l = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels) | |
| self.enc_p = PosteriorSFEncoder(1024, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
| self.enc_q = PosteriorAudioEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
| self.flow = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels) | |
| self.mel_decoder = MelDecoder(inter_channels, | |
| filter_channels, | |
| n_heads=2, | |
| n_layers=2, | |
| kernel_size=5, | |
| p_dropout=0.1, | |
| mel_size=self.mel_size, | |
| gin_channels=gin_channels) | |
| self.dec = Generator(inter_channels, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) | |
| self.sn = SourceNetwork(upsample_initial_channel//2) | |
| self.emb_g = StyleEncoder(in_dim=80, hidden_dim=256, out_dim=gin_channels) | |
| if cfg: | |
| self.emb = torch.nn.Embedding(1, 256) | |
| torch.nn.init.normal_(self.emb.weight, 0.0, 256 ** -0.5) | |
| self.null = torch.LongTensor([0]).cuda() | |
| self.uncond_ratio = uncond_ratio | |
| self.cfg = cfg | |
| def infer(self, x_mel, w2v, length, f0): | |
| x_mask = torch.unsqueeze(commons.sequence_mask(length, x_mel.size(2)), 1).to(x_mel.dtype) | |
| # Speaker embedding from mel (Style Encoder) | |
| g = self.emb_g(x_mel, x_mask).unsqueeze(-1) | |
| z, _, _ = self.enc_p_l(w2v, f0, x_mask, g=g) | |
| z = self.flow_l(z, x_mask, g=g, reverse=True) | |
| z = self.flow(z, x_mask, g=g, reverse=True) | |
| e, e_ = self.sn(z, g) | |
| o = self.dec(z, e, g=g) | |
| return o, e_ | |
| def voice_conversion(self, src, src_length, trg_mel, trg_length, f0, noise_scale = 0.333, uncond=False): | |
| trg_mask = torch.unsqueeze(commons.sequence_mask(trg_length, trg_mel.size(2)), 1).to(trg_mel.dtype) | |
| g = self.emb_g(trg_mel, trg_mask).unsqueeze(-1) | |
| y_mask = torch.unsqueeze(commons.sequence_mask(src_length, src.size(2)), 1).to(trg_mel.dtype) | |
| z, m_p, logs_p = self.enc_p_l(src, f0, y_mask, g=g) | |
| z = (m_p + torch.randn_like(m_p) * torch.exp(logs_p)*noise_scale) * y_mask | |
| z = self.flow_l(z, y_mask, g=g, reverse=True) | |
| z = self.flow(z, y_mask, g=g, reverse=True) | |
| if uncond: | |
| null_emb = self.emb(self.null) * math.sqrt(256) | |
| g = null_emb.unsqueeze(-1) | |
| e, _ = self.sn(z, g) | |
| o = self.dec(z, e, g=g) | |
| return o | |
| def voice_conversion_noise_control(self, src, src_length, trg_mel, trg_length, f0, noise_scale = 0.333, uncond=False, denoise_ratio = 0): | |
| trg_mask = torch.unsqueeze(commons.sequence_mask(trg_length, trg_mel.size(2)), 1).to(trg_mel.dtype) | |
| g = self.emb_g(trg_mel, trg_mask).unsqueeze(-1) | |
| g_org, g_denoise = g[:1, :, :], g[1:, :, :] | |
| g_interpolation = (1-denoise_ratio)*g_org + denoise_ratio*g_denoise | |
| y_mask = torch.unsqueeze(commons.sequence_mask(src_length, src.size(2)), 1).to(trg_mel.dtype) | |
| z, m_p, logs_p = self.enc_p_l(src, f0, y_mask, g=g_interpolation) | |
| z = (m_p + torch.randn_like(m_p) * torch.exp(logs_p)*noise_scale) * y_mask | |
| z = self.flow_l(z, y_mask, g=g_interpolation, reverse=True) | |
| z = self.flow(z, y_mask, g=g_interpolation, reverse=True) | |
| if uncond: | |
| null_emb = self.emb(self.null) * math.sqrt(256) | |
| g = null_emb.unsqueeze(-1) | |
| e, _ = self.sn(z, g_interpolation) | |
| o = self.dec(z, e, g=g_interpolation) | |
| return o | |
| def f0_extraction(self, x_linear, x_mel, length, x_audio, noise_scale = 0.333): | |
| x_mask = torch.unsqueeze(commons.sequence_mask(length, x_mel.size(2)), 1).to(x_mel.dtype) | |
| # Speaker embedding from mel (Style Encoder) | |
| g = self.emb_g(x_mel, x_mask).unsqueeze(-1) | |
| # posterior encoder from linear spec. | |
| _, m_q, logs_q= self.enc_q(x_linear, x_audio, x_mask, g=g) | |
| z = (m_q + torch.randn_like(m_q) * torch.exp(logs_q)*noise_scale) | |
| # Source Networks | |
| _, e_ = self.sn(z, g) | |
| return e_ | |