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| import copy | |
| import math | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| import commons | |
| import modules | |
| from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d | |
| from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
| from commons import init_weights, get_padding | |
| class ResidualCouplingBlock(nn.Module): | |
| def __init__(self, | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| 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.flows = nn.ModuleList() | |
| for i in range(n_flows): | |
| self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) | |
| self.flows.append(modules.Flip()) | |
| def forward(self, x, x_mask, g=None, reverse=False): | |
| 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 Encoder(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.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, x, x_lengths, g=None): | |
| x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) | |
| x = self.pre(x) * x_mask | |
| x = self.enc(x, 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, x_mask | |
| class Generator(torch.nn.Module): | |
| def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): | |
| super(Generator, self).__init__() | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) | |
| resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 | |
| 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)) | |
| 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) | |
| def forward(self, x, g=None): | |
| x = self.conv_pre(x) | |
| if g is not None: | |
| x = x + self.cond(g) | |
| for i in range(self.num_upsamples): | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| 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 = F.leaky_relu(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() | |
| 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 DiscriminatorS(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(DiscriminatorS, self).__init__() | |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
| self.convs = nn.ModuleList([ | |
| norm_f(Conv1d(1, 16, 15, 1, padding=7)), | |
| norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), | |
| norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), | |
| norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), | |
| norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), | |
| norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), | |
| ]) | |
| self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) | |
| def forward(self, x): | |
| fmap = [] | |
| 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] | |
| discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] | |
| 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 SpeakerEncoder(torch.nn.Module): | |
| def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256): | |
| super(SpeakerEncoder, self).__init__() | |
| self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) | |
| self.linear = nn.Linear(model_hidden_size, model_embedding_size) | |
| self.relu = nn.ReLU() | |
| def forward(self, mels): | |
| self.lstm.flatten_parameters() | |
| _, (hidden, _) = self.lstm(mels) | |
| embeds_raw = self.relu(self.linear(hidden[-1])) | |
| return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) | |
| def compute_partial_slices(self, total_frames, partial_frames, partial_hop): | |
| mel_slices = [] | |
| for i in range(0, total_frames-partial_frames, partial_hop): | |
| mel_range = torch.arange(i, i+partial_frames) | |
| mel_slices.append(mel_range) | |
| return mel_slices | |
| def embed_utterance(self, mel, partial_frames=128, partial_hop=64): | |
| mel_len = mel.size(1) | |
| last_mel = mel[:,-partial_frames:] | |
| if mel_len > partial_frames: | |
| mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop) | |
| mels = list(mel[:,s] for s in mel_slices) | |
| mels.append(last_mel) | |
| mels = torch.stack(tuple(mels), 0).squeeze(1) | |
| with torch.no_grad(): | |
| partial_embeds = self(mels) | |
| embed = torch.mean(partial_embeds, axis=0).unsqueeze(0) | |
| #embed = embed / torch.linalg.norm(embed, 2) | |
| else: | |
| with torch.no_grad(): | |
| embed = self(last_mel) | |
| return embed | |
| 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, | |
| ssl_dim, | |
| use_spk, | |
| **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.gin_channels = gin_channels | |
| self.ssl_dim = ssl_dim | |
| self.use_spk = use_spk | |
| self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16) | |
| self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) | |
| self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
| self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) | |
| if not self.use_spk: | |
| self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels) | |
| def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None): | |
| if c_lengths == None: | |
| c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) | |
| if spec_lengths == None: | |
| spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device) | |
| if not self.use_spk: | |
| g = self.enc_spk(mel.transpose(1,2)) | |
| g = g.unsqueeze(-1) | |
| _, m_p, logs_p, _ = self.enc_p(c, c_lengths) | |
| z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) | |
| z_p = self.flow(z, spec_mask, g=g) | |
| z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size) | |
| o = self.dec(z_slice, g=g) | |
| return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q) | |
| def infer(self, c, g=None, mel=None, c_lengths=None): | |
| if c_lengths == None: | |
| c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) | |
| if not self.use_spk: | |
| g = self.enc_spk.embed_utterance(mel.transpose(1,2)) | |
| g = g.unsqueeze(-1) | |
| z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths) | |
| z = self.flow(z_p, c_mask, g=g, reverse=True) | |
| o = self.dec(z * c_mask, g=g) | |
| return o | |