Spaces:
Sleeping
Sleeping
| import copy | |
| import math | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from module import commons | |
| from module import modules | |
| from module import attentions | |
| from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d | |
| from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
| from module.commons import init_weights, get_padding | |
| from module.mrte_model import MRTE | |
| from module.quantize import ResidualVectorQuantizer | |
| from text import symbols | |
| from torch.cuda.amp import autocast | |
| class StochasticDurationPredictor(nn.Module): | |
| def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): | |
| super().__init__() | |
| filter_channels = in_channels # it needs to be removed from future version. | |
| self.in_channels = in_channels | |
| self.filter_channels = filter_channels | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.n_flows = n_flows | |
| self.gin_channels = gin_channels | |
| self.log_flow = modules.Log() | |
| self.flows = nn.ModuleList() | |
| self.flows.append(modules.ElementwiseAffine(2)) | |
| for i in range(n_flows): | |
| self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) | |
| self.flows.append(modules.Flip()) | |
| self.post_pre = nn.Conv1d(1, filter_channels, 1) | |
| self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) | |
| self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) | |
| self.post_flows = nn.ModuleList() | |
| self.post_flows.append(modules.ElementwiseAffine(2)) | |
| for i in range(4): | |
| self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) | |
| self.post_flows.append(modules.Flip()) | |
| self.pre = nn.Conv1d(in_channels, filter_channels, 1) | |
| self.proj = nn.Conv1d(filter_channels, filter_channels, 1) | |
| self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) | |
| if gin_channels != 0: | |
| self.cond = nn.Conv1d(gin_channels, filter_channels, 1) | |
| def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): | |
| x = torch.detach(x) | |
| x = self.pre(x) | |
| if g is not None: | |
| g = torch.detach(g) | |
| x = x + self.cond(g) | |
| x = self.convs(x, x_mask) | |
| x = self.proj(x) * x_mask | |
| if not reverse: | |
| flows = self.flows | |
| assert w is not None | |
| logdet_tot_q = 0 | |
| h_w = self.post_pre(w) | |
| h_w = self.post_convs(h_w, x_mask) | |
| h_w = self.post_proj(h_w) * x_mask | |
| e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask | |
| z_q = e_q | |
| for flow in self.post_flows: | |
| z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) | |
| logdet_tot_q += logdet_q | |
| z_u, z1 = torch.split(z_q, [1, 1], 1) | |
| u = torch.sigmoid(z_u) * x_mask | |
| z0 = (w - u) * x_mask | |
| logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]) | |
| logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q | |
| logdet_tot = 0 | |
| z0, logdet = self.log_flow(z0, x_mask) | |
| logdet_tot += logdet | |
| z = torch.cat([z0, z1], 1) | |
| for flow in flows: | |
| z, logdet = flow(z, x_mask, g=x, reverse=reverse) | |
| logdet_tot = logdet_tot + logdet | |
| nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot | |
| return nll + logq # [b] | |
| else: | |
| flows = list(reversed(self.flows)) | |
| flows = flows[:-2] + [flows[-1]] # remove a useless vflow | |
| z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale | |
| for flow in flows: | |
| z = flow(z, x_mask, g=x, reverse=reverse) | |
| z0, z1 = torch.split(z, [1, 1], 1) | |
| logw = z0 | |
| return logw | |
| class DurationPredictor(nn.Module): | |
| def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.filter_channels = filter_channels | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.gin_channels = gin_channels | |
| self.drop = nn.Dropout(p_dropout) | |
| self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) | |
| self.norm_1 = modules.LayerNorm(filter_channels) | |
| self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) | |
| self.norm_2 = modules.LayerNorm(filter_channels) | |
| self.proj = nn.Conv1d(filter_channels, 1, 1) | |
| if gin_channels != 0: | |
| self.cond = nn.Conv1d(gin_channels, in_channels, 1) | |
| def forward(self, x, x_mask, g=None): | |
| x = torch.detach(x) | |
| if g is not None: | |
| g = torch.detach(g) | |
| x = x + self.cond(g) | |
| x = self.conv_1(x * x_mask) | |
| x = torch.relu(x) | |
| x = self.norm_1(x) | |
| x = self.drop(x) | |
| x = self.conv_2(x * x_mask) | |
| x = torch.relu(x) | |
| x = self.norm_2(x) | |
| x = self.drop(x) | |
| x = self.proj(x * x_mask) | |
| return x * x_mask | |
| class TextEncoder(nn.Module): | |
| def __init__(self, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| latent_channels=192): | |
| super().__init__() | |
| self.out_channels = out_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.latent_channels = latent_channels | |
| self.ssl_proj = nn.Conv1d(768, hidden_channels, 1) | |
| self.encoder_ssl = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers//2, | |
| kernel_size, | |
| p_dropout) | |
| self.encoder_text = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout) | |
| self.text_embedding = nn.Embedding(len(symbols), hidden_channels) | |
| self.mrte = MRTE() | |
| self.encoder2 = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers//2, | |
| kernel_size, | |
| p_dropout) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, y, y_lengths, text, text_lengths, ge, test=None): | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype) | |
| y = self.ssl_proj(y * y_mask) * y_mask | |
| y = self.encoder_ssl(y * y_mask, y_mask) | |
| text_mask = torch.unsqueeze(commons.sequence_mask(text_lengths, text.size(1)), 1).to(y.dtype) | |
| if test == 1 : | |
| text[:, :] = 0 | |
| text = self.text_embedding(text).transpose(1, 2) | |
| text = self.encoder_text(text * text_mask, text_mask) | |
| y = self.mrte(y, y_mask, text, text_mask, ge) | |
| y = self.encoder2(y * y_mask, y_mask) | |
| stats = self.proj(y) * y_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| return y, m, logs, y_mask | |
| def extract_latent(self, x): | |
| x = self.ssl_proj(x) | |
| quantized, codes, commit_loss, quantized_list = self.quantizer(x) | |
| return codes.transpose(0,1) | |
| def decode_latent(self, codes, y_mask, refer,refer_mask, ge): | |
| quantized = self.quantizer.decode(codes) | |
| y = self.vq_proj(quantized) * y_mask | |
| y = self.encoder_ssl(y * y_mask, y_mask) | |
| y = self.mrte(y, y_mask, refer, refer_mask, ge) | |
| y = self.encoder2(y * y_mask, y_mask) | |
| stats = self.proj(y) * y_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| return y, m, logs, y_mask, quantized | |
| 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 PosteriorEncoder(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): | |
| if(g!=None): | |
| g = g.detach() | |
| 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 WNEncoder(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, 1) | |
| self.norm = modules.LayerNorm(out_channels) | |
| 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) | |
| out = self.proj(x) * x_mask | |
| out = self.norm(out) | |
| return out | |
| 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 ReferenceEncoder(nn.Module): | |
| ''' | |
| inputs --- [N, Ty/r, n_mels*r] mels | |
| outputs --- [N, ref_enc_gru_size] | |
| ''' | |
| def __init__(self, spec_channels, gin_channels=0): | |
| super().__init__() | |
| self.spec_channels = spec_channels | |
| ref_enc_filters = [32, 32, 64, 64, 128, 128] | |
| K = len(ref_enc_filters) | |
| filters = [1] + ref_enc_filters | |
| convs = [weight_norm(nn.Conv2d(in_channels=filters[i], | |
| out_channels=filters[i + 1], | |
| kernel_size=(3, 3), | |
| stride=(2, 2), | |
| padding=(1, 1))) for i in range(K)] | |
| self.convs = nn.ModuleList(convs) | |
| # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) | |
| out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) | |
| self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels, | |
| hidden_size=256 // 2, | |
| batch_first=True) | |
| self.proj = nn.Linear(128, gin_channels) | |
| def forward(self, inputs): | |
| N = inputs.size(0) | |
| out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs] | |
| for conv in self.convs: | |
| out = conv(out) | |
| # out = wn(out) | |
| out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K] | |
| out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K] | |
| T = out.size(1) | |
| N = out.size(0) | |
| out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K] | |
| self.gru.flatten_parameters() | |
| memory, out = self.gru(out) # out --- [1, N, 128] | |
| return self.proj(out.squeeze(0)).unsqueeze(-1) | |
| def calculate_channels(self, L, kernel_size, stride, pad, n_convs): | |
| for i in range(n_convs): | |
| L = (L - kernel_size + 2 * pad) // stride + 1 | |
| return L | |
| class Quantizer_module(torch.nn.Module): | |
| def __init__(self, n_e, e_dim): | |
| super(Quantizer_module, self).__init__() | |
| self.embedding = nn.Embedding(n_e, e_dim) | |
| self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e) | |
| def forward(self, x): | |
| d = torch.sum(x ** 2, 1, keepdim=True) + torch.sum(self.embedding.weight ** 2, 1) - 2 * torch.matmul(x, self.embedding.weight.T) | |
| min_indicies = torch.argmin(d, 1) | |
| z_q = self.embedding(min_indicies) | |
| return z_q, min_indicies | |
| class Quantizer(torch.nn.Module): | |
| def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160): | |
| super(Quantizer, self).__init__() | |
| assert embed_dim % n_code_groups == 0 | |
| self.quantizer_modules = nn.ModuleList([ | |
| Quantizer_module(n_codes, embed_dim // n_code_groups) for _ in range(n_code_groups) | |
| ]) | |
| self.n_code_groups = n_code_groups | |
| self.embed_dim = embed_dim | |
| def forward(self, xin): | |
| #B, C, T | |
| B, C, T = xin.shape | |
| xin = xin.transpose(1, 2) | |
| x = xin.reshape(-1, self.embed_dim) | |
| x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1) | |
| min_indicies = [] | |
| z_q = [] | |
| for _x, m in zip(x, self.quantizer_modules): | |
| _z_q, _min_indicies = m(_x) | |
| z_q.append(_z_q) | |
| min_indicies.append(_min_indicies) #B * T, | |
| z_q = torch.cat(z_q, -1).reshape(xin.shape) | |
| loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean((z_q - xin.detach()) ** 2) | |
| z_q = xin + (z_q - xin).detach() | |
| z_q = z_q.transpose(1, 2) | |
| codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups) | |
| return z_q, loss, codes.transpose(1, 2) | |
| def embed(self, x): | |
| #idx: N, 4, T | |
| x=x.transpose(1, 2) | |
| x = torch.split(x, 1, 2) | |
| ret = [] | |
| for q, embed in zip(x, self.quantizer_modules): | |
| q = embed.embedding(q.squeeze(-1)) | |
| ret.append(q) | |
| ret = torch.cat(ret, -1) | |
| return ret.transpose(1, 2) #N, C, T | |
| class CodePredictor(nn.Module): | |
| def __init__(self, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| n_q=8, | |
| dims=1024, | |
| ssl_dim=768 | |
| ): | |
| 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.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1) | |
| self.ref_enc = modules.MelStyleEncoder(ssl_dim, style_vector_dim=hidden_channels) | |
| self.encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout) | |
| self.out_proj = nn.Conv1d(hidden_channels, (n_q-1) * dims, 1) | |
| self.n_q = n_q | |
| self.dims = dims | |
| def forward(self, x, x_mask, refer, codes, infer=False): | |
| x = x.detach() | |
| x = self.vq_proj(x * x_mask) * x_mask | |
| g = self.ref_enc(refer, x_mask) | |
| x = x + g | |
| x = self.encoder(x * x_mask, x_mask) | |
| x = self.out_proj(x * x_mask) * x_mask | |
| logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose(2, 3) | |
| target = codes[1:].transpose(0, 1) | |
| if not infer: | |
| logits = logits.reshape(-1, self.dims) | |
| target = target.reshape(-1) | |
| loss = torch.nn.functional.cross_entropy(logits, target) | |
| return loss | |
| else: | |
| _, top10_preds = torch.topk(logits, 10, dim=-1) | |
| correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1) | |
| top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item() | |
| print('Top-10 Accuracy:', top3_acc, "%") | |
| pred_codes = torch.argmax(logits, dim=-1) | |
| acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item() | |
| print('Top-1 Accuracy:', acc, "%") | |
| return pred_codes.transpose(0, 1) | |
| 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, | |
| n_speakers=0, | |
| gin_channels=0, | |
| use_sdp=True, | |
| semantic_frame_rate=None, | |
| freeze_quantizer=None, | |
| **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.n_speakers = n_speakers | |
| self.gin_channels = gin_channels | |
| self.use_sdp = use_sdp | |
| self.enc_p = TextEncoder( | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout) | |
| 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 = PosteriorEncoder(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) | |
| self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels) | |
| ssl_dim = 768 | |
| assert semantic_frame_rate in ['25hz', "50hz"] | |
| self.semantic_frame_rate = semantic_frame_rate | |
| if semantic_frame_rate == '25hz': | |
| self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2) | |
| else: | |
| self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1) | |
| self.quantizer = ResidualVectorQuantizer( | |
| dimension=ssl_dim, | |
| n_q=1, | |
| bins=1024 | |
| ) | |
| if freeze_quantizer: | |
| self.ssl_proj.requires_grad_(False) | |
| self.quantizer.requires_grad_(False) | |
| # self.enc_p.text_embedding.requires_grad_(False) | |
| # self.enc_p.encoder_text.requires_grad_(False) | |
| # self.enc_p.mrte.requires_grad_(False) | |
| def forward(self, ssl, y, y_lengths, text, text_lengths): | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype) | |
| ge = self.ref_enc(y * y_mask, y_mask) | |
| with autocast(enabled=False): | |
| ssl = self.ssl_proj(ssl) | |
| quantized, codes, commit_loss, quantized_list = self.quantizer(ssl, layers=[0]) | |
| if self.semantic_frame_rate == '25hz': | |
| quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest") | |
| x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge) | |
| z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge) | |
| z_p = self.flow(z, y_mask, g=ge) | |
| z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) | |
| o = self.dec(z_slice, g=ge) | |
| return o, commit_loss, ids_slice, y_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), quantized | |
| def infer(self, ssl, y, y_lengths, text, text_lengths, test=None, noise_scale=0.5): | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype) | |
| ge = self.ref_enc(y * y_mask, y_mask) | |
| ssl = self.ssl_proj(ssl) | |
| quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0]) | |
| if self.semantic_frame_rate == '25hz': | |
| quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest") | |
| x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, test=test) | |
| z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale | |
| z = self.flow(z_p, y_mask, g=ge, reverse=True) | |
| o = self.dec((z * y_mask)[:, :, :], g=ge) | |
| return o,y_mask, (z, z_p, m_p, logs_p) | |
| def decode(self, codes,text, refer, noise_scale=0.5): | |
| refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device) | |
| refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype) | |
| ge = self.ref_enc(refer * refer_mask, refer_mask) | |
| y_lengths = torch.LongTensor([codes.size(2)*2]).to(codes.device) | |
| text_lengths = torch.LongTensor([text.size(-1)]).to(text.device) | |
| quantized = self.quantizer.decode(codes) | |
| if self.semantic_frame_rate == '25hz': | |
| quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest") | |
| x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge) | |
| z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale | |
| z = self.flow(z_p, y_mask, g=ge, reverse=True) | |
| o = self.dec((z * y_mask)[:, :, :], g=ge) | |
| return o | |
| def extract_latent(self, x): | |
| ssl = self.ssl_proj(x) | |
| quantized, codes, commit_loss, quantized_list = self.quantizer(ssl) | |
| return codes.transpose(0,1) | |