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T4
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from ttv_v1 import modules | |
| import attentions | |
| from torch.nn import Conv1d, ConvTranspose1d | |
| from torch.nn.utils import weight_norm, remove_weight_norm | |
| from commons import init_weights | |
| import typing as tp | |
| import transformers | |
| import math | |
| from ttv_v1.styleencoder import StyleEncoder | |
| import commons | |
| from ttv_v1.modules import WN | |
| def get_2d_padding(kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1)): | |
| return (((kernel_size[0] - 1) * dilation[0]) // 2, ((kernel_size[1] - 1) * dilation[1]) // 2) | |
| class Wav2vec2(torch.nn.Module): | |
| def __init__(self, layer=7): | |
| """we use the intermediate features of xls-r-300m. | |
| More specifically, we used the output from the 12th layer of the 24-layer transformer encoder. | |
| """ | |
| super().__init__() | |
| # self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-xls-r-300m") | |
| self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/mms-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] | |
| y = y.permute((0, 2, 1)) | |
| return y | |
| class TextEncoder(nn.Module): | |
| def __init__(self, | |
| n_vocab, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout): | |
| super().__init__() | |
| self.n_vocab = n_vocab | |
| 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.emb = nn.Embedding(n_vocab, hidden_channels) | |
| nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) | |
| self.cond = nn.Conv1d(256, hidden_channels, 1) | |
| self.encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout) | |
| self.encoder2 = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout) | |
| self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, x, x_lengths, g): | |
| x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] | |
| x = torch.transpose(x, 1, -1) # [b, h, t] | |
| x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) | |
| x = self.encoder(x * x_mask, x_mask) | |
| x = x + self.cond(g) | |
| x = self.encoder2(x * x_mask, x_mask) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| return x, m, logs, x_mask | |
| 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, gin_channels=gin_channels, mean_only=True, attention_head=4)) | |
| 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 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_mask, g=None): | |
| 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 | |
| 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 W2VDecoder(nn.Module): | |
| def __init__(self, | |
| in_channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| output_size=1024, | |
| gin_channels=0, | |
| p_dropout=0): | |
| super().__init__() | |
| self.in_channels = in_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.p_dropout = p_dropout | |
| self.pre = nn.Conv1d(in_channels, hidden_channels, 1) | |
| self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, p_dropout=p_dropout) | |
| self.proj = nn.Conv1d(hidden_channels, output_size, 1) | |
| def forward(self, x, x_mask, g=None): | |
| x = self.pre(x * x_mask) * x_mask | |
| x = self.enc(x, x_mask, g=g) | |
| x = self.proj(x) * x_mask | |
| return x | |
| class PitchPredictor(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| resblock_kernel_sizes = [3,5,7] | |
| upsample_rates = [2,2] | |
| initial_channel = 1024 | |
| upsample_initial_channel = 256 | |
| 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 = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) | |
| resblock = modules.ResBlock1 | |
| 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) | |
| self.cond = Conv1d(256, upsample_initial_channel, 1) | |
| def forward(self, x, g): | |
| x = self.conv_pre(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) | |
| ## Predictor | |
| x = self.conv_post(x) | |
| return x | |
| 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, | |
| gin_channels=256, | |
| prosody_size=20, | |
| 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.segment_size = segment_size | |
| self.mel_size = prosody_size | |
| self.enc_q = PosteriorEncoder(1024, inter_channels, hidden_channels, 5, 1, 16, gin_channels=256) | |
| self.enc_p = TextEncoder(178, out_channels=inter_channels, hidden_channels=inter_channels, filter_channels=inter_channels*4, | |
| n_heads=4, n_layers=3, kernel_size=9, p_dropout=0.2) | |
| self.flow = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=256) | |
| self.w2v_decoder = W2VDecoder(inter_channels, inter_channels*2, 5, 1, 8, output_size=1024, p_dropout=0.1, gin_channels=256) | |
| self.emb_g = StyleEncoder(in_dim=80, hidden_dim=256, out_dim=256) | |
| self.dp = StochasticDurationPredictor(inter_channels, inter_channels, 3, 0.5, 4, gin_channels=256) | |
| self.pp = PitchPredictor() | |
| self.phoneme_classifier = Conv1d(inter_channels, 178, 1, bias=False) | |
| def infer(self, x, x_lengths, y_mel, y_length, noise_scale=1, noise_scale_w=1, length_scale=1): | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_length, y_mel.size(2)), 1).to(y_mel.dtype) | |
| # Speaker embedding from mel (Style Encoder) | |
| g = self.emb_g(y_mel, y_mask).unsqueeze(-1) | |
| x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g) | |
| logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) | |
| w = torch.exp(logw) * x_mask * length_scale | |
| w_ceil = torch.ceil(w) | |
| y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) | |
| attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) | |
| attn = commons.generate_path(w_ceil, attn_mask) | |
| m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] | |
| logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] | |
| z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale | |
| z = self.flow(z_p, y_mask, g=g, reverse=True) | |
| w2v = self.w2v_decoder(z, y_mask, g=g) | |
| pitch = self.pp(w2v, g) | |
| return w2v, pitch | |
| def infer_noise_control(self, x, x_lengths, y_mel, y_length, noise_scale=0.333, noise_scale_w=1, length_scale=1, denoise_ratio = 0): | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_length, y_mel.size(2)), 1).to(y_mel.dtype) | |
| # Speaker embedding from mel (Style Encoder) | |
| g = self.emb_g(y_mel, y_mask).unsqueeze(-1) | |
| g_org, g_denoise = g[:1, :, :], g[1:, :, :] | |
| g = (1-denoise_ratio)*g_org + denoise_ratio*g_denoise | |
| x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g) | |
| logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) | |
| w = torch.exp(logw) * x_mask * length_scale | |
| w_ceil = torch.ceil(w) | |
| y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) | |
| attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) | |
| attn = commons.generate_path(w_ceil, attn_mask) | |
| m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] | |
| logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] | |
| z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale | |
| z = self.flow(z_p, y_mask, g=g, reverse=True) | |
| w2v = self.w2v_decoder(z, y_mask, g=g) | |
| pitch = self.pp(w2v, g) | |
| return w2v, pitch | |