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 @torch.no_grad() 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) @torch.no_grad() 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 @torch.no_grad() 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