import math import torch from torch import nn from infer_pack.models import ( # type:ignore GeneratorNSF, PosteriorEncoder, ResidualCouplingBlock, Generator, ) from infer_pack import commons, attentions # type:ignore class TextEncoder(nn.Module): def __init__( self, out_channels, hidden_channels, filter_channels, emb_channels, n_heads, n_layers, kernel_size, p_dropout, f0=True, ): super().__init__() self.out_channels = out_channels self.hidden_channels = hidden_channels self.filter_channels = filter_channels self.emb_channels = emb_channels self.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout self.emb_phone = nn.Linear(emb_channels, hidden_channels) self.lrelu = nn.LeakyReLU(0.1, inplace=True) if f0 is True: self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 self.encoder = 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, phone, pitch, lengths): if pitch is None: x = self.emb_phone(phone) else: x = self.emb_phone(phone) + self.emb_pitch(pitch) x = x * math.sqrt(self.hidden_channels) # [b, t, h] x = self.lrelu(x) x = torch.transpose(x, 1, -1) # [b, h, t] x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( x.dtype ) x = self.encoder(x * x_mask, x_mask) stats = self.proj(x) * x_mask m, logs = torch.split(stats, self.out_channels, dim=1) return m, logs, x_mask class SynthesizerTrnMsNSFsid(nn.Module): 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, spk_embed_dim, gin_channels, emb_channels, sr, **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.emb_channels = emb_channels # self.hop_length = hop_length# self.spk_embed_dim = spk_embed_dim self.enc_p = TextEncoder( inter_channels, hidden_channels, filter_channels, emb_channels, n_heads, n_layers, kernel_size, p_dropout, ) self.dec = GeneratorNSF( inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels, sr=sr, is_half=kwargs["is_half"], ) 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, 3, gin_channels=gin_channels ) self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) def remove_weight_norm(self): self.dec.remove_weight_norm() self.flow.remove_weight_norm() self.enc_q.remove_weight_norm() def forward( self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds ): # 这里ds是id，[bs,1] # print(1,pitch.shape)#[bs,t] g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) z_p = self.flow(z, y_mask, g=g) z_slice, ids_slice = commons.rand_slice_segments( z, y_lengths, self.segment_size ) # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) # print(-2,pitchf.shape,z_slice.shape) o = self.dec(z_slice, pitchf, g=g) return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None): g = self.emb_g(sid).unsqueeze(-1) m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask z = self.flow(z_p, x_mask, g=g, reverse=True) o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) return o, x_mask, (z, z_p, m_p, logs_p) class SynthesizerTrnMsNSFsidNono(nn.Module): 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, spk_embed_dim, gin_channels, emb_channels, sr=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.gin_channels = gin_channels self.emb_channels = emb_channels # self.hop_length = hop_length# self.spk_embed_dim = spk_embed_dim self.enc_p = TextEncoder( inter_channels, hidden_channels, filter_channels, emb_channels, n_heads, n_layers, kernel_size, p_dropout, f0=False, ) 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, 3, gin_channels=gin_channels ) self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) def remove_weight_norm(self): self.dec.remove_weight_norm() self.flow.remove_weight_norm() self.enc_q.remove_weight_norm() def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1] g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) z_p = self.flow(z, y_mask, g=g) z_slice, ids_slice = commons.rand_slice_segments( z, y_lengths, self.segment_size ) o = self.dec(z_slice, g=g) return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) def infer(self, phone, phone_lengths, sid, max_len=None): g = self.emb_g(sid).unsqueeze(-1) m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask z = self.flow(z_p, x_mask, g=g, reverse=True) o = self.dec((z * x_mask)[:, :, :max_len], g=g) return o, x_mask, (z, z_p, m_p, logs_p)