import torch from torch import nn from torch.nn import functional as F import modules.attentions as attentions import modules.modules as modules from utils import f0_to_coarse 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 TextEncoder(nn.Module): def __init__(self, out_channels, hidden_channels, kernel_size, n_layers, gin_channels=0, filter_channels=None, n_heads=None, p_dropout=None): super().__init__() self.out_channels = out_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.n_layers = n_layers self.gin_channels = gin_channels self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) self.f0_emb = nn.Embedding(256, hidden_channels) self.enc_ = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout) def forward(self, x, x_mask, f0=None, z=None): x = x + self.f0_emb(f0).transpose(1, 2) x = self.enc_(x * x_mask, x_mask) stats = self.proj(x) * x_mask m, logs = torch.split(stats, self.out_channels, dim=1) z = (m + z * torch.exp(logs)) * x_mask return z, m, logs, x_mask class F0Decoder(nn.Module): def __init__(self, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, spk_channels=0): 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.spk_channels = spk_channels self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1) self.decoder = attentions.FFT( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout) self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1) self.cond = nn.Conv1d(spk_channels, hidden_channels, 1) def forward(self, x, norm_f0, x_mask, spk_emb=None): x = torch.detach(x) if (spk_emb is not None): x = x + self.cond(spk_emb) x += self.f0_prenet(norm_f0) x = self.prenet(x) * x_mask x = self.decoder(x * x_mask, x_mask) x = self.proj(x) * x_mask 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, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels, ssl_dim, n_speakers, sampling_rate=44100, vol_embedding=False, vocoder_name = "nsf-hifigan", **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.vol_embedding = vol_embedding self.emb_g = nn.Embedding(n_speakers, gin_channels) if vol_embedding: self.emb_vol = nn.Linear(1, hidden_channels) self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2) self.enc_p = TextEncoder( inter_channels, hidden_channels, filter_channels=filter_channels, n_heads=n_heads, n_layers=n_layers, kernel_size=kernel_size, p_dropout=p_dropout ) hps = { "sampling_rate": sampling_rate, "inter_channels": inter_channels, "resblock": resblock, "resblock_kernel_sizes": resblock_kernel_sizes, "resblock_dilation_sizes": resblock_dilation_sizes, "upsample_rates": upsample_rates, "upsample_initial_channel": upsample_initial_channel, "upsample_kernel_sizes": upsample_kernel_sizes, "gin_channels": gin_channels, } if vocoder_name == "nsf-hifigan": from vdecoder.hifigan.models import Generator self.dec = Generator(h=hps) elif vocoder_name == "nsf-snake-hifigan": from vdecoder.hifiganwithsnake.models import Generator self.dec = Generator(h=hps) else: print("[?] Unkown vocoder: use default(nsf-hifigan)") from vdecoder.hifigan.models import Generator self.dec = Generator(h=hps) self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) self.f0_decoder = F0Decoder( 1, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, spk_channels=gin_channels ) self.emb_uv = nn.Embedding(2, hidden_channels) self.predict_f0 = False self.speaker_map = [] self.export_mix = False def export_chara_mix(self, speakers_mix): self.speaker_map = torch.zeros((len(speakers_mix), 1, 1, self.gin_channels)) i = 0 for key in speakers_mix.keys(): spkidx = speakers_mix[key] self.speaker_map[i] = self.emb_g(torch.LongTensor([[spkidx]])) i = i + 1 self.speaker_map = self.speaker_map.unsqueeze(0) self.export_mix = True def forward(self, c, f0, mel2ph, uv, noise=None, g=None, vol = None): decoder_inp = F.pad(c, [0, 0, 1, 0]) mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]]) c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H] if self.export_mix: # [N, S] * [S, B, 1, H] g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1] g = g * self.speaker_map # [N, S, B, 1, H] g = torch.sum(g, dim=1) # [N, 1, B, 1, H] g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N] else: if g.dim() == 1: g = g.unsqueeze(0) g = self.emb_g(g).transpose(1, 2) x_mask = torch.unsqueeze(torch.ones_like(f0), 1).to(c.dtype) # vol proj vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol is not None and self.vol_embedding else 0 x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2) + vol z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise) z = self.flow(z_p, c_mask, g=g, reverse=True) o = self.dec(z * c_mask, g=g, f0=f0) return o