import math import torch import numpy as np from torch import nn from torch.nn import functional as F from torch.nn import Conv1d, ConvTranspose1d, Conv2d from lib.infer_pack import modules, attentions, commons from torch.nn.utils import weight_norm, remove_weight_norm from lib.infer_pack.commons import init_weights, get_padding from lib.infer_pack.commons import init_weights, sequence_mask from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from lib.infer_pack.modules import ResidualCouplingLayer, WN, ResBlock1, ResBlock2, LRELU_SLOPE class TextEncoder(nn.Module): def __init__( self, input_dim, out_channels, hidden_channels, filter_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.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout self.emb_phone = nn.Linear(input_dim, hidden_channels) self.lrelu = nn.LeakyReLU(0.1, inplace=True) if f0:self.emb_pitch = nn.Embedding(256, hidden_channels) 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): x = self.emb_phone(phone) + self.emb_pitch(pitch) if pitch is not None else self.emb_phone(phone) x *= math.sqrt(self.hidden_channels) x = self.lrelu(x) x = torch.transpose(x, 1, -1) x_mask = torch.unsqueeze(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 TextEncoder768(nn.Module): def __init__( self, out_channels, hidden_channels, filter_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.n_heads = n_heads self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout self.emb_phone = nn.Linear(768, hidden_channels) self.lrelu = nn.LeakyReLU(0.1, inplace=True) if f0 == True:self.emb_pitch = nn.Embedding(256, hidden_channels) 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) x = self.lrelu(x) x = torch.transpose(x, 1, -1) 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 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 _ 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 def remove_weight_norm(self): for i in range(self.n_flows): self.flows[i * 2].remove_weight_norm() 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): 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 def remove_weight_norm(self): self.enc.remove_weight_norm() 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 k, d in 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): for l in self.ups: remove_weight_norm(l) for l in self.resblocks: l.remove_weight_norm() class SineGen(torch.nn.Module): def __init__( self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=0.003, voiced_threshold=0, ): super(SineGen, self).__init__() self.sine_amp = sine_amp self.noise_std = noise_std self.harmonic_num = harmonic_num self.dim = self.harmonic_num + 1 self.sampling_rate = samp_rate self.voiced_threshold = voiced_threshold def _f02uv(self, f0): uv = torch.ones_like(f0) uv = uv * (f0 > self.voiced_threshold) return uv def forward(self, f0, upp): with torch.no_grad(): f0 = f0[:, None].transpose(1, 2) f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) f0_buf[:, :, 0] = f0[:, :, 0] for idx in np.arange(self.harmonic_num): f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2) rad_values = (f0_buf / self.sampling_rate) % 1 rand_ini = torch.rand(f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device) rand_ini[:, 0] = 0 rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini tmp_over_one = torch.cumsum(rad_values, 1) tmp_over_one *= upp tmp_over_one = F.interpolate(tmp_over_one.transpose(2, 1), scale_factor=upp, mode="linear", align_corners=True).transpose(2, 1) rad_values = F.interpolate(rad_values.transpose(2, 1), scale_factor=upp, mode="nearest").transpose(2, 1) tmp_over_one %= 1 tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 cumsum_shift = torch.zeros_like(rad_values) cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi) sine_waves = sine_waves * self.sine_amp uv = self._f02uv(f0) uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode="nearest").transpose(2, 1) noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 noise = noise_amp * torch.randn_like(sine_waves) sine_waves = sine_waves * uv + noise return sine_waves, uv, noise class SourceModuleHnNSF(torch.nn.Module): def __init__( self, sampling_rate, harmonic_num=0, sine_amp=0.1, add_noise_std=0.003, voiced_threshod=0, is_half=True, ): super(SourceModuleHnNSF, self).__init__() self.sine_amp = sine_amp self.noise_std = add_noise_std self.is_half = is_half self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod) self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) self.l_tanh = torch.nn.Tanh() def forward(self, x, upp=None): sine_wavs, uv, _ = self.l_sin_gen(x, upp) if self.is_half: sine_wavs = sine_wavs.half() sine_merge = self.l_tanh(self.l_linear(sine_wavs)) return sine_merge, None, None class GeneratorNSF(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, sr, is_half=False, ): super(GeneratorNSF, self).__init__() self.num_kernels = len(resblock_kernel_sizes) self.num_upsamples = len(upsample_rates) self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) self.m_source = SourceModuleHnNSF(sampling_rate=sr, harmonic_num=0, is_half=is_half) self.noise_convs = nn.ModuleList() 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)): c_cur = upsample_initial_channel // (2 ** (i + 1)) self.ups.append(weight_norm(ConvTranspose1d(upsample_initial_channel // (2**i), upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2,))) if i + 1 < len(upsample_rates): stride_f0 = np.prod(upsample_rates[i + 1 :]) self.noise_convs.append(Conv1d(1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2,)) else: self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = upsample_initial_channel // (2 ** (i + 1)) for k, d in 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) self.upp = np.prod(upsample_rates) def forward(self, x, f0, g=None): har_source, noi_source, uv = self.m_source(f0, self.upp) har_source = har_source.transpose(1, 2) 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) x_source = self.noise_convs[i](har_source) x = x + x_source 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): for l in self.ups: remove_weight_norm(l) for l in self.resblocks: l.remove_weight_norm() sr2sr = {"32k": 32000,"40k": 40000,"48k": 48000,} class SynthesizerTrnMsNSFsidM(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, sr, version, **kwargs ): super().__init__() if type(sr) == type("strr"): sr = sr2sr[sr] 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.spk_embed_dim = spk_embed_dim if version == "v1": self.enc_p = TextEncoder(inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout) else: self.enc_p = TextEncoder768(inter_channels, hidden_channels, filter_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) self.speaker_map = None def remove_weight_norm(self): self.dec.remove_weight_norm() self.flow.remove_weight_norm() self.enc_q.remove_weight_norm() def construct_spkmixmap(self, n_speaker): self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels)) for i in range(n_speaker): self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]])) self.speaker_map = self.speaker_map.unsqueeze(0) def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None): if self.speaker_map is not None: g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) g = g * self.speaker_map g = torch.sum(g, dim=1) g = g.transpose(0, -1).transpose(0, -2).squeeze(0) else: g = g.unsqueeze(0) g = self.emb_g(g).transpose(1, 2) m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask z = self.flow(z_p, x_mask, g=g, reverse=True) return self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) class MultiPeriodDiscriminator(torch.nn.Module): def __init__(self, use_spectral_norm=False): super(MultiPeriodDiscriminator, self).__init__() periods = [2, 3, 5, 7, 11, 17] discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] 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 d in 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 MultiPeriodDiscriminatorV2(torch.nn.Module): def __init__(self, use_spectral_norm=False): super(MultiPeriodDiscriminatorV2, self).__init__() periods = [2, 3, 5, 7, 11, 17, 23, 37] discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] 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 d in 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 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 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 = [] b, c, t = x.shape if t % self.period != 0: 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