import numpy as np import torch from torch import nn from torch.nn import functional as F from .modules import ResidualCouplingLayer, Flip, WN, ResBlock1, ResBlock2, LRELU_SLOPE from torch.nn import Conv1d, ConvTranspose1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from .commons import init_weights, get_padding, sequence_mask from .generator import SiFiGANGenerator from .features import SignalGenerator, dilated_factor class TextEncoder(nn.Module): def __init__(self, out_channels, hidden_channels, requires_grad=True): super().__init__() self.out_channels = out_channels self.hidden_channels = hidden_channels self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) # パラメータを学習しない if requires_grad is False: for param in self.parameters(): param.requires_grad = False def forward(self, x, x_lengths): x = torch.transpose(x.half(), 1, -1) # [b, h, t] x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) stats = self.proj(x) * x_mask m, logs = torch.split(stats, self.out_channels, dim=1) return x, 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, requires_grad=True): 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(ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) self.flows.append(Flip()) # パラメータを学習しない if requires_grad is False: for param in self.parameters(): param.requires_grad = False 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 PosteriorEncoder(nn.Module): def __init__(self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, requires_grad=True): 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 = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) # パラメータを学習しない if requires_grad is False: for param in self.parameters(): param.requires_grad = False def forward(self, x, x_lengths, g=None): x_mask = torch.unsqueeze(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 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, requires_grad=True): 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 = ResBlock1 if resblock == "1" else 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 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) if requires_grad is False: for param in self.parameters(): param.requires_grad = False def forward(self, x, g=None): x = self.conv_pre(x) for i in range(self.num_upsamples): x = F.leaky_relu(x, 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): print("Removing weight norm...") for l in self.ups: remove_weight_norm(l) for l in self.resblocks: l.remove_weight_norm() 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 is 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 = [] # 1d to 2d b, c, t = x.shape if t % self.period != 0: # pad first 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, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap class DiscriminatorS(torch.nn.Module): def __init__(self, use_spectral_norm=False): super(DiscriminatorS, self).__init__() norm_f = weight_norm if use_spectral_norm is 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, LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) fmap.append(x) x = torch.flatten(x, 1, -1) return x, fmap class MultiPeriodDiscriminator(torch.nn.Module): def __init__(self, use_spectral_norm=False): super(MultiPeriodDiscriminator, self).__init__() # periods = [2,3,5,7,11] periods = [3, 5, 7, 11, 13] discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] self.discriminators = nn.ModuleList(discs) def forward(self, y, y_hat, flag=True): if flag: y_d_rs = [] y_d_gs = [] fmap_rs = [] fmap_gs = [] for i, d in enumerate(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 else: y_d_gs = [] with torch.no_grad(): for i, d in enumerate(self.discriminators): y_d_g, _ = d(y_hat) y_d_gs.append(y_d_g) return y_d_gs class SynthesizerTrn(nn.Module): """ Synthesizer for Training """ def __init__( self, spec_channels, segment_size, inter_channels, hidden_channels, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, n_flow, dec_out_channels=1, dec_kernel_size=7, n_speakers=0, gin_channels=0, requires_grad_pe=True, requires_grad_flow=True, requires_grad_text_enc=True, requires_grad_dec=True, requires_grad_emb_g=True, sample_rate=24000, hop_size=128, sine_amp=0.1, noise_amp=0.003, signal_types=["sine"], dense_factors=[0.5, 1, 4, 8], upsample_scales=[8, 4, 2, 2], ): super().__init__() self.spec_channels = spec_channels self.hidden_channels = hidden_channels 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.dec_out_channels = dec_out_channels self.dec_kernel_size = dec_kernel_size self.n_speakers = n_speakers self.gin_channels = gin_channels self.requires_grad_pe = requires_grad_pe self.requires_grad_flow = requires_grad_flow self.requires_grad_text_enc = requires_grad_text_enc self.requires_grad_dec = requires_grad_dec self.requires_grad_emb_g = requires_grad_emb_g self.sample_rate = sample_rate self.hop_size = hop_size self.sine_amp = sine_amp self.noise_amp = noise_amp self.signal_types = signal_types self.dense_factors = dense_factors self.upsample_scales = upsample_scales self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels, requires_grad=requires_grad_pe) self.enc_p = TextEncoder(inter_channels, hidden_channels, requires_grad=requires_grad_text_enc) self.dec = SiFiGANGenerator(in_channels=inter_channels, out_channels=dec_out_channels, channels=upsample_initial_channel, kernel_size=dec_kernel_size, upsample_scales=upsample_rates, upsample_kernel_sizes=upsample_kernel_sizes, requires_grad=requires_grad_dec) self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, n_flows=n_flow, gin_channels=gin_channels, requires_grad=requires_grad_flow) self.signal_generator = SignalGenerator(sample_rate=sample_rate, hop_size=hop_size, noise_amp=noise_amp, signal_types=signal_types) if n_speakers > 1: self.emb_g = nn.Embedding(n_speakers, gin_channels) self.emb_g.requires_grad = requires_grad_emb_g def forward(self, x, x_lengths, y, y_lengths, f0, slice_id, sid=None, target_ids=None): pass # sin, d = self.make_sin_d(f0) # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) # # target sid 作成 # target_sids = self.make_random_target_sids(target_ids, sid) # if self.n_speakers > 0: # g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] # tgt_g = self.emb_g(target_sids).unsqueeze(-1) # [b, h, 1] # else: # g = None # # PE # z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) # # Flow # z_p = self.flow(z, y_mask, g=g) # # VC # tgt_z = self.flow(z_p, y_mask, g=tgt_g, reverse=True) # # アライメントの作成 # liner_alignment = F.one_hot(torch.arange(0, x.shape[2] + 2)).cuda() # liner_alignment = torch.stack([liner_alignment for _ in range(x.shape[0])], axis=0) # liner_alignment = F.interpolate(liner_alignment.float(), size=(z.shape[2]), mode="linear", align_corners=True) # liner_alignment = liner_alignment[:, 1:-1, :] # # TextEncとPEのshape合わせ # m_p = torch.matmul(m_p, liner_alignment) # logs_p = torch.matmul(logs_p, liner_alignment) # # slice # z_slice = slice_segments(z, slice_id, self.segment_size) # # targetのslice # tgt_z_slice = slice_segments(tgt_z, slice_id, self.segment_size) # # Dec # o = self.dec(sin, z_slice, d, sid=g) # tgt_o = self.dec(sin, tgt_z_slice, d, sid=tgt_g) # return (o, tgt_o), slice_id, x_mask, y_mask, ((z, z_p, m_p), logs_p, m_q, logs_q) def make_sin_d(self, f0): # f0 から sin と d を作成 # f0 : [b, 1, t] # sin : [b, 1, t] # d : [4][b, 1, t] prod_upsample_scales = np.cumprod(self.upsample_scales) dfs_batch = [] for df, us in zip(self.dense_factors, prod_upsample_scales): dilated_tensor = dilated_factor(f0, self.sample_rate, df) # result += [torch.repeat_interleave(dilated_tensor, us, dim=1)] result = [torch.stack([dilated_tensor for _ in range(us)], -1).reshape(dilated_tensor.shape[0], -1)] dfs_batch.append(torch.cat(result, dim=0).unsqueeze(1)) in_batch = self.signal_generator(f0) return in_batch, dfs_batch def make_random_target_sids(self, target_ids, sid): # target_sids は target_ids をランダムで埋める target_sids = torch.zeros_like(sid) for i in range(len(target_sids)): source_id = sid[i] deleted_target_ids = target_ids[target_ids != source_id] # source_id と target_id が同じにならないよう sid と同じものを削除 if len(deleted_target_ids) >= 1: target_sids[i] = deleted_target_ids[torch.randint(len(deleted_target_ids), (1,))] else: # target_id 候補が無いときは仕方ないので sid を使う target_sids[i] = source_id return target_sids def voice_conversion(self, y, y_lengths, f0, sid_src, sid_tgt): assert self.n_speakers > 0, "n_speakers have to be larger than 0." sin, d = self.make_sin_d(f0) g_src = self.emb_g(sid_src).unsqueeze(-1) g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) z, _, _, y_mask = self.enc_q(y, y_lengths, g=g_src) z_p = self.flow(z, y_mask, g=g_src) z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) # print("VC", sin.device, d[0].device, g_tgt.device) o_hat = self.dec(sin, z_hat * y_mask, d, sid=g_tgt) return o_hat[0] def voice_ra_pa_db(self, y, y_lengths, sid_src, sid_tgt): assert self.n_speakers > 0, "n_speakers have to be larger than 0." g_src = self.emb_g(sid_src).unsqueeze(-1) g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) o_hat = self.dec(z * y_mask, g=g_tgt) return o_hat, y_mask, (z) def voice_ra_pa_da(self, y, y_lengths, sid_src, sid_tgt): assert self.n_speakers > 0, "n_speakers have to be larger than 0." g_src = self.emb_g(sid_src).unsqueeze(-1) # g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) o_hat = self.dec(z * y_mask, g=g_src) return o_hat, y_mask, (z) def voice_conversion_cycle(self, y, y_lengths, sid_src, sid_tgt): assert self.n_speakers > 0, "n_speakers have to be larger than 0." g_src = self.emb_g(sid_src).unsqueeze(-1) g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) z_p = self.flow(z, y_mask, g=g_src) z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) z_p_hat = self.flow(z_hat, y_mask, g=g_tgt) z_hat_hat = self.flow(z_p_hat, y_mask, g=g_src, reverse=True) o_hat = self.dec(z_hat_hat * y_mask, g=g_tgt) return o_hat, y_mask, (z, z_p, z_hat) def save_synthesizer(self, path): enc_q = self.enc_q.state_dict() dec = self.dec.state_dict() emb_g = self.emb_g.state_dict() torch.save({"enc_q": enc_q, "dec": dec, "emb_g": emb_g}, path) def load_synthesizer(self, path): dict = torch.load(path, map_location="cpu") enc_q = dict["enc_q"] dec = dict["dec"] emb_g = dict["emb_g"] self.enc_q.load_state_dict(enc_q) self.dec.load_state_dict(dec) self.emb_g.load_state_dict(emb_g)