From d4d0082b05cb5f6d28381dcb2ed0684d7b026ba9 Mon Sep 17 00:00:00 2001 From: Lengyue Date: Tue, 5 Sep 2023 01:21:17 -0400 Subject: [PATCH] lint code (no other modify) --- models.py | 762 +++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 520 insertions(+), 242 deletions(-) diff --git a/models.py b/models.py index 9a4e1e7..cd83ea4 100644 --- a/models.py +++ b/models.py @@ -1,4 +1,3 @@ -import copy import math import torch from torch import nn @@ -9,91 +8,102 @@ import modules import attentions import monotonic_align -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +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 from text import symbols, num_tones, num_languages -class DurationDiscriminator(nn.Module): #vits2 - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.dur_proj = nn.Conv1d(1, filter_channels, 1) +class DurationDiscriminator(nn.Module): # vits2 + def __init__( + self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 + ): + super().__init__() - self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.pre_out_norm_1 = modules.LayerNorm(filter_channels) - self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.pre_out_norm_2 = modules.LayerNorm(filter_channels) + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.gin_channels = gin_channels - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) + self.drop = nn.Dropout(p_dropout) + self.conv_1 = nn.Conv1d( + in_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.norm_1 = modules.LayerNorm(filter_channels) + self.conv_2 = nn.Conv1d( + filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.norm_2 = modules.LayerNorm(filter_channels) + self.dur_proj = nn.Conv1d(1, filter_channels, 1) - self.output_layer = nn.Sequential( - nn.Linear(filter_channels, 1), - nn.Sigmoid() - ) + self.pre_out_conv_1 = nn.Conv1d( + 2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.pre_out_norm_1 = modules.LayerNorm(filter_channels) + self.pre_out_conv_2 = nn.Conv1d( + filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.pre_out_norm_2 = modules.LayerNorm(filter_channels) - def forward_probability(self, x, x_mask, dur, g=None): - dur = self.dur_proj(dur) - x = torch.cat([x, dur], dim=1) - x = self.pre_out_conv_1(x * x_mask) - x = torch.relu(x) - x = self.pre_out_norm_1(x) - x = self.drop(x) - x = self.pre_out_conv_2(x * x_mask) - x = torch.relu(x) - x = self.pre_out_norm_2(x) - x = self.drop(x) - x = x * x_mask - x = x.transpose(1, 2) - output_prob = self.output_layer(x) - return output_prob + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, in_channels, 1) - def forward(self, x, x_mask, dur_r, dur_hat, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) + self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid()) - output_probs = [] - for dur in [dur_r, dur_hat]: - output_prob = self.forward_probability(x, x_mask, dur, g) - output_probs.append(output_prob) + def forward_probability(self, x, x_mask, dur, g=None): + dur = self.dur_proj(dur) + x = torch.cat([x, dur], dim=1) + x = self.pre_out_conv_1(x * x_mask) + x = torch.relu(x) + x = self.pre_out_norm_1(x) + x = self.drop(x) + x = self.pre_out_conv_2(x * x_mask) + x = torch.relu(x) + x = self.pre_out_norm_2(x) + x = self.drop(x) + x = x * x_mask + x = x.transpose(1, 2) + output_prob = self.output_layer(x) + return output_prob + + def forward(self, x, x_mask, dur_r, dur_hat, g=None): + x = torch.detach(x) + if g is not None: + g = torch.detach(g) + x = x + self.cond(g) + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + + output_probs = [] + for dur in [dur_r, dur_hat]: + output_prob = self.forward_probability(x, x_mask, dur, g) + output_probs.append(output_prob) + + return output_probs - return output_probs class TransformerCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - n_flows=4, - gin_channels=0, - share_parameter=False - ): + def __init__( + self, + channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + n_flows=4, + gin_channels=0, + share_parameter=False, + ): super().__init__() self.channels = channels @@ -105,11 +115,36 @@ class TransformerCouplingBlock(nn.Module): self.flows = nn.ModuleList() - self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = self.gin_channels) if share_parameter else None + self.wn = ( + attentions.FFT( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + isflow=True, + gin_channels=self.gin_channels, + ) + if share_parameter + else None + ) for i in range(n_flows): self.flows.append( - modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout, filter_channels, mean_only=True, wn_sharing_parameter=self.wn, gin_channels = self.gin_channels)) + modules.TransformerCouplingLayer( + channels, + hidden_channels, + kernel_size, + n_layers, + n_heads, + p_dropout, + filter_channels, + mean_only=True, + wn_sharing_parameter=self.wn, + gin_channels=self.gin_channels, + ) + ) self.flows.append(modules.Flip()) def forward(self, x, x_mask, g=None, reverse=False): @@ -121,8 +156,17 @@ class TransformerCouplingBlock(nn.Module): x = flow(x, x_mask, g=g, reverse=reverse) return x + class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): + 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 @@ -136,21 +180,29 @@ class StochasticDurationPredictor(nn.Module): 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.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_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.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) + 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) @@ -171,7 +223,10 @@ class StochasticDurationPredictor(nn.Module): 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 + 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)) @@ -179,8 +234,13 @@ class StochasticDurationPredictor(nn.Module): 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_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) @@ -189,12 +249,18 @@ class StochasticDurationPredictor(nn.Module): 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 + 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 + 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) @@ -203,7 +269,9 @@ class StochasticDurationPredictor(nn.Module): class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): + def __init__( + self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 + ): super().__init__() self.in_channels = in_channels @@ -213,9 +281,13 @@ class DurationPredictor(nn.Module): self.gin_channels = gin_channels self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.conv_1 = nn.Conv1d( + in_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.conv_2 = nn.Conv1d( + filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) self.norm_2 = modules.LayerNorm(filter_channels) self.proj = nn.Conv1d(filter_channels, 1, 1) @@ -240,16 +312,18 @@ class DurationPredictor(nn.Module): class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - gin_channels=0): + def __init__( + self, + n_vocab, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + gin_channels=0, + ): super().__init__() self.n_vocab = n_vocab self.out_channels = out_channels @@ -261,14 +335,14 @@ class TextEncoder(nn.Module): self.p_dropout = p_dropout self.gin_channels = gin_channels self.emb = nn.Embedding(len(symbols), hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5) + nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) self.tone_emb = nn.Embedding(num_tones, hidden_channels) - nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5) + nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5) self.language_emb = nn.Embedding(num_languages, hidden_channels) - nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5) + nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5) self.bert_proj = nn.Conv1d(1024, hidden_channels, 1) self.ja_bert_proj = nn.Conv1d(768, hidden_channels, 1) - + self.encoder = attentions.Encoder( hidden_channels, filter_channels, @@ -276,16 +350,26 @@ class TextEncoder(nn.Module): n_layers, kernel_size, p_dropout, - gin_channels=self.gin_channels) + gin_channels=self.gin_channels, + ) self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, x, x_lengths, tone, language, bert, ja_bert, g=None): - bert_emb = self.zh_bert_proj(bert).transpose(1,2) - ja_bert_emb = = self.ja_bert_proj(ja_bert).transpose(1,2) - x = (self.emb(x)+ self.tone_emb(tone)+ self.language_emb(language) - + bert_emb +ja_bert_emb) * math.sqrt(self.hidden_channels) # [b, t, h] + bert_emb = self.zh_bert_proj(bert).transpose(1, 2) + ja_bert_emb = self.ja_bert_proj(ja_bert).transpose(1, 2) + x = ( + self.emb(x) + + self.tone_emb(tone) + + self.language_emb(language) + + bert_emb + + ja_bert_emb + ) * 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_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( + x.dtype + ) x = self.encoder(x * x_mask, x_mask, g=g) stats = self.proj(x) * x_mask @@ -295,14 +379,16 @@ class TextEncoder(nn.Module): class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): + 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 @@ -315,8 +401,16 @@ class ResidualCouplingBlock(nn.Module): 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)) + 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): @@ -330,14 +424,16 @@ class ResidualCouplingBlock(nn.Module): class PosteriorEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): + 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 @@ -348,11 +444,19 @@ class PosteriorEncoder(nn.Module): 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.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_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 @@ -362,24 +466,45 @@ class PosteriorEncoder(nn.Module): 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): + 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.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.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)): + 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) @@ -410,11 +535,11 @@ class Generator(torch.nn.Module): 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() + print("Removing weight norm...") + for layer in self.ups: + remove_weight_norm(layer) + for layer in self.resblocks: + layer.remove_weight_norm() class DiscriminatorP(torch.nn.Module): @@ -422,14 +547,56 @@ class DiscriminatorP(torch.nn.Module): 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))), - ]) + 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): @@ -443,8 +610,8 @@ class DiscriminatorP(torch.nn.Module): t = t + n_pad x = x.view(b, c, t // self.period, self.period) - for l in self.convs: - x = l(x) + for layer in self.convs: + x = layer(x) x = F.leaky_relu(x, modules.LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) @@ -457,22 +624,24 @@ class DiscriminatorP(torch.nn.Module): 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)), - ]) + 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) + for layer in self.convs: + x = layer(x) x = F.leaky_relu(x, modules.LRELU_SLOPE) fmap.append(x) x = self.conv_post(x) @@ -488,7 +657,9 @@ class MultiPeriodDiscriminator(torch.nn.Module): periods = [2, 3, 5, 7, 11] discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] + 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): @@ -506,11 +677,12 @@ class MultiPeriodDiscriminator(torch.nn.Module): return y_d_rs, y_d_gs, fmap_rs, fmap_gs + class ReferenceEncoder(nn.Module): - ''' + """ inputs --- [N, Ty/r, n_mels*r] mels outputs --- [N, ref_enc_gru_size] - ''' + """ def __init__(self, spec_channels, gin_channels=0): @@ -519,18 +691,27 @@ class ReferenceEncoder(nn.Module): ref_enc_filters = [32, 32, 64, 64, 128, 128] K = len(ref_enc_filters) filters = [1] + ref_enc_filters - convs = [weight_norm(nn.Conv2d(in_channels=filters[i], - out_channels=filters[i + 1], - kernel_size=(3, 3), - stride=(2, 2), - padding=(1, 1))) for i in range(K)] + convs = [ + weight_norm( + nn.Conv2d( + in_channels=filters[i], + out_channels=filters[i + 1], + kernel_size=(3, 3), + stride=(2, 2), + padding=(1, 1), + ) + ) + for i in range(K) + ] self.convs = nn.ModuleList(convs) - # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) + # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501 out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) - self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels, - hidden_size=256 // 2, - batch_first=True) + self.gru = nn.GRU( + input_size=ref_enc_filters[-1] * out_channels, + hidden_size=256 // 2, + batch_first=True, + ) self.proj = nn.Linear(128, gin_channels) def forward(self, inputs, mask=None): @@ -559,34 +740,36 @@ class ReferenceEncoder(nn.Module): class SynthesizerTrn(nn.Module): """ - Synthesizer for Training - """ + Synthesizer for Training + """ - def __init__(self, - n_vocab, - 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, - n_speakers=256, - gin_channels=256, - use_sdp=True, - n_flow_layer = 4, - n_layers_trans_flow = 6, - flow_share_parameter = False, - use_transformer_flow = True, - **kwargs): + def __init__( + self, + n_vocab, + 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, + n_speakers=256, + gin_channels=256, + use_sdp=True, + n_flow_layer=4, + n_layers_trans_flow=6, + flow_share_parameter=False, + use_transformer_flow=True, + **kwargs + ): super().__init__() self.n_vocab = n_vocab @@ -608,7 +791,9 @@ class SynthesizerTrn(nn.Module): self.n_speakers = n_speakers self.gin_channels = gin_channels self.n_layers_trans_flow = n_layers_trans_flow - self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True) + self.use_spk_conditioned_encoder = kwargs.get( + "use_spk_conditioned_encoder", True + ) self.use_sdp = use_sdp self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False) self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01) @@ -616,26 +801,65 @@ class SynthesizerTrn(nn.Module): self.current_mas_noise_scale = self.mas_noise_scale_initial if self.use_spk_conditioned_encoder and gin_channels > 0: self.enc_gin_channels = gin_channels - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - gin_channels=self.enc_gin_channels) - 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.enc_p = TextEncoder( + n_vocab, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + gin_channels=self.enc_gin_channels, + ) + 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, + ) if use_transformer_flow: - self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads, n_layers_trans_flow, 5, p_dropout, n_flow_layer, gin_channels=gin_channels,share_parameter= flow_share_parameter) + self.flow = TransformerCouplingBlock( + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers_trans_flow, + 5, + p_dropout, + n_flow_layer, + gin_channels=gin_channels, + share_parameter=flow_share_parameter, + ) else: - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels) - self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - + self.flow = ResidualCouplingBlock( + inter_channels, + hidden_channels, + 5, + 1, + n_flow_layer, + gin_channels=gin_channels, + ) + self.sdp = StochasticDurationPredictor( + hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels + ) + self.dp = DurationPredictor( + hidden_channels, 256, 3, 0.5, gin_channels=gin_channels + ) + if n_speakers > 1: self.emb_g = nn.Embedding(n_speakers, gin_channels) else: @@ -645,35 +869,54 @@ class SynthesizerTrn(nn.Module): if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: - g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1) - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, ja_bert, g=g) + g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1) + x, m_p, logs_p, x_mask = self.enc_p( + x, x_lengths, tone, language, bert, ja_bert, g=g + ) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) z_p = self.flow(z, y_mask, g=g) with torch.no_grad(): # negative cross-entropy s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] - neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] - neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), - s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] + neg_cent1 = torch.sum( + -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True + ) # [b, 1, t_s] + neg_cent2 = torch.matmul( + -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r + ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] + neg_cent3 = torch.matmul( + z_p.transpose(1, 2), (m_p * s_p_sq_r) + ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] + neg_cent4 = torch.sum( + -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True + ) # [b, 1, t_s] neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 if self.use_noise_scaled_mas: - epsilon = torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale - neg_cent = neg_cent + epsilon + epsilon = ( + torch.std(neg_cent) + * torch.randn_like(neg_cent) + * self.current_mas_noise_scale + ) + neg_cent = neg_cent + epsilon attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() + attn = ( + monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)) + .unsqueeze(1) + .detach() + ) w = attn.sum(2) l_length_sdp = self.sdp(x, x_mask, w, g=g) l_length_sdp = l_length_sdp / torch.sum(x_mask) - + logw_ = torch.log(w + 1e-6) * x_mask logw = self.dp(x, x_mask, g=g) - l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging + l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum( + x_mask + ) # for averaging l_length = l_length_dp + l_length_sdp @@ -681,29 +924,64 @@ class SynthesizerTrn(nn.Module): m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) - z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) + z_slice, ids_slice = commons.rand_slice_segments( + z, y_lengths, self.segment_size + ) o = self.dec(z_slice, g=g) - return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_) - - def infer(self, x, x_lengths, sid, tone, language, bert, ja_bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0,y=None): - #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert) + return ( + o, + l_length, + attn, + ids_slice, + x_mask, + y_mask, + (z, z_p, m_p, logs_p, m_q, logs_q), + (x, logw, logw_), + ) + + def infer( + self, + x, + x_lengths, + sid, + tone, + language, + bert, + ja_bert, + noise_scale=0.667, + length_scale=1, + noise_scale_w=0.8, + max_len=None, + sdp_ratio=0, + y=None, + ): + # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert) # g = self.gst(y) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] else: - g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1) - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, ja_bert, g=g) - logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio) + g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1) + x, m_p, logs_p, x_mask = self.enc_p( + x, x_lengths, tone, language, bert, ja_bert, g=g + ) + logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * ( + sdp_ratio + ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio) 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) + 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'] + 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)