import math import torch from torch import nn from torch.nn import functional as F import commons import modules from . import attentions_onnx 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 from vector_quantize_pytorch import VectorQuantize 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) self.LSTM = nn.LSTM( 2 * filter_channels, filter_channels, batch_first=True, bidirectional=True ) if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, in_channels, 1) self.output_layer = nn.Sequential( nn.Linear(2 * filter_channels, 1), nn.Sigmoid() ) def forward_probability(self, x, dur): dur = self.dur_proj(dur) x = torch.cat([x, dur], dim=1) x = x.transpose(1, 2) x, _ = self.LSTM(x) 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, dur) output_probs.append(output_prob) 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, ): super().__init__() self.channels = channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.n_layers = n_layers self.n_flows = n_flows self.gin_channels = gin_channels self.flows = nn.ModuleList() self.wn = ( attentions_onnx.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, ) ) self.flows.append(modules.Flip()) def forward(self, x, x_mask, g=None, reverse=True): 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 StochasticDurationPredictor(nn.Module): 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 self.filter_channels = filter_channels self.kernel_size = kernel_size self.p_dropout = p_dropout self.n_flows = n_flows self.gin_channels = gin_channels self.log_flow = modules.Log() 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.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_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.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 ) if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, filter_channels, 1) def forward(self, x, x_mask, z, g=None): x = torch.detach(x) x = self.pre(x) if g is not None: g = torch.detach(g) x = x + self.cond(g) x = self.convs(x, x_mask) x = self.proj(x) * x_mask flows = list(reversed(self.flows)) flows = flows[:-2] + [flows[-1]] # remove a useless vflow for flow in flows: z = flow(z, x_mask, g=x, reverse=True) z0, z1 = torch.split(z, [1, 1], 1) logw = z0 return logw class DurationPredictor(nn.Module): 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.proj = nn.Conv1d(filter_channels, 1, 1) if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, in_channels, 1) def forward(self, x, x_mask, 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) x = self.proj(x * x_mask) return x * x_mask class Bottleneck(nn.Sequential): def __init__(self, in_dim, hidden_dim): c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False) c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False) super().__init__(*[c_fc1, c_fc2]) class Block(nn.Module): def __init__(self, in_dim, hidden_dim) -> None: super().__init__() self.norm = nn.LayerNorm(in_dim) self.mlp = MLP(in_dim, hidden_dim) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x + self.mlp(self.norm(x)) return x class MLP(nn.Module): def __init__(self, in_dim, hidden_dim): super().__init__() self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False) self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False) self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False) def forward(self, x: torch.Tensor): x = F.silu(self.c_fc1(x)) * self.c_fc2(x) x = self.c_proj(x) return x 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, ): super().__init__() self.n_vocab = n_vocab 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.gin_channels = gin_channels self.emb = nn.Embedding(len(symbols), hidden_channels) 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) self.language_emb = nn.Embedding(num_languages, hidden_channels) nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5) self.bert_proj = nn.Conv1d(1024, hidden_channels, 1) self.bert_pre_proj = nn.Conv1d(2048, 1024, 1) self.in_feature_net = nn.Sequential( # input is assumed to an already normalized embedding nn.Linear(512, 1028, bias=False), nn.GELU(), nn.LayerNorm(1028), *[Block(1028, 512) for _ in range(1)], nn.Linear(1028, 512, bias=False), # normalize before passing to VQ? # nn.GELU(), # nn.LayerNorm(512), ) self.emo_vq = VectorQuantize( dim=512, codebook_size=64, codebook_dim=32, commitment_weight=0.1, decay=0.85, heads=32, kmeans_iters=20, separate_codebook_per_head=True, stochastic_sample_codes=True, threshold_ema_dead_code=2, ) self.out_feature_net = nn.Linear(512, hidden_channels) self.encoder = attentions_onnx.Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, gin_channels=self.gin_channels, ) self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, x, x_lengths, tone, language, bert, emo, g=None): x_mask = torch.ones_like(x).unsqueeze(0) bert_emb = self.bert_proj( self.bert_pre_proj(bert.transpose(0, 1).unsqueeze(0)) ).transpose(1, 2) emo_emb = self.in_feature_net(emo.transpose(0, 1)) emo_emb, _, _ = self.emo_vq(emo_emb.unsqueeze(1)) emo_emb = self.out_feature_net(emo_emb) x = ( self.emb(x) + self.tone_emb(tone) + self.language_emb(language) + bert_emb + emo_emb ) * math.sqrt( self.hidden_channels ) # [b, t, h] x = torch.transpose(x, 1, -1) # [b, h, t] x_mask = x_mask.to(x.dtype) x = self.encoder(x * x_mask, x_mask, g=g) 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, ): 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=True): 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, ): 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 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 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 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): 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): 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 layer in self.convs: x = layer(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 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 layer in self.convs: x = layer(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 MultiPeriodDiscriminator(torch.nn.Module): def __init__(self, use_spectral_norm=False): super(MultiPeriodDiscriminator, self).__init__() 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 ] self.discriminators = nn.ModuleList(discs) def forward(self, y, y_hat): 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 class WavLMDiscriminator(nn.Module): """docstring for Discriminator.""" def __init__( self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False ): super(WavLMDiscriminator, self).__init__() norm_f = weight_norm if use_spectral_norm == False else spectral_norm self.pre = norm_f( Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0) ) self.convs = nn.ModuleList( [ norm_f( nn.Conv1d( initial_channel, initial_channel * 2, kernel_size=5, padding=2 ) ), norm_f( nn.Conv1d( initial_channel * 2, initial_channel * 4, kernel_size=5, padding=2, ) ), norm_f( nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2) ), ] ) self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1)) def forward(self, x): x = self.pre(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) x = torch.flatten(x, 1, -1) return x 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): super().__init__() self.spec_channels = spec_channels 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) ] self.convs = nn.ModuleList(convs) # 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.proj = nn.Linear(128, gin_channels) def forward(self, inputs, mask=None): N = inputs.size(0) out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs] for conv in self.convs: out = conv(out) # out = wn(out) out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K] out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K] T = out.size(1) N = out.size(0) out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K] self.gru.flatten_parameters() memory, out = self.gru(out) # out --- [1, N, 128] return self.proj(out.squeeze(0)) def calculate_channels(self, L, kernel_size, stride, pad, n_convs): for i in range(n_convs): L = (L - kernel_size + 2 * pad) // stride + 1 return L class SynthesizerTrn(nn.Module): """ 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, ): super().__init__() self.n_vocab = n_vocab 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.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_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) self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6) 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, ) 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, ) 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 ) if n_speakers >= 1: self.emb_g = nn.Embedding(n_speakers, gin_channels) else: self.ref_enc = ReferenceEncoder(spec_channels, gin_channels) def export_onnx( self, path, max_len=None, sdp_ratio=0, y=None, ): noise_scale = 0.667 length_scale = 1 noise_scale_w = 0.8 x = ( torch.LongTensor( [ 0, 97, 0, 8, 0, 78, 0, 8, 0, 76, 0, 37, 0, 40, 0, 97, 0, 8, 0, 23, 0, 8, 0, 74, 0, 26, 0, 104, 0, ] ) .unsqueeze(0) .cpu() ) tone = torch.zeros_like(x).cpu() language = torch.zeros_like(x).cpu() x_lengths = torch.LongTensor([x.shape[1]]).cpu() sid = torch.LongTensor([0]).cpu() bert = torch.randn(size=(x.shape[1], 2048)).cpu() emo = torch.randn(512, 1) if self.n_speakers > 0: g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] torch.onnx.export( self.emb_g, (sid), f"onnx/{path}/{path}_emb.onnx", input_names=["sid"], output_names=["g"], verbose=True, ) else: g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1) torch.onnx.export( self.enc_p, (x, x_lengths, tone, language, bert, emo, g), f"onnx/{path}/{path}_enc_p.onnx", input_names=[ "x", "x_lengths", "t", "language", "bert_0", "emo", "g", ], output_names=["xout", "m_p", "logs_p", "x_mask"], dynamic_axes={ "x": [0, 1], "t": [0, 1], "language": [0, 1], "bert_0": [0], "xout": [0, 2], "m_p": [0, 2], "logs_p": [0, 2], "x_mask": [0, 2], }, verbose=True, opset_version=16, ) x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, emo, g) zinput = ( torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale_w ) torch.onnx.export( self.sdp, (x, x_mask, zinput, g), f"onnx/{path}/{path}_sdp.onnx", input_names=["x", "x_mask", "zin", "g"], output_names=["logw"], dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "zin": [0, 2], "logw": [0, 2]}, verbose=True, ) torch.onnx.export( self.dp, (x, x_mask, g), f"onnx/{path}/{path}_dp.onnx", input_names=["x", "x_mask", "g"], output_names=["logw"], dynamic_axes={"x": [0, 2], "x_mask": [0, 2], "logw": [0, 2]}, verbose=True, ) logw = self.sdp(x, x_mask, zinput, g=g) * (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 ) 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'] z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale torch.onnx.export( self.flow, (z_p, y_mask, g), f"onnx/{path}/{path}_flow.onnx", input_names=["z_p", "y_mask", "g"], output_names=["z"], dynamic_axes={"z_p": [0, 2], "y_mask": [0, 2], "z": [0, 2]}, verbose=True, ) z = self.flow(z_p, y_mask, g=g, reverse=True) z_in = (z * y_mask)[:, :, :max_len] torch.onnx.export( self.dec, (z_in, g), f"onnx/{path}/{path}_dec.onnx", input_names=["z_in", "g"], output_names=["o"], dynamic_axes={"z_in": [0, 2], "o": [0, 2]}, verbose=True, ) o = self.dec((z * y_mask)[:, :, :max_len], g=g)