From 9a64971d7f0e0c280be7213ce449c5cb82d32937 Mon Sep 17 00:00:00 2001 From: Owen Song Date: Wed, 17 Jun 2026 23:54:36 +0000 Subject: [PATCH] Remove obsolete package file tiny_tts/models/synthesizer.py --- tiny_tts/models/synthesizer.py | 718 --------------------------------- 1 file changed, 718 deletions(-) delete mode 100644 tiny_tts/models/synthesizer.py diff --git a/tiny_tts/models/synthesizer.py b/tiny_tts/models/synthesizer.py deleted file mode 100644 index 76d7b0a..0000000 --- a/tiny_tts/models/synthesizer.py +++ /dev/null @@ -1,718 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F - -from tiny_tts.nn import commons -from tiny_tts.nn import modules -from tiny_tts.nn import attentions - -from torch.nn import Conv1d, ConvTranspose1d -from torch.nn.utils import weight_norm, remove_weight_norm - -from tiny_tts.nn.commons import initialize_weights, compute_padding -import tiny_tts.alignment as alignment - - -class AttentionFlowBlock(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.FeedForward( - 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.FlipTransform()) - - 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 VariationalDurationModel(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 - 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.LogTransform() - self.flows = nn.ModuleList() - self.flows.append(modules.AffineCoupling(2)) - for i in range(n_flows): - self.flows.append( - modules.ConvolutionalFlow(2, filter_channels, kernel_size, n_layers=3) - ) - self.flows.append(modules.FlipTransform()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DepthwiseSepConv( - filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout - ) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.AffineCoupling(2)) - for i in range(4): - self.post_flows.append( - modules.ConvolutionalFlow(2, filter_channels, kernel_size, n_layers=3) - ) - self.post_flows.append(modules.FlipTransform()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DepthwiseSepConv( - 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, w=None, g=None, reverse=False, noise_scale=1.0): - 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 - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - 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 - ) - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - 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 = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - 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 - ) - return nll + logq - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] - 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) - logw = z0 - return logw - - -class DurationEstimator(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.ChannelNorm(filter_channels) - self.conv_2 = nn.Conv1d( - filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 - ) - self.norm_2 = modules.ChannelNorm(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 PhonemeEncoder(nn.Module): - def __init__( - self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - gin_channels=0, - num_languages=None, - num_tones=None, - ): - super().__init__() - if num_languages is None: - from tiny_tts.text import num_languages - if num_tones is None: - from tiny_tts.text import num_tones - 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(n_vocab, 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.ja_bert_proj = nn.Conv1d(768, hidden_channels, 1) - - self.encoder = attentions.TransformerBlock( - 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, ja_bert, g=None): - bert_emb = self.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 - ) - x = torch.transpose(x, 1, -1) - x_mask = torch.unsqueeze(commons.create_length_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 - - m, logs = torch.split(stats, self.out_channels, dim=1) - return x, m, logs, x_mask - - -class FlowBlock(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.FlowCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.FlipTransform()) - - 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 LatentEncoder(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.WaveNet( - 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, tau=1.0): - x_mask = torch.unsqueeze(commons.create_length_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) * tau * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class WaveformDecoder(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(WaveformDecoder, 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.ConvResBlock if resblock == "1" else modules.ConvResBlockLight - - 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(initialize_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 layer in self.ups: - remove_weight_norm(layer) - for layer in self.resblocks: - layer.remove_weight_norm() - - -class StyleEncoder(nn.Module): - def __init__(self, spec_channels, gin_channels=0, layernorm=False): - 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) - - 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) - if layernorm: - self.layernorm = nn.LayerNorm(self.spec_channels) - else: - self.layernorm = None - - def forward(self, inputs, mask=None): - N = inputs.size(0) - - out = inputs.view(N, 1, -1, self.spec_channels) - if self.layernorm is not None: - out = self.layernorm(out) - - for conv in self.convs: - out = conv(out) - out = F.relu(out) - - out = out.transpose(1, 2) - T = out.size(1) - N = out.size(0) - out = out.contiguous().view(N, T, -1) - - self.gru.flatten_parameters() - memory, out = self.gru(out) - - 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 VoiceSynthesizer(nn.Module): - """Voice synthesis model for inference.""" - - 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, - use_vc=False, - num_languages=None, - num_tones=None, - norm_refenc=False, - **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 - else: - self.enc_gin_channels = 0 - self.enc_p = PhonemeEncoder( - n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - gin_channels=self.enc_gin_channels, - num_languages=num_languages, - num_tones=num_tones, - ) - self.dec = WaveformDecoder( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - self.enc_q = LatentEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - if use_transformer_flow: - self.flow = AttentionFlowBlock( - 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 = FlowBlock( - inter_channels, - hidden_channels, - 5, - 1, - n_flow_layer, - gin_channels=gin_channels, - ) - self.sdp = VariationalDurationModel( - hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels - ) - self.dp = DurationEstimator( - hidden_channels, 256, 3, 0.5, gin_channels=gin_channels - ) - - if n_speakers > 0: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - else: - self.ref_enc = StyleEncoder(spec_channels, gin_channels, layernorm=norm_refenc) - self.use_vc = use_vc - - 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, - g=None, - ): - if g is None: - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) - else: - g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1) - if self.use_vc: - g_p = None - else: - g_p = g - x, m_p, logs_p, x_mask = self.enc_p( - x, x_lengths, tone, language, bert, ja_bert, g=g_p - ) - 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.create_length_mask(y_lengths, None), 1).to( - x_mask.dtype - ) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.compute_alignment_path(w_ceil, attn_mask) - - 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_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) - o = self.dec((z * y_mask)[:, :, :max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p)