import os import sys import math import torch from typing import Optional from torch.nn.utils import remove_weight_norm from torch.nn.utils.parametrizations import weight_norm now_dir = os.getcwd() sys.path.append(now_dir) from .modules import WaveNet from .residuals import ResidualCouplingBlock, ResBlock1, ResBlock2, LRELU_SLOPE from .commons import init_weights, slice_segments, rand_slice_segments, sequence_mask, convert_pad_shape 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 = torch.nn.Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) resblock = ResBlock1 if resblock == "1" else ResBlock2 self.ups_and_resblocks = torch.nn.ModuleList() for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): self.ups_and_resblocks.append(weight_norm(torch.nn.ConvTranspose1d(upsample_initial_channel // (2**i), upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2))) ch = upsample_initial_channel // (2 ** (i + 1)) for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): self.ups_and_resblocks.append(resblock(ch, k, d)) self.conv_post = torch.nn.Conv1d(ch, 1, 7, 1, padding=3, bias=False) self.ups_and_resblocks.apply(init_weights) if gin_channels != 0: self.cond = torch.nn.Conv1d(gin_channels, upsample_initial_channel, 1) def forward(self, x: torch.Tensor, g: Optional[torch.Tensor] = None): x = self.conv_pre(x) if g is not None: x = x + self.cond(g) resblock_idx = 0 for _ in range(self.num_upsamples): x = torch.nn.functional.leaky_relu(x, LRELU_SLOPE) x = self.ups_and_resblocks[resblock_idx](x) resblock_idx += 1 xs = 0 for _ in range(self.num_kernels): xs += self.ups_and_resblocks[resblock_idx](x) resblock_idx += 1 x = xs / self.num_kernels x = torch.nn.functional.leaky_relu(x) x = self.conv_post(x) x = torch.tanh(x) return x def __prepare_scriptable__(self): for l in self.ups_and_resblocks: for hook in l._forward_pre_hooks.values(): if (hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" and hook.__class__.__name__ == "WeightNorm"): torch.nn.utils.remove_weight_norm(l) return self def remove_weight_norm(self): for l in self.ups_and_resblocks: remove_weight_norm(l) class SineGen(torch.nn.Module): def __init__(self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=0.003, voiced_threshold=0, flag_for_pulse=False): 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.sample_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: torch.Tensor, upp: int): 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] f0_buf[:, :, 1:] = (f0_buf[:, :, 0:1] * torch.arange(2, self.harmonic_num + 2, device=f0.device)[None, None, :]) rad_values = (f0_buf / float(self.sample_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 = torch.nn.functional.interpolate(tmp_over_one.transpose(2, 1), scale_factor=float(upp), mode="linear", align_corners=True).transpose(2, 1) rad_values = torch.nn.functional.interpolate(rad_values.transpose(2, 1), scale_factor=float(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 * torch.pi) sine_waves = sine_waves * self.sine_amp uv = self._f02uv(f0) uv = torch.nn.functional.interpolate(uv.transpose(2, 1), scale_factor=float(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, sample_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(sample_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: torch.Tensor, upsample_factor: int = 1): sine_wavs, uv, _ = self.l_sin_gen(x, upsample_factor) sine_wavs = sine_wavs.to(dtype=self.l_linear.weight.dtype) 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=math.prod(upsample_rates)) self.m_source = SourceModuleHnNSF(sample_rate=sr, harmonic_num=0, is_half=is_half) self.conv_pre = torch.nn.Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) resblock_cls = ResBlock1 if resblock == "1" else ResBlock2 self.ups = torch.nn.ModuleList() self.noise_convs = torch.nn.ModuleList() channels = [upsample_initial_channel // (2 ** (i + 1)) for i in range(len(upsample_rates))] stride_f0s = [math.prod(upsample_rates[i + 1 :]) if i + 1 < len(upsample_rates) else 1 for i in range(len(upsample_rates))] for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): self.ups.append(weight_norm(torch.nn.ConvTranspose1d(upsample_initial_channel // (2**i), channels[i], k, u, padding=(k - u) // 2))) self.noise_convs.append(torch.nn.Conv1d(1, channels[i], kernel_size=(stride_f0s[i] * 2 if stride_f0s[i] > 1 else 1), stride=stride_f0s[i], padding=(stride_f0s[i] // 2 if stride_f0s[i] > 1 else 0))) self.resblocks = torch.nn.ModuleList([resblock_cls(channels[i], k, d) for i in range(len(self.ups)) for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes)]) self.conv_post = torch.nn.Conv1d(channels[-1], 1, 7, 1, padding=3, bias=False) self.ups.apply(init_weights) if gin_channels != 0: self.cond = torch.nn.Conv1d(gin_channels, upsample_initial_channel, 1) self.upp = math.prod(upsample_rates) self.lrelu_slope = LRELU_SLOPE def forward(self, x, f0, g: Optional[torch.Tensor] = None): har_source, _, _ = 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, (ups, noise_convs) in enumerate(zip(self.ups, self.noise_convs)): x = torch.nn.functional.leaky_relu(x, self.lrelu_slope) x = ups(x) x = x + noise_convs(har_source) xs = sum([resblock(x) for j, resblock in enumerate(self.resblocks) if j in range(i * self.num_kernels, (i + 1) * self.num_kernels)]) x = xs / self.num_kernels x = torch.nn.functional.leaky_relu(x) x = torch.tanh(self.conv_post(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() def __prepare_scriptable__(self): for l in self.ups: for hook in l._forward_pre_hooks.values(): if (hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" and hook.__class__.__name__ == "WeightNorm"): remove_weight_norm(l) for l in self.resblocks: for hook in l._forward_pre_hooks.values(): if (hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" and hook.__class__.__name__ == "WeightNorm"): remove_weight_norm(l) return self class LayerNorm(torch.nn.Module): def __init__(self, channels, eps=1e-5): super().__init__() self.eps = eps self.gamma = torch.nn.Parameter(torch.ones(channels)) self.beta = torch.nn.Parameter(torch.zeros(channels)) def forward(self, x): x = x.transpose(1, -1) x = torch.nn.functional.layer_norm(x, (x.size(-1),), self.gamma, self.beta, self.eps) return x.transpose(1, -1) class MultiHeadAttention(torch.nn.Module): def __init__(self, channels, out_channels, n_heads, p_dropout=0.0, window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False): super().__init__() assert channels % n_heads == 0 self.channels = channels self.out_channels = out_channels self.n_heads = n_heads self.p_dropout = p_dropout self.window_size = window_size self.heads_share = heads_share self.block_length = block_length self.proximal_bias = proximal_bias self.proximal_init = proximal_init self.attn = None self.k_channels = channels // n_heads self.conv_q = torch.nn.Conv1d(channels, channels, 1) self.conv_k = torch.nn.Conv1d(channels, channels, 1) self.conv_v = torch.nn.Conv1d(channels, channels, 1) self.conv_o = torch.nn.Conv1d(channels, out_channels, 1) self.drop = torch.nn.Dropout(p_dropout) if window_size is not None: n_heads_rel = 1 if heads_share else n_heads rel_stddev = self.k_channels**-0.5 self.emb_rel_k = torch.nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.emb_rel_v = torch.nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) torch.nn.init.xavier_uniform_(self.conv_q.weight) torch.nn.init.xavier_uniform_(self.conv_k.weight) torch.nn.init.xavier_uniform_(self.conv_v.weight) if proximal_init: with torch.no_grad(): self.conv_k.weight.copy_(self.conv_q.weight) self.conv_k.bias.copy_(self.conv_q.bias) def forward(self, x, c, attn_mask=None): q = self.conv_q(x) k = self.conv_k(c) v = self.conv_v(c) x, self.attn = self.attention(q, k, v, mask=attn_mask) x = self.conv_o(x) return x def attention(self, query, key, value, mask=None): b, d, t_s, t_t = (*key.size(), query.size(2)) query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) if self.window_size is not None: assert (t_s == t_t), "(t_s == t_t)" key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings) scores_local = self._relative_position_to_absolute_position(rel_logits) scores = scores + scores_local if self.proximal_bias: assert t_s == t_t, "t_s == t_t" scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) if mask is not None: scores = scores.masked_fill(mask == 0, -1e4) if self.block_length is not None: assert (t_s == t_t), "(t_s == t_t)" block_mask = (torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)) scores = scores.masked_fill(block_mask == 0, -1e4) p_attn = torch.nn.functional.softmax(scores, dim=-1) p_attn = self.drop(p_attn) output = torch.matmul(p_attn, value) if self.window_size is not None: relative_weights = self._absolute_position_to_relative_position(p_attn) value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) output = (output.transpose(2, 3).contiguous().view(b, d, t_t)) return output, p_attn def _matmul_with_relative_values(self, x, y): ret = torch.matmul(x, y.unsqueeze(0)) return ret def _matmul_with_relative_keys(self, x, y): ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) return ret def _get_relative_embeddings(self, relative_embeddings, length): pad_length = max(length - (self.window_size + 1), 0) slice_start_position = max((self.window_size + 1) - length, 0) slice_end_position = slice_start_position + 2 * length - 1 if pad_length > 0: padded_relative_embeddings = torch.nn.functional.pad(relative_embeddings, convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) else: padded_relative_embeddings = relative_embeddings used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] return used_relative_embeddings def _relative_position_to_absolute_position(self, x): batch, heads, length, _ = x.size() x = torch.nn.functional.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) x_flat = x.view([batch, heads, length * 2 * length]) x_flat = torch.nn.functional.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :] return x_final def _absolute_position_to_relative_position(self, x): batch, heads, length, _ = x.size() x = torch.nn.functional.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) x_flat = torch.nn.functional.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] return x_final def _attention_bias_proximal(self, length): r = torch.arange(length, dtype=torch.float32) diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) class FFN(torch.nn.Module): def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0, activation=None, causal=False): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.filter_channels = filter_channels self.kernel_size = kernel_size self.p_dropout = p_dropout self.activation = activation self.causal = causal if causal: self.padding = self._causal_padding else: self.padding = self._same_padding self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size) self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size) self.drop = torch.nn.Dropout(p_dropout) def forward(self, x, x_mask): x = self.conv_1(self.padding(x * x_mask)) if self.activation == "gelu": x = x * torch.sigmoid(1.702 * x) else: x = torch.relu(x) x = self.drop(x) x = self.conv_2(self.padding(x * x_mask)) return x * x_mask def _causal_padding(self, x): if self.kernel_size == 1: return x pad_l = self.kernel_size - 1 pad_r = 0 padding = [[0, 0], [0, 0], [pad_l, pad_r]] x = torch.nn.functional.pad(x, convert_pad_shape(padding)) return x def _same_padding(self, x): if self.kernel_size == 1: return x pad_l = (self.kernel_size - 1) // 2 pad_r = self.kernel_size // 2 padding = [[0, 0], [0, 0], [pad_l, pad_r]] x = torch.nn.functional.pad(x, convert_pad_shape(padding)) return x class Encoder(torch.nn.Module): def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.0, window_size=10, **kwargs): super().__init__() 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.window_size = window_size self.drop = torch.nn.Dropout(p_dropout) self.attn_layers = torch.nn.ModuleList() self.norm_layers_1 = torch.nn.ModuleList() self.ffn_layers = torch.nn.ModuleList() self.norm_layers_2 = torch.nn.ModuleList() for _ in range(self.n_layers): self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size)) self.norm_layers_1.append(LayerNorm(hidden_channels)) self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) self.norm_layers_2.append(LayerNorm(hidden_channels)) def forward(self, x, x_mask): attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) x = x * x_mask for i in range(self.n_layers): y = self.attn_layers[i](x, x, attn_mask) y = self.drop(y) x = self.norm_layers_1[i](x + y) y = self.ffn_layers[i](x, x_mask) y = self.drop(y) x = self.norm_layers_2[i](x + y) x = x * x_mask return x class TextEncoder(torch.nn.Module): def __init__(self, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, embedding_dim, f0=True): super(TextEncoder, self).__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 = float(p_dropout) self.emb_phone = torch.nn.Linear(embedding_dim, hidden_channels) self.lrelu = torch.nn.LeakyReLU(0.1, inplace=True) if f0: self.emb_pitch = torch.nn.Embedding(256, hidden_channels) self.encoder = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, float(p_dropout)) self.proj = torch.nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, phone: torch.Tensor, pitch: Optional[torch.Tensor], lengths: torch.Tensor): 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(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 PosteriorEncoder(torch.nn.Module): def __init__(self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0): super(PosteriorEncoder, self).__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 = torch.nn.Conv1d(in_channels, hidden_channels, 1) self.enc = WaveNet(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) self.proj = torch.nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, x: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = 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 def remove_weight_norm(self): self.enc.remove_weight_norm() def __prepare_scriptable__(self): for hook in self.enc._forward_pre_hooks.values(): if (hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" and hook.__class__.__name__ == "WeightNorm"): torch.nn.utils.remove_weight_norm(self.enc) return self class Synthesizer(torch.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, use_f0, text_enc_hidden_dim=768, **kwargs): super(Synthesizer, self).__init__() 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 = float(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 self.use_f0 = use_f0 self.enc_p = TextEncoder(inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, float(p_dropout), text_enc_hidden_dim, f0=use_f0) if use_f0: 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"]) else: 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.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels) self.emb_g = torch.nn.Embedding(self.spk_embed_dim, gin_channels) def remove_weight_norm(self): self.dec.remove_weight_norm() self.flow.remove_weight_norm() self.enc_q.remove_weight_norm() def __prepare_scriptable__(self): for hook in self.dec._forward_pre_hooks.values(): if (hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" and hook.__class__.__name__ == "WeightNorm"): torch.nn.utils.remove_weight_norm(self.dec) for hook in self.flow._forward_pre_hooks.values(): if (hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" and hook.__class__.__name__ == "WeightNorm"): torch.nn.utils.remove_weight_norm(self.flow) if hasattr(self, "enc_q"): for hook in self.enc_q._forward_pre_hooks.values(): if (hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" and hook.__class__.__name__ == "WeightNorm"): torch.nn.utils.remove_weight_norm(self.enc_q) return self @torch.jit.ignore def forward(self, phone: torch.Tensor, phone_lengths: torch.Tensor, pitch: Optional[torch.Tensor] = None, pitchf: Optional[torch.Tensor] = None, y: torch.Tensor = None, y_lengths: torch.Tensor = None, ds: Optional[torch.Tensor] = None): g = self.emb_g(ds).unsqueeze(-1) m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) if y is not None: z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) z_p = self.flow(z, y_mask, g=g) z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size) if self.use_f0: pitchf = slice_segments(pitchf, ids_slice, self.segment_size, 2) o = self.dec(z_slice, pitchf, g=g) else: o = self.dec(z_slice, g=g) return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) else: return None, None, x_mask, None, (None, None, m_p, logs_p, None, None) @torch.jit.export def infer(self, phone: torch.Tensor, phone_lengths: torch.Tensor, pitch: Optional[torch.Tensor] = None, nsff0: Optional[torch.Tensor] = None, sid: torch.Tensor = None, rate: Optional[torch.Tensor] = None): g = self.emb_g(sid).unsqueeze(-1) m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask if rate is not None: assert isinstance(rate, torch.Tensor) head = int(z_p.shape[2] * (1.0 - rate.item())) z_p = z_p[:, :, head:] x_mask = x_mask[:, :, head:] if self.use_f0: nsff0 = nsff0[:, head:] if self.use_f0: z = self.flow(z_p, x_mask, g=g, reverse=True) o = self.dec(z * x_mask, nsff0, g=g) else: z = self.flow(z_p, x_mask, g=g, reverse=True) o = self.dec(z * x_mask, g=g) return o, x_mask, (z, z_p, m_p, logs_p)