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| import math | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from . import modules | |
| from . import attentions | |
| from . import commons | |
| from .commons import init_weights, get_padding | |
| from torch.nn import Conv1d, ConvTranspose1d, Conv2d | |
| from torch.nn.utils import remove_weight_norm | |
| from torch.nn.utils.parametrizations import spectral_norm, weight_norm | |
| from typing import Optional | |
| has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available()) | |
| class TextEncoder256(nn.Module): | |
| def __init__( | |
| self, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| f0=True, | |
| ): | |
| super(TextEncoder256, 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 = nn.Linear(256, hidden_channels) | |
| self.lrelu = nn.LeakyReLU(0.1, inplace=True) | |
| if f0 == True: | |
| self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 | |
| self.encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| float(p_dropout), | |
| ) | |
| self.proj = 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) # [b, t, h] | |
| x = self.lrelu(x) | |
| x = torch.transpose(x, 1, -1) # [b, h, t] | |
| x_mask = torch.unsqueeze(commons.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 TextEncoder768(nn.Module): | |
| def __init__( | |
| self, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| f0=True, | |
| ): | |
| super(TextEncoder768, 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 = nn.Linear(768, hidden_channels) | |
| self.lrelu = nn.LeakyReLU(0.1, inplace=True) | |
| if f0 == True: | |
| self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 | |
| self.encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| float(p_dropout), | |
| ) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, phone: torch.Tensor, pitch: 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) # [b, t, h] | |
| x = self.lrelu(x) | |
| x = torch.transpose(x, 1, -1) # [b, h, t] | |
| x_mask = torch.unsqueeze(commons.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 ResidualCouplingBlock(nn.Module): | |
| def __init__( | |
| self, | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| n_flows=4, | |
| gin_channels=0, | |
| ): | |
| super(ResidualCouplingBlock, self).__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: torch.Tensor, | |
| x_mask: torch.Tensor, | |
| g: Optional[torch.Tensor] = None, | |
| reverse: bool = False, | |
| ): | |
| if not reverse: | |
| for flow in self.flows: | |
| x, _ = flow(x, x_mask, g=g, reverse=reverse) | |
| else: | |
| for flow in self.flows[::-1]: | |
| x = flow.forward(x, x_mask, g=g, reverse=reverse) | |
| return x | |
| def remove_weight_norm(self): | |
| for i in range(self.n_flows): | |
| self.flows[i * 2].remove_weight_norm() | |
| def __prepare_scriptable__(self): | |
| for i in range(self.n_flows): | |
| for hook in self.flows[i * 2]._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.flows[i * 2]) | |
| return self | |
| class PosteriorEncoder(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 = 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: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = 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 | |
| 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 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: torch.Tensor, g: Optional[torch.Tensor] = 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 __prepare_scriptable__(self): | |
| for l in self.ups: | |
| for hook in l._forward_pre_hooks.values(): | |
| # The hook we want to remove is an instance of WeightNorm class, so | |
| # normally we would do `if isinstance(...)` but this class is not accessible | |
| # because of shadowing, so we check the module name directly. | |
| # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 | |
| if ( | |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.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" | |
| ): | |
| torch.nn.utils.remove_weight_norm(l) | |
| return self | |
| def remove_weight_norm(self): | |
| for l in self.ups: | |
| remove_weight_norm(l) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| class SineGen(torch.nn.Module): | |
| """Definition of sine generator | |
| SineGen(samp_rate, harmonic_num = 0, | |
| sine_amp = 0.1, noise_std = 0.003, | |
| voiced_threshold = 0, | |
| flag_for_pulse=False) | |
| samp_rate: sampling rate in Hz | |
| harmonic_num: number of harmonic overtones (default 0) | |
| sine_amp: amplitude of sine-wavefrom (default 0.1) | |
| noise_std: std of Gaussian noise (default 0.003) | |
| voiced_thoreshold: F0 threshold for U/V classification (default 0) | |
| flag_for_pulse: this SinGen is used inside PulseGen (default False) | |
| Note: when flag_for_pulse is True, the first time step of a voiced | |
| segment is always sin(torch.pi) or cos(0) | |
| """ | |
| 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.sampling_rate = samp_rate | |
| self.voiced_threshold = voiced_threshold | |
| def _f02uv(self, f0): | |
| # generate uv signal | |
| uv = torch.ones_like(f0) | |
| uv = uv * (f0 > self.voiced_threshold) | |
| if uv.device.type == "privateuseone": # for DirectML | |
| uv = uv.float() | |
| return uv | |
| def forward(self, f0: torch.Tensor, upp: int): | |
| """sine_tensor, uv = forward(f0) | |
| input F0: tensor(batchsize=1, length, dim=1) | |
| f0 for unvoiced steps should be 0 | |
| output sine_tensor: tensor(batchsize=1, length, dim) | |
| output uv: tensor(batchsize=1, length, 1) | |
| """ | |
| 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) | |
| # fundamental component | |
| f0_buf[:, :, 0] = f0[:, :, 0] | |
| for idx in range(self.harmonic_num): | |
| f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * ( | |
| idx + 2 | |
| ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic | |
| rad_values = (f0_buf / float(self.sampling_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 = F.interpolate( | |
| tmp_over_one.transpose(2, 1), | |
| scale_factor=float(upp), | |
| mode="linear", | |
| align_corners=True, | |
| ).transpose(2, 1) | |
| rad_values = F.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 = F.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): | |
| """SourceModule for hn-nsf | |
| SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, | |
| add_noise_std=0.003, voiced_threshod=0) | |
| sampling_rate: sampling_rate in Hz | |
| harmonic_num: number of harmonic above F0 (default: 0) | |
| sine_amp: amplitude of sine source signal (default: 0.1) | |
| add_noise_std: std of additive Gaussian noise (default: 0.003) | |
| note that amplitude of noise in unvoiced is decided | |
| by sine_amp | |
| voiced_threshold: threhold to set U/V given F0 (default: 0) | |
| Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) | |
| F0_sampled (batchsize, length, 1) | |
| Sine_source (batchsize, length, 1) | |
| noise_source (batchsize, length 1) | |
| uv (batchsize, length, 1) | |
| """ | |
| def __init__( | |
| self, | |
| sampling_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 | |
| # to produce sine waveforms | |
| self.l_sin_gen = SineGen( | |
| sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod | |
| ) | |
| # to merge source harmonics into a single excitation | |
| self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) | |
| self.l_tanh = torch.nn.Tanh() | |
| # self.ddtype:int = -1 | |
| def forward(self, x: torch.Tensor, upp: int = 1): | |
| # if self.ddtype ==-1: | |
| # self.ddtype = self.l_linear.weight.dtype | |
| sine_wavs, uv, _ = self.l_sin_gen(x, upp) | |
| # print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype) | |
| # if self.is_half: | |
| # sine_wavs = sine_wavs.half() | |
| # sine_merge = self.l_tanh(self.l_linear(sine_wavs.to(x))) | |
| # print(sine_wavs.dtype,self.ddtype) | |
| # if sine_wavs.dtype != self.l_linear.weight.dtype: | |
| 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 # noise, uv | |
| 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( | |
| sampling_rate=sr, harmonic_num=0, is_half=is_half | |
| ) | |
| self.noise_convs = nn.ModuleList() | |
| 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)): | |
| c_cur = upsample_initial_channel // (2 ** (i + 1)) | |
| self.ups.append( | |
| weight_norm( | |
| ConvTranspose1d( | |
| upsample_initial_channel // (2**i), | |
| upsample_initial_channel // (2 ** (i + 1)), | |
| k, | |
| u, | |
| padding=(k - u) // 2, | |
| ) | |
| ) | |
| ) | |
| if i + 1 < len(upsample_rates): | |
| stride_f0 = math.prod(upsample_rates[i + 1 :]) | |
| self.noise_convs.append( | |
| Conv1d( | |
| 1, | |
| c_cur, | |
| kernel_size=stride_f0 * 2, | |
| stride=stride_f0, | |
| padding=stride_f0 // 2, | |
| ) | |
| ) | |
| else: | |
| self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) | |
| 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) | |
| self.upp = math.prod(upsample_rates) | |
| self.lrelu_slope = modules.LRELU_SLOPE | |
| def forward(self, x, f0, g: Optional[torch.Tensor] = None): | |
| har_source, noi_source, uv = 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) | |
| # torch.jit.script() does not support direct indexing of torch modules | |
| # That's why I wrote this | |
| for i, (ups, noise_convs) in enumerate(zip(self.ups, self.noise_convs)): | |
| if i < self.num_upsamples: | |
| x = F.leaky_relu(x, self.lrelu_slope) | |
| x = ups(x) | |
| x_source = noise_convs(har_source) | |
| x = x + x_source | |
| xs: Optional[torch.Tensor] = None | |
| l = [i * self.num_kernels + j for j in range(self.num_kernels)] | |
| for j, resblock in enumerate(self.resblocks): | |
| if j in l: | |
| if xs is None: | |
| xs = resblock(x) | |
| else: | |
| xs += resblock(x) | |
| # This assertion cannot be ignored! \ | |
| # If ignored, it will cause torch.jit.script() compilation errors | |
| assert isinstance(xs, torch.Tensor) | |
| 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 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(): | |
| # The hook we want to remove is an instance of WeightNorm class, so | |
| # normally we would do `if isinstance(...)` but this class is not accessible | |
| # because of shadowing, so we check the module name directly. | |
| # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 | |
| if ( | |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.remove_weight_norm(l) | |
| for l in self.resblocks: | |
| for hook in self.resblocks._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 | |
| sr2sr = { | |
| "32k": 32000, | |
| "40k": 40000, | |
| "48k": 48000, | |
| } | |
| class SynthesizerTrnMs256NSFsid(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, | |
| **kwargs | |
| ): | |
| super(SynthesizerTrnMs256NSFsid, self).__init__() | |
| if isinstance(sr, str): | |
| sr = sr2sr[sr] | |
| 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.hop_length = hop_length# | |
| self.spk_embed_dim = spk_embed_dim | |
| self.enc_p = TextEncoder256( | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| float(p_dropout), | |
| ) | |
| 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"], | |
| ) | |
| 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 = 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(): | |
| # The hook we want to remove is an instance of WeightNorm class, so | |
| # normally we would do `if isinstance(...)` but this class is not accessible | |
| # because of shadowing, so we check the module name directly. | |
| # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 | |
| 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 | |
| def forward( | |
| self, | |
| phone: torch.Tensor, | |
| phone_lengths: torch.Tensor, | |
| pitch: torch.Tensor, | |
| pitchf: torch.Tensor, | |
| y: torch.Tensor, | |
| y_lengths: torch.Tensor, | |
| ds: Optional[torch.Tensor] = None, | |
| ): # 这里ds是id，[bs,1] | |
| # print(1,pitch.shape)#[bs,t] | |
| g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 | |
| m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) | |
| 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 = commons.rand_slice_segments( | |
| z, y_lengths, self.segment_size | |
| ) | |
| # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) | |
| pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) | |
| # print(-2,pitchf.shape,z_slice.shape) | |
| o = self.dec(z_slice, pitchf, g=g) | |
| return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) | |
| def infer( | |
| self, | |
| phone: torch.Tensor, | |
| phone_lengths: torch.Tensor, | |
| pitch: torch.Tensor, | |
| nsff0: torch.Tensor, | |
| sid: torch.Tensor, | |
| 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 - rate.item())) | |
| z_p = z_p[:, :, head:] | |
| x_mask = x_mask[:, :, head:] | |
| nsff0 = nsff0[:, head:] | |
| z = self.flow(z_p, x_mask, g=g, reverse=True) | |
| o = self.dec(z * x_mask, nsff0, g=g) | |
| return o, x_mask, (z, z_p, m_p, logs_p) | |
| class SynthesizerTrnMs768NSFsid(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, | |
| **kwargs | |
| ): | |
| super(SynthesizerTrnMs768NSFsid, self).__init__() | |
| if isinstance(sr, str): | |
| sr = sr | |
| 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.hop_length = hop_length# | |
| self.spk_embed_dim = spk_embed_dim | |
| self.enc_p = TextEncoder768( | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| float(p_dropout), | |
| ) | |
| 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"], | |
| ) | |
| 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 = 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(): | |
| # The hook we want to remove is an instance of WeightNorm class, so | |
| # normally we would do `if isinstance(...)` but this class is not accessible | |
| # because of shadowing, so we check the module name directly. | |
| # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 | |
| 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 | |
| def forward( | |
| self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds | |
| ): # 这里ds是id，[bs,1] | |
| # print(1,pitch.shape)#[bs,t] | |
| g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 | |
| m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) | |
| 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 = commons.rand_slice_segments( | |
| z, y_lengths, self.segment_size | |
| ) | |
| # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) | |
| pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) | |
| # print(-2,pitchf.shape,z_slice.shape) | |
| o = self.dec(z_slice, pitchf, g=g) | |
| return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) | |
| def infer( | |
| self, | |
| phone: torch.Tensor, | |
| phone_lengths: torch.Tensor, | |
| pitch: torch.Tensor, | |
| nsff0: torch.Tensor, | |
| sid: torch.Tensor, | |
| 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: | |
| head = int(z_p.shape[2] * (1.0 - rate.item())) | |
| z_p = z_p[:, :, head:] | |
| x_mask = x_mask[:, :, head:] | |
| nsff0 = nsff0[:, head:] | |
| z = self.flow(z_p, x_mask, g=g, reverse=True) | |
| o = self.dec(z * x_mask, nsff0, g=g) | |
| return o, x_mask, (z, z_p, m_p, logs_p) | |
| class SynthesizerTrnMs256NSFsid_nono(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=None, | |
| **kwargs | |
| ): | |
| super(SynthesizerTrnMs256NSFsid_nono, 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.hop_length = hop_length# | |
| self.spk_embed_dim = spk_embed_dim | |
| self.enc_p = TextEncoder256( | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| float(p_dropout), | |
| f0=False, | |
| ) | |
| 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 = 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(): | |
| # The hook we want to remove is an instance of WeightNorm class, so | |
| # normally we would do `if isinstance(...)` but this class is not accessible | |
| # because of shadowing, so we check the module name directly. | |
| # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 | |
| 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 | |
| def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1] | |
| g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 | |
| m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) | |
| 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 = commons.rand_slice_segments( | |
| z, y_lengths, self.segment_size | |
| ) | |
| 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) | |
| def infer( | |
| self, | |
| phone: torch.Tensor, | |
| phone_lengths: torch.Tensor, | |
| sid: torch.Tensor, | |
| rate: Optional[torch.Tensor] = None, | |
| ): | |
| g = self.emb_g(sid).unsqueeze(-1) | |
| m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) | |
| z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask | |
| if rate is not None: | |
| head = int(z_p.shape[2] * (1.0 - rate.item())) | |
| z_p = z_p[:, :, head:] | |
| x_mask = x_mask[:, :, head:] | |
| 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) | |
| class SynthesizerTrnMs768NSFsid_nono(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=None, | |
| **kwargs | |
| ): | |
| super(SynthesizerTrnMs768NSFsid_nono, 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.hop_length = hop_length# | |
| self.spk_embed_dim = spk_embed_dim | |
| self.enc_p = TextEncoder768( | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| float(p_dropout), | |
| f0=False, | |
| ) | |
| 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 = 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(): | |
| # The hook we want to remove is an instance of WeightNorm class, so | |
| # normally we would do `if isinstance(...)` but this class is not accessible | |
| # because of shadowing, so we check the module name directly. | |
| # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 | |
| 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 | |
| def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1] | |
| g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 | |
| m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) | |
| 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 = commons.rand_slice_segments( | |
| z, y_lengths, self.segment_size | |
| ) | |
| 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) | |
| def infer( | |
| self, | |
| phone: torch.Tensor, | |
| phone_lengths: torch.Tensor, | |
| sid: torch.Tensor, | |
| rate: Optional[torch.Tensor] = None, | |
| ): | |
| g = self.emb_g(sid).unsqueeze(-1) | |
| m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) | |
| z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask | |
| if rate is not None: | |
| head = int(z_p.shape[2] * (1.0 - rate.item())) | |
| z_p = z_p[:, :, head:] | |
| x_mask = x_mask[:, :, head:] | |
| 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) | |
| class MultiPeriodDiscriminator(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(MultiPeriodDiscriminator, self).__init__() | |
| periods = [2, 3, 5, 7, 11, 17] | |
| # periods = [3, 5, 7, 11, 17, 23, 37] | |
| 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) | |
| # for j in range(len(fmap_r)): | |
| # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) | |
| 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 MultiPeriodDiscriminatorV2(torch.nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(MultiPeriodDiscriminatorV2, self).__init__() | |
| # periods = [2, 3, 5, 7, 11, 17] | |
| periods = [2, 3, 5, 7, 11, 17, 23, 37] | |
| 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) | |
| # for j in range(len(fmap_r)): | |
| # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) | |
| 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 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)), | |
| ] | |
| ) | |
| self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) | |
| def forward(self, x): | |
| fmap = [] | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| 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 == 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) | |
| if has_xpu and x.dtype == torch.bfloat16: | |
| x = F.pad(x.to(dtype=torch.float16), (0, n_pad), "reflect").to( | |
| dtype=torch.bfloat16 | |
| ) | |
| else: | |
| x = F.pad(x, (0, n_pad), "reflect") | |
| t = t + n_pad | |
| x = x.view(b, c, t // self.period, self.period) | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, modules.LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |