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| import torch | |
| import torch.nn.functional as F | |
| import torch.nn as nn | |
| from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d | |
| from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
| from utils import init_weights, get_padding | |
| import numpy as np | |
| from stft import TorchSTFT | |
| LRELU_SLOPE = 0.1 | |
| def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): | |
| n_channels_int = n_channels[0] | |
| in_act = input_a + input_b | |
| t_act = torch.tanh(in_act[:, :n_channels_int, :]) | |
| s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) | |
| acts = t_act * s_act | |
| return acts | |
| class WN(torch.nn.Module): | |
| def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): | |
| super(WN, self).__init__() | |
| assert(kernel_size % 2 == 1) | |
| 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.p_dropout = p_dropout | |
| self.in_layers = torch.nn.ModuleList() | |
| self.res_skip_layers = torch.nn.ModuleList() | |
| self.drop = nn.Dropout(p_dropout) | |
| if gin_channels != 0: | |
| cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) | |
| self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') | |
| for i in range(n_layers): | |
| dilation = dilation_rate ** i | |
| padding = int((kernel_size * dilation - dilation) / 2) | |
| in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, | |
| dilation=dilation, padding=padding) | |
| in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') | |
| self.in_layers.append(in_layer) | |
| # last one is not necessary | |
| if i < n_layers - 1: | |
| res_skip_channels = 2 * hidden_channels | |
| else: | |
| res_skip_channels = hidden_channels | |
| res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) | |
| res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') | |
| self.res_skip_layers.append(res_skip_layer) | |
| def forward(self, x, x_mask, g=None, **kwargs): | |
| output = torch.zeros_like(x) | |
| n_channels_tensor = torch.IntTensor([self.hidden_channels]) | |
| if g is not None: | |
| g = self.cond_layer(g) | |
| for i in range(self.n_layers): | |
| x_in = self.in_layers[i](x) | |
| if g is not None: | |
| cond_offset = i * 2 * self.hidden_channels | |
| g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] | |
| else: | |
| g_l = torch.zeros_like(x_in) | |
| acts = fused_add_tanh_sigmoid_multiply( | |
| x_in, | |
| g_l, | |
| n_channels_tensor) | |
| acts = self.drop(acts) | |
| res_skip_acts = self.res_skip_layers[i](acts) | |
| if i < self.n_layers - 1: | |
| res_acts = res_skip_acts[:,:self.hidden_channels,:] | |
| x = (x + res_acts) * x_mask | |
| output = output + res_skip_acts[:,self.hidden_channels:,:] | |
| else: | |
| output = output + res_skip_acts | |
| return output * x_mask | |
| def remove_weight_norm(self): | |
| if self.gin_channels != 0: | |
| torch.nn.utils.remove_weight_norm(self.cond_layer) | |
| for l in self.in_layers: | |
| torch.nn.utils.remove_weight_norm(l) | |
| for l in self.res_skip_layers: | |
| torch.nn.utils.remove_weight_norm(l) | |
| class Encoder(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 = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels, 1) | |
| def forward(self, x, x_mask=1, 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) | |
| x = self.proj(x) * x_mask | |
| return x | |
| class ResBlock1(torch.nn.Module): | |
| def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): | |
| super(ResBlock1, self).__init__() | |
| self.h = h | |
| self.convs1 = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], | |
| padding=get_padding(kernel_size, dilation[2]))) | |
| ]) | |
| self.convs1.apply(init_weights) | |
| self.convs2 = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))) | |
| ]) | |
| self.convs2.apply(init_weights) | |
| self.alpha1 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))]) | |
| self.alpha2 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))]) | |
| def forward(self, x): | |
| for c1, c2, a1, a2 in zip(self.convs1, self.convs2, self.alpha1, self.alpha2): | |
| xt = x + (1 / a1) * (torch.sin(a1 * x) ** 2) # Snake1D | |
| xt = c1(xt) | |
| xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2) # Snake1D | |
| xt = c2(xt) | |
| x = xt + x | |
| return x | |
| def remove_weight_norm(self): | |
| for l in self.convs1: | |
| remove_weight_norm(l) | |
| for l in self.convs2: | |
| remove_weight_norm(l) | |
| class ResBlock1_old(torch.nn.Module): | |
| def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): | |
| super(ResBlock1, self).__init__() | |
| self.h = h | |
| self.convs1 = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], | |
| padding=get_padding(kernel_size, dilation[2]))) | |
| ]) | |
| self.convs1.apply(init_weights) | |
| self.convs2 = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, | |
| padding=get_padding(kernel_size, 1))) | |
| ]) | |
| self.convs2.apply(init_weights) | |
| def forward(self, x): | |
| for c1, c2 in zip(self.convs1, self.convs2): | |
| xt = F.leaky_relu(x, LRELU_SLOPE) | |
| xt = c1(xt) | |
| xt = F.leaky_relu(xt, LRELU_SLOPE) | |
| xt = c2(xt) | |
| x = xt + x | |
| return x | |
| def remove_weight_norm(self): | |
| for l in self.convs1: | |
| remove_weight_norm(l) | |
| for l in self.convs2: | |
| remove_weight_norm(l) | |
| class ResBlock2(torch.nn.Module): | |
| def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): | |
| super(ResBlock2, self).__init__() | |
| self.h = h | |
| self.convs = nn.ModuleList([ | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], | |
| padding=get_padding(kernel_size, dilation[0]))), | |
| weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], | |
| padding=get_padding(kernel_size, dilation[1]))) | |
| ]) | |
| self.convs.apply(init_weights) | |
| def forward(self, x): | |
| for c in self.convs: | |
| xt = F.leaky_relu(x, LRELU_SLOPE) | |
| xt = c(xt) | |
| x = xt + x | |
| return x | |
| def remove_weight_norm(self): | |
| for l in self.convs: | |
| remove_weight_norm(l) | |
| 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(np.pi) or cos(0) | |
| """ | |
| def __init__(self, samp_rate, upsample_scale, 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 | |
| self.flag_for_pulse = flag_for_pulse | |
| self.upsample_scale = upsample_scale | |
| def _f02uv(self, f0): | |
| # generate uv signal | |
| uv = (f0 > self.voiced_threshold).type(torch.float32) | |
| return uv | |
| def _f02sine(self, f0_values): | |
| """ f0_values: (batchsize, length, dim) | |
| where dim indicates fundamental tone and overtones | |
| """ | |
| # convert to F0 in rad. The interger part n can be ignored | |
| # because 2 * np.pi * n doesn't affect phase | |
| rad_values = (f0_values / self.sampling_rate) % 1 | |
| # initial phase noise (no noise for fundamental component) | |
| rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ | |
| device=f0_values.device) | |
| rand_ini[:, 0] = 0 | |
| rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini | |
| # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) | |
| if not self.flag_for_pulse: | |
| # # for normal case | |
| # # To prevent torch.cumsum numerical overflow, | |
| # # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. | |
| # # Buffer tmp_over_one_idx indicates the time step to add -1. | |
| # # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi | |
| # tmp_over_one = torch.cumsum(rad_values, 1) % 1 | |
| # tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 | |
| # cumsum_shift = torch.zeros_like(rad_values) | |
| # cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 | |
| # phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi | |
| rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), | |
| scale_factor=1/self.upsample_scale, | |
| mode="linear").transpose(1, 2) | |
| # tmp_over_one = torch.cumsum(rad_values, 1) % 1 | |
| # tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 | |
| # cumsum_shift = torch.zeros_like(rad_values) | |
| # cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 | |
| phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi | |
| phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, | |
| scale_factor=self.upsample_scale, mode="linear").transpose(1, 2) | |
| sines = torch.sin(phase) | |
| else: | |
| # If necessary, make sure that the first time step of every | |
| # voiced segments is sin(pi) or cos(0) | |
| # This is used for pulse-train generation | |
| # identify the last time step in unvoiced segments | |
| uv = self._f02uv(f0_values) | |
| uv_1 = torch.roll(uv, shifts=-1, dims=1) | |
| uv_1[:, -1, :] = 1 | |
| u_loc = (uv < 1) * (uv_1 > 0) | |
| # get the instantanouse phase | |
| tmp_cumsum = torch.cumsum(rad_values, dim=1) | |
| # different batch needs to be processed differently | |
| for idx in range(f0_values.shape[0]): | |
| temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] | |
| temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] | |
| # stores the accumulation of i.phase within | |
| # each voiced segments | |
| tmp_cumsum[idx, :, :] = 0 | |
| tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum | |
| # rad_values - tmp_cumsum: remove the accumulation of i.phase | |
| # within the previous voiced segment. | |
| i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) | |
| # get the sines | |
| sines = torch.cos(i_phase * 2 * np.pi) | |
| return sines | |
| def forward(self, f0): | |
| """ 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) | |
| """ | |
| f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, | |
| device=f0.device) | |
| # fundamental component | |
| fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) | |
| # generate sine waveforms | |
| sine_waves = self._f02sine(fn) * self.sine_amp | |
| # generate uv signal | |
| # uv = torch.ones(f0.shape) | |
| # uv = uv * (f0 > self.voiced_threshold) | |
| uv = self._f02uv(f0) | |
| # noise: for unvoiced should be similar to sine_amp | |
| # std = self.sine_amp/3 -> max value ~ self.sine_amp | |
| # . for voiced regions is self.noise_std | |
| noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 | |
| noise = noise_amp * torch.randn_like(sine_waves) | |
| # first: set the unvoiced part to 0 by uv | |
| # then: additive noise | |
| 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, upsample_scale, harmonic_num=0, sine_amp=0.1, | |
| add_noise_std=0.003, voiced_threshod=0): | |
| super(SourceModuleHnNSF, self).__init__() | |
| self.sine_amp = sine_amp | |
| self.noise_std = add_noise_std | |
| # to produce sine waveforms | |
| self.l_sin_gen = SineGen(sampling_rate, upsample_scale, 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() | |
| def forward(self, x): | |
| """ | |
| Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) | |
| F0_sampled (batchsize, length, 1) | |
| Sine_source (batchsize, length, 1) | |
| noise_source (batchsize, length 1) | |
| """ | |
| # source for harmonic branch | |
| with torch.no_grad(): | |
| sine_wavs, uv, _ = self.l_sin_gen(x) | |
| sine_merge = self.l_tanh(self.l_linear(sine_wavs)) | |
| # source for noise branch, in the same shape as uv | |
| noise = torch.randn_like(uv) * self.sine_amp / 3 | |
| return sine_merge, noise, uv | |
| def padDiff(x): | |
| return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) | |
| class Generator(torch.nn.Module): | |
| def __init__(self, h, F0_model): | |
| super(Generator, self).__init__() | |
| self.h = h | |
| self.num_kernels = len(h.resblock_kernel_sizes) | |
| self.num_upsamples = len(h.upsample_rates) | |
| resblock = ResBlock1 if h.resblock == '1' else ResBlock2 | |
| self.m_source = SourceModuleHnNSF( | |
| sampling_rate=h.sampling_rate, | |
| upsample_scale=np.prod(h.upsample_rates) * h.gen_istft_hop_size, | |
| harmonic_num=8, voiced_threshod=10) | |
| self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h.upsample_rates) * h.gen_istft_hop_size) | |
| self.noise_convs = nn.ModuleList() | |
| self.noise_res = nn.ModuleList() | |
| self.F0_model = F0_model | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): | |
| self.ups.append(weight_norm( | |
| ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)), | |
| k, u, padding=(k-u)//2))) | |
| c_cur = h.upsample_initial_channel // (2 ** (i + 1)) | |
| if i + 1 < len(h.upsample_rates): # | |
| stride_f0 = np.prod(h.upsample_rates[i + 1:]) | |
| self.noise_convs.append(Conv1d( | |
| h.gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2)) | |
| self.noise_res.append(resblock(h, c_cur, 7, [1,3,5])) | |
| else: | |
| self.noise_convs.append(Conv1d(h.gen_istft_n_fft + 2, c_cur, kernel_size=1)) | |
| self.noise_res.append(resblock(h, c_cur, 11, [1,3,5])) | |
| self.resblocks = nn.ModuleList() | |
| for i in range(len(self.ups)): | |
| ch = h.upsample_initial_channel//(2**(i+1)) | |
| for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): | |
| self.resblocks.append(resblock(h, ch, k, d)) | |
| self.post_n_fft = h.gen_istft_n_fft | |
| self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3)) | |
| self.ups.apply(init_weights) | |
| self.conv_post.apply(init_weights) | |
| self.reflection_pad = torch.nn.ReflectionPad1d((1, 0)) | |
| self.stft = TorchSTFT(filter_length=h.gen_istft_n_fft, hop_length=h.gen_istft_hop_size, win_length=h.gen_istft_n_fft) | |
| gin_channels = 256 | |
| inter_channels = hidden_channels = h.upsample_initial_channel - gin_channels | |
| self.embed_spk = nn.Embedding(108, gin_channels) | |
| self.enc = Encoder(768, inter_channels, hidden_channels, 5, 1, 4) | |
| self.dec = Encoder(inter_channels, inter_channels, hidden_channels, 5, 1, 20, gin_channels=gin_channels) | |
| def forward(self, x, mel, spk_emb, spk_id): | |
| g = self.embed_spk(spk_id).transpose(1, 2) | |
| g = g + spk_emb.unsqueeze(-1) | |
| f0, _, _ = self.F0_model(mel.unsqueeze(1)) | |
| f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t | |
| har_source, _, _ = self.m_source(f0) | |
| har_source = har_source.transpose(1, 2).squeeze(1) | |
| har_spec, har_phase = self.stft.transform(har_source) | |
| har = torch.cat([har_spec, har_phase], dim=1) | |
| x = self.enc(x) | |
| x = self.dec(x, g=g) | |
| g = g.repeat(1, 1, x.shape[-1]) | |
| x = torch.cat([x, g], dim=1) | |
| for i in range(self.num_upsamples): | |
| x = F.leaky_relu(x, LRELU_SLOPE) | |
| x_source = self.noise_convs[i](har) | |
| x_source = self.noise_res[i](x_source) | |
| x = self.ups[i](x) | |
| if i == self.num_upsamples - 1: | |
| x = self.reflection_pad(x) | |
| x = x + x_source | |
| 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) | |
| spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) | |
| phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) | |
| return spec, phase | |
| def get_f0(self, mel, f0_mean_tgt, voiced_threshold=10): | |
| f0, _, _ = self.F0_model(mel.unsqueeze(1)) | |
| voiced = f0 > voiced_threshold | |
| lf0 = torch.log(f0) | |
| lf0_ = lf0 * voiced.float() | |
| lf0_mean = lf0_.sum(1) / voiced.float().sum(1) | |
| lf0_mean = lf0_mean.unsqueeze(1) | |
| lf0_adj = lf0 - lf0_mean + torch.log(f0_mean_tgt) | |
| f0_adj = torch.exp(lf0_adj) | |
| energy = mel.sum(1) | |
| unsilent = energy > -700 | |
| unsilent = unsilent | voiced # simple vad | |
| f0_adj = f0_adj * unsilent.float() | |
| return f0_adj | |
| def get_x(self, x, spk_emb, spk_id): | |
| g = self.embed_spk(spk_id).transpose(1, 2) | |
| g = g + spk_emb.unsqueeze(-1) | |
| x = self.enc(x) | |
| x = self.dec(x, g=g) | |
| g = g.repeat(1, 1, x.shape[-1]) | |
| x = torch.cat([x, g], dim=1) | |
| return x | |
| def infer(self, x, f0): | |
| f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t | |
| har_source, _, _ = self.m_source(f0) | |
| har_source = har_source.transpose(1, 2).squeeze(1) | |
| har_spec, har_phase = self.stft.transform(har_source) | |
| har = torch.cat([har_spec, har_phase], dim=1) | |
| for i in range(self.num_upsamples): | |
| x = F.leaky_relu(x, LRELU_SLOPE) | |
| x_source = self.noise_convs[i](har) | |
| x_source = self.noise_res[i](x_source) | |
| x = self.ups[i](x) | |
| if i == self.num_upsamples - 1: | |
| x = self.reflection_pad(x) | |
| x = x + x_source | |
| 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) | |
| spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) | |
| phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) | |
| y = self.stft.inverse(spec, phase) | |
| return y | |
| def remove_weight_norm(self): | |
| print('Removing weight norm...') | |
| for l in self.ups: | |
| remove_weight_norm(l) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| remove_weight_norm(self.conv_post) | |
| def stft(x, fft_size, hop_size, win_length, window): | |
| """Perform STFT and convert to magnitude spectrogram. | |
| Args: | |
| x (Tensor): Input signal tensor (B, T). | |
| fft_size (int): FFT size. | |
| hop_size (int): Hop size. | |
| win_length (int): Window length. | |
| window (str): Window function type. | |
| Returns: | |
| Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). | |
| """ | |
| x_stft = torch.stft(x, fft_size, hop_size, win_length, window, | |
| return_complex=True) | |
| real = x_stft[..., 0] | |
| imag = x_stft[..., 1] | |
| # NOTE(kan-bayashi): clamp is needed to avoid nan or inf | |
| return torch.abs(x_stft).transpose(2, 1) | |
| class SpecDiscriminator(nn.Module): | |
| """docstring for Discriminator.""" | |
| def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False): | |
| super(SpecDiscriminator, self).__init__() | |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
| self.fft_size = fft_size | |
| self.shift_size = shift_size | |
| self.win_length = win_length | |
| self.window = getattr(torch, window)(win_length) | |
| self.discriminators = nn.ModuleList([ | |
| norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))), | |
| norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), | |
| norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), | |
| norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), | |
| norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1,1), padding=(1, 1))), | |
| ]) | |
| self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1)) | |
| def forward(self, y): | |
| fmap = [] | |
| y = y.squeeze(1) | |
| y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device())) | |
| y = y.unsqueeze(1) | |
| for i, d in enumerate(self.discriminators): | |
| y = d(y) | |
| y = F.leaky_relu(y, LRELU_SLOPE) | |
| fmap.append(y) | |
| y = self.out(y) | |
| fmap.append(y) | |
| return torch.flatten(y, 1, -1), fmap | |
| class MultiResSpecDiscriminator(torch.nn.Module): | |
| def __init__(self, | |
| fft_sizes=[1024, 2048, 512], | |
| hop_sizes=[120, 240, 50], | |
| win_lengths=[600, 1200, 240], | |
| window="hann_window"): | |
| super(MultiResSpecDiscriminator, self).__init__() | |
| self.discriminators = nn.ModuleList([ | |
| SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window), | |
| SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window), | |
| SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window) | |
| ]) | |
| 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) | |
| fmap_rs.append(fmap_r) | |
| y_d_gs.append(y_d_g) | |
| fmap_gs.append(fmap_g) | |
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
| 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 | |
| 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(5, 1), 0))), | |
| norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), | |
| norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), | |
| norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), | |
| norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 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 l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, 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): | |
| super(MultiPeriodDiscriminator, self).__init__() | |
| self.discriminators = nn.ModuleList([ | |
| DiscriminatorP(2), | |
| DiscriminatorP(3), | |
| DiscriminatorP(5), | |
| DiscriminatorP(7), | |
| DiscriminatorP(11), | |
| ]) | |
| 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) | |
| fmap_rs.append(fmap_r) | |
| y_d_gs.append(y_d_g) | |
| 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, 128, 15, 1, padding=7)), | |
| norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), | |
| norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), | |
| norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), | |
| norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), | |
| norm_f(Conv1d(1024, 1024, 41, 1, groups=16, 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, LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| class MultiScaleDiscriminator(torch.nn.Module): | |
| def __init__(self): | |
| super(MultiScaleDiscriminator, self).__init__() | |
| self.discriminators = nn.ModuleList([ | |
| DiscriminatorS(use_spectral_norm=True), | |
| DiscriminatorS(), | |
| DiscriminatorS(), | |
| ]) | |
| self.meanpools = nn.ModuleList([ | |
| AvgPool1d(4, 2, padding=2), | |
| AvgPool1d(4, 2, padding=2) | |
| ]) | |
| def forward(self, y, y_hat): | |
| y_d_rs = [] | |
| y_d_gs = [] | |
| fmap_rs = [] | |
| fmap_gs = [] | |
| for i, d in enumerate(self.discriminators): | |
| if i != 0: | |
| y = self.meanpools[i-1](y) | |
| y_hat = self.meanpools[i-1](y_hat) | |
| y_d_r, fmap_r = d(y) | |
| y_d_g, fmap_g = d(y_hat) | |
| y_d_rs.append(y_d_r) | |
| fmap_rs.append(fmap_r) | |
| y_d_gs.append(y_d_g) | |
| fmap_gs.append(fmap_g) | |
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
| def feature_loss(fmap_r, fmap_g): | |
| loss = 0 | |
| for dr, dg in zip(fmap_r, fmap_g): | |
| for rl, gl in zip(dr, dg): | |
| loss += torch.mean(torch.abs(rl - gl)) | |
| return loss*2 | |
| def discriminator_loss(disc_real_outputs, disc_generated_outputs): | |
| loss = 0 | |
| r_losses = [] | |
| g_losses = [] | |
| for dr, dg in zip(disc_real_outputs, disc_generated_outputs): | |
| r_loss = torch.mean((1-dr)**2) | |
| g_loss = torch.mean(dg**2) | |
| loss += (r_loss + g_loss) | |
| r_losses.append(r_loss.item()) | |
| g_losses.append(g_loss.item()) | |
| return loss, r_losses, g_losses | |
| def generator_loss(disc_outputs): | |
| loss = 0 | |
| gen_losses = [] | |
| for dg in disc_outputs: | |
| l = torch.mean((1-dg)**2) | |
| gen_losses.append(l) | |
| loss += l | |
| return loss, gen_losses | |
| def discriminator_TPRLS_loss(disc_real_outputs, disc_generated_outputs): | |
| loss = 0 | |
| for dr, dg in zip(disc_real_outputs, disc_generated_outputs): | |
| tau = 0.04 | |
| m_DG = torch.median((dr-dg)) | |
| L_rel = torch.mean((((dr - dg) - m_DG)**2)[dr < dg + m_DG]) | |
| loss += tau - F.relu(tau - L_rel) | |
| return loss | |
| def generator_TPRLS_loss(disc_real_outputs, disc_generated_outputs): | |
| loss = 0 | |
| for dg, dr in zip(disc_real_outputs, disc_generated_outputs): | |
| tau = 0.04 | |
| m_DG = torch.median((dr-dg)) | |
| L_rel = torch.mean((((dr - dg) - m_DG)**2)[dr < dg + m_DG]) | |
| loss += tau - F.relu(tau - L_rel) | |
| return loss | |