| import copy |
| import math |
| import torch |
| from torch import nn |
| from torch.nn import functional as F |
|
|
| import modules.attentions as attentions |
| import modules.commons as commons |
| import modules.modules as modules |
|
|
| from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d |
| from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm |
|
|
| import utils |
| from modules.commons import init_weights, get_padding |
| from vdecoder.hifigan.models import Generator |
| from utils import f0_to_coarse |
|
|
| class ResidualCouplingBlock(nn.Module): |
| def __init__(self, |
| channels, |
| hidden_channels, |
| kernel_size, |
| dilation_rate, |
| n_layers, |
| n_flows=4, |
| gin_channels=0): |
| super().__init__() |
| self.channels = channels |
| self.hidden_channels = hidden_channels |
| self.kernel_size = kernel_size |
| self.dilation_rate = dilation_rate |
| self.n_layers = n_layers |
| self.n_flows = n_flows |
| self.gin_channels = gin_channels |
|
|
| self.flows = nn.ModuleList() |
| for i in range(n_flows): |
| self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) |
| self.flows.append(modules.Flip()) |
|
|
| def forward(self, x, x_mask, g=None, reverse=False): |
| if not reverse: |
| for flow in self.flows: |
| x, _ = flow(x, x_mask, g=g, reverse=reverse) |
| else: |
| for flow in reversed(self.flows): |
| x = flow(x, x_mask, g=g, reverse=reverse) |
| return x |
|
|
|
|
| class 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 = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) |
|
|
| def forward(self, x, x_lengths, g=None): |
| |
| x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) |
| x = self.pre(x) * x_mask |
| x = self.enc(x, x_mask, g=g) |
| stats = self.proj(x) * x_mask |
| m, logs = torch.split(stats, self.out_channels, dim=1) |
| z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask |
| return z, m, logs, x_mask |
|
|
|
|
| class TextEncoder(nn.Module): |
| def __init__(self, |
| out_channels, |
| hidden_channels, |
| kernel_size, |
| n_layers, |
| gin_channels=0, |
| filter_channels=None, |
| n_heads=None, |
| p_dropout=None): |
| super().__init__() |
| self.out_channels = out_channels |
| self.hidden_channels = hidden_channels |
| self.kernel_size = kernel_size |
| self.n_layers = n_layers |
| self.gin_channels = gin_channels |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) |
| self.f0_emb = nn.Embedding(256, hidden_channels) |
|
|
| self.enc_ = attentions.Encoder( |
| hidden_channels, |
| filter_channels, |
| n_heads, |
| n_layers, |
| kernel_size, |
| p_dropout) |
|
|
| def forward(self, x, x_mask, f0=None, noice_scale=1): |
| x = x + self.f0_emb(f0).transpose(1,2) |
| x = self.enc_(x * x_mask, x_mask) |
| 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) * noice_scale) * x_mask |
|
|
| return z, m, logs, x_mask |
|
|
|
|
|
|
| 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 = [] |
|
|
| |
| b, c, t = x.shape |
| if t % self.period != 0: |
| 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, modules.LRELU_SLOPE) |
| fmap.append(x) |
| x = self.conv_post(x) |
| fmap.append(x) |
| x = torch.flatten(x, 1, -1) |
|
|
| return x, fmap |
|
|
|
|
| class DiscriminatorS(torch.nn.Module): |
| def __init__(self, use_spectral_norm=False): |
| super(DiscriminatorS, self).__init__() |
| norm_f = weight_norm if use_spectral_norm == 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 MultiPeriodDiscriminator(torch.nn.Module): |
| def __init__(self, use_spectral_norm=False): |
| super(MultiPeriodDiscriminator, self).__init__() |
| periods = [2,3,5,7,11] |
|
|
| discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] |
| discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] |
| self.discriminators = nn.ModuleList(discs) |
|
|
| def forward(self, y, y_hat): |
| y_d_rs = [] |
| y_d_gs = [] |
| fmap_rs = [] |
| fmap_gs = [] |
| for i, d in enumerate(self.discriminators): |
| y_d_r, fmap_r = d(y) |
| y_d_g, fmap_g = d(y_hat) |
| y_d_rs.append(y_d_r) |
| y_d_gs.append(y_d_g) |
| fmap_rs.append(fmap_r) |
| fmap_gs.append(fmap_g) |
|
|
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
|
|
|
|
| class SpeakerEncoder(torch.nn.Module): |
| def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256): |
| super(SpeakerEncoder, self).__init__() |
| self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) |
| self.linear = nn.Linear(model_hidden_size, model_embedding_size) |
| self.relu = nn.ReLU() |
|
|
| def forward(self, mels): |
| self.lstm.flatten_parameters() |
| _, (hidden, _) = self.lstm(mels) |
| embeds_raw = self.relu(self.linear(hidden[-1])) |
| return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) |
|
|
| def compute_partial_slices(self, total_frames, partial_frames, partial_hop): |
| mel_slices = [] |
| for i in range(0, total_frames-partial_frames, partial_hop): |
| mel_range = torch.arange(i, i+partial_frames) |
| mel_slices.append(mel_range) |
|
|
| return mel_slices |
|
|
| def embed_utterance(self, mel, partial_frames=128, partial_hop=64): |
| mel_len = mel.size(1) |
| last_mel = mel[:,-partial_frames:] |
|
|
| if mel_len > partial_frames: |
| mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop) |
| mels = list(mel[:,s] for s in mel_slices) |
| mels.append(last_mel) |
| mels = torch.stack(tuple(mels), 0).squeeze(1) |
|
|
| with torch.no_grad(): |
| partial_embeds = self(mels) |
| embed = torch.mean(partial_embeds, axis=0).unsqueeze(0) |
| |
| else: |
| with torch.no_grad(): |
| embed = self(last_mel) |
|
|
| return embed |
|
|
| class F0Decoder(nn.Module): |
| def __init__(self, |
| out_channels, |
| hidden_channels, |
| filter_channels, |
| n_heads, |
| n_layers, |
| kernel_size, |
| p_dropout, |
| spk_channels=0): |
| super().__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 = p_dropout |
| self.spk_channels = spk_channels |
|
|
| self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1) |
| self.decoder = attentions.FFT( |
| hidden_channels, |
| filter_channels, |
| n_heads, |
| n_layers, |
| kernel_size, |
| p_dropout) |
| self.proj = nn.Conv1d(hidden_channels, out_channels, 1) |
| self.f0_prenet = nn.Conv1d(1, hidden_channels , 3, padding=1) |
| self.cond = nn.Conv1d(spk_channels, hidden_channels, 1) |
|
|
| def forward(self, x, norm_f0, x_mask, spk_emb=None): |
| x = torch.detach(x) |
| if (spk_emb is not None): |
| x = x + self.cond(spk_emb) |
| x += self.f0_prenet(norm_f0) |
| x = self.prenet(x) * x_mask |
| x = self.decoder(x * x_mask, x_mask) |
| x = self.proj(x) * x_mask |
| return x |
|
|
|
|
| class SynthesizerTrn(nn.Module): |
| """ |
| Synthesizer for Training |
| """ |
|
|
| 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, |
| gin_channels, |
| ssl_dim, |
| n_speakers, |
| sampling_rate=44100, |
| **kwargs): |
|
|
| super().__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 = 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.ssl_dim = ssl_dim |
| self.emb_g = nn.Embedding(n_speakers, gin_channels) |
|
|
| self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2) |
|
|
| self.enc_p = TextEncoder( |
| inter_channels, |
| hidden_channels, |
| filter_channels=filter_channels, |
| n_heads=n_heads, |
| n_layers=n_layers, |
| kernel_size=kernel_size, |
| p_dropout=p_dropout |
| ) |
| hps = { |
| "sampling_rate": sampling_rate, |
| "inter_channels": inter_channels, |
| "resblock": resblock, |
| "resblock_kernel_sizes": resblock_kernel_sizes, |
| "resblock_dilation_sizes": resblock_dilation_sizes, |
| "upsample_rates": upsample_rates, |
| "upsample_initial_channel": upsample_initial_channel, |
| "upsample_kernel_sizes": upsample_kernel_sizes, |
| "gin_channels": gin_channels, |
| } |
| self.dec = Generator(h=hps) |
| self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) |
| self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) |
| self.f0_decoder = F0Decoder( |
| 1, |
| hidden_channels, |
| filter_channels, |
| n_heads, |
| n_layers, |
| kernel_size, |
| p_dropout, |
| spk_channels=gin_channels |
| ) |
| self.emb_uv = nn.Embedding(2, hidden_channels) |
|
|
| def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None): |
| g = self.emb_g(g).transpose(1,2) |
| |
| x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype) |
| x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2) |
|
|
| |
| lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500 |
| norm_lf0 = utils.normalize_f0(lf0, x_mask, uv) |
| pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g) |
|
|
| |
| z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0)) |
| z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) |
|
|
| |
| z_p = self.flow(z, spec_mask, g=g) |
| z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size) |
|
|
| |
| o = self.dec(z_slice, g=g, f0=pitch_slice) |
|
|
| return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 |
|
|
| def infer(self, c, f0, uv, g=None, noice_scale=0.35, predict_f0=False): |
| c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) |
| g = self.emb_g(g).transpose(1,2) |
| x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype) |
| x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2) |
|
|
| if predict_f0: |
| lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500 |
| norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False) |
| pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g) |
| f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1) |
|
|
| z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale) |
| z = self.flow(z_p, c_mask, g=g, reverse=True) |
| o = self.dec(z * c_mask, g=g, f0=f0) |
| return o |
|
|
