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| import warnings | |
| from logging import getLogger | |
| from typing import Any, Literal, Sequence | |
| import torch | |
| from torch import nn | |
| import so_vits_svc_fork.f0 | |
| from so_vits_svc_fork.f0 import f0_to_coarse | |
| from so_vits_svc_fork.modules import commons as commons | |
| from so_vits_svc_fork.modules.decoders.f0 import F0Decoder | |
| from so_vits_svc_fork.modules.decoders.hifigan import NSFHifiGANGenerator | |
| from so_vits_svc_fork.modules.decoders.mb_istft import ( | |
| Multiband_iSTFT_Generator, | |
| Multistream_iSTFT_Generator, | |
| iSTFT_Generator, | |
| ) | |
| from so_vits_svc_fork.modules.encoders import Encoder, TextEncoder | |
| from so_vits_svc_fork.modules.flows import ResidualCouplingBlock | |
| LOG = getLogger(__name__) | |
| class SynthesizerTrn(nn.Module): | |
| """ | |
| Synthesizer for Training | |
| """ | |
| def __init__( | |
| self, | |
| spec_channels: int, | |
| segment_size: int, | |
| inter_channels: int, | |
| hidden_channels: int, | |
| filter_channels: int, | |
| n_heads: int, | |
| n_layers: int, | |
| kernel_size: int, | |
| p_dropout: int, | |
| resblock: str, | |
| resblock_kernel_sizes: Sequence[int], | |
| resblock_dilation_sizes: Sequence[Sequence[int]], | |
| upsample_rates: Sequence[int], | |
| upsample_initial_channel: int, | |
| upsample_kernel_sizes: Sequence[int], | |
| gin_channels: int, | |
| ssl_dim: int, | |
| n_speakers: int, | |
| sampling_rate: int = 44100, | |
| type_: Literal["hifi-gan", "istft", "ms-istft", "mb-istft"] = "hifi-gan", | |
| gen_istft_n_fft: int = 16, | |
| gen_istft_hop_size: int = 4, | |
| subbands: int = 4, | |
| **kwargs: Any, | |
| ): | |
| 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.n_speakers = n_speakers | |
| self.sampling_rate = sampling_rate | |
| self.type_ = type_ | |
| self.gen_istft_n_fft = gen_istft_n_fft | |
| self.gen_istft_hop_size = gen_istft_hop_size | |
| self.subbands = subbands | |
| if kwargs: | |
| warnings.warn(f"Unused arguments: {kwargs}") | |
| self.emb_g = nn.Embedding(n_speakers, gin_channels) | |
| if ssl_dim is None: | |
| self.pre = nn.LazyConv1d(hidden_channels, kernel_size=5, padding=2) | |
| else: | |
| 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, | |
| ) | |
| LOG.info(f"Decoder type: {type_}") | |
| if type_ == "hifi-gan": | |
| 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 = NSFHifiGANGenerator(h=hps) | |
| self.mb = False | |
| else: | |
| hps = { | |
| "initial_channel": 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, | |
| "gen_istft_n_fft": gen_istft_n_fft, | |
| "gen_istft_hop_size": gen_istft_hop_size, | |
| "subbands": subbands, | |
| } | |
| # gen_istft_n_fft, gen_istft_hop_size, subbands | |
| if type_ == "istft": | |
| del hps["subbands"] | |
| self.dec = iSTFT_Generator(**hps) | |
| elif type_ == "ms-istft": | |
| self.dec = Multistream_iSTFT_Generator(**hps) | |
| elif type_ == "mb-istft": | |
| self.dec = Multiband_iSTFT_Generator(**hps) | |
| else: | |
| raise ValueError(f"Unknown type: {type_}") | |
| self.mb = True | |
| 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) | |
| # ssl prenet | |
| 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) | |
| # f0 predict | |
| lf0 = 2595.0 * torch.log10(1.0 + f0.unsqueeze(1) / 700.0) / 500 | |
| norm_lf0 = so_vits_svc_fork.f0.normalize_f0(lf0, x_mask, uv) | |
| pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g) | |
| # encoder | |
| 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) | |
| # flow | |
| 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 | |
| ) | |
| # MB-iSTFT-VITS | |
| if self.mb: | |
| o, o_mb = self.dec(z_slice, g=g) | |
| # HiFi-GAN | |
| else: | |
| o = self.dec(z_slice, g=g, f0=pitch_slice) | |
| o_mb = None | |
| return ( | |
| o, | |
| o_mb, | |
| 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 = 2595.0 * torch.log10(1.0 + f0.unsqueeze(1) / 700.0) / 500 | |
| norm_lf0 = so_vits_svc_fork.f0.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) | |
| # MB-iSTFT-VITS | |
| if self.mb: | |
| o, o_mb = self.dec(z * c_mask, g=g) | |
| else: | |
| o = self.dec(z * c_mask, g=g, f0=f0) | |
| return o | |