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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # This code is modified from https://github.com/svc-develop-team/so-vits-svc/blob/4.1-Stable/models.py | |
| import copy | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from utils.util import * | |
| from modules.transformer.attentions import Encoder | |
| from models.tts.vits.vits import ResidualCouplingBlock, PosteriorEncoder | |
| from models.vocoders.gan.generator.bigvgan import BigVGAN | |
| from models.vocoders.gan.generator.hifigan import HiFiGAN | |
| from models.vocoders.gan.generator.nsfhifigan import NSFHiFiGAN | |
| from models.vocoders.gan.generator.melgan import MelGAN | |
| from models.vocoders.gan.generator.apnet import APNet | |
| from modules.encoder.condition_encoder import ConditionEncoder | |
| def slice_pitch_segments(x, ids_str, segment_size=4): | |
| ret = torch.zeros_like(x[:, :segment_size]) | |
| for i in range(x.size(0)): | |
| idx_str = ids_str[i] | |
| idx_end = idx_str + segment_size | |
| ret[i] = x[i, idx_str:idx_end] | |
| return ret | |
| def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4): | |
| b, d, t = x.size() | |
| if x_lengths is None: | |
| x_lengths = t | |
| ids_str_max = x_lengths - segment_size + 1 | |
| ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) | |
| ret = slice_segments(x, ids_str, segment_size) | |
| ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size) | |
| return ret, ret_pitch, ids_str | |
| class ContentEncoder(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.f0_emb = nn.Embedding(256, hidden_channels) | |
| self.enc_ = Encoder( | |
| hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout | |
| ) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| # condition_encoder ver. | |
| def forward(self, x, x_mask, noice_scale=1): | |
| 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 SynthesizerTrn(nn.Module): | |
| """ | |
| Synthesizer for Training | |
| """ | |
| def __init__(self, spec_channels, segment_size, cfg): | |
| super().__init__() | |
| self.spec_channels = spec_channels | |
| self.segment_size = segment_size | |
| self.cfg = cfg | |
| self.inter_channels = cfg.model.vits.inter_channels | |
| self.hidden_channels = cfg.model.vits.hidden_channels | |
| self.filter_channels = cfg.model.vits.filter_channels | |
| self.n_heads = cfg.model.vits.n_heads | |
| self.n_layers = cfg.model.vits.n_layers | |
| self.kernel_size = cfg.model.vits.kernel_size | |
| self.p_dropout = cfg.model.vits.p_dropout | |
| self.n_flow_layer = cfg.model.vits.n_flow_layer | |
| self.gin_channels = cfg.model.vits.gin_channels | |
| self.n_speakers = cfg.model.vits.n_speakers | |
| # f0 | |
| self.n_bins = cfg.preprocess.pitch_bin | |
| self.f0_min = cfg.preprocess.f0_min | |
| self.f0_max = cfg.preprocess.f0_max | |
| # TODO: sort out the config | |
| self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min | |
| self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max | |
| self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) | |
| self.emb_g = nn.Embedding(self.n_speakers, self.gin_channels) | |
| self.enc_p = ContentEncoder( | |
| self.inter_channels, | |
| self.hidden_channels, | |
| filter_channels=self.filter_channels, | |
| n_heads=self.n_heads, | |
| n_layers=self.n_layers, | |
| kernel_size=self.kernel_size, | |
| p_dropout=self.p_dropout, | |
| ) | |
| assert cfg.model.generator in [ | |
| "bigvgan", | |
| "hifigan", | |
| "melgan", | |
| "nsfhifigan", | |
| "apnet", | |
| ] | |
| self.dec_name = cfg.model.generator | |
| temp_cfg = copy.deepcopy(cfg) | |
| temp_cfg.preprocess.n_mel = self.inter_channels | |
| if cfg.model.generator == "bigvgan": | |
| temp_cfg.model.bigvgan = cfg.model.generator_config.bigvgan | |
| self.dec = BigVGAN(temp_cfg) | |
| elif cfg.model.generator == "hifigan": | |
| temp_cfg.model.hifigan = cfg.model.generator_config.hifigan | |
| self.dec = HiFiGAN(temp_cfg) | |
| elif cfg.model.generator == "melgan": | |
| temp_cfg.model.melgan = cfg.model.generator_config.melgan | |
| self.dec = MelGAN(temp_cfg) | |
| elif cfg.model.generator == "nsfhifigan": | |
| temp_cfg.model.nsfhifigan = cfg.model.generator_config.nsfhifigan | |
| self.dec = NSFHiFiGAN(temp_cfg) # TODO: nsf need f0 | |
| elif cfg.model.generator == "apnet": | |
| temp_cfg.model.apnet = cfg.model.generator_config.apnet | |
| self.dec = APNet(temp_cfg) | |
| self.enc_q = PosteriorEncoder( | |
| self.spec_channels, | |
| self.inter_channels, | |
| self.hidden_channels, | |
| 5, | |
| 1, | |
| 16, | |
| gin_channels=self.gin_channels, | |
| ) | |
| self.flow = ResidualCouplingBlock( | |
| self.inter_channels, | |
| self.hidden_channels, | |
| 5, | |
| 1, | |
| self.n_flow_layer, | |
| gin_channels=self.gin_channels, | |
| ) | |
| def forward(self, data): | |
| """VitsSVC forward function. | |
| Args: | |
| data (dict): condition data & audio data, including: | |
| B: batch size, T: target length | |
| { | |
| "spk_id": [B, singer_table_size] | |
| "target_len": [B] | |
| "mask": [B, T, 1] | |
| "mel": [B, T, n_mel] | |
| "linear": [B, T, n_fft // 2 + 1] | |
| "frame_pitch": [B, T] | |
| "frame_uv": [B, T] | |
| "audio": [B, audio_len] | |
| "audio_len": [B] | |
| "contentvec_feat": [B, T, contentvec_dim] | |
| "whisper_feat": [B, T, whisper_dim] | |
| ... | |
| } | |
| """ | |
| # TODO: elegantly handle the dimensions | |
| spec = data["linear"].transpose(1, 2) | |
| g = data["spk_id"] | |
| g = self.emb_g(g).transpose(1, 2) | |
| c_lengths = data["target_len"] | |
| spec_lengths = data["target_len"] | |
| f0 = data["frame_pitch"] | |
| # condition_encoder ver. | |
| x = self.condition_encoder(data).transpose(1, 2) | |
| x_mask = torch.unsqueeze(sequence_mask(c_lengths, f0.size(1)), 1).to(x.dtype) | |
| # prior encoder | |
| z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask) | |
| # posterior encoder | |
| 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 = rand_slice_segments_with_pitch( | |
| z, f0, spec_lengths, self.segment_size | |
| ) | |
| if self.dec_name == "nsfhifigan": | |
| o = self.dec(z_slice, f0=f0.float()) | |
| elif self.dec_name == "apnet": | |
| _, _, _, _, o = self.dec(z_slice) | |
| else: | |
| o = self.dec(z_slice) | |
| outputs = { | |
| "y_hat": o, | |
| "ids_slice": ids_slice, | |
| "x_mask": x_mask, | |
| "z_mask": data["mask"].transpose(1, 2), | |
| "z": z, | |
| "z_p": z_p, | |
| "m_p": m_p, | |
| "logs_p": logs_p, | |
| "m_q": m_q, | |
| "logs_q": logs_q, | |
| } | |
| return outputs | |
| def infer(self, data, noise_scale=0.35, seed=52468): | |
| # c, f0, uv, g | |
| f0 = data["frame_pitch"] | |
| g = data["spk_id"] | |
| if f0.device == torch.device("cuda"): | |
| torch.cuda.manual_seed_all(seed) | |
| else: | |
| torch.manual_seed(seed) | |
| c_lengths = (torch.ones(f0.size(0)) * f0.size(-1)).to(f0.device) | |
| if g.dim() == 1: | |
| g = g.unsqueeze(0) | |
| g = self.emb_g(g).transpose(1, 2) | |
| # condition_encoder ver. | |
| x = self.condition_encoder(data).transpose(1, 2) | |
| x_mask = torch.unsqueeze(sequence_mask(c_lengths, f0.size(1)), 1).to(x.dtype) | |
| z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, noice_scale=noise_scale) | |
| z = self.flow(z_p, c_mask, g=g, reverse=True) | |
| if self.dec_name == "nsfhifigan": | |
| o = self.dec(z * c_mask, f0=f0.float()) | |
| elif self.dec_name == "apnet": | |
| _, _, _, _, o = self.dec(z * c_mask) | |
| else: | |
| o = self.dec(z * c_mask) | |
| return o, f0 | |