import torch from torch import nn from torch.nn import Conv1d, Conv2d, ConvTranspose1d from torch.nn import functional as F from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm from fish_speech.models.vits_decoder.modules import attentions, commons, modules from .commons import get_padding, init_weights from .mrte import MRTE from .vq_encoder import VQEncoder class TextEncoder(nn.Module): def __init__( self, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, latent_channels=192, codebook_size=264, ): 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.latent_channels = latent_channels self.ssl_proj = nn.Conv1d(768, hidden_channels, 1) self.encoder_ssl = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers // 2, kernel_size, p_dropout, ) self.encoder_text = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout ) self.text_embedding = nn.Embedding(codebook_size, hidden_channels) self.mrte = MRTE() self.encoder2 = attentions.Encoder( hidden_channels, filter_channels, n_heads, n_layers // 2, kernel_size, p_dropout, ) self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) def forward(self, y, y_lengths, text, text_lengths, ge): y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to( y.dtype ) y = self.ssl_proj(y * y_mask) * y_mask y = self.encoder_ssl(y * y_mask, y_mask) text_mask = torch.unsqueeze( commons.sequence_mask(text_lengths, text.size(1)), 1 ).to(y.dtype) text = self.text_embedding(text).transpose(1, 2) text = self.encoder_text(text * text_mask, text_mask) print(y.shape,text.shape) y = self.mrte(y, y_mask, text, text_mask, ge) print(y.shape) y = self.encoder2(y * y_mask, y_mask) print(y.shape) # exit(0) stats = self.proj(y) * y_mask m, logs = torch.split(stats, self.out_channels, dim=1) return y, m, logs, y_mask 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 PosteriorEncoder(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): if g != None: g = g.detach() 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 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, g=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 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() 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) 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 EnsembledDiscriminator(torch.nn.Module): def __init__(self, periods=(2, 3, 5, 7, 11), use_spectral_norm=False): super().__init__() 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 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=0, codebook_size=264, vq_mask_ratio=0.0, ref_mask_ratio=0.0, ): 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.vq_mask_ratio = vq_mask_ratio self.ref_mask_ratio = ref_mask_ratio self.enc_p = TextEncoder( inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, codebook_size=codebook_size, ) 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, 4, gin_channels=gin_channels ) self.ref_enc = modules.MelStyleEncoder( spec_channels, style_vector_dim=gin_channels ) self.vq = VQEncoder() for param in self.vq.parameters(): param.requires_grad = False def forward( self, audio, audio_lengths, gt_specs, gt_spec_lengths, text, text_lengths ): y_mask = torch.unsqueeze( commons.sequence_mask(gt_spec_lengths, gt_specs.size(2)), 1 ).to(gt_specs.dtype) ge = self.ref_enc(gt_specs * y_mask, y_mask) if self.training and self.ref_mask_ratio > 0: bs = audio.size(0) mask_speaker_len = int(bs * self.ref_mask_ratio) mask_indices = torch.randperm(bs)[:mask_speaker_len] audio[mask_indices] = 0 quantized = self.vq(audio, audio_lengths) # Block masking, block_size = 4 block_size = 4 if self.training and self.vq_mask_ratio > 0: reduced_length = quantized.size(-1) // block_size mask_length = int(reduced_length * self.vq_mask_ratio) mask_indices = torch.randperm(reduced_length)[:mask_length] short_mask = torch.zeros( quantized.size(0), quantized.size(1), reduced_length, device=quantized.device, dtype=torch.float, ) short_mask[:, :, mask_indices] = 1.0 long_mask = short_mask.repeat_interleave(block_size, dim=-1) long_mask = F.interpolate( long_mask, size=quantized.size(-1), mode="nearest" ) quantized = quantized.masked_fill(long_mask > 0.5, 0) x, m_p, logs_p, y_mask = self.enc_p( quantized, gt_spec_lengths, text, text_lengths, ge ) z, m_q, logs_q, y_mask = self.enc_q(gt_specs, gt_spec_lengths, g=ge) z_p = self.flow(z, y_mask, g=ge) z_slice, ids_slice = commons.rand_slice_segments( z, gt_spec_lengths, self.segment_size ) o = self.dec(z_slice, g=ge) return ( o, ids_slice, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), ) @torch.no_grad() def infer( self, audio, audio_lengths, gt_specs, gt_spec_lengths, text, text_lengths, noise_scale=0.5, ): quantized = self.vq(audio, audio_lengths) quantized_lengths = audio_lengths // 512 ge = self.encode_ref(gt_specs, gt_spec_lengths) return self.decode( quantized, quantized_lengths, text, text_lengths, noise_scale=noise_scale, ge=ge, ) @torch.no_grad() def infer_posterior( self, gt_specs, gt_spec_lengths, ): y_mask = torch.unsqueeze( commons.sequence_mask(gt_spec_lengths, gt_specs.size(2)), 1 ).to(gt_specs.dtype) ge = self.ref_enc(gt_specs * y_mask, y_mask) z, m_q, logs_q, y_mask = self.enc_q(gt_specs, gt_spec_lengths, g=ge) o = self.dec(z * y_mask, g=ge) return o @torch.no_grad() def decode( self, quantized, quantized_lengths, text, text_lengths, noise_scale=0.5, ge=None, ): x, m_p, logs_p, y_mask = self.enc_p( quantized, quantized_lengths, text, text_lengths, ge ) print(x.shape) z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale print(z_p.shape) z = self.flow(z_p, y_mask, g=ge, reverse=True) print(z.shape) o = self.dec(z * y_mask, g=ge) print(o.shape) return o @torch.no_grad() def encode_ref(self, gt_specs, gt_spec_lengths): y_mask = torch.unsqueeze( commons.sequence_mask(gt_spec_lengths, gt_specs.size(2)), 1 ).to(gt_specs.dtype) ge = self.ref_enc(gt_specs * y_mask, y_mask) return ge if __name__ == "__main__": import librosa from transformers import AutoTokenizer from fish_speech.utils.spectrogram import LinearSpectrogram model = SynthesizerTrn( spec_channels=1025, segment_size=20480 // 640, inter_channels=192, hidden_channels=192, filter_channels=768, n_heads=2, n_layers=6, kernel_size=3, p_dropout=0.1, resblock="1", resblock_kernel_sizes=[3, 7, 11], resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], upsample_rates=[8, 8, 2, 2, 2], upsample_initial_channel=512, upsample_kernel_sizes=[16, 16, 8, 2, 2], gin_channels=512, ) ckpt = "checkpoints/Bert-VITS2/G_0.pth" # Try to load the model print(f"Loading model from {ckpt}") checkpoint = torch.load(ckpt, map_location="cpu", weights_only=True)["model"] # d_checkpoint = torch.load( # "checkpoints/Bert-VITS2/D_0.pth", map_location="cpu", weights_only=True # )["model"] # print(checkpoint.keys()) checkpoint.pop("dec.cond.weight") checkpoint.pop("enc_q.enc.cond_layer.weight_v") # new_checkpoint = {} # for k, v in checkpoint.items(): # new_checkpoint["generator." + k] = v # for k, v in d_checkpoint.items(): # new_checkpoint["discriminator." + k] = v # torch.save(new_checkpoint, "checkpoints/Bert-VITS2/ensemble.pth") # exit() print(model.load_state_dict(checkpoint, strict=False)) # Test ref_audio = librosa.load( "data/source/云天河/云天河-旁白/《薄太太》第0025集-yth_24.wav", sr=32000 )[0] input_audio = librosa.load( "data/source/云天河/云天河-旁白/《薄太太》第0025集-yth_24.wav", sr=32000 )[0] ref_audio = input_audio text = "博兴只知道身边的小女人没睡着，他又凑到她耳边压低了声线。阮苏眉睁眼，不觉得你老公像英雄吗？阮苏还是没反应，这男人是不是有病？刚才那冰冷又强势的样子，和现在这幼稚无赖的样子，根本就判若二人。" encoded_text = AutoTokenizer.from_pretrained("fishaudio/fish-speech-1") spec = LinearSpectrogram(n_fft=2048, hop_length=640, win_length=2048) ref_audio = torch.tensor(ref_audio).unsqueeze(0).unsqueeze(0) ref_spec = spec(ref_audio) input_audio = torch.tensor(input_audio).unsqueeze(0).unsqueeze(0) text = encoded_text(text, return_tensors="pt")["input_ids"] print(ref_audio.size(), ref_spec.size(), input_audio.size(), text.size()) o, y_mask, (z, z_p, m_p, logs_p) = model.infer( input_audio, torch.LongTensor([input_audio.size(2)]), ref_spec, torch.LongTensor([ref_spec.size(2)]), text, torch.LongTensor([text.size(1)]), ) print(o.size(), y_mask.size(), z.size(), z_p.size(), m_p.size(), logs_p.size()) # Save output # import soundfile as sf # sf.write("output.wav", o.squeeze().detach().numpy(), 32000)