687 lines
21 KiB
Python
687 lines
21 KiB
Python
import torch
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from torch import nn
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from torch.nn import Conv1d, Conv2d, ConvTranspose1d
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from torch.nn import functional as F
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from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
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from fish_speech.models.vits_decoder.modules import attentions, commons, modules
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from .commons import get_padding, init_weights
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from .mrte import MRTE
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from .vq_encoder import VQEncoder
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class TextEncoder(nn.Module):
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def __init__(
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self,
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out_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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latent_channels=192,
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codebook_size=264,
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):
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super().__init__()
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.latent_channels = latent_channels
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self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
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self.encoder_ssl = attentions.Encoder(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers // 2,
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kernel_size,
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p_dropout,
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)
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self.encoder_text = attentions.Encoder(
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hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
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)
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self.text_embedding = nn.Embedding(codebook_size, hidden_channels)
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self.mrte = MRTE()
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self.encoder2 = attentions.Encoder(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers // 2,
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kernel_size,
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p_dropout,
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)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, y, y_lengths, text, text_lengths, ge):
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
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y.dtype
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)
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y = self.ssl_proj(y * y_mask) * y_mask
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y = self.encoder_ssl(y * y_mask, y_mask)
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text_mask = torch.unsqueeze(
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commons.sequence_mask(text_lengths, text.size(1)), 1
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).to(y.dtype)
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text = self.text_embedding(text).transpose(1, 2)
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text = self.encoder_text(text * text_mask, text_mask)
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y = self.mrte(y, y_mask, text, text_mask, ge)
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y = self.encoder2(y * y_mask, y_mask)
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stats = self.proj(y) * y_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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return y, m, logs, y_mask
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class ResidualCouplingBlock(nn.Module):
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def __init__(
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self,
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channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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n_flows=4,
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gin_channels=0,
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):
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super().__init__()
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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.flows = nn.ModuleList()
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for i in range(n_flows):
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self.flows.append(
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modules.ResidualCouplingLayer(
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channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=gin_channels,
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mean_only=True,
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)
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)
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self.flows.append(modules.Flip())
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def forward(self, x, x_mask, g=None, reverse=False):
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if not reverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, reverse=reverse)
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return x
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class PosteriorEncoder(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=0,
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
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self.enc = modules.WN(
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=gin_channels,
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)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, x, x_lengths, g=None):
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if g != None:
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g = g.detach()
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
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x.dtype
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)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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class Generator(torch.nn.Module):
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def __init__(
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self,
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initial_channel,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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gin_channels=0,
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):
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super(Generator, self).__init__()
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_rates)
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self.conv_pre = Conv1d(
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initial_channel, upsample_initial_channel, 7, 1, padding=3
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)
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resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
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self.ups.append(
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weight_norm(
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ConvTranspose1d(
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upsample_initial_channel // (2**i),
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upsample_initial_channel // (2 ** (i + 1)),
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k,
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u,
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padding=(k - u) // 2,
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)
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)
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)
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(
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zip(resblock_kernel_sizes, resblock_dilation_sizes)
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):
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self.resblocks.append(resblock(ch, k, d))
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
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self.ups.apply(init_weights)
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if gin_channels != 0:
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
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def forward(self, x, g=None):
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x = self.conv_pre(x)
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if g is not None:
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x = x + self.cond(g)
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for i in range(self.num_upsamples):
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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x = self.ups[i](x)
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xs = None
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for j in range(self.num_kernels):
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if xs is None:
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xs = self.resblocks[i * self.num_kernels + j](x)
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else:
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xs += self.resblocks[i * self.num_kernels + j](x)
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x = xs / self.num_kernels
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x = F.leaky_relu(x)
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x = self.conv_post(x)
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x = torch.tanh(x)
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return x
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def remove_weight_norm(self):
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print("Removing weight norm...")
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for l in self.ups:
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remove_weight_norm(l)
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for l in self.resblocks:
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l.remove_weight_norm()
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class DiscriminatorP(torch.nn.Module):
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
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super(DiscriminatorP, self).__init__()
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self.period = period
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self.use_spectral_norm = use_spectral_norm
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.convs = nn.ModuleList(
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[
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norm_f(
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Conv2d(
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1,
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32,
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(kernel_size, 1),
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(stride, 1),
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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norm_f(
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Conv2d(
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32,
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128,
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(kernel_size, 1),
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(stride, 1),
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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norm_f(
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Conv2d(
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128,
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512,
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(kernel_size, 1),
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(stride, 1),
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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norm_f(
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Conv2d(
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512,
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1024,
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(kernel_size, 1),
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(stride, 1),
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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norm_f(
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Conv2d(
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1024,
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1024,
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(kernel_size, 1),
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1,
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padding=(get_padding(kernel_size, 1), 0),
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)
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),
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]
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)
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
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def forward(self, x):
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fmap = []
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class DiscriminatorS(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(DiscriminatorS, self).__init__()
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.convs = nn.ModuleList(
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[
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norm_f(Conv1d(1, 16, 15, 1, padding=7)),
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norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
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norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
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norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
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]
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)
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self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
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def forward(self, x):
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fmap = []
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class EnsembledDiscriminator(torch.nn.Module):
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def __init__(self, periods=(2, 3, 5, 7, 11), use_spectral_norm=False):
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super().__init__()
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discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
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discs = discs + [
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DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
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]
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self.discriminators = nn.ModuleList(discs)
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def forward(self, y, y_hat):
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y_d_rs = []
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y_d_gs = []
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fmap_rs = []
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fmap_gs = []
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for i, d in enumerate(self.discriminators):
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y_d_r, fmap_r = d(y)
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y_d_g, fmap_g = d(y_hat)
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y_d_rs.append(y_d_r)
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y_d_gs.append(y_d_g)
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fmap_rs.append(fmap_r)
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fmap_gs.append(fmap_g)
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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class SynthesizerTrn(nn.Module):
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"""
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Synthesizer for Training
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"""
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def __init__(
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self,
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*,
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spec_channels,
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segment_size,
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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gin_channels=0,
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codebook_size=264,
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vq_mask_ratio=0.0,
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ref_mask_ratio=0.0,
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):
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super().__init__()
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self.spec_channels = spec_channels
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self.inter_channels = inter_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.resblock = resblock
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self.resblock_kernel_sizes = resblock_kernel_sizes
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self.resblock_dilation_sizes = resblock_dilation_sizes
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self.upsample_rates = upsample_rates
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self.upsample_initial_channel = upsample_initial_channel
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self.upsample_kernel_sizes = upsample_kernel_sizes
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self.segment_size = segment_size
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self.gin_channels = gin_channels
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self.vq_mask_ratio = vq_mask_ratio
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self.ref_mask_ratio = ref_mask_ratio
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self.enc_p = TextEncoder(
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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codebook_size=codebook_size,
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)
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self.dec = Generator(
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inter_channels,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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gin_channels=gin_channels,
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)
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self.enc_q = PosteriorEncoder(
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spec_channels,
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inter_channels,
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hidden_channels,
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5,
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1,
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16,
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gin_channels=gin_channels,
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)
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self.flow = ResidualCouplingBlock(
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inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
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)
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self.ref_enc = modules.MelStyleEncoder(
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spec_channels, style_vector_dim=gin_channels
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)
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self.vq = VQEncoder()
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for param in self.vq.parameters():
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param.requires_grad = False
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def forward(
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self, audio, audio_lengths, gt_specs, gt_spec_lengths, text, text_lengths
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):
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y_mask = torch.unsqueeze(
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commons.sequence_mask(gt_spec_lengths, gt_specs.size(2)), 1
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).to(gt_specs.dtype)
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ge = self.ref_enc(gt_specs * y_mask, y_mask)
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if self.training and self.ref_mask_ratio > 0:
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bs = audio.size(0)
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mask_speaker_len = int(bs * self.ref_mask_ratio)
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mask_indices = torch.randperm(bs)[:mask_speaker_len]
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audio[mask_indices] = 0
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quantized = self.vq(audio, audio_lengths)
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# Block masking, block_size = 4
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block_size = 4
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if self.training and self.vq_mask_ratio > 0:
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reduced_length = quantized.size(-1) // block_size
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mask_length = int(reduced_length * self.vq_mask_ratio)
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mask_indices = torch.randperm(reduced_length)[:mask_length]
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short_mask = torch.zeros(
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quantized.size(0),
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quantized.size(1),
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reduced_length,
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device=quantized.device,
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dtype=torch.float,
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)
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short_mask[:, :, mask_indices] = 1.0
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long_mask = short_mask.repeat_interleave(block_size, dim=-1)
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long_mask = F.interpolate(
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long_mask, size=quantized.size(-1), mode="nearest"
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)
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quantized = quantized.masked_fill(long_mask > 0.5, 0)
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x, m_p, logs_p, y_mask = self.enc_p(
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quantized, gt_spec_lengths, text, text_lengths, ge
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)
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z, m_q, logs_q, y_mask = self.enc_q(gt_specs, gt_spec_lengths, g=ge)
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z_p = self.flow(z, y_mask, g=ge)
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|
|
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
|
|
)
|
|
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
|
|
|
z = self.flow(z_p, y_mask, g=ge, reverse=True)
|
|
|
|
o = self.dec(z * y_mask, g=ge)
|
|
|
|
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)
|