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GPT_SoVITS/module/__init__.py
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GPT_SoVITS/module/__init__.py
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514
GPT_SoVITS/module/attentions.py
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GPT_SoVITS/module/attentions.py
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import math
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import torch
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from torch import nn
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from torch.nn import functional as F
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from module import commons
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from module. modules import LayerNorm
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class Encoder(nn.Module):
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def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4,isflow=False, **kwargs):
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super().__init__()
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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.window_size = window_size
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self.drop = nn.Dropout(p_dropout)
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self.attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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if isflow:
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cond_layer = torch.nn.Conv1d(kwargs["gin_channels"], 2*hidden_channels*n_layers, 1)
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self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
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self.cond_layer = weight_norm_modules(cond_layer, name='weight')
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self.gin_channels = kwargs["gin_channels"]
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def forward(self, x, x_mask, g=None):
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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if g is not None:
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g = self.cond_layer(g)
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for i in range(self.n_layers):
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if g is not None:
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x = self.cond_pre(x)
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cond_offset = i * 2 * self.hidden_channels
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g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
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x = commons.fused_add_tanh_sigmoid_multiply(
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x,
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g_l,
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torch.IntTensor([self.hidden_channels]))
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y = self.attn_layers[i](x, x, attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class Decoder(nn.Module):
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def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
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super().__init__()
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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.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.drop = nn.Dropout(p_dropout)
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self.self_attn_layers = nn.ModuleList()
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self.norm_layers_0 = nn.ModuleList()
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self.encdec_attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
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self.norm_layers_0.append(LayerNorm(hidden_channels))
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self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask, h, h_mask):
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"""
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x: decoder input
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h: encoder output
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"""
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self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
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encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.self_attn_layers[i](x, x, self_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_0[i](x + y)
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y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class MultiHeadAttention(nn.Module):
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def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
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super().__init__()
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assert channels % n_heads == 0
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self.channels = channels
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self.out_channels = out_channels
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self.n_heads = n_heads
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self.p_dropout = p_dropout
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self.window_size = window_size
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self.heads_share = heads_share
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self.block_length = block_length
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self.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.attn = None
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self.k_channels = channels // n_heads
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self.conv_q = nn.Conv1d(channels, channels, 1)
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self.conv_k = nn.Conv1d(channels, channels, 1)
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self.conv_v = nn.Conv1d(channels, channels, 1)
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self.conv_o = nn.Conv1d(channels, out_channels, 1)
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self.drop = nn.Dropout(p_dropout)
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if window_size is not None:
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n_heads_rel = 1 if heads_share else n_heads
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rel_stddev = self.k_channels**-0.5
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self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
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self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
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nn.init.xavier_uniform_(self.conv_q.weight)
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nn.init.xavier_uniform_(self.conv_k.weight)
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nn.init.xavier_uniform_(self.conv_v.weight)
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if proximal_init:
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with torch.no_grad():
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self.conv_k.weight.copy_(self.conv_q.weight)
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self.conv_k.bias.copy_(self.conv_q.bias)
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def forward(self, x, c, attn_mask=None):
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q = self.conv_q(x)
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k = self.conv_k(c)
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v = self.conv_v(c)
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x, self.attn = self.attention(q, k, v, mask=attn_mask)
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x = self.conv_o(x)
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return x
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def attention(self, query, key, value, mask=None):
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# reshape [b, d, t] -> [b, n_h, t, d_k]
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b, d, t_s, t_t = (*key.size(), query.size(2))
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query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
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key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
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if self.window_size is not None:
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assert t_s == t_t, "Relative attention is only available for self-attention."
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key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
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rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
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scores_local = self._relative_position_to_absolute_position(rel_logits)
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scores = scores + scores_local
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if self.proximal_bias:
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assert t_s == t_t, "Proximal bias is only available for self-attention."
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scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
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if mask is not None:
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scores = scores.masked_fill(mask == 0, -1e4)
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if self.block_length is not None:
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assert t_s == t_t, "Local attention is only available for self-attention."
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block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
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scores = scores.masked_fill(block_mask == 0, -1e4)
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p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
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p_attn = self.drop(p_attn)
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output = torch.matmul(p_attn, value)
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if self.window_size is not None:
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relative_weights = self._absolute_position_to_relative_position(p_attn)
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value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
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output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
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output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
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return output, p_attn
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def _matmul_with_relative_values(self, x, y):
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"""
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x: [b, h, l, m]
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y: [h or 1, m, d]
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ret: [b, h, l, d]
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"""
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ret = torch.matmul(x, y.unsqueeze(0))
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return ret
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def _matmul_with_relative_keys(self, x, y):
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"""
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x: [b, h, l, d]
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y: [h or 1, m, d]
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ret: [b, h, l, m]
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"""
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ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
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return ret
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def _get_relative_embeddings(self, relative_embeddings, length):
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max_relative_position = 2 * self.window_size + 1
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# Pad first before slice to avoid using cond ops.
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pad_length = max(length - (self.window_size + 1), 0)
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slice_start_position = max((self.window_size + 1) - length, 0)
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slice_end_position = slice_start_position + 2 * length - 1
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if pad_length > 0:
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padded_relative_embeddings = F.pad(
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relative_embeddings,
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commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
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else:
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padded_relative_embeddings = relative_embeddings
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used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
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return used_relative_embeddings
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def _relative_position_to_absolute_position(self, x):
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"""
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x: [b, h, l, 2*l-1]
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ret: [b, h, l, l]
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"""
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batch, heads, length, _ = x.size()
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# Concat columns of pad to shift from relative to absolute indexing.
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x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
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# Concat extra elements so to add up to shape (len+1, 2*len-1).
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x_flat = x.view([batch, heads, length * 2 * length])
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x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
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# Reshape and slice out the padded elements.
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x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
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return x_final
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def _absolute_position_to_relative_position(self, x):
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"""
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x: [b, h, l, l]
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ret: [b, h, l, 2*l-1]
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"""
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batch, heads, length, _ = x.size()
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# padd along column
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x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
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x_flat = x.view([batch, heads, length**2 + length*(length -1)])
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# add 0's in the beginning that will skew the elements after reshape
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x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
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x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
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return x_final
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def _attention_bias_proximal(self, length):
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"""Bias for self-attention to encourage attention to close positions.
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Args:
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length: an integer scalar.
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Returns:
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a Tensor with shape [1, 1, length, length]
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"""
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r = torch.arange(length, dtype=torch.float32)
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diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
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return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
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class FFN(nn.Module):
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def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
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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.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.activation = activation
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self.causal = causal
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if causal:
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self.padding = self._causal_padding
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else:
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self.padding = self._same_padding
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
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self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
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self.drop = nn.Dropout(p_dropout)
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def forward(self, x, x_mask):
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x = self.conv_1(self.padding(x * x_mask))
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if self.activation == "gelu":
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x = x * torch.sigmoid(1.702 * x)
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else:
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x = torch.relu(x)
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x = self.drop(x)
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x = self.conv_2(self.padding(x * x_mask))
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return x * x_mask
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def _causal_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l = self.kernel_size - 1
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pad_r = 0
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padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(x, commons.convert_pad_shape(padding))
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return x
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def _same_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l = (self.kernel_size - 1) // 2
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pad_r = self.kernel_size // 2
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padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(x, commons.convert_pad_shape(padding))
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return x
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import torch.nn as nn
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from torch.nn.utils import remove_weight_norm, weight_norm
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class Depthwise_Separable_Conv1D(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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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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bias=True,
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padding_mode='zeros', # TODO: refine this type
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device=None,
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dtype=None
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):
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super().__init__()
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self.depth_conv = nn.Conv1d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size,
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groups=in_channels, stride=stride, padding=padding, dilation=dilation, bias=bias,
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padding_mode=padding_mode, device=device, dtype=dtype)
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self.point_conv = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias,
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device=device, dtype=dtype)
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def forward(self, input):
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return self.point_conv(self.depth_conv(input))
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def weight_norm(self):
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self.depth_conv = weight_norm(self.depth_conv, name='weight')
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self.point_conv = weight_norm(self.point_conv, name='weight')
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def remove_weight_norm(self):
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self.depth_conv = remove_weight_norm(self.depth_conv, name='weight')
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self.point_conv = remove_weight_norm(self.point_conv, name='weight')
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class Depthwise_Separable_TransposeConv1D(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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kernel_size,
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stride=1,
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padding=0,
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output_padding=0,
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bias=True,
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dilation=1,
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padding_mode='zeros', # TODO: refine this type
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device=None,
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dtype=None
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):
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super().__init__()
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self.depth_conv = nn.ConvTranspose1d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size,
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groups=in_channels, stride=stride, output_padding=output_padding,
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padding=padding, dilation=dilation, bias=bias, padding_mode=padding_mode,
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device=device, dtype=dtype)
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self.point_conv = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias,
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device=device, dtype=dtype)
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def forward(self, input):
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return self.point_conv(self.depth_conv(input))
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def weight_norm(self):
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self.depth_conv = weight_norm(self.depth_conv, name='weight')
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self.point_conv = weight_norm(self.point_conv, name='weight')
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def remove_weight_norm(self):
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remove_weight_norm(self.depth_conv, name='weight')
|
||||
remove_weight_norm(self.point_conv, name='weight')
|
||||
|
||||
|
||||
def weight_norm_modules(module, name='weight', dim=0):
|
||||
if isinstance(module, Depthwise_Separable_Conv1D) or isinstance(module, Depthwise_Separable_TransposeConv1D):
|
||||
module.weight_norm()
|
||||
return module
|
||||
else:
|
||||
return weight_norm(module, name, dim)
|
||||
|
||||
|
||||
def remove_weight_norm_modules(module, name='weight'):
|
||||
if isinstance(module, Depthwise_Separable_Conv1D) or isinstance(module, Depthwise_Separable_TransposeConv1D):
|
||||
module.remove_weight_norm()
|
||||
else:
|
||||
remove_weight_norm(module, name)
|
||||
|
||||
|
||||
class FFT(nn.Module):
|
||||
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
|
||||
proximal_bias=False, proximal_init=True, isflow = False, **kwargs):
|
||||
super().__init__()
|
||||
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.proximal_bias = proximal_bias
|
||||
self.proximal_init = proximal_init
|
||||
if isflow:
|
||||
cond_layer = torch.nn.Conv1d(kwargs["gin_channels"], 2*hidden_channels*n_layers, 1)
|
||||
self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
|
||||
self.cond_layer = weight_norm_modules(cond_layer, name='weight')
|
||||
self.gin_channels = kwargs["gin_channels"]
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.self_attn_layers = nn.ModuleList()
|
||||
self.norm_layers_0 = nn.ModuleList()
|
||||
self.ffn_layers = nn.ModuleList()
|
||||
self.norm_layers_1 = nn.ModuleList()
|
||||
for i in range(self.n_layers):
|
||||
self.self_attn_layers.append(
|
||||
MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
|
||||
proximal_init=proximal_init))
|
||||
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
||||
self.ffn_layers.append(
|
||||
FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
||||
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
||||
|
||||
def forward(self, x, x_mask, g = None):
|
||||
"""
|
||||
x: decoder input
|
||||
h: encoder output
|
||||
"""
|
||||
if g is not None:
|
||||
g = self.cond_layer(g)
|
||||
|
||||
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
x = x * x_mask
|
||||
for i in range(self.n_layers):
|
||||
if g is not None:
|
||||
x = self.cond_pre(x)
|
||||
cond_offset = i * 2 * self.hidden_channels
|
||||
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
||||
x = commons.fused_add_tanh_sigmoid_multiply(
|
||||
x,
|
||||
g_l,
|
||||
torch.IntTensor([self.hidden_channels]))
|
||||
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_0[i](x + y)
|
||||
|
||||
y = self.ffn_layers[i](x, x_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_1[i](x + y)
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class TransformerCouplingLayer(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
n_layers,
|
||||
n_heads,
|
||||
p_dropout=0,
|
||||
filter_channels=0,
|
||||
mean_only=False,
|
||||
wn_sharing_parameter=None,
|
||||
gin_channels = 0
|
||||
):
|
||||
assert channels % 2 == 0, "channels should be divisible by 2"
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.half_channels = channels // 2
|
||||
self.mean_only = mean_only
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
||||
self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
|
||||
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
||||
self.post.weight.data.zero_()
|
||||
self.post.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
||||
h = self.pre(x0) * x_mask
|
||||
h = self.enc(h, x_mask, g=g)
|
||||
stats = self.post(h) * x_mask
|
||||
if not self.mean_only:
|
||||
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
||||
else:
|
||||
m = stats
|
||||
logs = torch.zeros_like(m)
|
||||
|
||||
if not reverse:
|
||||
x1 = m + x1 * torch.exp(logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
logdet = torch.sum(logs, [1,2])
|
||||
return x, logdet
|
||||
else:
|
||||
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
return x
|
||||
189
GPT_SoVITS/module/commons.py
Normal file
189
GPT_SoVITS/module/commons.py
Normal file
@@ -0,0 +1,189 @@
|
||||
import math
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size*dilation - dilation)/2)
|
||||
|
||||
|
||||
def convert_pad_shape(pad_shape):
|
||||
l = pad_shape[::-1]
|
||||
pad_shape = [item for sublist in l for item in sublist]
|
||||
return pad_shape
|
||||
|
||||
|
||||
def intersperse(lst, item):
|
||||
result = [item] * (len(lst) * 2 + 1)
|
||||
result[1::2] = lst
|
||||
return result
|
||||
|
||||
|
||||
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
||||
"""KL(P||Q)"""
|
||||
kl = (logs_q - logs_p) - 0.5
|
||||
kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
|
||||
return kl
|
||||
|
||||
|
||||
def rand_gumbel(shape):
|
||||
"""Sample from the Gumbel distribution, protect from overflows."""
|
||||
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
||||
return -torch.log(-torch.log(uniform_samples))
|
||||
|
||||
|
||||
def rand_gumbel_like(x):
|
||||
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
||||
return g
|
||||
|
||||
|
||||
def slice_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(x, 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)
|
||||
return ret, ids_str
|
||||
|
||||
|
||||
def get_timing_signal_1d(
|
||||
length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
||||
position = torch.arange(length, dtype=torch.float)
|
||||
num_timescales = channels // 2
|
||||
log_timescale_increment = (
|
||||
math.log(float(max_timescale) / float(min_timescale)) /
|
||||
(num_timescales - 1))
|
||||
inv_timescales = min_timescale * torch.exp(
|
||||
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
|
||||
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
||||
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
||||
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
||||
signal = signal.view(1, channels, length)
|
||||
return signal
|
||||
|
||||
|
||||
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
||||
b, channels, length = x.size()
|
||||
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
||||
return x + signal.to(dtype=x.dtype, device=x.device)
|
||||
|
||||
|
||||
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
||||
b, channels, length = x.size()
|
||||
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
||||
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
||||
|
||||
|
||||
def subsequent_mask(length):
|
||||
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
||||
return mask
|
||||
|
||||
|
||||
@torch.jit.script
|
||||
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
||||
n_channels_int = n_channels[0]
|
||||
in_act = input_a + input_b
|
||||
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
||||
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
||||
acts = t_act * s_act
|
||||
return acts
|
||||
|
||||
|
||||
def convert_pad_shape(pad_shape):
|
||||
l = pad_shape[::-1]
|
||||
pad_shape = [item for sublist in l for item in sublist]
|
||||
return pad_shape
|
||||
|
||||
|
||||
def shift_1d(x):
|
||||
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
||||
return x
|
||||
|
||||
|
||||
def sequence_mask(length, max_length=None):
|
||||
if max_length is None:
|
||||
max_length = length.max()
|
||||
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
||||
return x.unsqueeze(0) < length.unsqueeze(1)
|
||||
|
||||
|
||||
def generate_path(duration, mask):
|
||||
"""
|
||||
duration: [b, 1, t_x]
|
||||
mask: [b, 1, t_y, t_x]
|
||||
"""
|
||||
device = duration.device
|
||||
|
||||
b, _, t_y, t_x = mask.shape
|
||||
cum_duration = torch.cumsum(duration, -1)
|
||||
|
||||
cum_duration_flat = cum_duration.view(b * t_x)
|
||||
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
||||
path = path.view(b, t_x, t_y)
|
||||
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
||||
path = path.unsqueeze(1).transpose(2,3) * mask
|
||||
return path
|
||||
|
||||
|
||||
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
||||
if isinstance(parameters, torch.Tensor):
|
||||
parameters = [parameters]
|
||||
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
||||
norm_type = float(norm_type)
|
||||
if clip_value is not None:
|
||||
clip_value = float(clip_value)
|
||||
|
||||
total_norm = 0
|
||||
for p in parameters:
|
||||
param_norm = p.grad.data.norm(norm_type)
|
||||
total_norm += param_norm.item() ** norm_type
|
||||
if clip_value is not None:
|
||||
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
||||
total_norm = total_norm ** (1. / norm_type)
|
||||
return total_norm
|
||||
|
||||
|
||||
def squeeze(x, x_mask=None, n_sqz=2):
|
||||
b, c, t = x.size()
|
||||
|
||||
t = (t // n_sqz) * n_sqz
|
||||
x = x[:, :, :t]
|
||||
x_sqz = x.view(b, c, t // n_sqz, n_sqz)
|
||||
x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
|
||||
|
||||
if x_mask is not None:
|
||||
x_mask = x_mask[:, :, n_sqz - 1::n_sqz]
|
||||
else:
|
||||
x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
|
||||
return x_sqz * x_mask, x_mask
|
||||
|
||||
|
||||
def unsqueeze(x, x_mask=None, n_sqz=2):
|
||||
b, c, t = x.size()
|
||||
|
||||
x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
|
||||
x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
|
||||
|
||||
if x_mask is not None:
|
||||
x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
|
||||
else:
|
||||
x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
|
||||
return x_unsqz * x_mask, x_mask
|
||||
367
GPT_SoVITS/module/core_vq.py
Normal file
367
GPT_SoVITS/module/core_vq.py
Normal file
@@ -0,0 +1,367 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
#
|
||||
# This implementation is inspired from
|
||||
# https://github.com/lucidrains/vector-quantize-pytorch
|
||||
# which is released under MIT License. Hereafter, the original license:
|
||||
# MIT License
|
||||
#
|
||||
# Copyright (c) 2020 Phil Wang
|
||||
#
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
#
|
||||
# The above copyright notice and this permission notice shall be included in all
|
||||
# copies or substantial portions of the Software.
|
||||
#
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
# SOFTWARE.
|
||||
|
||||
"""Core vector quantization implementation."""
|
||||
import typing as tp
|
||||
|
||||
from einops import rearrange, repeat
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def default(val: tp.Any, d: tp.Any) -> tp.Any:
|
||||
return val if val is not None else d
|
||||
|
||||
|
||||
def ema_inplace(moving_avg, new, decay: float):
|
||||
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
|
||||
|
||||
|
||||
def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
|
||||
return (x + epsilon) / (x.sum() + n_categories * epsilon)
|
||||
|
||||
|
||||
def uniform_init(*shape: int):
|
||||
t = torch.empty(shape)
|
||||
nn.init.kaiming_uniform_(t)
|
||||
return t
|
||||
|
||||
|
||||
def sample_vectors(samples, num: int):
|
||||
num_samples, device = samples.shape[0], samples.device
|
||||
|
||||
if num_samples >= num:
|
||||
indices = torch.randperm(num_samples, device=device)[:num]
|
||||
else:
|
||||
indices = torch.randint(0, num_samples, (num,), device=device)
|
||||
|
||||
return samples[indices]
|
||||
|
||||
|
||||
def kmeans(samples, num_clusters: int, num_iters: int = 10):
|
||||
dim, dtype = samples.shape[-1], samples.dtype
|
||||
max_kmeans_samples = 500
|
||||
samples = samples[:max_kmeans_samples, :]
|
||||
means = sample_vectors(samples, num_clusters)
|
||||
|
||||
print("kmeans start ... ")
|
||||
for _ in tqdm(range(num_iters)):
|
||||
diffs = rearrange(samples, "n d -> n () d") - rearrange(
|
||||
means, "c d -> () c d"
|
||||
)
|
||||
dists = -(diffs ** 2).sum(dim=-1)
|
||||
|
||||
buckets = dists.max(dim=-1).indices
|
||||
bins = torch.bincount(buckets, minlength=num_clusters)
|
||||
zero_mask = bins == 0
|
||||
bins_min_clamped = bins.masked_fill(zero_mask, 1)
|
||||
|
||||
new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
|
||||
new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
|
||||
new_means = new_means / bins_min_clamped[..., None]
|
||||
|
||||
means = torch.where(zero_mask[..., None], means, new_means)
|
||||
|
||||
return means, bins
|
||||
|
||||
|
||||
class EuclideanCodebook(nn.Module):
|
||||
"""Codebook with Euclidean distance.
|
||||
Args:
|
||||
dim (int): Dimension.
|
||||
codebook_size (int): Codebook size.
|
||||
kmeans_init (bool): Whether to use k-means to initialize the codebooks.
|
||||
If set to true, run the k-means algorithm on the first training batch and use
|
||||
the learned centroids as initialization.
|
||||
kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
|
||||
decay (float): Decay for exponential moving average over the codebooks.
|
||||
epsilon (float): Epsilon value for numerical stability.
|
||||
threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
|
||||
that have an exponential moving average cluster size less than the specified threshold with
|
||||
randomly selected vector from the current batch.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
codebook_size: int,
|
||||
kmeans_init: int = False,
|
||||
kmeans_iters: int = 10,
|
||||
decay: float = 0.99,
|
||||
epsilon: float = 1e-5,
|
||||
threshold_ema_dead_code: int = 2,
|
||||
):
|
||||
super().__init__()
|
||||
self.decay = decay
|
||||
init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
|
||||
embed = init_fn(codebook_size, dim)
|
||||
|
||||
self.codebook_size = codebook_size
|
||||
|
||||
self.kmeans_iters = kmeans_iters
|
||||
self.epsilon = epsilon
|
||||
self.threshold_ema_dead_code = threshold_ema_dead_code
|
||||
|
||||
self.register_buffer("inited", torch.Tensor([not kmeans_init]))
|
||||
self.register_buffer("cluster_size", torch.zeros(codebook_size))
|
||||
self.register_buffer("embed", embed)
|
||||
self.register_buffer("embed_avg", embed.clone())
|
||||
|
||||
@torch.jit.ignore
|
||||
def init_embed_(self, data):
|
||||
if self.inited:
|
||||
return
|
||||
|
||||
embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
|
||||
self.embed.data.copy_(embed)
|
||||
self.embed_avg.data.copy_(embed.clone())
|
||||
self.cluster_size.data.copy_(cluster_size)
|
||||
self.inited.data.copy_(torch.Tensor([True]))
|
||||
# Make sure all buffers across workers are in sync after initialization
|
||||
#broadcast_tensors(self.buffers())
|
||||
|
||||
def replace_(self, samples, mask):
|
||||
modified_codebook = torch.where(
|
||||
mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
|
||||
)
|
||||
self.embed.data.copy_(modified_codebook)
|
||||
|
||||
def expire_codes_(self, batch_samples):
|
||||
if self.threshold_ema_dead_code == 0:
|
||||
return
|
||||
|
||||
expired_codes = self.cluster_size < self.threshold_ema_dead_code
|
||||
if not torch.any(expired_codes):
|
||||
return
|
||||
|
||||
batch_samples = rearrange(batch_samples, "... d -> (...) d")
|
||||
self.replace_(batch_samples, mask=expired_codes)
|
||||
#broadcast_tensors(self.buffers())
|
||||
|
||||
def preprocess(self, x):
|
||||
x = rearrange(x, "... d -> (...) d")
|
||||
return x
|
||||
|
||||
def quantize(self, x):
|
||||
embed = self.embed.t()
|
||||
dist = -(
|
||||
x.pow(2).sum(1, keepdim=True)
|
||||
- 2 * x @ embed
|
||||
+ embed.pow(2).sum(0, keepdim=True)
|
||||
)
|
||||
embed_ind = dist.max(dim=-1).indices
|
||||
return embed_ind
|
||||
|
||||
def postprocess_emb(self, embed_ind, shape):
|
||||
return embed_ind.view(*shape[:-1])
|
||||
|
||||
def dequantize(self, embed_ind):
|
||||
quantize = F.embedding(embed_ind, self.embed)
|
||||
return quantize
|
||||
|
||||
def encode(self, x):
|
||||
shape = x.shape
|
||||
# pre-process
|
||||
x = self.preprocess(x)
|
||||
# quantize
|
||||
embed_ind = self.quantize(x)
|
||||
# post-process
|
||||
embed_ind = self.postprocess_emb(embed_ind, shape)
|
||||
return embed_ind
|
||||
|
||||
def decode(self, embed_ind):
|
||||
quantize = self.dequantize(embed_ind)
|
||||
return quantize
|
||||
|
||||
def forward(self, x):
|
||||
shape, dtype = x.shape, x.dtype
|
||||
x = self.preprocess(x)
|
||||
|
||||
self.init_embed_(x)
|
||||
|
||||
embed_ind = self.quantize(x)
|
||||
embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
|
||||
embed_ind = self.postprocess_emb(embed_ind, shape)
|
||||
quantize = self.dequantize(embed_ind)
|
||||
|
||||
if self.training:
|
||||
# We do the expiry of code at that point as buffers are in sync
|
||||
# and all the workers will take the same decision.
|
||||
self.expire_codes_(x)
|
||||
ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
|
||||
embed_sum = x.t() @ embed_onehot
|
||||
ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
|
||||
cluster_size = (
|
||||
laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
|
||||
* self.cluster_size.sum()
|
||||
)
|
||||
embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
|
||||
self.embed.data.copy_(embed_normalized)
|
||||
|
||||
return quantize, embed_ind
|
||||
|
||||
|
||||
class VectorQuantization(nn.Module):
|
||||
"""Vector quantization implementation.
|
||||
Currently supports only euclidean distance.
|
||||
Args:
|
||||
dim (int): Dimension
|
||||
codebook_size (int): Codebook size
|
||||
codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
|
||||
decay (float): Decay for exponential moving average over the codebooks.
|
||||
epsilon (float): Epsilon value for numerical stability.
|
||||
kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
|
||||
kmeans_iters (int): Number of iterations used for kmeans initialization.
|
||||
threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
|
||||
that have an exponential moving average cluster size less than the specified threshold with
|
||||
randomly selected vector from the current batch.
|
||||
commitment_weight (float): Weight for commitment loss.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
codebook_size: int,
|
||||
codebook_dim: tp.Optional[int] = None,
|
||||
decay: float = 0.99,
|
||||
epsilon: float = 1e-5,
|
||||
kmeans_init: bool = True,
|
||||
kmeans_iters: int = 50,
|
||||
threshold_ema_dead_code: int = 2,
|
||||
commitment_weight: float = 1.,
|
||||
):
|
||||
super().__init__()
|
||||
_codebook_dim: int = default(codebook_dim, dim)
|
||||
|
||||
requires_projection = _codebook_dim != dim
|
||||
self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity())
|
||||
self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity())
|
||||
|
||||
self.epsilon = epsilon
|
||||
self.commitment_weight = commitment_weight
|
||||
|
||||
self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size,
|
||||
kmeans_init=kmeans_init, kmeans_iters=kmeans_iters,
|
||||
decay=decay, epsilon=epsilon,
|
||||
threshold_ema_dead_code=threshold_ema_dead_code)
|
||||
self.codebook_size = codebook_size
|
||||
|
||||
@property
|
||||
def codebook(self):
|
||||
return self._codebook.embed
|
||||
|
||||
def encode(self, x):
|
||||
x = rearrange(x, "b d n -> b n d")
|
||||
x = self.project_in(x)
|
||||
embed_in = self._codebook.encode(x)
|
||||
return embed_in
|
||||
|
||||
def decode(self, embed_ind):
|
||||
quantize = self._codebook.decode(embed_ind)
|
||||
quantize = self.project_out(quantize)
|
||||
quantize = rearrange(quantize, "b n d -> b d n")
|
||||
return quantize
|
||||
|
||||
def forward(self, x):
|
||||
device = x.device
|
||||
x = rearrange(x, "b d n -> b n d")
|
||||
x = self.project_in(x)
|
||||
|
||||
quantize, embed_ind = self._codebook(x)
|
||||
|
||||
if self.training:
|
||||
quantize = x + (quantize - x).detach()
|
||||
|
||||
loss = torch.tensor([0.0], device=device, requires_grad=self.training)
|
||||
|
||||
if self.training:
|
||||
if self.commitment_weight > 0:
|
||||
commit_loss = F.mse_loss(quantize.detach(), x)
|
||||
loss = loss + commit_loss * self.commitment_weight
|
||||
|
||||
quantize = self.project_out(quantize)
|
||||
quantize = rearrange(quantize, "b n d -> b d n")
|
||||
return quantize, embed_ind, loss
|
||||
|
||||
|
||||
class ResidualVectorQuantization(nn.Module):
|
||||
"""Residual vector quantization implementation.
|
||||
Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
|
||||
"""
|
||||
def __init__(self, *, num_quantizers, **kwargs):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList(
|
||||
[VectorQuantization(**kwargs) for _ in range(num_quantizers)]
|
||||
)
|
||||
|
||||
def forward(self, x, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None):
|
||||
quantized_out = 0.0
|
||||
residual = x
|
||||
|
||||
all_losses = []
|
||||
all_indices = []
|
||||
out_quantized = []
|
||||
|
||||
n_q = n_q or len(self.layers)
|
||||
|
||||
for i, layer in enumerate(self.layers[:n_q]):
|
||||
quantized, indices, loss = layer(residual)
|
||||
residual = residual - quantized
|
||||
quantized_out = quantized_out + quantized
|
||||
|
||||
all_indices.append(indices)
|
||||
all_losses.append(loss)
|
||||
if layers and i in layers:
|
||||
out_quantized.append(quantized)
|
||||
|
||||
out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
|
||||
return quantized_out, out_indices, out_losses, out_quantized
|
||||
|
||||
def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int]= None) -> torch.Tensor:
|
||||
residual = x
|
||||
all_indices = []
|
||||
n_q = n_q or len(self.layers)
|
||||
st = st or 0
|
||||
for layer in self.layers[st:n_q]:
|
||||
indices = layer.encode(residual)
|
||||
quantized = layer.decode(indices)
|
||||
residual = residual - quantized
|
||||
all_indices.append(indices)
|
||||
out_indices = torch.stack(all_indices)
|
||||
return out_indices
|
||||
|
||||
def decode(self, q_indices: torch.Tensor, st: int=0) -> torch.Tensor:
|
||||
quantized_out = torch.tensor(0.0, device=q_indices.device)
|
||||
for i, indices in enumerate(q_indices):
|
||||
layer = self.layers[st + i]
|
||||
quantized = layer.decode(indices)
|
||||
quantized_out = quantized_out + quantized
|
||||
return quantized_out
|
||||
326
GPT_SoVITS/module/data_utils.py
Normal file
326
GPT_SoVITS/module/data_utils.py
Normal file
@@ -0,0 +1,326 @@
|
||||
import time,logging
|
||||
import os
|
||||
import random,traceback
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.utils.data
|
||||
from tqdm import tqdm
|
||||
|
||||
from module import commons
|
||||
from module.mel_processing import spectrogram_torch
|
||||
from text import cleaned_text_to_sequence
|
||||
from utils import load_wav_to_torch, load_filepaths_and_text
|
||||
import torch.nn.functional as F
|
||||
from functools import lru_cache
|
||||
import torch
|
||||
import requests
|
||||
from scipy.io import wavfile
|
||||
from io import BytesIO
|
||||
# from config import exp_dir
|
||||
from my_utils import load_audio
|
||||
|
||||
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
|
||||
"""
|
||||
1) loads audio, speaker_id, text pairs
|
||||
2) normalizes text and converts them to sequences of integers
|
||||
3) computes spectrograms from audio files.
|
||||
"""
|
||||
|
||||
def __init__(self, hparams, val=False):
|
||||
exp_dir=hparams.exp_dir
|
||||
self.path2="%s/2-name2text.txt"%exp_dir
|
||||
self.path4="%s/4-cnhubert"%exp_dir
|
||||
self.path5="%s/5-wav32k"%exp_dir
|
||||
assert os.path.exists(self.path2)
|
||||
assert os.path.exists(self.path4)
|
||||
assert os.path.exists(self.path5)
|
||||
names4=set([name[:-3]for name in list(os.listdir(self.path4))])#去除.pt后缀
|
||||
names5=set(os.listdir(self.path5))
|
||||
self.phoneme_data={}
|
||||
with open(self.path2,"r",encoding="utf8")as f:
|
||||
lines=f.read().strip("\n").split("\n")
|
||||
|
||||
for line in lines:
|
||||
tmp=line.split("\t")
|
||||
if(len(tmp)!=4):continue
|
||||
self.phoneme_data[tmp[0]]=[tmp[1]]
|
||||
|
||||
self.audiopaths_sid_text=list(set(self.phoneme_data)&names4&names5)
|
||||
tmp=self.audiopaths_sid_text
|
||||
leng=len(tmp)
|
||||
min_num=100
|
||||
if(leng<min_num):
|
||||
self.audiopaths_sid_text=[]
|
||||
for _ in range(max(2, int(min_num / leng))):
|
||||
self.audiopaths_sid_text += tmp
|
||||
self.max_wav_value = hparams.max_wav_value
|
||||
self.sampling_rate = hparams.sampling_rate
|
||||
self.filter_length = hparams.filter_length
|
||||
self.hop_length = hparams.hop_length
|
||||
self.win_length = hparams.win_length
|
||||
self.sampling_rate = hparams.sampling_rate
|
||||
self.val = val
|
||||
|
||||
random.seed(1234)
|
||||
random.shuffle(self.audiopaths_sid_text)
|
||||
|
||||
print("phoneme_data_len:", len(self.phoneme_data.keys()))
|
||||
print("wav_data_len:", len(self.audiopaths_sid_text))
|
||||
|
||||
audiopaths_sid_text_new = []
|
||||
lengths = []
|
||||
skipped_phone = 0
|
||||
skipped_dur = 0
|
||||
for audiopath in tqdm(self.audiopaths_sid_text):
|
||||
try:
|
||||
phoneme = self.phoneme_data[audiopath][0]
|
||||
phoneme = phoneme.split(' ')
|
||||
phoneme_ids = cleaned_text_to_sequence(phoneme)
|
||||
except Exception:
|
||||
print(f"{audiopath} not in self.phoneme_data !")
|
||||
skipped_phone += 1
|
||||
continue
|
||||
size=os.path.getsize("%s/%s"%(self.path5,audiopath))
|
||||
duration = size / self.sampling_rate / 2
|
||||
if (54 > duration > 0.6 or self.val):
|
||||
audiopaths_sid_text_new.append([audiopath, phoneme_ids])
|
||||
lengths.append(size // (2 * self.hop_length))
|
||||
else:
|
||||
skipped_dur += 1
|
||||
continue
|
||||
print("skipped_phone: ", skipped_phone, ", skipped_dur: ", skipped_dur)
|
||||
print("total left: ", len(audiopaths_sid_text_new))
|
||||
assert len(audiopaths_sid_text_new)>1#至少能凑够batch size，这里todo
|
||||
self.audiopaths_sid_text = audiopaths_sid_text_new
|
||||
self.lengths = lengths
|
||||
|
||||
def get_audio_text_speaker_pair(self, audiopath_sid_text):
|
||||
audiopath, phoneme_ids = audiopath_sid_text
|
||||
text = torch.FloatTensor(phoneme_ids)
|
||||
try:
|
||||
spec, wav = self.get_audio("%s/%s"%(self.path5,audiopath))
|
||||
with torch.no_grad():
|
||||
ssl = torch.load("%s/%s.pt"%(self.path4,audiopath),map_location="cpu")
|
||||
if(ssl.shape[-1]!=spec.shape[-1]):
|
||||
typee=ssl.dtype
|
||||
ssl=F.pad(ssl.float(),(0,1),mode="replicate").to(typee)
|
||||
ssl.requires_grad=False
|
||||
except:
|
||||
traceback.print_exc()
|
||||
spec = torch.zeros(1025, 100)
|
||||
wav = torch.zeros(1, 100*self.hop_length)
|
||||
ssl=torch.zeros(1,768,100)
|
||||
text=text[-1:]
|
||||
print("load audio or ssl error!!!!!!", audiopath)
|
||||
# print(ssl.requires_grad,spec.requires_grad,wav.requires_grad,text.requires_grad)
|
||||
return (ssl, spec, wav, text)
|
||||
|
||||
def get_audio(self, filename):
|
||||
audio_array = load_audio(filename,self.sampling_rate)#load_audio的方法是已经归一化到-1~1之间的，不用再/32768
|
||||
# print(filename,audio_array.max(),audio_array.min(),audio_array.mean())
|
||||
audio=torch.FloatTensor(audio_array)#/32768
|
||||
audio_norm = audio
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec = spectrogram_torch(audio_norm, self.filter_length,self.sampling_rate, self.hop_length, self.win_length,center=False)
|
||||
spec = torch.squeeze(spec, 0)
|
||||
return spec, audio_norm
|
||||
|
||||
def get_sid(self, sid):
|
||||
sid = torch.LongTensor([int(sid)])
|
||||
return sid
|
||||
|
||||
def __getitem__(self, index):
|
||||
# with torch.no_grad():
|
||||
return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
|
||||
|
||||
def __len__(self):
|
||||
return len(self.audiopaths_sid_text)
|
||||
|
||||
def random_slice(self, ssl, wav, mel):
|
||||
assert abs(ssl.shape[-1]- wav.shape[-1]//self.hop_length) < 3, ("first", ssl.shape, wav.shape)
|
||||
|
||||
len_mel = mel.shape[1]
|
||||
if self.val:
|
||||
reference_mel = mel[:, :len_mel//3]
|
||||
return reference_mel, ssl, wav, mel
|
||||
dir = random.randint(0, 1)
|
||||
sep_point = random.randint(int(len_mel//3), int(len_mel//3*2))
|
||||
|
||||
if dir == 0:
|
||||
reference_mel = mel[:, :sep_point]
|
||||
ssl = ssl[:, :, sep_point:]
|
||||
wav2 = wav[:, sep_point*self.hop_length:]
|
||||
mel = mel[:, sep_point:]
|
||||
else:
|
||||
reference_mel = mel[:, sep_point:]
|
||||
ssl = ssl[:, :, :sep_point]
|
||||
wav2 = wav[:, :sep_point*self.hop_length]
|
||||
mel = mel[:, :sep_point]
|
||||
|
||||
assert abs(ssl.shape[-1]- wav2.shape[-1]//self.hop_length) < 3, (ssl.shape, wav.shape,wav2.shape, mel.shape, sep_point,self.hop_length, sep_point*self.hop_length, dir)
|
||||
return reference_mel, ssl, wav2, mel
|
||||
|
||||
|
||||
class TextAudioSpeakerCollate():
|
||||
""" Zero-pads model inputs and targets
|
||||
"""
|
||||
|
||||
def __init__(self, return_ids=False):
|
||||
self.return_ids = return_ids
|
||||
|
||||
def __call__(self, batch):
|
||||
"""Collate's training batch from normalized text, audio and speaker identities
|
||||
PARAMS
|
||||
------
|
||||
batch: [text_normalized, spec_normalized, wav_normalized, sid]
|
||||
"""
|
||||
# Right zero-pad all one-hot text sequences to max input length
|
||||
_, ids_sorted_decreasing = torch.sort(
|
||||
torch.LongTensor([x[1].size(1) for x in batch]),
|
||||
dim=0, descending=True)
|
||||
|
||||
max_ssl_len = max([x[0].size(2) for x in batch])
|
||||
max_ssl_len = int(2 * ((max_ssl_len // 2) + 1))
|
||||
max_spec_len = max([x[1].size(1) for x in batch])
|
||||
max_spec_len = int(2 * ((max_spec_len // 2) + 1))
|
||||
max_wav_len = max([x[2].size(1) for x in batch])
|
||||
max_text_len = max([x[3].size(0) for x in batch])
|
||||
|
||||
ssl_lengths = torch.LongTensor(len(batch))
|
||||
spec_lengths = torch.LongTensor(len(batch))
|
||||
wav_lengths = torch.LongTensor(len(batch))
|
||||
text_lengths = torch.LongTensor(len(batch))
|
||||
|
||||
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
||||
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
||||
ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len)
|
||||
text_padded = torch.LongTensor(len(batch), max_text_len)
|
||||
|
||||
spec_padded.zero_()
|
||||
wav_padded.zero_()
|
||||
ssl_padded.zero_()
|
||||
text_padded.zero_()
|
||||
|
||||
for i in range(len(ids_sorted_decreasing)):
|
||||
row = batch[ids_sorted_decreasing[i]]
|
||||
|
||||
ssl = row[0]
|
||||
ssl_padded[i, :, :ssl.size(2)] = ssl[0, :, :]
|
||||
ssl_lengths[i] = ssl.size(2)
|
||||
|
||||
spec = row[1]
|
||||
spec_padded[i, :, :spec.size(1)] = spec
|
||||
spec_lengths[i] = spec.size(1)
|
||||
|
||||
wav = row[2]
|
||||
wav_padded[i, :, :wav.size(1)] = wav
|
||||
wav_lengths[i] = wav.size(1)
|
||||
|
||||
text = row[3]
|
||||
text_padded[i, :text.size(0)] = text
|
||||
text_lengths[i] = text.size(0)
|
||||
|
||||
|
||||
return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths
|
||||
|
||||
|
||||
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
|
||||
"""
|
||||
Maintain similar input lengths in a batch.
|
||||
Length groups are specified by boundaries.
|
||||
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
|
||||
|
||||
It removes samples which are not included in the boundaries.
|
||||
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
|
||||
"""
|
||||
|
||||
def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
|
||||
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
|
||||
self.lengths = dataset.lengths
|
||||
# print(233333333333333,self.lengths,dir(dataset))
|
||||
self.batch_size = batch_size
|
||||
self.boundaries = boundaries
|
||||
|
||||
self.buckets, self.num_samples_per_bucket = self._create_buckets()
|
||||
self.total_size = sum(self.num_samples_per_bucket)
|
||||
self.num_samples = self.total_size // self.num_replicas
|
||||
|
||||
def _create_buckets(self):
|
||||
buckets = [[] for _ in range(len(self.boundaries) - 1)]
|
||||
for i in range(len(self.lengths)):
|
||||
length = self.lengths[i]
|
||||
idx_bucket = self._bisect(length)
|
||||
if idx_bucket != -1:
|
||||
buckets[idx_bucket].append(i)
|
||||
|
||||
for i in range(len(buckets) - 1, 0, -1):
|
||||
# for i in range(len(buckets) - 1, -1, -1):
|
||||
if len(buckets[i]) == 0:
|
||||
buckets.pop(i)
|
||||
self.boundaries.pop(i + 1)
|
||||
|
||||
num_samples_per_bucket = []
|
||||
for i in range(len(buckets)):
|
||||
len_bucket = len(buckets[i])
|
||||
total_batch_size = self.num_replicas * self.batch_size
|
||||
rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
|
||||
num_samples_per_bucket.append(len_bucket + rem)
|
||||
return buckets, num_samples_per_bucket
|
||||
|
||||
def __iter__(self):
|
||||
# deterministically shuffle based on epoch
|
||||
g = torch.Generator()
|
||||
g.manual_seed(self.epoch)
|
||||
|
||||
indices = []
|
||||
if self.shuffle:
|
||||
for bucket in self.buckets:
|
||||
indices.append(torch.randperm(len(bucket), generator=g).tolist())
|
||||
else:
|
||||
for bucket in self.buckets:
|
||||
indices.append(list(range(len(bucket))))
|
||||
|
||||
batches = []
|
||||
for i in range(len(self.buckets)):
|
||||
bucket = self.buckets[i]
|
||||
len_bucket = len(bucket)
|
||||
ids_bucket = indices[i]
|
||||
num_samples_bucket = self.num_samples_per_bucket[i]
|
||||
|
||||
# add extra samples to make it evenly divisible
|
||||
rem = num_samples_bucket - len_bucket
|
||||
ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
|
||||
|
||||
# subsample
|
||||
ids_bucket = ids_bucket[self.rank::self.num_replicas]
|
||||
|
||||
# batching
|
||||
for j in range(len(ids_bucket) // self.batch_size):
|
||||
batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
|
||||
batches.append(batch)
|
||||
|
||||
if self.shuffle:
|
||||
batch_ids = torch.randperm(len(batches), generator=g).tolist()
|
||||
batches = [batches[i] for i in batch_ids]
|
||||
self.batches = batches
|
||||
|
||||
assert len(self.batches) * self.batch_size == self.num_samples
|
||||
return iter(self.batches)
|
||||
|
||||
def _bisect(self, x, lo=0, hi=None):
|
||||
if hi is None:
|
||||
hi = len(self.boundaries) - 1
|
||||
|
||||
if hi > lo:
|
||||
mid = (hi + lo) // 2
|
||||
if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
|
||||
return mid
|
||||
elif x <= self.boundaries[mid]:
|
||||
return self._bisect(x, lo, mid)
|
||||
else:
|
||||
return self._bisect(x, mid + 1, hi)
|
||||
else:
|
||||
return -1
|
||||
|
||||
def __len__(self):
|
||||
return self.num_samples // self.batch_size
|
||||
68
GPT_SoVITS/module/losses.py
Normal file
68
GPT_SoVITS/module/losses.py
Normal file
@@ -0,0 +1,68 @@
|
||||
import math
|
||||
|
||||
import torch
|
||||
from torch.nn import functional as F
|
||||
|
||||
|
||||
def feature_loss(fmap_r, fmap_g):
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
rl = rl.float().detach()
|
||||
gl = gl.float()
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
dr = dr.float()
|
||||
dg = dg.float()
|
||||
r_loss = torch.mean((1-dr)**2)
|
||||
g_loss = torch.mean(dg**2)
|
||||
loss += (r_loss + g_loss)
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
def generator_loss(disc_outputs):
|
||||
loss = 0
|
||||
gen_losses = []
|
||||
for dg in disc_outputs:
|
||||
dg = dg.float()
|
||||
l = torch.mean((1-dg)**2)
|
||||
gen_losses.append(l)
|
||||
loss += l
|
||||
|
||||
return loss, gen_losses
|
||||
|
||||
|
||||
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
|
||||
"""
|
||||
z_p, logs_q: [b, h, t_t]
|
||||
m_p, logs_p: [b, h, t_t]
|
||||
"""
|
||||
z_p = z_p.float()
|
||||
logs_q = logs_q.float()
|
||||
m_p = m_p.float()
|
||||
logs_p = logs_p.float()
|
||||
z_mask = z_mask.float()
|
||||
|
||||
kl = logs_p - logs_q - 0.5
|
||||
kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
|
||||
kl = torch.sum(kl * z_mask)
|
||||
l = kl / torch.sum(z_mask)
|
||||
return l
|
||||
|
||||
def mle_loss(z, m, logs, logdet, mask):
|
||||
l = torch.sum(logs) + 0.5 * torch.sum(torch.exp(-2 * logs) * ((z - m)**2)) # neg normal likelihood w/o the constant term
|
||||
l = l - torch.sum(logdet) # log jacobian determinant
|
||||
l = l / torch.sum(torch.ones_like(z) * mask) # averaging across batch, channel and time axes
|
||||
l = l + 0.5 * math.log(2 * math.pi) # add the remaining constant term
|
||||
return l
|
||||
111
GPT_SoVITS/module/mel_processing.py
Normal file
111
GPT_SoVITS/module/mel_processing.py
Normal file
@@ -0,0 +1,111 @@
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.data
|
||||
import numpy as np
|
||||
import librosa
|
||||
import librosa.util as librosa_util
|
||||
from librosa.util import normalize, pad_center, tiny
|
||||
from scipy.signal import get_window
|
||||
from scipy.io.wavfile import read
|
||||
from librosa.filters import mel as librosa_mel_fn
|
||||
|
||||
MAX_WAV_VALUE = 32768.0
|
||||
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
C: compression factor
|
||||
"""
|
||||
return torch.log(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
|
||||
def dynamic_range_decompression_torch(x, C=1):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
C: compression factor used to compress
|
||||
"""
|
||||
return torch.exp(x) / C
|
||||
|
||||
|
||||
def spectral_normalize_torch(magnitudes):
|
||||
output = dynamic_range_compression_torch(magnitudes)
|
||||
return output
|
||||
|
||||
|
||||
def spectral_de_normalize_torch(magnitudes):
|
||||
output = dynamic_range_decompression_torch(magnitudes)
|
||||
return output
|
||||
|
||||
|
||||
mel_basis = {}
|
||||
hann_window = {}
|
||||
|
||||
|
||||
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
global hann_window
|
||||
dtype_device = str(y.dtype) + '_' + str(y.device)
|
||||
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
||||
if wnsize_dtype_device not in hann_window:
|
||||
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
||||
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
||||
return spec
|
||||
|
||||
|
||||
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
|
||||
global mel_basis
|
||||
dtype_device = str(spec.dtype) + '_' + str(spec.device)
|
||||
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
||||
if fmax_dtype_device not in mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
|
||||
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
|
||||
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
||||
spec = spectral_normalize_torch(spec)
|
||||
return spec
|
||||
|
||||
|
||||
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
global mel_basis, hann_window
|
||||
dtype_device = str(y.dtype) + '_' + str(y.device)
|
||||
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
||||
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
||||
if fmax_dtype_device not in mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
|
||||
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
|
||||
if wnsize_dtype_device not in hann_window:
|
||||
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
||||
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
||||
|
||||
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
||||
spec = spectral_normalize_torch(spec)
|
||||
|
||||
return spec
|
||||
784
GPT_SoVITS/module/models.py
Normal file
784
GPT_SoVITS/module/models.py
Normal file
@@ -0,0 +1,784 @@
|
||||
import copy
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from module import commons
|
||||
from module import modules
|
||||
from module import attentions
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from module.commons import init_weights, get_padding
|
||||
from module.mrte_model import MRTE
|
||||
from module.quantize import ResidualVectorQuantizer
|
||||
from text import symbols
|
||||
from torch.cuda.amp import autocast
|
||||
|
||||
class StochasticDurationPredictor(nn.Module):
|
||||
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
|
||||
super().__init__()
|
||||
filter_channels = in_channels # it needs to be removed from future version.
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.log_flow = modules.Log()
|
||||
self.flows = nn.ModuleList()
|
||||
self.flows.append(modules.ElementwiseAffine(2))
|
||||
for i in range(n_flows):
|
||||
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
||||
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
||||
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
||||
self.post_flows = nn.ModuleList()
|
||||
self.post_flows.append(modules.ElementwiseAffine(2))
|
||||
for i in range(4):
|
||||
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
||||
self.post_flows.append(modules.Flip())
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
||||
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
||||
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
||||
|
||||
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
||||
x = torch.detach(x)
|
||||
x = self.pre(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.convs(x, x_mask)
|
||||
x = self.proj(x) * x_mask
|
||||
|
||||
if not reverse:
|
||||
flows = self.flows
|
||||
assert w is not None
|
||||
|
||||
logdet_tot_q = 0
|
||||
h_w = self.post_pre(w)
|
||||
h_w = self.post_convs(h_w, x_mask)
|
||||
h_w = self.post_proj(h_w) * x_mask
|
||||
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
||||
z_q = e_q
|
||||
for flow in self.post_flows:
|
||||
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
||||
logdet_tot_q += logdet_q
|
||||
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
||||
u = torch.sigmoid(z_u) * x_mask
|
||||
z0 = (w - u) * x_mask
|
||||
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
|
||||
logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
|
||||
|
||||
logdet_tot = 0
|
||||
z0, logdet = self.log_flow(z0, x_mask)
|
||||
logdet_tot += logdet
|
||||
z = torch.cat([z0, z1], 1)
|
||||
for flow in flows:
|
||||
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
||||
logdet_tot = logdet_tot + logdet
|
||||
nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
|
||||
return nll + logq # [b]
|
||||
else:
|
||||
flows = list(reversed(self.flows))
|
||||
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
||||
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
||||
for flow in flows:
|
||||
z = flow(z, x_mask, g=x, reverse=reverse)
|
||||
z0, z1 = torch.split(z, [1, 1], 1)
|
||||
logw = z0
|
||||
return logw
|
||||
|
||||
|
||||
class DurationPredictor(nn.Module):
|
||||
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
||||
super().__init__()
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
||||
self.norm_1 = modules.LayerNorm(filter_channels)
|
||||
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
||||
self.norm_2 = modules.LayerNorm(filter_channels)
|
||||
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
x = torch.detach(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.conv_1(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_1(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_2(x)
|
||||
x = self.drop(x)
|
||||
x = self.proj(x * x_mask)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
latent_channels=192):
|
||||
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(len(symbols), 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, test=None):
|
||||
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)
|
||||
if test == 1 :
|
||||
text[:, :] = 0
|
||||
text = self.text_embedding(text).transpose(1, 2)
|
||||
text = self.encoder_text(text * text_mask, text_mask)
|
||||
y = self.mrte(y, y_mask, text, text_mask, ge)
|
||||
|
||||
y = self.encoder2(y * y_mask, y_mask)
|
||||
|
||||
stats = self.proj(y) * y_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return y, m, logs, y_mask
|
||||
|
||||
def extract_latent(self, x):
|
||||
x = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(x)
|
||||
return codes.transpose(0,1)
|
||||
def decode_latent(self, codes, y_mask, refer,refer_mask, ge):
|
||||
|
||||
quantized = self.quantizer.decode(codes)
|
||||
|
||||
y = self.vq_proj(quantized) * y_mask
|
||||
y = self.encoder_ssl(y * y_mask, y_mask)
|
||||
|
||||
y = self.mrte(y, y_mask, refer, refer_mask, ge)
|
||||
|
||||
y = self.encoder2(y * y_mask, y_mask)
|
||||
|
||||
stats = self.proj(y) * y_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return y, m, logs, y_mask, quantized
|
||||
|
||||
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 WNEncoder(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, 1)
|
||||
self.norm = modules.LayerNorm(out_channels)
|
||||
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)
|
||||
out = self.proj(x) * x_mask
|
||||
out = self.norm(out)
|
||||
return out
|
||||
|
||||
|
||||
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 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 ReferenceEncoder(nn.Module):
|
||||
'''
|
||||
inputs --- [N, Ty/r, n_mels*r] mels
|
||||
outputs --- [N, ref_enc_gru_size]
|
||||
'''
|
||||
|
||||
def __init__(self, spec_channels, gin_channels=0):
|
||||
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
ref_enc_filters = [32, 32, 64, 64, 128, 128]
|
||||
K = len(ref_enc_filters)
|
||||
filters = [1] + ref_enc_filters
|
||||
convs = [weight_norm(nn.Conv2d(in_channels=filters[i],
|
||||
out_channels=filters[i + 1],
|
||||
kernel_size=(3, 3),
|
||||
stride=(2, 2),
|
||||
padding=(1, 1))) for i in range(K)]
|
||||
self.convs = nn.ModuleList(convs)
|
||||
# self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
|
||||
|
||||
out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
|
||||
self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels,
|
||||
hidden_size=256 // 2,
|
||||
batch_first=True)
|
||||
self.proj = nn.Linear(128, gin_channels)
|
||||
|
||||
def forward(self, inputs):
|
||||
N = inputs.size(0)
|
||||
out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
|
||||
for conv in self.convs:
|
||||
out = conv(out)
|
||||
# out = wn(out)
|
||||
out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
|
||||
|
||||
out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
|
||||
T = out.size(1)
|
||||
N = out.size(0)
|
||||
out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
|
||||
|
||||
self.gru.flatten_parameters()
|
||||
memory, out = self.gru(out) # out --- [1, N, 128]
|
||||
|
||||
return self.proj(out.squeeze(0)).unsqueeze(-1)
|
||||
|
||||
def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
|
||||
for i in range(n_convs):
|
||||
L = (L - kernel_size + 2 * pad) // stride + 1
|
||||
return L
|
||||
|
||||
|
||||
class Quantizer_module(torch.nn.Module):
|
||||
def __init__(self, n_e, e_dim):
|
||||
super(Quantizer_module, self).__init__()
|
||||
self.embedding = nn.Embedding(n_e, e_dim)
|
||||
self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e)
|
||||
|
||||
def forward(self, x):
|
||||
d = torch.sum(x ** 2, 1, keepdim=True) + torch.sum(self.embedding.weight ** 2, 1) - 2 * torch.matmul(x, self.embedding.weight.T)
|
||||
min_indicies = torch.argmin(d, 1)
|
||||
z_q = self.embedding(min_indicies)
|
||||
return z_q, min_indicies
|
||||
|
||||
class Quantizer(torch.nn.Module):
|
||||
def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160):
|
||||
super(Quantizer, self).__init__()
|
||||
assert embed_dim % n_code_groups == 0
|
||||
self.quantizer_modules = nn.ModuleList([
|
||||
Quantizer_module(n_codes, embed_dim // n_code_groups) for _ in range(n_code_groups)
|
||||
])
|
||||
self.n_code_groups = n_code_groups
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
def forward(self, xin):
|
||||
#B, C, T
|
||||
B, C, T = xin.shape
|
||||
xin = xin.transpose(1, 2)
|
||||
x = xin.reshape(-1, self.embed_dim)
|
||||
x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1)
|
||||
min_indicies = []
|
||||
z_q = []
|
||||
for _x, m in zip(x, self.quantizer_modules):
|
||||
_z_q, _min_indicies = m(_x)
|
||||
z_q.append(_z_q)
|
||||
min_indicies.append(_min_indicies) #B * T,
|
||||
z_q = torch.cat(z_q, -1).reshape(xin.shape)
|
||||
loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean((z_q - xin.detach()) ** 2)
|
||||
z_q = xin + (z_q - xin).detach()
|
||||
z_q = z_q.transpose(1, 2)
|
||||
codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups)
|
||||
return z_q, loss, codes.transpose(1, 2)
|
||||
|
||||
def embed(self, x):
|
||||
#idx: N, 4, T
|
||||
x=x.transpose(1, 2)
|
||||
x = torch.split(x, 1, 2)
|
||||
ret = []
|
||||
for q, embed in zip(x, self.quantizer_modules):
|
||||
q = embed.embedding(q.squeeze(-1))
|
||||
ret.append(q)
|
||||
ret = torch.cat(ret, -1)
|
||||
return ret.transpose(1, 2) #N, C, T
|
||||
|
||||
|
||||
class CodePredictor(nn.Module):
|
||||
def __init__(self,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
n_q=8,
|
||||
dims=1024,
|
||||
ssl_dim=768
|
||||
):
|
||||
super().__init__()
|
||||
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.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1)
|
||||
self.ref_enc = modules.MelStyleEncoder(ssl_dim, style_vector_dim=hidden_channels)
|
||||
|
||||
self.encoder = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
|
||||
self.out_proj = nn.Conv1d(hidden_channels, (n_q-1) * dims, 1)
|
||||
self.n_q = n_q
|
||||
self.dims = dims
|
||||
def forward(self, x, x_mask, refer, codes, infer=False):
|
||||
x = x.detach()
|
||||
x = self.vq_proj(x * x_mask) * x_mask
|
||||
g = self.ref_enc(refer, x_mask)
|
||||
x = x + g
|
||||
x = self.encoder(x * x_mask, x_mask)
|
||||
x = self.out_proj(x * x_mask) * x_mask
|
||||
logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose(2, 3)
|
||||
target = codes[1:].transpose(0, 1)
|
||||
if not infer:
|
||||
logits = logits.reshape(-1, self.dims)
|
||||
target = target.reshape(-1)
|
||||
loss = torch.nn.functional.cross_entropy(logits, target)
|
||||
return loss
|
||||
else:
|
||||
_, top10_preds = torch.topk(logits, 10, dim=-1)
|
||||
correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1)
|
||||
top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item()
|
||||
|
||||
print('Top-10 Accuracy:', top3_acc, "%")
|
||||
|
||||
pred_codes = torch.argmax(logits, dim=-1)
|
||||
acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item()
|
||||
print('Top-1 Accuracy:', acc, "%")
|
||||
|
||||
return pred_codes.transpose(0, 1)
|
||||
|
||||
|
||||
|
||||
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,
|
||||
n_speakers=0,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
semantic_frame_rate=None,
|
||||
freeze_quantizer=None,
|
||||
**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.n_speakers = n_speakers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.use_sdp = use_sdp
|
||||
self.enc_p = TextEncoder(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
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)
|
||||
|
||||
ssl_dim = 768
|
||||
assert semantic_frame_rate in ['25hz', "50hz"]
|
||||
self.semantic_frame_rate = semantic_frame_rate
|
||||
if semantic_frame_rate == '25hz':
|
||||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
|
||||
else:
|
||||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
|
||||
|
||||
self.quantizer = ResidualVectorQuantizer(
|
||||
dimension=ssl_dim,
|
||||
n_q=1,
|
||||
bins=1024
|
||||
)
|
||||
if freeze_quantizer:
|
||||
self.ssl_proj.requires_grad_(False)
|
||||
self.quantizer.requires_grad_(False)
|
||||
# self.enc_p.text_embedding.requires_grad_(False)
|
||||
# self.enc_p.encoder_text.requires_grad_(False)
|
||||
# self.enc_p.mrte.requires_grad_(False)
|
||||
|
||||
def forward(self, ssl, y, y_lengths, text, text_lengths):
|
||||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
|
||||
ge = self.ref_enc(y * y_mask, y_mask)
|
||||
|
||||
with autocast(enabled=False):
|
||||
ssl = self.ssl_proj(ssl)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl, layers=[0])
|
||||
|
||||
if self.semantic_frame_rate == '25hz':
|
||||
quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
|
||||
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
|
||||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge)
|
||||
z_p = self.flow(z, y_mask, g=ge)
|
||||
|
||||
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
||||
o = self.dec(z_slice, g=ge)
|
||||
return o, commit_loss, ids_slice, y_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), quantized
|
||||
|
||||
def infer(self, ssl, y, y_lengths, text, text_lengths, test=None, noise_scale=0.5):
|
||||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
|
||||
ge = self.ref_enc(y * y_mask, y_mask)
|
||||
|
||||
ssl = self.ssl_proj(ssl)
|
||||
quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0])
|
||||
if self.semantic_frame_rate == '25hz':
|
||||
quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
|
||||
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, test=test)
|
||||
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,y_mask, (z, z_p, m_p, logs_p)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def decode(self, codes,text, refer, noise_scale=0.5):
|
||||
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
|
||||
refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
|
||||
ge = self.ref_enc(refer * refer_mask, refer_mask)
|
||||
|
||||
y_lengths = torch.LongTensor([codes.size(2)*2]).to(codes.device)
|
||||
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
|
||||
|
||||
quantized = self.quantizer.decode(codes)
|
||||
if self.semantic_frame_rate == '25hz':
|
||||
quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
|
||||
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_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
|
||||
|
||||
def extract_latent(self, x):
|
||||
ssl = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||||
return codes.transpose(0,1)
|
||||
769
GPT_SoVITS/module/modules.py
Normal file
769
GPT_SoVITS/module/modules.py
Normal file
@@ -0,0 +1,769 @@
|
||||
import math
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from torch.nn import Conv1d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm
|
||||
|
||||
from module import commons
|
||||
from module.commons import init_weights, get_padding
|
||||
from module.transforms import piecewise_rational_quadratic_transform
|
||||
import torch.distributions as D
|
||||
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.gamma = nn.Parameter(torch.ones(channels))
|
||||
self.beta = nn.Parameter(torch.zeros(channels))
|
||||
|
||||
def forward(self, x):
|
||||
x = x.transpose(1, -1)
|
||||
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
||||
return x.transpose(1, -1)
|
||||
|
||||
|
||||
class ConvReluNorm(nn.Module):
|
||||
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
assert n_layers > 1, "Number of layers should be larger than 0."
|
||||
|
||||
self.conv_layers = nn.ModuleList()
|
||||
self.norm_layers = nn.ModuleList()
|
||||
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.relu_drop = nn.Sequential(
|
||||
nn.ReLU(),
|
||||
nn.Dropout(p_dropout))
|
||||
for _ in range(n_layers-1):
|
||||
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x_org = x
|
||||
for i in range(self.n_layers):
|
||||
x = self.conv_layers[i](x * x_mask)
|
||||
x = self.norm_layers[i](x)
|
||||
x = self.relu_drop(x)
|
||||
x = x_org + self.proj(x)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class DDSConv(nn.Module):
|
||||
"""
|
||||
Dialted and Depth-Separable Convolution
|
||||
"""
|
||||
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.convs_sep = nn.ModuleList()
|
||||
self.convs_1x1 = nn.ModuleList()
|
||||
self.norms_1 = nn.ModuleList()
|
||||
self.norms_2 = nn.ModuleList()
|
||||
for i in range(n_layers):
|
||||
dilation = kernel_size ** i
|
||||
padding = (kernel_size * dilation - dilation) // 2
|
||||
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
||||
groups=channels, dilation=dilation, padding=padding
|
||||
))
|
||||
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
||||
self.norms_1.append(LayerNorm(channels))
|
||||
self.norms_2.append(LayerNorm(channels))
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
if g is not None:
|
||||
x = x + g
|
||||
for i in range(self.n_layers):
|
||||
y = self.convs_sep[i](x * x_mask)
|
||||
y = self.norms_1[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.convs_1x1[i](y)
|
||||
y = self.norms_2[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.drop(y)
|
||||
x = x + y
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class WN(torch.nn.Module):
|
||||
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
||||
super(WN, self).__init__()
|
||||
assert(kernel_size % 2 == 1)
|
||||
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.p_dropout = p_dropout
|
||||
|
||||
self.in_layers = torch.nn.ModuleList()
|
||||
self.res_skip_layers = torch.nn.ModuleList()
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
if gin_channels != 0:
|
||||
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
||||
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
||||
|
||||
for i in range(n_layers):
|
||||
dilation = dilation_rate ** i
|
||||
padding = int((kernel_size * dilation - dilation) / 2)
|
||||
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
||||
dilation=dilation, padding=padding)
|
||||
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
||||
self.in_layers.append(in_layer)
|
||||
|
||||
# last one is not necessary
|
||||
if i < n_layers - 1:
|
||||
res_skip_channels = 2 * hidden_channels
|
||||
else:
|
||||
res_skip_channels = hidden_channels
|
||||
|
||||
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
||||
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
||||
self.res_skip_layers.append(res_skip_layer)
|
||||
|
||||
def forward(self, x, x_mask, g=None, **kwargs):
|
||||
output = torch.zeros_like(x)
|
||||
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
||||
|
||||
if g is not None:
|
||||
g = self.cond_layer(g)
|
||||
|
||||
for i in range(self.n_layers):
|
||||
x_in = self.in_layers[i](x)
|
||||
if g is not None:
|
||||
cond_offset = i * 2 * self.hidden_channels
|
||||
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
||||
else:
|
||||
g_l = torch.zeros_like(x_in)
|
||||
|
||||
acts = commons.fused_add_tanh_sigmoid_multiply(
|
||||
x_in,
|
||||
g_l,
|
||||
n_channels_tensor)
|
||||
acts = self.drop(acts)
|
||||
|
||||
res_skip_acts = self.res_skip_layers[i](acts)
|
||||
if i < self.n_layers - 1:
|
||||
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
||||
x = (x + res_acts) * x_mask
|
||||
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
||||
else:
|
||||
output = output + res_skip_acts
|
||||
return output * x_mask
|
||||
|
||||
def remove_weight_norm(self):
|
||||
if self.gin_channels != 0:
|
||||
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
||||
for l in self.in_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
for l in self.res_skip_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock1(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
||||
super(ResBlock1, self).__init__()
|
||||
self.convs1 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2])))
|
||||
])
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1)))
|
||||
])
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c1, c2 in zip(self.convs1, self.convs2):
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c1(xt)
|
||||
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock2(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
||||
super(ResBlock2, self).__init__()
|
||||
self.convs = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1])))
|
||||
])
|
||||
self.convs.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c in self.convs:
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class Log(nn.Module):
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
||||
logdet = torch.sum(-y, [1, 2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = torch.exp(x) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class Flip(nn.Module):
|
||||
def forward(self, x, *args, reverse=False, **kwargs):
|
||||
x = torch.flip(x, [1])
|
||||
if not reverse:
|
||||
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
class ElementwiseAffine(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.m = nn.Parameter(torch.zeros(channels,1))
|
||||
self.logs = nn.Parameter(torch.zeros(channels,1))
|
||||
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = self.m + torch.exp(self.logs) * x
|
||||
y = y * x_mask
|
||||
logdet = torch.sum(self.logs * x_mask, [1,2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class ResidualCouplingLayer(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
p_dropout=0,
|
||||
gin_channels=0,
|
||||
mean_only=False):
|
||||
assert channels % 2 == 0, "channels should be divisible by 2"
|
||||
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.half_channels = channels // 2
|
||||
self.mean_only = mean_only
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
||||
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
||||
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
||||
self.post.weight.data.zero_()
|
||||
self.post.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
||||
h = self.pre(x0) * x_mask
|
||||
h = self.enc(h, x_mask, g=g)
|
||||
stats = self.post(h) * x_mask
|
||||
if not self.mean_only:
|
||||
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
||||
else:
|
||||
m = stats
|
||||
logs = torch.zeros_like(m)
|
||||
|
||||
if not reverse:
|
||||
x1 = m + x1 * torch.exp(logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
logdet = torch.sum(logs, [1,2])
|
||||
return x, logdet
|
||||
else:
|
||||
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
return x
|
||||
|
||||
|
||||
class ConvFlow(nn.Module):
|
||||
def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.num_bins = num_bins
|
||||
self.tail_bound = tail_bound
|
||||
self.half_channels = in_channels // 2
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
||||
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
|
||||
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
||||
h = self.pre(x0)
|
||||
h = self.convs(h, x_mask, g=g)
|
||||
h = self.proj(h) * x_mask
|
||||
|
||||
b, c, t = x0.shape
|
||||
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
||||
|
||||
unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
|
||||
unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
|
||||
unnormalized_derivatives = h[..., 2 * self.num_bins:]
|
||||
|
||||
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=reverse,
|
||||
tails='linear',
|
||||
tail_bound=self.tail_bound
|
||||
)
|
||||
|
||||
x = torch.cat([x0, x1], 1) * x_mask
|
||||
logdet = torch.sum(logabsdet * x_mask, [1,2])
|
||||
if not reverse:
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class LinearNorm(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
bias=True,
|
||||
spectral_norm=False,
|
||||
):
|
||||
super(LinearNorm, self).__init__()
|
||||
self.fc = nn.Linear(in_channels, out_channels, bias)
|
||||
|
||||
if spectral_norm:
|
||||
self.fc = nn.utils.spectral_norm(self.fc)
|
||||
|
||||
def forward(self, input):
|
||||
out = self.fc(input)
|
||||
return out
|
||||
|
||||
|
||||
class Mish(nn.Module):
|
||||
def __init__(self):
|
||||
super(Mish, self).__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return x * torch.tanh(F.softplus(x))
|
||||
|
||||
|
||||
class Conv1dGLU(nn.Module):
|
||||
'''
|
||||
Conv1d + GLU(Gated Linear Unit) with residual connection.
|
||||
For GLU refer to https://arxiv.org/abs/1612.08083 paper.
|
||||
'''
|
||||
|
||||
def __init__(self, in_channels, out_channels, kernel_size, dropout):
|
||||
super(Conv1dGLU, self).__init__()
|
||||
self.out_channels = out_channels
|
||||
self.conv1 = ConvNorm(in_channels, 2 * out_channels, kernel_size=kernel_size)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
x = self.conv1(x)
|
||||
x1, x2 = torch.split(x, split_size_or_sections=self.out_channels, dim=1)
|
||||
x = x1 * torch.sigmoid(x2)
|
||||
x = residual + self.dropout(x)
|
||||
return x
|
||||
|
||||
|
||||
class ConvNorm(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=None,
|
||||
dilation=1,
|
||||
bias=True,
|
||||
spectral_norm=False,
|
||||
):
|
||||
super(ConvNorm, self).__init__()
|
||||
|
||||
if padding is None:
|
||||
assert (kernel_size % 2 == 1)
|
||||
padding = int(dilation * (kernel_size - 1) / 2)
|
||||
|
||||
self.conv = torch.nn.Conv1d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=bias)
|
||||
|
||||
if spectral_norm:
|
||||
self.conv = nn.utils.spectral_norm(self.conv)
|
||||
|
||||
def forward(self, input):
|
||||
out = self.conv(input)
|
||||
return out
|
||||
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
''' Multi-Head Attention module '''
|
||||
|
||||
def __init__(self, n_head, d_model, d_k, d_v, dropout=0., spectral_norm=False):
|
||||
super().__init__()
|
||||
|
||||
self.n_head = n_head
|
||||
self.d_k = d_k
|
||||
self.d_v = d_v
|
||||
|
||||
self.w_qs = nn.Linear(d_model, n_head * d_k)
|
||||
self.w_ks = nn.Linear(d_model, n_head * d_k)
|
||||
self.w_vs = nn.Linear(d_model, n_head * d_v)
|
||||
|
||||
self.attention = ScaledDotProductAttention(temperature=np.power(d_model, 0.5), dropout=dropout)
|
||||
|
||||
self.fc = nn.Linear(n_head * d_v, d_model)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
if spectral_norm:
|
||||
self.w_qs = nn.utils.spectral_norm(self.w_qs)
|
||||
self.w_ks = nn.utils.spectral_norm(self.w_ks)
|
||||
self.w_vs = nn.utils.spectral_norm(self.w_vs)
|
||||
self.fc = nn.utils.spectral_norm(self.fc)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
|
||||
sz_b, len_x, _ = x.size()
|
||||
|
||||
residual = x
|
||||
|
||||
q = self.w_qs(x).view(sz_b, len_x, n_head, d_k)
|
||||
k = self.w_ks(x).view(sz_b, len_x, n_head, d_k)
|
||||
v = self.w_vs(x).view(sz_b, len_x, n_head, d_v)
|
||||
q = q.permute(2, 0, 1, 3).contiguous().view(-1,
|
||||
len_x, d_k) # (n*b) x lq x dk
|
||||
k = k.permute(2, 0, 1, 3).contiguous().view(-1,
|
||||
len_x, d_k) # (n*b) x lk x dk
|
||||
v = v.permute(2, 0, 1, 3).contiguous().view(-1,
|
||||
len_x, d_v) # (n*b) x lv x dv
|
||||
|
||||
if mask is not None:
|
||||
slf_mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
|
||||
else:
|
||||
slf_mask = None
|
||||
output, attn = self.attention(q, k, v, mask=slf_mask)
|
||||
|
||||
output = output.view(n_head, sz_b, len_x, d_v)
|
||||
output = output.permute(1, 2, 0, 3).contiguous().view(
|
||||
sz_b, len_x, -1) # b x lq x (n*dv)
|
||||
|
||||
output = self.fc(output)
|
||||
|
||||
output = self.dropout(output) + residual
|
||||
return output, attn
|
||||
|
||||
|
||||
class ScaledDotProductAttention(nn.Module):
|
||||
''' Scaled Dot-Product Attention '''
|
||||
|
||||
def __init__(self, temperature, dropout):
|
||||
super().__init__()
|
||||
self.temperature = temperature
|
||||
self.softmax = nn.Softmax(dim=2)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, q, k, v, mask=None):
|
||||
attn = torch.bmm(q, k.transpose(1, 2))
|
||||
attn = attn / self.temperature
|
||||
|
||||
if mask is not None:
|
||||
attn = attn.masked_fill(mask, -np.inf)
|
||||
|
||||
attn = self.softmax(attn)
|
||||
p_attn = self.dropout(attn)
|
||||
|
||||
output = torch.bmm(p_attn, v)
|
||||
return output, attn
|
||||
|
||||
|
||||
class MelStyleEncoder(nn.Module):
|
||||
''' MelStyleEncoder '''
|
||||
|
||||
def __init__(self, n_mel_channels=80,
|
||||
style_hidden=128,
|
||||
style_vector_dim=256,
|
||||
style_kernel_size=5,
|
||||
style_head=2,
|
||||
dropout=0.1):
|
||||
super(MelStyleEncoder, self).__init__()
|
||||
self.in_dim = n_mel_channels
|
||||
self.hidden_dim = style_hidden
|
||||
self.out_dim = style_vector_dim
|
||||
self.kernel_size = style_kernel_size
|
||||
self.n_head = style_head
|
||||
self.dropout = dropout
|
||||
|
||||
self.spectral = nn.Sequential(
|
||||
LinearNorm(self.in_dim, self.hidden_dim),
|
||||
Mish(),
|
||||
nn.Dropout(self.dropout),
|
||||
LinearNorm(self.hidden_dim, self.hidden_dim),
|
||||
Mish(),
|
||||
nn.Dropout(self.dropout)
|
||||
)
|
||||
|
||||
self.temporal = nn.Sequential(
|
||||
Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
|
||||
Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
|
||||
)
|
||||
|
||||
self.slf_attn = MultiHeadAttention(self.n_head, self.hidden_dim,
|
||||
self.hidden_dim // self.n_head, self.hidden_dim // self.n_head,
|
||||
self.dropout)
|
||||
|
||||
self.fc = LinearNorm(self.hidden_dim, self.out_dim)
|
||||
|
||||
def temporal_avg_pool(self, x, mask=None):
|
||||
if mask is None:
|
||||
out = torch.mean(x, dim=1)
|
||||
else:
|
||||
len_ = (~mask).sum(dim=1).unsqueeze(1)
|
||||
x = x.masked_fill(mask.unsqueeze(-1), 0)
|
||||
x = x.sum(dim=1)
|
||||
out = torch.div(x, len_)
|
||||
return out
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
x = x.transpose(1,2)
|
||||
if mask is not None:
|
||||
mask = (mask.int()==0).squeeze(1)
|
||||
max_len = x.shape[1]
|
||||
slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) if mask is not None else None
|
||||
|
||||
# spectral
|
||||
x = self.spectral(x)
|
||||
# temporal
|
||||
x = x.transpose(1, 2)
|
||||
x = self.temporal(x)
|
||||
x = x.transpose(1, 2)
|
||||
# self-attention
|
||||
if mask is not None:
|
||||
x = x.masked_fill(mask.unsqueeze(-1), 0)
|
||||
x, _ = self.slf_attn(x, mask=slf_attn_mask)
|
||||
# fc
|
||||
x = self.fc(x)
|
||||
# temoral average pooling
|
||||
w = self.temporal_avg_pool(x, mask=mask)
|
||||
|
||||
return w.unsqueeze(-1)
|
||||
|
||||
|
||||
class MelStyleEncoderVAE(nn.Module):
|
||||
def __init__(self, spec_channels, z_latent_dim, emb_dim):
|
||||
super().__init__()
|
||||
self.ref_encoder = MelStyleEncoder(spec_channels, style_vector_dim=emb_dim)
|
||||
self.fc1 = nn.Linear(emb_dim, z_latent_dim)
|
||||
self.fc2 = nn.Linear(emb_dim, z_latent_dim)
|
||||
self.fc3 = nn.Linear(z_latent_dim, emb_dim)
|
||||
self.z_latent_dim = z_latent_dim
|
||||
|
||||
def reparameterize(self, mu, logvar):
|
||||
if self.training:
|
||||
std = torch.exp(0.5 * logvar)
|
||||
eps = torch.randn_like(std)
|
||||
return eps.mul(std).add_(mu)
|
||||
else:
|
||||
return mu
|
||||
|
||||
def forward(self, inputs, mask=None):
|
||||
enc_out = self.ref_encoder(inputs.squeeze(-1), mask).squeeze(-1)
|
||||
mu = self.fc1(enc_out)
|
||||
logvar = self.fc2(enc_out)
|
||||
posterior = D.Normal(mu, torch.exp(logvar))
|
||||
kl_divergence = D.kl_divergence(posterior, D.Normal(torch.zeros_like(mu), torch.ones_like(logvar)))
|
||||
loss_kl = kl_divergence.mean()
|
||||
|
||||
z = posterior.rsample()
|
||||
style_embed = self.fc3(z)
|
||||
|
||||
return style_embed.unsqueeze(-1), loss_kl
|
||||
|
||||
def infer(self, inputs=None, random_sample=False, manual_latent=None):
|
||||
if manual_latent is None:
|
||||
if random_sample:
|
||||
dev = next(self.parameters()).device
|
||||
posterior = D.Normal(torch.zeros(1, self.z_latent_dim, device=dev),
|
||||
torch.ones(1, self.z_latent_dim, device=dev))
|
||||
z = posterior.rsample()
|
||||
else:
|
||||
|
||||
enc_out = self.ref_encoder(inputs.transpose(1, 2))
|
||||
mu = self.fc1(enc_out)
|
||||
z = mu
|
||||
else:
|
||||
z = manual_latent
|
||||
style_embed = self.fc3(z)
|
||||
return style_embed.unsqueeze(-1), z
|
||||
|
||||
|
||||
class ActNorm(nn.Module):
|
||||
def __init__(self, channels, ddi=False, **kwargs):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.initialized = not ddi
|
||||
|
||||
self.logs = nn.Parameter(torch.zeros(1, channels, 1))
|
||||
self.bias = nn.Parameter(torch.zeros(1, channels, 1))
|
||||
|
||||
def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs):
|
||||
if x_mask is None:
|
||||
x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
x_len = torch.sum(x_mask, [1, 2])
|
||||
if not self.initialized:
|
||||
self.initialize(x, x_mask)
|
||||
self.initialized = True
|
||||
|
||||
if reverse:
|
||||
z = (x - self.bias) * torch.exp(-self.logs) * x_mask
|
||||
logdet = None
|
||||
return z
|
||||
else:
|
||||
z = (self.bias + torch.exp(self.logs) * x) * x_mask
|
||||
logdet = torch.sum(self.logs) * x_len # [b]
|
||||
return z, logdet
|
||||
|
||||
def store_inverse(self):
|
||||
pass
|
||||
|
||||
def set_ddi(self, ddi):
|
||||
self.initialized = not ddi
|
||||
|
||||
def initialize(self, x, x_mask):
|
||||
with torch.no_grad():
|
||||
denom = torch.sum(x_mask, [0, 2])
|
||||
m = torch.sum(x * x_mask, [0, 2]) / denom
|
||||
m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom
|
||||
v = m_sq - (m ** 2)
|
||||
logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6))
|
||||
|
||||
bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
|
||||
logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype)
|
||||
|
||||
self.bias.data.copy_(bias_init)
|
||||
self.logs.data.copy_(logs_init)
|
||||
|
||||
|
||||
class InvConvNear(nn.Module):
|
||||
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
|
||||
super().__init__()
|
||||
assert (n_split % 2 == 0)
|
||||
self.channels = channels
|
||||
self.n_split = n_split
|
||||
self.no_jacobian = no_jacobian
|
||||
|
||||
w_init = torch.linalg.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0]
|
||||
if torch.det(w_init) < 0:
|
||||
w_init[:, 0] = -1 * w_init[:, 0]
|
||||
self.weight = nn.Parameter(w_init)
|
||||
|
||||
def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs):
|
||||
b, c, t = x.size()
|
||||
assert (c % self.n_split == 0)
|
||||
if x_mask is None:
|
||||
x_mask = 1
|
||||
x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
|
||||
else:
|
||||
x_len = torch.sum(x_mask, [1, 2])
|
||||
|
||||
x = x.view(b, 2, c // self.n_split, self.n_split // 2, t)
|
||||
x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t)
|
||||
|
||||
if reverse:
|
||||
if hasattr(self, "weight_inv"):
|
||||
weight = self.weight_inv
|
||||
else:
|
||||
weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
|
||||
logdet = None
|
||||
else:
|
||||
weight = self.weight
|
||||
if self.no_jacobian:
|
||||
logdet = 0
|
||||
else:
|
||||
logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b]
|
||||
|
||||
weight = weight.view(self.n_split, self.n_split, 1, 1)
|
||||
z = F.conv2d(x, weight)
|
||||
|
||||
z = z.view(b, 2, self.n_split // 2, c // self.n_split, t)
|
||||
z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
|
||||
if reverse:
|
||||
return z
|
||||
else:
|
||||
return z, logdet
|
||||
|
||||
def store_inverse(self):
|
||||
self.weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
|
||||
160
GPT_SoVITS/module/mrte_model.py
Normal file
160
GPT_SoVITS/module/mrte_model.py
Normal file
@@ -0,0 +1,160 @@
|
||||
# This is Multi-reference timbre encoder
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn.utils import remove_weight_norm, weight_norm
|
||||
from module.attentions import MultiHeadAttention
|
||||
|
||||
class MRTE(nn.Module):
|
||||
def __init__(self,
|
||||
content_enc_channels=192,
|
||||
hidden_size=512,
|
||||
out_channels=192,
|
||||
kernel_size=5,
|
||||
n_heads=4,
|
||||
ge_layer = 2
|
||||
):
|
||||
super(MRTE, self).__init__()
|
||||
self.cross_attention = MultiHeadAttention(hidden_size,hidden_size,n_heads)
|
||||
self.c_pre = nn.Conv1d(content_enc_channels,hidden_size, 1)
|
||||
self.text_pre = nn.Conv1d(content_enc_channels,hidden_size, 1)
|
||||
self.c_post = nn.Conv1d(hidden_size,out_channels, 1)
|
||||
|
||||
def forward(self, ssl_enc, ssl_mask, text, text_mask, ge, test=None):
|
||||
if(ge==None):ge=0
|
||||
attn_mask = text_mask.unsqueeze(2) * ssl_mask.unsqueeze(-1)
|
||||
|
||||
ssl_enc = self.c_pre(ssl_enc * ssl_mask)
|
||||
text_enc = self.text_pre(text * text_mask)
|
||||
if test != None:
|
||||
if test == 0:
|
||||
x = self.cross_attention(ssl_enc * ssl_mask, text_enc * text_mask, attn_mask) + ssl_enc + ge
|
||||
elif test == 1:
|
||||
x = ssl_enc + ge
|
||||
elif test ==2:
|
||||
x = self.cross_attention(ssl_enc*0 * ssl_mask, text_enc * text_mask, attn_mask) + ge
|
||||
else:
|
||||
raise ValueError("test should be 0,1,2")
|
||||
else:
|
||||
x = self.cross_attention(ssl_enc * ssl_mask, text_enc * text_mask, attn_mask) + ssl_enc + ge
|
||||
x = self.c_post(x * ssl_mask)
|
||||
return x
|
||||
|
||||
|
||||
class SpeakerEncoder(torch.nn.Module):
|
||||
def __init__(self, mel_n_channels=80, model_num_layers=2, 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.transpose(-1, -2))
|
||||
embeds_raw = self.relu(self.linear(hidden[-1]))
|
||||
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
|
||||
|
||||
|
||||
class MELEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers):
|
||||
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.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
|
||||
def forward(self, x):
|
||||
# print(x.shape,x_lengths.shape)
|
||||
x = self.pre(x)
|
||||
x = self.enc(x)
|
||||
x = self.proj(x)
|
||||
return x
|
||||
|
||||
|
||||
class WN(torch.nn.Module):
|
||||
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers):
|
||||
super(WN, self).__init__()
|
||||
assert(kernel_size % 2 == 1)
|
||||
self.hidden_channels =hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
|
||||
self.in_layers = torch.nn.ModuleList()
|
||||
self.res_skip_layers = torch.nn.ModuleList()
|
||||
|
||||
for i in range(n_layers):
|
||||
dilation = dilation_rate ** i
|
||||
padding = int((kernel_size * dilation - dilation) / 2)
|
||||
in_layer = nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
||||
dilation=dilation, padding=padding)
|
||||
in_layer = weight_norm(in_layer)
|
||||
self.in_layers.append(in_layer)
|
||||
|
||||
# last one is not necessary
|
||||
if i < n_layers - 1:
|
||||
res_skip_channels = 2 * hidden_channels
|
||||
else:
|
||||
res_skip_channels = hidden_channels
|
||||
|
||||
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
||||
res_skip_layer = weight_norm(res_skip_layer, name='weight')
|
||||
self.res_skip_layers.append(res_skip_layer)
|
||||
|
||||
def forward(self, x):
|
||||
output = torch.zeros_like(x)
|
||||
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
||||
|
||||
for i in range(self.n_layers):
|
||||
x_in = self.in_layers[i](x)
|
||||
|
||||
acts = fused_add_tanh_sigmoid_multiply(
|
||||
x_in,
|
||||
n_channels_tensor)
|
||||
|
||||
res_skip_acts = self.res_skip_layers[i](acts)
|
||||
if i < self.n_layers - 1:
|
||||
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
||||
x = (x + res_acts)
|
||||
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
||||
else:
|
||||
output = output + res_skip_acts
|
||||
return output
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.in_layers:
|
||||
remove_weight_norm(l)
|
||||
for l in self.res_skip_layers:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
@torch.jit.script
|
||||
def fused_add_tanh_sigmoid_multiply(input, n_channels):
|
||||
n_channels_int = n_channels[0]
|
||||
t_act = torch.tanh(input[:, :n_channels_int, :])
|
||||
s_act = torch.sigmoid(input[:, n_channels_int:, :])
|
||||
acts = t_act * s_act
|
||||
return acts
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
content_enc = torch.randn(3,192,100)
|
||||
content_mask = torch.ones(3,1,100)
|
||||
ref_mel = torch.randn(3,128,30)
|
||||
ref_mask = torch.ones(3,1,30)
|
||||
model = MRTE()
|
||||
out = model(content_enc,content_mask,ref_mel,ref_mask)
|
||||
print(out.shape)
|
||||
108
GPT_SoVITS/module/quantize.py
Normal file
108
GPT_SoVITS/module/quantize.py
Normal file
@@ -0,0 +1,108 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
"""Residual vector quantizer implementation."""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
import math
|
||||
import typing as tp
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from module.core_vq import ResidualVectorQuantization
|
||||
|
||||
|
||||
@dataclass
|
||||
class QuantizedResult:
|
||||
quantized: torch.Tensor
|
||||
codes: torch.Tensor
|
||||
bandwidth: torch.Tensor # bandwidth in kb/s used, per batch item.
|
||||
penalty: tp.Optional[torch.Tensor] = None
|
||||
metrics: dict = field(default_factory=dict)
|
||||
|
||||
|
||||
class ResidualVectorQuantizer(nn.Module):
|
||||
"""Residual Vector Quantizer.
|
||||
Args:
|
||||
dimension (int): Dimension of the codebooks.
|
||||
n_q (int): Number of residual vector quantizers used.
|
||||
bins (int): Codebook size.
|
||||
decay (float): Decay for exponential moving average over the codebooks.
|
||||
kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
|
||||
kmeans_iters (int): Number of iterations used for kmeans initialization.
|
||||
threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
|
||||
that have an exponential moving average cluster size less than the specified threshold with
|
||||
randomly selected vector from the current batch.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
dimension: int = 256,
|
||||
n_q: int = 8,
|
||||
bins: int = 1024,
|
||||
decay: float = 0.99,
|
||||
kmeans_init: bool = True,
|
||||
kmeans_iters: int = 50,
|
||||
threshold_ema_dead_code: int = 2,
|
||||
):
|
||||
super().__init__()
|
||||
self.n_q = n_q
|
||||
self.dimension = dimension
|
||||
self.bins = bins
|
||||
self.decay = decay
|
||||
self.kmeans_init = kmeans_init
|
||||
self.kmeans_iters = kmeans_iters
|
||||
self.threshold_ema_dead_code = threshold_ema_dead_code
|
||||
self.vq = ResidualVectorQuantization(
|
||||
dim=self.dimension,
|
||||
codebook_size=self.bins,
|
||||
num_quantizers=self.n_q,
|
||||
decay=self.decay,
|
||||
kmeans_init=self.kmeans_init,
|
||||
kmeans_iters=self.kmeans_iters,
|
||||
threshold_ema_dead_code=self.threshold_ema_dead_code,
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None) -> QuantizedResult:
|
||||
"""Residual vector quantization on the given input tensor.
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
n_q (int): Number of quantizer used to quantize. Default: All quantizers.
|
||||
layers (list): Layer that need to return quantized. Defalt: None.
|
||||
Returns:
|
||||
QuantizedResult:
|
||||
The quantized (or approximately quantized) representation with
|
||||
the associated numbert quantizers and layer quantized required to return.
|
||||
"""
|
||||
n_q = n_q if n_q else self.n_q
|
||||
if layers and max(layers) >= n_q:
|
||||
raise ValueError(f'Last layer index in layers: A {max(layers)}. Number of quantizers in RVQ: B {self.n_q}. A must less than B.')
|
||||
quantized, codes, commit_loss, quantized_list = self.vq(x, n_q=n_q, layers=layers)
|
||||
return quantized, codes, torch.mean(commit_loss), quantized_list
|
||||
|
||||
|
||||
def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None) -> torch.Tensor:
|
||||
"""Encode a given input tensor with the specified sample rate at the given bandwidth.
|
||||
The RVQ encode method sets the appropriate number of quantizer to use
|
||||
and returns indices for each quantizer.
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
n_q (int): Number of quantizer used to quantize. Default: All quantizers.
|
||||
st (int): Start to encode input from which layers. Default: 0.
|
||||
"""
|
||||
n_q = n_q if n_q else self.n_q
|
||||
st = st or 0
|
||||
codes = self.vq.encode(x, n_q=n_q, st=st)
|
||||
return codes
|
||||
|
||||
def decode(self, codes: torch.Tensor, st: int = 0) -> torch.Tensor:
|
||||
"""Decode the given codes to the quantized representation.
|
||||
Args:
|
||||
codes (torch.Tensor): Input indices for each quantizer.
|
||||
st (int): Start to decode input codes from which layers. Default: 0.
|
||||
"""
|
||||
quantized = self.vq.decode(codes, st=st)
|
||||
return quantized
|
||||
193
GPT_SoVITS/module/transforms.py
Normal file
193
GPT_SoVITS/module/transforms.py
Normal file
@@ -0,0 +1,193 @@
|
||||
import torch
|
||||
from torch.nn import functional as F
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
DEFAULT_MIN_BIN_WIDTH = 1e-3
|
||||
DEFAULT_MIN_BIN_HEIGHT = 1e-3
|
||||
DEFAULT_MIN_DERIVATIVE = 1e-3
|
||||
|
||||
|
||||
def piecewise_rational_quadratic_transform(inputs,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=False,
|
||||
tails=None,
|
||||
tail_bound=1.,
|
||||
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
||||
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
||||
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
||||
|
||||
if tails is None:
|
||||
spline_fn = rational_quadratic_spline
|
||||
spline_kwargs = {}
|
||||
else:
|
||||
spline_fn = unconstrained_rational_quadratic_spline
|
||||
spline_kwargs = {
|
||||
'tails': tails,
|
||||
'tail_bound': tail_bound
|
||||
}
|
||||
|
||||
outputs, logabsdet = spline_fn(
|
||||
inputs=inputs,
|
||||
unnormalized_widths=unnormalized_widths,
|
||||
unnormalized_heights=unnormalized_heights,
|
||||
unnormalized_derivatives=unnormalized_derivatives,
|
||||
inverse=inverse,
|
||||
min_bin_width=min_bin_width,
|
||||
min_bin_height=min_bin_height,
|
||||
min_derivative=min_derivative,
|
||||
**spline_kwargs
|
||||
)
|
||||
return outputs, logabsdet
|
||||
|
||||
|
||||
def searchsorted(bin_locations, inputs, eps=1e-6):
|
||||
bin_locations[..., -1] += eps
|
||||
return torch.sum(
|
||||
inputs[..., None] >= bin_locations,
|
||||
dim=-1
|
||||
) - 1
|
||||
|
||||
|
||||
def unconstrained_rational_quadratic_spline(inputs,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=False,
|
||||
tails='linear',
|
||||
tail_bound=1.,
|
||||
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
||||
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
||||
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
||||
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
||||
outside_interval_mask = ~inside_interval_mask
|
||||
|
||||
outputs = torch.zeros_like(inputs)
|
||||
logabsdet = torch.zeros_like(inputs)
|
||||
|
||||
if tails == 'linear':
|
||||
unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
||||
constant = np.log(np.exp(1 - min_derivative) - 1)
|
||||
unnormalized_derivatives[..., 0] = constant
|
||||
unnormalized_derivatives[..., -1] = constant
|
||||
|
||||
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
||||
logabsdet[outside_interval_mask] = 0
|
||||
else:
|
||||
raise RuntimeError('{} tails are not implemented.'.format(tails))
|
||||
|
||||
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
|
||||
inputs=inputs[inside_interval_mask],
|
||||
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
||||
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
||||
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
||||
inverse=inverse,
|
||||
left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
|
||||
min_bin_width=min_bin_width,
|
||||
min_bin_height=min_bin_height,
|
||||
min_derivative=min_derivative
|
||||
)
|
||||
|
||||
return outputs, logabsdet
|
||||
|
||||
def rational_quadratic_spline(inputs,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=False,
|
||||
left=0., right=1., bottom=0., top=1.,
|
||||
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
||||
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
||||
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
||||
if torch.min(inputs) < left or torch.max(inputs) > right:
|
||||
raise ValueError('Input to a transform is not within its domain')
|
||||
|
||||
num_bins = unnormalized_widths.shape[-1]
|
||||
|
||||
if min_bin_width * num_bins > 1.0:
|
||||
raise ValueError('Minimal bin width too large for the number of bins')
|
||||
if min_bin_height * num_bins > 1.0:
|
||||
raise ValueError('Minimal bin height too large for the number of bins')
|
||||
|
||||
widths = F.softmax(unnormalized_widths, dim=-1)
|
||||
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
||||
cumwidths = torch.cumsum(widths, dim=-1)
|
||||
cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
|
||||
cumwidths = (right - left) * cumwidths + left
|
||||
cumwidths[..., 0] = left
|
||||
cumwidths[..., -1] = right
|
||||
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
||||
|
||||
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
||||
|
||||
heights = F.softmax(unnormalized_heights, dim=-1)
|
||||
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
||||
cumheights = torch.cumsum(heights, dim=-1)
|
||||
cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
|
||||
cumheights = (top - bottom) * cumheights + bottom
|
||||
cumheights[..., 0] = bottom
|
||||
cumheights[..., -1] = top
|
||||
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
||||
|
||||
if inverse:
|
||||
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
||||
else:
|
||||
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
||||
|
||||
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
||||
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
||||
|
||||
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
||||
delta = heights / widths
|
||||
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
||||
|
||||
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
||||
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
||||
|
||||
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
||||
|
||||
if inverse:
|
||||
a = (((inputs - input_cumheights) * (input_derivatives
|
||||
+ input_derivatives_plus_one
|
||||
- 2 * input_delta)
|
||||
+ input_heights * (input_delta - input_derivatives)))
|
||||
b = (input_heights * input_derivatives
|
||||
- (inputs - input_cumheights) * (input_derivatives
|
||||
+ input_derivatives_plus_one
|
||||
- 2 * input_delta))
|
||||
c = - input_delta * (inputs - input_cumheights)
|
||||
|
||||
discriminant = b.pow(2) - 4 * a * c
|
||||
assert (discriminant >= 0).all()
|
||||
|
||||
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
||||
outputs = root * input_bin_widths + input_cumwidths
|
||||
|
||||
theta_one_minus_theta = root * (1 - root)
|
||||
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
||||
* theta_one_minus_theta)
|
||||
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
|
||||
+ 2 * input_delta * theta_one_minus_theta
|
||||
+ input_derivatives * (1 - root).pow(2))
|
||||
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
||||
|
||||
return outputs, -logabsdet
|
||||
else:
|
||||
theta = (inputs - input_cumwidths) / input_bin_widths
|
||||
theta_one_minus_theta = theta * (1 - theta)
|
||||
|
||||
numerator = input_heights * (input_delta * theta.pow(2)
|
||||
+ input_derivatives * theta_one_minus_theta)
|
||||
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
||||
* theta_one_minus_theta)
|
||||
outputs = input_cumheights + numerator / denominator
|
||||
|
||||
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
|
||||
+ 2 * input_delta * theta_one_minus_theta
|
||||
+ input_derivatives * (1 - theta).pow(2))
|
||||
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
||||
|
||||
return outputs, logabsdet
|
||||
Reference in New Issue
Block a user