411 lines
14 KiB
Python
411 lines
14 KiB
Python
""" from https://github.com/jaywalnut310/glow-tts """
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import math
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import torch
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import torch.nn as nn
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from einops import rearrange
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import matcha.utils as utils
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from matcha.utils.model import sequence_mask
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log = utils.get_pylogger(__name__)
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class LayerNorm(nn.Module):
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def __init__(self, channels, eps=1e-4):
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super().__init__()
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self.channels = channels
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self.eps = eps
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self.gamma = torch.nn.Parameter(torch.ones(channels))
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self.beta = torch.nn.Parameter(torch.zeros(channels))
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def forward(self, x):
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n_dims = len(x.shape)
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mean = torch.mean(x, 1, keepdim=True)
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variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
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x = (x - mean) * torch.rsqrt(variance + self.eps)
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shape = [1, -1] + [1] * (n_dims - 2)
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x = x * self.gamma.view(*shape) + self.beta.view(*shape)
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return x
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class ConvReluNorm(nn.Module):
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def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
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super().__init__()
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self.in_channels = in_channels
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self.hidden_channels = hidden_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.p_dropout = p_dropout
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self.conv_layers = torch.nn.ModuleList()
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self.norm_layers = torch.nn.ModuleList()
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self.conv_layers.append(torch.nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
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self.norm_layers.append(LayerNorm(hidden_channels))
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self.relu_drop = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Dropout(p_dropout))
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for _ in range(n_layers - 1):
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self.conv_layers.append(
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torch.nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)
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)
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self.norm_layers.append(LayerNorm(hidden_channels))
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self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1)
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self.proj.weight.data.zero_()
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self.proj.bias.data.zero_()
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def forward(self, x, x_mask):
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x_org = x
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for i in range(self.n_layers):
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x = self.conv_layers[i](x * x_mask)
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x = self.norm_layers[i](x)
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x = self.relu_drop(x)
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x = x_org + self.proj(x)
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return x * x_mask
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class DurationPredictor(nn.Module):
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def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
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super().__init__()
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self.in_channels = in_channels
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self.filter_channels = filter_channels
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self.p_dropout = p_dropout
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self.drop = torch.nn.Dropout(p_dropout)
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self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
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self.norm_1 = LayerNorm(filter_channels)
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self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
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self.norm_2 = LayerNorm(filter_channels)
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self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
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def forward(self, x, x_mask):
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x = self.conv_1(x * x_mask)
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x = torch.relu(x)
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x = self.norm_1(x)
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x = self.drop(x)
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x = self.conv_2(x * x_mask)
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x = torch.relu(x)
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x = self.norm_2(x)
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x = self.drop(x)
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x = self.proj(x * x_mask)
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return x * x_mask
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class RotaryPositionalEmbeddings(nn.Module):
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"""
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## RoPE module
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Rotary encoding transforms pairs of features by rotating in the 2D plane.
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That is, it organizes the $d$ features as $\frac{d}{2}$ pairs.
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Each pair can be considered a coordinate in a 2D plane, and the encoding will rotate it
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by an angle depending on the position of the token.
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"""
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def __init__(self, d: int, base: int = 10_000):
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r"""
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* `d` is the number of features $d$
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* `base` is the constant used for calculating $\Theta$
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"""
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super().__init__()
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self.base = base
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self.d = int(d)
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self.cos_cached = None
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self.sin_cached = None
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def _build_cache(self, x: torch.Tensor):
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r"""
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Cache $\cos$ and $\sin$ values
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"""
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# Return if cache is already built
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if self.cos_cached is not None and x.shape[0] <= self.cos_cached.shape[0]:
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return
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# Get sequence length
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seq_len = x.shape[0]
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# $\Theta = {\theta_i = 10000^{-\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
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theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2).float() / self.d)).to(x.device)
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# Create position indexes `[0, 1, ..., seq_len - 1]`
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seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device)
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# Calculate the product of position index and $\theta_i$
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idx_theta = torch.einsum("n,d->nd", seq_idx, theta)
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# Concatenate so that for row $m$ we have
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# $[m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}, m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}]$
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idx_theta2 = torch.cat([idx_theta, idx_theta], dim=1)
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# Cache them
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self.cos_cached = idx_theta2.cos()[:, None, None, :]
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self.sin_cached = idx_theta2.sin()[:, None, None, :]
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def _neg_half(self, x: torch.Tensor):
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# $\frac{d}{2}$
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d_2 = self.d // 2
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# Calculate $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$
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return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1)
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def forward(self, x: torch.Tensor):
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"""
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* `x` is the Tensor at the head of a key or a query with shape `[seq_len, batch_size, n_heads, d]`
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"""
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# Cache $\cos$ and $\sin$ values
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x = rearrange(x, "b h t d -> t b h d")
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self._build_cache(x)
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# Split the features, we can choose to apply rotary embeddings only to a partial set of features.
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x_rope, x_pass = x[..., : self.d], x[..., self.d :]
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# Calculate
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# $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$
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neg_half_x = self._neg_half(x_rope)
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x_rope = (x_rope * self.cos_cached[: x.shape[0]]) + (neg_half_x * self.sin_cached[: x.shape[0]])
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return rearrange(torch.cat((x_rope, x_pass), dim=-1), "t b h d -> b h t d")
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class MultiHeadAttention(nn.Module):
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def __init__(
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self,
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channels,
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out_channels,
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n_heads,
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heads_share=True,
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p_dropout=0.0,
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proximal_bias=False,
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proximal_init=False,
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):
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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.heads_share = heads_share
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self.proximal_bias = proximal_bias
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self.p_dropout = p_dropout
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self.attn = None
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self.k_channels = channels // n_heads
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self.conv_q = torch.nn.Conv1d(channels, channels, 1)
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self.conv_k = torch.nn.Conv1d(channels, channels, 1)
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self.conv_v = torch.nn.Conv1d(channels, channels, 1)
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# from https://nn.labml.ai/transformers/rope/index.html
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self.query_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5)
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self.key_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5)
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self.conv_o = torch.nn.Conv1d(channels, out_channels, 1)
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self.drop = torch.nn.Dropout(p_dropout)
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torch.nn.init.xavier_uniform_(self.conv_q.weight)
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torch.nn.init.xavier_uniform_(self.conv_k.weight)
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if proximal_init:
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self.conv_k.weight.data.copy_(self.conv_q.weight.data)
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self.conv_k.bias.data.copy_(self.conv_q.bias.data)
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torch.nn.init.xavier_uniform_(self.conv_v.weight)
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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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b, d, t_s, t_t = (*key.size(), query.size(2))
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query = rearrange(query, "b (h c) t-> b h t c", h=self.n_heads)
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key = rearrange(key, "b (h c) t-> b h t c", h=self.n_heads)
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value = rearrange(value, "b (h c) t-> b h t c", h=self.n_heads)
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query = self.query_rotary_pe(query)
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key = self.key_rotary_pe(key)
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scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
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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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p_attn = torch.nn.functional.softmax(scores, dim=-1)
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p_attn = self.drop(p_attn)
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output = torch.matmul(p_attn, value)
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output = output.transpose(2, 3).contiguous().view(b, d, t_t)
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return output, p_attn
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@staticmethod
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def _attention_bias_proximal(length):
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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=0.0):
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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.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
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self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size, padding=kernel_size // 2)
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self.drop = torch.nn.Dropout(p_dropout)
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def forward(self, x, x_mask):
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x = self.conv_1(x * x_mask)
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x = torch.relu(x)
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x = self.drop(x)
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x = self.conv_2(x * x_mask)
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return x * x_mask
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class Encoder(nn.Module):
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def __init__(
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self,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size=1,
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p_dropout=0.0,
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**kwargs,
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):
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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.drop = torch.nn.Dropout(p_dropout)
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self.attn_layers = torch.nn.ModuleList()
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self.norm_layers_1 = torch.nn.ModuleList()
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self.ffn_layers = torch.nn.ModuleList()
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self.norm_layers_2 = torch.nn.ModuleList()
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for _ in range(self.n_layers):
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self.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(
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FFN(
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hidden_channels,
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hidden_channels,
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filter_channels,
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kernel_size,
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p_dropout=p_dropout,
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)
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)
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask):
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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for i in range(self.n_layers):
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x = x * x_mask
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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 TextEncoder(nn.Module):
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def __init__(
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self,
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encoder_type,
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encoder_params,
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duration_predictor_params,
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n_vocab,
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n_spks=1,
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spk_emb_dim=128,
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):
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super().__init__()
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self.encoder_type = encoder_type
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self.n_vocab = n_vocab
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self.n_feats = encoder_params.n_feats
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self.n_channels = encoder_params.n_channels
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self.spk_emb_dim = spk_emb_dim
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self.n_spks = n_spks
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self.emb = torch.nn.Embedding(n_vocab, self.n_channels)
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torch.nn.init.normal_(self.emb.weight, 0.0, self.n_channels**-0.5)
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if encoder_params.prenet:
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self.prenet = ConvReluNorm(
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self.n_channels,
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self.n_channels,
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self.n_channels,
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kernel_size=5,
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n_layers=3,
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p_dropout=0.5,
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)
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else:
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self.prenet = lambda x, x_mask: x
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self.encoder = Encoder(
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encoder_params.n_channels + (spk_emb_dim if n_spks > 1 else 0),
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encoder_params.filter_channels,
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encoder_params.n_heads,
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encoder_params.n_layers,
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encoder_params.kernel_size,
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encoder_params.p_dropout,
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)
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self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1)
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self.proj_w = DurationPredictor(
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self.n_channels + (spk_emb_dim if n_spks > 1 else 0),
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duration_predictor_params.filter_channels_dp,
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duration_predictor_params.kernel_size,
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duration_predictor_params.p_dropout,
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)
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def forward(self, x, x_lengths, spks=None):
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"""Run forward pass to the transformer based encoder and duration predictor
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Args:
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x (torch.Tensor): text input
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shape: (batch_size, max_text_length)
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x_lengths (torch.Tensor): text input lengths
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shape: (batch_size,)
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spks (torch.Tensor, optional): speaker ids. Defaults to None.
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shape: (batch_size,)
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Returns:
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mu (torch.Tensor): average output of the encoder
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shape: (batch_size, n_feats, max_text_length)
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logw (torch.Tensor): log duration predicted by the duration predictor
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shape: (batch_size, 1, max_text_length)
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x_mask (torch.Tensor): mask for the text input
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shape: (batch_size, 1, max_text_length)
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"""
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x = self.emb(x) * math.sqrt(self.n_channels)
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x = torch.transpose(x, 1, -1)
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x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.prenet(x, x_mask)
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if self.n_spks > 1:
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x = torch.cat([x, spks.unsqueeze(-1).repeat(1, 1, x.shape[-1])], dim=1)
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x = self.encoder(x, x_mask)
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mu = self.proj_m(x) * x_mask
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x_dp = torch.detach(x)
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logw = self.proj_w(x_dp, x_mask)
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return mu, logw, x_mask
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