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""" from https://github.com/jaywalnut310/glow-tts """
import math
import torch
import torch.nn as nn
from einops import rearrange
import matcha.utils as utils
from matcha.utils.model import sequence_mask
log = utils.get_pylogger(__name__)
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-4):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = torch.nn.Parameter(torch.ones(channels))
self.beta = torch.nn.Parameter(torch.zeros(channels))
def forward(self, x):
n_dims = len(x.shape)
mean = torch.mean(x, 1, keepdim=True)
variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
x = (x - mean) * torch.rsqrt(variance + self.eps)
shape = [1, -1] + [1] * (n_dims - 2)
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
return x
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
self.conv_layers = torch.nn.ModuleList()
self.norm_layers = torch.nn.ModuleList()
self.conv_layers.append(torch.nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
self.norm_layers.append(LayerNorm(hidden_channels))
self.relu_drop = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Dropout(p_dropout))
for _ in range(n_layers - 1):
self.conv_layers.append(
torch.nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)
)
self.norm_layers.append(LayerNorm(hidden_channels))
self.proj = torch.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 DurationPredictor(nn.Module):
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.p_dropout = p_dropout
self.drop = torch.nn.Dropout(p_dropout)
self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.norm_1 = LayerNorm(filter_channels)
self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.norm_2 = LayerNorm(filter_channels)
self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
def forward(self, x, x_mask):
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 RotaryPositionalEmbeddings(nn.Module):
"""
## RoPE module
Rotary encoding transforms pairs of features by rotating in the 2D plane.
That is, it organizes the $d$ features as $\frac{d}{2}$ pairs.
Each pair can be considered a coordinate in a 2D plane, and the encoding will rotate it
by an angle depending on the position of the token.
"""
def __init__(self, d: int, base: int = 10_000):
r"""
* `d` is the number of features $d$
* `base` is the constant used for calculating $\Theta$
"""
super().__init__()
self.base = base
self.d = int(d)
self.cos_cached = None
self.sin_cached = None
def _build_cache(self, x: torch.Tensor):
r"""
Cache $\cos$ and $\sin$ values
"""
# Return if cache is already built
if self.cos_cached is not None and x.shape[0] <= self.cos_cached.shape[0]:
return
# Get sequence length
seq_len = x.shape[0]
# $\Theta = {\theta_i = 10000^{-\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2).float() / self.d)).to(x.device)
# Create position indexes `[0, 1, ..., seq_len - 1]`
seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device)
# Calculate the product of position index and $\theta_i$
idx_theta = torch.einsum("n,d->nd", seq_idx, theta)
# Concatenate so that for row $m$ we have
# $[m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}, m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}]$
idx_theta2 = torch.cat([idx_theta, idx_theta], dim=1)
# Cache them
self.cos_cached = idx_theta2.cos()[:, None, None, :]
self.sin_cached = idx_theta2.sin()[:, None, None, :]
def _neg_half(self, x: torch.Tensor):
# $\frac{d}{2}$
d_2 = self.d // 2
# Calculate $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$
return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1)
def forward(self, x: torch.Tensor):
"""
* `x` is the Tensor at the head of a key or a query with shape `[seq_len, batch_size, n_heads, d]`
"""
# Cache $\cos$ and $\sin$ values
x = rearrange(x, "b h t d -> t b h d")
self._build_cache(x)
# Split the features, we can choose to apply rotary embeddings only to a partial set of features.
x_rope, x_pass = x[..., : self.d], x[..., self.d :]
# Calculate
# $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$
neg_half_x = self._neg_half(x_rope)
x_rope = (x_rope * self.cos_cached[: x.shape[0]]) + (neg_half_x * self.sin_cached[: x.shape[0]])
return rearrange(torch.cat((x_rope, x_pass), dim=-1), "t b h d -> b h t d")
class MultiHeadAttention(nn.Module):
def __init__(
self,
channels,
out_channels,
n_heads,
heads_share=True,
p_dropout=0.0,
proximal_bias=False,
proximal_init=False,
):
super().__init__()
assert channels % n_heads == 0
self.channels = channels
self.out_channels = out_channels
self.n_heads = n_heads
self.heads_share = heads_share
self.proximal_bias = proximal_bias
self.p_dropout = p_dropout
self.attn = None
self.k_channels = channels // n_heads
self.conv_q = torch.nn.Conv1d(channels, channels, 1)
self.conv_k = torch.nn.Conv1d(channels, channels, 1)
self.conv_v = torch.nn.Conv1d(channels, channels, 1)
# from https://nn.labml.ai/transformers/rope/index.html
self.query_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5)
self.key_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5)
self.conv_o = torch.nn.Conv1d(channels, out_channels, 1)
self.drop = torch.nn.Dropout(p_dropout)
torch.nn.init.xavier_uniform_(self.conv_q.weight)
torch.nn.init.xavier_uniform_(self.conv_k.weight)
if proximal_init:
self.conv_k.weight.data.copy_(self.conv_q.weight.data)
self.conv_k.bias.data.copy_(self.conv_q.bias.data)
torch.nn.init.xavier_uniform_(self.conv_v.weight)
def forward(self, x, c, attn_mask=None):
q = self.conv_q(x)
k = self.conv_k(c)
v = self.conv_v(c)
x, self.attn = self.attention(q, k, v, mask=attn_mask)
x = self.conv_o(x)
return x
def attention(self, query, key, value, mask=None):
b, d, t_s, t_t = (*key.size(), query.size(2))
query = rearrange(query, "b (h c) t-> b h t c", h=self.n_heads)
key = rearrange(key, "b (h c) t-> b h t c", h=self.n_heads)
value = rearrange(value, "b (h c) t-> b h t c", h=self.n_heads)
query = self.query_rotary_pe(query)
key = self.key_rotary_pe(key)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
if self.proximal_bias:
assert t_s == t_t, "Proximal bias is only available for self-attention."
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e4)
p_attn = torch.nn.functional.softmax(scores, dim=-1)
p_attn = self.drop(p_attn)
output = torch.matmul(p_attn, value)
output = output.transpose(2, 3).contiguous().view(b, d, t_t)
return output, p_attn
@staticmethod
def _attention_bias_proximal(length):
r = torch.arange(length, dtype=torch.float32)
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
class FFN(nn.Module):
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size, padding=kernel_size // 2)
self.drop = torch.nn.Dropout(p_dropout)
def forward(self, x, x_mask):
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.drop(x)
x = self.conv_2(x * x_mask)
return x * x_mask
class Encoder(nn.Module):
def __init__(
self,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size=1,
p_dropout=0.0,
**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.drop = torch.nn.Dropout(p_dropout)
self.attn_layers = torch.nn.ModuleList()
self.norm_layers_1 = torch.nn.ModuleList()
self.ffn_layers = torch.nn.ModuleList()
self.norm_layers_2 = torch.nn.ModuleList()
for _ in range(self.n_layers):
self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
self.norm_layers_1.append(LayerNorm(hidden_channels))
self.ffn_layers.append(
FFN(
hidden_channels,
hidden_channels,
filter_channels,
kernel_size,
p_dropout=p_dropout,
)
)
self.norm_layers_2.append(LayerNorm(hidden_channels))
def forward(self, x, x_mask):
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
for i in range(self.n_layers):
x = x * x_mask
y = self.attn_layers[i](x, x, attn_mask)
y = self.drop(y)
x = self.norm_layers_1[i](x + y)
y = self.ffn_layers[i](x, x_mask)
y = self.drop(y)
x = self.norm_layers_2[i](x + y)
x = x * x_mask
return x
class TextEncoder(nn.Module):
def __init__(
self,
encoder_type,
encoder_params,
duration_predictor_params,
n_vocab,
n_spks=1,
spk_emb_dim=128,
):
super().__init__()
self.encoder_type = encoder_type
self.n_vocab = n_vocab
self.n_feats = encoder_params.n_feats
self.n_channels = encoder_params.n_channels
self.spk_emb_dim = spk_emb_dim
self.n_spks = n_spks
self.emb = torch.nn.Embedding(n_vocab, self.n_channels)
torch.nn.init.normal_(self.emb.weight, 0.0, self.n_channels**-0.5)
if encoder_params.prenet:
self.prenet = ConvReluNorm(
self.n_channels,
self.n_channels,
self.n_channels,
kernel_size=5,
n_layers=3,
p_dropout=0.5,
)
else:
self.prenet = lambda x, x_mask: x
self.encoder = Encoder(
encoder_params.n_channels + (spk_emb_dim if n_spks > 1 else 0),
encoder_params.filter_channels,
encoder_params.n_heads,
encoder_params.n_layers,
encoder_params.kernel_size,
encoder_params.p_dropout,
)
self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1)
self.proj_w = DurationPredictor(
self.n_channels + (spk_emb_dim if n_spks > 1 else 0),
duration_predictor_params.filter_channels_dp,
duration_predictor_params.kernel_size,
duration_predictor_params.p_dropout,
)
def forward(self, x, x_lengths, spks=None):
x = self.emb(x) * math.sqrt(self.n_channels)
x = torch.transpose(x, 1, -1)
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.prenet(x, x_mask)
if self.n_spks > 1:
x = torch.cat([x, spks.unsqueeze(-1).repeat(1, 1, x.shape[-1])], dim=1)
x = self.encoder(x, x_mask)
mu = self.proj_m(x) * x_mask
x_dp = torch.detach(x)
logw = self.proj_w(x_dp, x_mask)
return mu, logw, x_mask