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import os
import sys
import torch.nn as nn
import torch.nn.functional as F
sys.path.append(os.getcwd())
from main.library.predictors.FCPE.attentions import SelfAttention
from main.library.predictors.FCPE.utils import calc_same_padding, Transpose, GLU, Swish
class ConformerConvModule_LEGACY(nn.Module):
def __init__(
self,
dim,
causal=False,
expansion_factor=2,
kernel_size=31,
dropout=0.0
):
super().__init__()
inner_dim = dim * expansion_factor
self.net = nn.Sequential(
nn.LayerNorm(dim),
Transpose((1, 2)),
nn.Conv1d(dim, inner_dim * 2, 1),
GLU(dim=1),
DepthWiseConv1d_LEGACY(
inner_dim,
inner_dim,
kernel_size=kernel_size,
padding=(
calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0)
)
),
Swish(),
nn.Conv1d(inner_dim, dim, 1),
Transpose((1, 2)),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class ConformerConvModule(nn.Module):
def __init__(
self,
dim,
expansion_factor=2,
kernel_size=31,
dropout=0
):
super().__init__()
inner_dim = dim * expansion_factor
self.net = nn.Sequential(
nn.LayerNorm(dim),
Transpose((1, 2)),
nn.Conv1d(dim, inner_dim * 2, 1),
nn.GLU(dim=1),
DepthWiseConv1d(
inner_dim,
inner_dim,
kernel_size=kernel_size,
padding=calc_same_padding(kernel_size)[0],
groups=inner_dim
),
nn.SiLU(),
nn.Conv1d(inner_dim, dim, 1),
Transpose((1, 2)),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class DepthWiseConv1d_LEGACY(nn.Module):
def __init__(
self,
chan_in,
chan_out,
kernel_size,
padding
):
super().__init__()
self.padding = padding
self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups=chan_in)
def forward(self, x):
return self.conv(F.pad(x, self.padding))
class DepthWiseConv1d(nn.Module):
def __init__(
self,
chan_in,
chan_out,
kernel_size,
padding,
groups
):
super().__init__()
self.conv = nn.Conv1d(chan_in, chan_out, kernel_size=kernel_size, padding=padding, groups=groups)
def forward(self, x):
return self.conv(x)
class EncoderLayer(nn.Module):
def __init__(
self,
parent
):
super().__init__()
self.conformer = ConformerConvModule_LEGACY(parent.dim_model)
self.norm = nn.LayerNorm(parent.dim_model)
self.dropout = nn.Dropout(parent.residual_dropout)
self.attn = SelfAttention(dim=parent.dim_model, heads=parent.num_heads, causal=False)
def forward(self, phone, mask=None):
phone = phone + (self.attn(self.norm(phone), mask=mask))
return phone + (self.conformer(phone))
class ConformerNaiveEncoder(nn.Module):
def __init__(
self,
num_layers,
num_heads,
dim_model,
use_norm = False,
conv_only = False,
conv_dropout = 0,
atten_dropout = 0
):
super().__init__()
self.num_layers = num_layers
self.num_heads = num_heads
self.dim_model = dim_model
self.use_norm = use_norm
self.residual_dropout = 0.1
self.attention_dropout = 0.1
self.encoder_layers = nn.ModuleList([
CFNEncoderLayer(dim_model, num_heads, use_norm, conv_only, conv_dropout, atten_dropout)
for _ in range(num_layers)
])
def forward(self, x, mask=None):
for (_, layer) in enumerate(self.encoder_layers):
x = layer(x, mask)
return x
class CFNEncoderLayer(nn.Module):
def __init__(
self,
dim_model,
num_heads = 8,
use_norm = False,
conv_only = False,
conv_dropout = 0,
atten_dropout = 0
):
super().__init__()
self.conformer = (
nn.Sequential(
ConformerConvModule(dim_model),
nn.Dropout(conv_dropout)
)
) if conv_dropout > 0 else (
ConformerConvModule(dim_model)
)
self.norm = nn.LayerNorm(dim_model)
self.dropout = nn.Dropout(0.1)
self.attn = SelfAttention(
dim=dim_model,
heads=num_heads,
causal=False,
use_norm=use_norm,
dropout=atten_dropout
) if not conv_only else None
def forward(self, x, mask=None):
if self.attn is not None: x = x + (self.attn(self.norm(x), mask=mask))
return x + (self.conformer(x)) |