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Zero
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import torch
from torch import nn
class GxlConv1dSubsampling2(nn.Module):
"""Conv1d subsampling module.
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
"""
def __init__(self, idim: int, odim: int):
"""Construct an Conv1dSubsampling object."""
super().__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv1d(idim, odim, 3, 1),
torch.nn.GELU(),
torch.nn.Conv1d(odim, odim, 3, 2),
torch.nn.GELU(),
)
def forward(self, x):
"""
Args:
x: (B, T, idim)
Returns:
"""
x = x.transpose(1, 2)
x = self.conv(x)
x = x.transpose(1, 2)
return x
class GxlConv1dSubsampling4(nn.Module):
"""Conv1d subsampling module.
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
"""
def __init__(self, idim: int, odim: int):
"""Construct an Conv1dSubsampling object."""
super().__init__()
self.conv = torch.nn.Sequential(
torch.nn.ConstantPad1d((2, 0), 0.0),
torch.nn.Conv1d(idim, odim, 3, 1),
torch.nn.GELU(),
torch.nn.ConstantPad1d((2, 0), 0.0),
torch.nn.Conv1d(odim, odim, 3, 2),
torch.nn.GELU(),
torch.nn.ConstantPad1d((2, 0), 0.0),
torch.nn.Conv1d(odim, odim, 3, 2),
torch.nn.GELU(),
)
def forward(self, x, mask_pad):
"""
Args:
x: (B, T, idim)
Returns:
"""
x = x.transpose(1, 2)
x = self.conv(x)
x = x.transpose(1, 2)
mask_pad = mask_pad[:, :, 0::2]
mask_pad = mask_pad[:, :, 0::2]
return x, mask_pad
class GxlConv1dSubsampling6(nn.Module):
"""Conv1d subsampling module.
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
"""
def __init__(self, idim: int, odim: int):
"""Construct an Conv1dSubsampling object."""
super().__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv1d(idim, odim, 3, 1),
torch.nn.GELU(),
torch.nn.Conv1d(odim, odim, 3, 2),
torch.nn.GELU(),
torch.nn.Conv1d(odim, odim, 3, 3),
torch.nn.GELU(),
)
def forward(self, x):
"""
Args:
x: (B, T, idim)
Returns:
"""
x = x.transpose(1, 2)
x = self.conv(x)
x = x.transpose(1, 2)
return x
class GxlConv1dSubsampling8(nn.Module):
"""Conv1d subsampling module.
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
"""
def __init__(self, idim: int, odim: int):
"""Construct an Conv1dSubsampling object."""
super().__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv1d(idim, odim, 3, 1),
torch.nn.GELU(),
torch.nn.Conv1d(odim, odim, 3, 2),
torch.nn.GELU(),
torch.nn.Conv1d(odim, odim, 3, 2),
torch.nn.GELU(),
torch.nn.Conv1d(odim, odim, 3, 2),
torch.nn.GELU(),
)
def forward(self, x):
"""
Args:
x: (B, T, idim)
Returns:
"""
x = x.transpose(1, 2)
x = self.conv(x)
x = x.transpose(1, 2)
return x
class LyzConv1dSubsampling(torch.nn.Module):
def __init__(
self,
enc_out_dim: int = 512,
llm_embed_dim: int = 4096,
kernel_size: int = 5,
activation_func: str = 'relu',
norm: str = 'batch',
):
super().__init__()
if enc_out_dim * 4 < llm_embed_dim:
self.left_padding1 = nn.ConstantPad1d((kernel_size - 1, 0), 0.0)
self.conv1d1 = nn.Conv1d(enc_out_dim, 2 * enc_out_dim, kernel_size, 1, 0)
self.bn1 = nn.BatchNorm1d(2 * enc_out_dim, eps=1e-3, momentum=0.99)
self.relu1 = nn.ReLU()
self.left_padding2 = nn.ConstantPad1d((kernel_size - 1, 0), 0.0)
self.conv1d2 = nn.Conv1d(2 * enc_out_dim, 4 * enc_out_dim, kernel_size, 2, 0)
self.bn2 = nn.BatchNorm1d(4 * enc_out_dim, eps=1e-3, momentum=0.99)
self.relu2 = nn.ReLU()
self.project = nn.Linear(4 * enc_out_dim, llm_embed_dim)
self.cnn_num = 2
else:
self.left_padding2 = nn.ConstantPad1d((kernel_size - 1, 0), 0.0)
self.conv1d2 = nn.Conv1d(enc_out_dim, 2 * enc_out_dim, kernel_size, 2, 0)
if norm == 'batch':
self.bn2 = nn.BatchNorm1d(2 * enc_out_dim, eps=1e-3, momentum=0.99)
elif norm == 'layer':
self.bn2 = nn.LayerNorm(2 * enc_out_dim, eps=1e-3)
if activation_func == 'gelu':
self.relu2 = nn.GELU()
else:
self.relu2 = nn.ReLU()
self.project = nn.Linear(2 * enc_out_dim, llm_embed_dim)
self.cnn_num = 1
def forward(self, x, mask_pad):
"""
x: B, T, enc_out_dim
mask: (B, T) or (B, 1, T)
"""
x = x.transpose(1, 2) # B, channels, T
# mask batch padding
if mask_pad.size(2) > 0: # time > 0
x.masked_fill_(~mask_pad, 0.0)
if self.cnn_num == 2:
x = self.left_padding1(x)
x = self.conv1d1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.left_padding2(x)
x = self.conv1d2(x)
if isinstance(self.bn2, nn.LayerNorm):
x = x.transpose(1, 2)
x = self.bn2(x)
if isinstance(self.bn2, nn.LayerNorm):
x = x.transpose(1, 2)
x = self.relu2(x)
x = x.transpose(1, 2)
x = self.project(x)
return x, mask_pad[:, :, 0::2]
def get_downsampler(downsample_rate, ndim=1280):
down_sample_2 = nn.Identity()
if downsample_rate == 2:
down_sample_2 = GxlConv1dSubsampling2(ndim, ndim)
elif downsample_rate == 4:
down_sample_2 = GxlConv1dSubsampling4(ndim, ndim)
elif downsample_rate == 8:
down_sample_2 = GxlConv1dSubsampling8(ndim, ndim)
elif downsample_rate == 6:
down_sample_2 = GxlConv1dSubsampling6(ndim, ndim)
return down_sample_2 |