Source code for torchaudio.models.conformer
from typing import Optional, Tuple
import torch
__all__ = ["Conformer"]
def _lengths_to_padding_mask(lengths: torch.Tensor) -> torch.Tensor:
batch_size = lengths.shape[0]
max_length = int(torch.max(lengths).item())
padding_mask = torch.arange(max_length, device=lengths.device, dtype=lengths.dtype).expand(
batch_size, max_length
) >= lengths.unsqueeze(1)
return padding_mask
class _ConvolutionModule(torch.nn.Module):
r"""Conformer convolution module.
Args:
input_dim (int): input dimension.
num_channels (int): number of depthwise convolution layer input channels.
depthwise_kernel_size (int): kernel size of depthwise convolution layer.
bias (bool, optional): indicates whether to add bias term to each convolution layer. (Default: ``False``)
"""
def __init__(
self,
input_dim: int,
num_channels: int,
depthwise_kernel_size: int,
bias: bool = False,
dropout: float = 0.0,
) -> None:
super().__init__()
assert (depthwise_kernel_size - 1) % 2 == 0, "depthwise_kernel_size must be odd to achieve 'SAME' padding."
self.layer_norm = torch.nn.LayerNorm(input_dim)
self.sequential = torch.nn.Sequential(
torch.nn.Conv1d(
input_dim,
2 * num_channels,
1,
stride=1,
padding=0,
bias=bias,
),
torch.nn.GLU(dim=1),
torch.nn.Conv1d(
num_channels,
num_channels,
depthwise_kernel_size,
stride=1,
padding=(depthwise_kernel_size - 1) // 2,
groups=num_channels,
bias=bias,
),
torch.nn.BatchNorm1d(num_channels),
torch.nn.SiLU(),
torch.nn.Conv1d(
num_channels,
input_dim,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
),
torch.nn.Dropout(dropout),
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
r"""
Args:
input (torch.Tensor): with shape `(B, T, D)`.
Returns:
torch.Tensor: output, with shape `(B, T, D)`.
"""
x = self.layer_norm(input)
x = x.transpose(1, 2)
x = self.sequential(x)
return x.transpose(1, 2)
class _FeedForwardModule(torch.nn.Module):
r"""Positionwise feed forward layer.
Args:
input_dim (int): input dimension.
hidden_dim (int): hidden dimension.
dropout (float, optional): dropout probability. (Default: 0.0)
"""
def __init__(self, input_dim: int, hidden_dim: int, dropout: float = 0.0) -> None:
super().__init__()
self.sequential = torch.nn.Sequential(
torch.nn.LayerNorm(input_dim),
torch.nn.Linear(input_dim, hidden_dim, bias=True),
torch.nn.SiLU(),
torch.nn.Dropout(dropout),
torch.nn.Linear(hidden_dim, input_dim, bias=True),
torch.nn.Dropout(dropout),
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
r"""
Args:
input (torch.Tensor): with shape `(*, D)`.
Returns:
torch.Tensor: output, with shape `(*, D)`.
"""
return self.sequential(input)
class ConformerLayer(torch.nn.Module):
r"""Conformer layer that constitutes Conformer.
Args:
input_dim (int): input dimension.
ffn_dim (int): hidden layer dimension of feedforward network.
num_attention_heads (int): number of attention heads.
depthwise_conv_kernel_size (int): kernel size of depthwise convolution layer.
dropout (float, optional): dropout probability. (Default: 0.0)
"""
def __init__(
self,
input_dim: int,
ffn_dim: int,
num_attention_heads: int,
depthwise_conv_kernel_size: int,
dropout: float = 0.0,
) -> None:
super().__init__()
self.ffn1 = _FeedForwardModule(input_dim, ffn_dim, dropout=dropout)
self.self_attn_layer_norm = torch.nn.LayerNorm(input_dim)
self.self_attn = torch.nn.MultiheadAttention(input_dim, num_attention_heads, dropout=dropout)
self.self_attn_dropout = torch.nn.Dropout(dropout)
self.conv_module = _ConvolutionModule(
input_dim=input_dim,
num_channels=input_dim,
depthwise_kernel_size=depthwise_conv_kernel_size,
bias=True,
dropout=dropout,
)
self.ffn2 = _FeedForwardModule(input_dim, ffn_dim, dropout=dropout)
self.final_layer_norm = torch.nn.LayerNorm(input_dim)
def forward(self, input: torch.Tensor, key_padding_mask: Optional[torch.Tensor]) -> torch.Tensor:
r"""
Args:
input (torch.Tensor): input, with shape `(T, B, D)`.
key_padding_mask (torch.Tensor or None): key padding mask to use in self attention layer.
Returns:
torch.Tensor: output, with shape `(T, B, D)`.
"""
residual = input
x = self.ffn1(input)
x = x * 0.5 + residual
residual = x
x = self.self_attn_layer_norm(x)
x, _ = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=key_padding_mask,
need_weights=False,
)
x = self.self_attn_dropout(x)
x = x + residual
residual = x
x = x.transpose(0, 1)
x = self.conv_module(x)
x = x.transpose(0, 1)
x = residual + x
residual = x
x = self.ffn2(x)
x = x * 0.5 + residual
x = self.final_layer_norm(x)
return x
[docs]class Conformer(torch.nn.Module):
r"""Implements the Conformer architecture introduced in
*Conformer: Convolution-augmented Transformer for Speech Recognition*
[:footcite:`gulati2020conformer`].
Args:
input_dim (int): input dimension.
num_heads (int): number of attention heads in each Conformer layer.
ffn_dim (int): hidden layer dimension of feedforward networks.
num_layers (int): number of Conformer layers to instantiate.
depthwise_conv_kernel_size (int): kernel size of each Conformer layer's depthwise convolution layer.
dropout (float, optional): dropout probability. (Default: 0.0)
Examples:
>>> conformer = Conformer(
>>> input_dim=80,
>>> num_heads=4,
>>> ffn_dim=128,
>>> num_layers=4,
>>> depthwise_conv_kernel_size=31,
>>> )
>>> lengths = torch.randint(1, 400, (10,)) # (batch,)
>>> input = torch.rand(10, int(lengths.max()), input_dim) # (batch, num_frames, input_dim)
>>> output = conformer(input, lengths)
"""
def __init__(
self,
input_dim: int,
num_heads: int,
ffn_dim: int,
num_layers: int,
depthwise_conv_kernel_size: int,
dropout: float = 0.0,
):
super().__init__()
self.conformer_layers = torch.nn.ModuleList(
[
ConformerLayer(
input_dim,
ffn_dim,
num_heads,
depthwise_conv_kernel_size,
dropout,
)
for _ in range(num_layers)
]
)
[docs] def forward(self, input: torch.Tensor, lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Args:
input (torch.Tensor): with shape `(B, T, input_dim)`.
lengths (torch.Tensor): with shape `(B,)` and i-th element representing
number of valid frames for i-th batch element in ``input``.
Returns:
(torch.Tensor, torch.Tensor)
torch.Tensor
output frames, with shape `(B, T, input_dim)`
torch.Tensor
output lengths, with shape `(B,)` and i-th element representing
number of valid frames for i-th batch element in output frames.
"""
encoder_padding_mask = _lengths_to_padding_mask(lengths)
x = input.transpose(0, 1)
for layer in self.conformer_layers:
x = layer(x, encoder_padding_mask)
return x.transpose(0, 1), lengths
