Source code for espnet2.spk.encoder.conformer_encoder
# Copyright 2023 Jee-weon Jung
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Conformer encoder definition."""
import logging
from typing import List, Optional, Tuple, Union
import torch
from typeguard import typechecked
from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule
from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer
from espnet.nets.pytorch_backend.nets_utils import get_activation
from espnet.nets.pytorch_backend.transformer.attention import (
LegacyRelPositionMultiHeadedAttention,
MultiHeadedAttention,
RelPositionMultiHeadedAttention,
)
from espnet.nets.pytorch_backend.transformer.embedding import (
LegacyRelPositionalEncoding,
PositionalEncoding,
RelPositionalEncoding,
ScaledPositionalEncoding,
)
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import (
Conv1dLinear,
MultiLayeredConv1d,
)
from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
PositionwiseFeedForward,
)
from espnet.nets.pytorch_backend.transformer.repeat import repeat
from espnet.nets.pytorch_backend.transformer.subsampling import (
Conv2dSubsampling,
Conv2dSubsampling1,
Conv2dSubsampling2,
Conv2dSubsampling6,
Conv2dSubsampling8,
)
[docs]class MfaConformerEncoder(AbsEncoder):
"""Conformer encoder module for MFA-Conformer.
Paper: Y. Zhang et al., ``Mfa-conformer: Multi-scale feature aggregation
conformer for automatic speaker verification,'' in Proc. INTERSPEECH, 2022.
Args:
input_size (int): Input dimension.
output_size (int): Dimension of attention.
attention_heads (int): The number of heads of multi head attention.
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of encoder blocks.
dropout_rate (float): Dropout rate.
attention_dropout_rate (float): Dropout rate in attention.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
input_layer (Union[str, torch.nn.Module]): Input layer type.
normalize_before (bool): Whether to use layer_norm before the first block.
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
rel_pos_type (str): Whether to use the latest relative positional encoding or
the legacy one. The legacy relative positional encoding will be deprecated
in the future. More Details can be found in
https://github.com/espnet/espnet/pull/2816.
encoder_pos_enc_layer_type (str): Encoder positional encoding layer type.
encoder_attn_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
macaron_style (bool): Whether to use macaron style for positionwise layer.
use_cnn_module (bool): Whether to use convolution module.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
cnn_module_kernel (int): Kernerl size of convolution module.
padding_idx (int): Padding idx for input_layer=embed.
"""
@typechecked
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: Optional[str] = "conv2d2",
normalize_before: bool = True,
positionwise_layer_type: str = "linear",
positionwise_conv_kernel_size: int = 3,
macaron_style: bool = False,
rel_pos_type: str = "legacy",
pos_enc_layer_type: str = "rel_pos",
selfattention_layer_type: str = "rel_selfattn",
activation_type: str = "swish",
use_cnn_module: bool = True,
zero_triu: bool = False,
cnn_module_kernel: int = 31,
stochastic_depth_rate: Union[float, List[float]] = 0.0,
layer_drop_rate: float = 0.0,
max_pos_emb_len: int = 5000,
padding_idx: Optional[int] = None,
):
super().__init__()
self._output_size = output_size * num_blocks
if rel_pos_type == "legacy":
if pos_enc_layer_type == "rel_pos":
pos_enc_layer_type = "legacy_rel_pos"
if selfattention_layer_type == "rel_selfattn":
selfattention_layer_type = "legacy_rel_selfattn"
elif rel_pos_type == "latest":
assert selfattention_layer_type != "legacy_rel_selfattn"
assert pos_enc_layer_type != "legacy_rel_pos"
else:
raise ValueError("unknown rel_pos_type: " + rel_pos_type)
activation = get_activation(activation_type)
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "scaled_abs_pos":
pos_enc_class = ScaledPositionalEncoding
elif pos_enc_layer_type == "rel_pos":
assert selfattention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "legacy_rel_pos":
assert selfattention_layer_type == "legacy_rel_selfattn"
pos_enc_class = LegacyRelPositionalEncoding
logging.warning(
"Using legacy_rel_pos and it will be deprecated in the future."
)
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(input_size, output_size),
torch.nn.LayerNorm(output_size),
torch.nn.Dropout(dropout_rate),
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d1":
self.embed = Conv2dSubsampling1(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d2":
self.embed = Conv2dSubsampling2(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d6":
self.embed = Conv2dSubsampling6(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "conv2d8":
self.embed = Conv2dSubsampling8(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif isinstance(input_layer, torch.nn.Module):
self.embed = torch.nn.Sequential(
input_layer,
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
)
elif input_layer is None:
self.embed = torch.nn.Sequential(
pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len)
)
else:
raise ValueError("unknown input_layer: " + input_layer)
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
activation,
)
elif positionwise_layer_type == "conv1d":
positionwise_layer = MultiLayeredConv1d
positionwise_layer_args = (
output_size,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
elif positionwise_layer_type == "conv1d-linear":
positionwise_layer = Conv1dLinear
positionwise_layer_args = (
output_size,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
else:
raise NotImplementedError("Support only linear or conv1d.")
if selfattention_layer_type == "selfattn":
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
)
elif selfattention_layer_type == "legacy_rel_selfattn":
assert pos_enc_layer_type == "legacy_rel_pos"
encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
)
logging.warning(
"Using legacy_rel_selfattn and it will be deprecated in the future."
)
elif selfattention_layer_type == "rel_selfattn":
assert pos_enc_layer_type == "rel_pos"
encoder_selfattn_layer = RelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
zero_triu,
)
else:
raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type)
convolution_layer = ConvolutionModule
convolution_layer_args = (output_size, cnn_module_kernel, activation)
if isinstance(stochastic_depth_rate, float):
stochastic_depth_rate = [stochastic_depth_rate] * num_blocks
if len(stochastic_depth_rate) != num_blocks:
raise ValueError(
f"Length of stochastic_depth_rate ({len(stochastic_depth_rate)}) "
f"should be equal to num_blocks ({num_blocks})"
)
self.encoders = repeat(
num_blocks,
lambda lnum: EncoderLayer(
output_size,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
positionwise_layer(*positionwise_layer_args),
positionwise_layer(*positionwise_layer_args) if macaron_style else None,
convolution_layer(*convolution_layer_args) if use_cnn_module else None,
dropout_rate,
normalize_before,
False,
stochastic_depth_rate[lnum],
),
layer_drop_rate,
)
self.ln = LayerNorm(output_size * num_blocks)
[docs] def forward(
self,
x: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
"""Calculate forward propagation.
Args:
x (torch.Tensor): Input tensor (#batch, L, input_size).
Returns:
torch.Tensor: Output tensor (#batch, L, output_size).
"""
masks = None
if (
isinstance(self.embed, Conv2dSubsampling)
or isinstance(self.embed, Conv2dSubsampling1)
or isinstance(self.embed, Conv2dSubsampling2)
or isinstance(self.embed, Conv2dSubsampling6)
or isinstance(self.embed, Conv2dSubsampling8)
):
xs_pad, _ = self.embed(x, masks)
else:
raise NotImplementedError(
"Supposed to be one of the Conv subsampling layers"
)
intermediate_outs = []
for layer_idx, encoder_layer in enumerate(self.encoders):
x, _ = encoder_layer(xs_pad, masks)
intermediate_outs.append(x[0])
x = torch.cat(intermediate_outs, dim=-1)
x = self.ln(x).transpose(1, 2) # (#batch, L, output_size)
# to (#batch, output_size, L)
return x