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Zero
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# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
# 2022 Xingchen Song ([email protected])
# 2023 NetEase Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from ESPnet(https://github.com/espnet/espnet)
"""Encoder definition."""
from typing import Optional, Tuple
import torch
from wenet.utils.mask import make_pad_mask
from wenet.transformer.encoder import TransformerEncoder, ConformerEncoder
class DualTransformerEncoder(TransformerEncoder):
"""Transformer encoder module."""
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: str = "conv2d",
pos_enc_layer_type: str = "abs_pos",
normalize_before: bool = True,
static_chunk_size: int = 0,
use_dynamic_chunk: bool = False,
global_cmvn: torch.nn.Module = None,
use_dynamic_left_chunk: bool = False,
query_bias: bool = True,
key_bias: bool = True,
value_bias: bool = True,
activation_type: str = "relu",
gradient_checkpointing: bool = False,
use_sdpa: bool = False,
layer_norm_type: str = 'layer_norm',
norm_eps: float = 1e-5,
n_kv_head: Optional[int] = None,
head_dim: Optional[int] = None,
selfattention_layer_type: str = "selfattn",
mlp_type: str = 'position_wise_feed_forward',
mlp_bias: bool = True,
n_expert: int = 8,
n_expert_activated: int = 2,
):
""" Construct DualTransformerEncoder
Support both the full context mode and the streaming mode separately
"""
super().__init__(input_size, output_size, attention_heads,
linear_units, num_blocks, dropout_rate,
positional_dropout_rate, attention_dropout_rate,
input_layer, pos_enc_layer_type, normalize_before,
static_chunk_size, use_dynamic_chunk, global_cmvn,
use_dynamic_left_chunk, query_bias, key_bias,
value_bias, activation_type, gradient_checkpointing,
use_sdpa, layer_norm_type, norm_eps, n_kv_head,
head_dim, selfattention_layer_type, mlp_type,
mlp_bias, n_expert, n_expert_activated)
def forward_full(
self,
xs: torch.Tensor,
xs_lens: torch.Tensor,
decoding_chunk_size: int = 0,
num_decoding_left_chunks: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
T = xs.size(1)
masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
xs, pos_emb, masks = self.embed(xs, masks)
mask_pad = masks # (B, 1, T/subsample_rate)
for layer in self.encoders:
xs, masks, _, _ = layer(xs, masks, pos_emb, mask_pad)
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks
class DualConformerEncoder(ConformerEncoder):
"""Conformer encoder module."""
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: str = "conv2d",
pos_enc_layer_type: str = "rel_pos",
normalize_before: bool = True,
static_chunk_size: int = 0,
use_dynamic_chunk: bool = False,
global_cmvn: torch.nn.Module = None,
use_dynamic_left_chunk: bool = False,
positionwise_conv_kernel_size: int = 1,
macaron_style: bool = True,
selfattention_layer_type: str = "rel_selfattn",
activation_type: str = "swish",
use_cnn_module: bool = True,
cnn_module_kernel: int = 15,
causal: bool = False,
cnn_module_norm: str = "batch_norm",
query_bias: bool = True,
key_bias: bool = True,
value_bias: bool = True,
conv_bias: bool = True,
gradient_checkpointing: bool = False,
use_sdpa: bool = False,
layer_norm_type: str = 'layer_norm',
norm_eps: float = 1e-5,
n_kv_head: Optional[int] = None,
head_dim: Optional[int] = None,
mlp_type: str = 'position_wise_feed_forward',
mlp_bias: bool = True,
n_expert: int = 8,
n_expert_activated: int = 2,
):
""" Construct DualConformerEncoder
Support both the full context mode and the streaming mode separately
"""
super().__init__(
input_size, output_size, attention_heads, linear_units, num_blocks,
dropout_rate, positional_dropout_rate, attention_dropout_rate,
input_layer, pos_enc_layer_type, normalize_before,
static_chunk_size, use_dynamic_chunk, global_cmvn,
use_dynamic_left_chunk, positionwise_conv_kernel_size,
macaron_style, selfattention_layer_type, activation_type,
use_cnn_module, cnn_module_kernel, causal, cnn_module_norm,
query_bias, key_bias, value_bias, conv_bias,
gradient_checkpointing, use_sdpa, layer_norm_type, norm_eps,
n_kv_head, head_dim, mlp_type, mlp_bias, n_expert,
n_expert_activated)
def forward_full(
self,
xs: torch.Tensor,
xs_lens: torch.Tensor,
decoding_chunk_size: int = 0,
num_decoding_left_chunks: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
T = xs.size(1)
masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
xs, pos_emb, masks = self.embed(xs, masks)
mask_pad = masks # (B, 1, T/subsample_rate)
for layer in self.encoders:
xs, masks, _, _ = layer(xs, masks, pos_emb, mask_pad)
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks
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