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Zero
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# Copyright (c) 2022 Yifan Peng (Carnegie Mellon University)
# 2023 Voicecomm Inc (Kai Li)
# 2023 Lucky Wong
#
# 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."""
import torch
from typing import List, Optional, Union
from wenet.branchformer.encoder import LayerDropModuleList
from wenet.e_branchformer.encoder_layer import EBranchformerEncoderLayer
from wenet.branchformer.cgmlp import ConvolutionalGatingMLP
from wenet.transformer.encoder import ConformerEncoder
from wenet.utils.class_utils import (
WENET_ACTIVATION_CLASSES,
WENET_ATTENTION_CLASSES,
WENET_MLP_CLASSES,
)
class EBranchformerEncoder(ConformerEncoder):
"""E-Branchformer encoder module."""
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
selfattention_layer_type: str = "rel_selfattn",
pos_enc_layer_type: str = "rel_pos",
activation_type: str = "swish",
cgmlp_linear_units: int = 2048,
cgmlp_conv_kernel: int = 31,
use_linear_after_conv: bool = False,
gate_activation: str = "identity",
num_blocks: int = 12,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: str = "conv2d",
stochastic_depth_rate: Union[float, List[float]] = 0.0,
static_chunk_size: int = 0,
use_dynamic_chunk: bool = False,
global_cmvn: torch.nn.Module = None,
use_dynamic_left_chunk: bool = False,
causal: bool = False,
merge_conv_kernel: int = 3,
use_ffn: bool = True,
macaron_style: bool = True,
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,
):
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,
True,
static_chunk_size,
use_dynamic_chunk,
global_cmvn,
use_dynamic_left_chunk,
1,
macaron_style,
selfattention_layer_type,
activation_type,
query_bias=query_bias,
key_bias=key_bias,
value_bias=value_bias,
conv_bias=conv_bias,
gradient_checkpointing=gradient_checkpointing,
use_sdpa=use_sdpa,
layer_norm_type=layer_norm_type,
norm_eps=norm_eps,
n_kv_head=n_kv_head,
head_dim=head_dim,
mlp_type=mlp_type,
mlp_bias=mlp_bias,
n_expert=n_expert,
n_expert_activated=n_expert_activated)
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
query_bias,
key_bias,
value_bias,
use_sdpa,
n_kv_head,
head_dim,
)
cgmlp_layer = ConvolutionalGatingMLP
cgmlp_layer_args = (output_size, cgmlp_linear_units, cgmlp_conv_kernel,
dropout_rate, use_linear_after_conv,
gate_activation, causal)
# feed-forward module definition
mlp_class = WENET_MLP_CLASSES[mlp_type]
activation = WENET_ACTIVATION_CLASSES[activation_type]()
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
activation,
mlp_bias,
n_expert,
n_expert_activated,
)
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 = LayerDropModuleList(
p=stochastic_depth_rate,
modules=[
EBranchformerEncoderLayer(
output_size,
WENET_ATTENTION_CLASSES[selfattention_layer_type](
*encoder_selfattn_layer_args),
cgmlp_layer(*cgmlp_layer_args),
mlp_class(*positionwise_layer_args) if use_ffn else None,
mlp_class(*positionwise_layer_args)
if use_ffn and macaron_style else None,
dropout_rate,
merge_conv_kernel=merge_conv_kernel,
causal=causal,
stochastic_depth_rate=stochastic_depth_rate[lnum],
) for lnum in range(num_blocks)
])
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