speech_recognition/FunASR
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嘉渊 2023-07-11 00:37:39 +08:00
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6
egs/aishell/e_branchformer/conf/decode_asr_transformer.yaml Normal file
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beam_size: 10
penalty: 0.0
maxlenratio: 0.0
minlenratio: 0.0
ctc_weight: 0.4
lm_weight: 0.0

101
egs/aishell/e_branchformer/conf/train_asr_e_branchformer.yaml Normal file
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# network architecture
# encoder related
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.
src_attention_dropout_rate: 0.
# frontend related
frontend: wav_frontend
frontend_conf:
fs: 16000
window: hamming
n_mels: 80
frame_length: 25
frame_shift: 10
lfr_m: 1
lfr_n: 1
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
# optimization related
accum_grad: 1
grad_clip: 5
max_epoch: 180
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
optim: adam
optim_conf:
lr: 0.001
weight_decay: 0.000001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 35000
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.
- 0.05
num_time_mask: 10
dataset_conf:
data_names: speech,text
data_types: sound,text
shuffle: True
shuffle_conf:
shuffle_size: 2048
sort_size: 500
batch_conf:
batch_type: token
batch_size: 10000
num_workers: 8
log_interval: 50
normalize: None

66
egs/aishell/e_branchformer/local/aishell_data_prep.sh Executable file
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#!/bin/bash
# Copyright 2017 Xingyu Na
# Apache 2.0
#. ./path.sh || exit 1;
if [ $# != 3 ]; then
echo "Usage: $0 <audio-path> <text-path> <output-path>"
echo " $0 /export/a05/xna/data/data_aishell/wav /export/a05/xna/data/data_aishell/transcript data"
exit 1;
fi
aishell_audio_dir=$1
aishell_text=$2/aishell_transcript_v0.8.txt
output_dir=$3
train_dir=$output_dir/data/local/train
dev_dir=$output_dir/data/local/dev
test_dir=$output_dir/data/local/test
tmp_dir=$output_dir/data/local/tmp
mkdir -p $train_dir
mkdir -p $dev_dir
mkdir -p $test_dir
mkdir -p $tmp_dir
# data directory check
if [ ! -d $aishell_audio_dir ] || [ ! -f $aishell_text ]; then
echo "Error: $0 requires two directory arguments"
exit 1;
fi
# find wav audio file for train, dev and test resp.
find $aishell_audio_dir -iname "*.wav" > $tmp_dir/wav.flist
n=`cat $tmp_dir/wav.flist | wc -l`
[ $n -ne 141925 ] && \
echo Warning: expected 141925 data data files, found $n
grep -i "wav/train" $tmp_dir/wav.flist > $train_dir/wav.flist || exit 1;
grep -i "wav/dev" $tmp_dir/wav.flist > $dev_dir/wav.flist || exit 1;
grep -i "wav/test" $tmp_dir/wav.flist > $test_dir/wav.flist || exit 1;
rm -r $tmp_dir
# Transcriptions preparation
for dir in $train_dir $dev_dir $test_dir; do
echo Preparing $dir transcriptions
sed -e 's/\.wav//' $dir/wav.flist | awk -F '/' '{print $NF}' > $dir/utt.list
paste -d' ' $dir/utt.list $dir/wav.flist > $dir/wav.scp_all
utils/filter_scp.pl -f 1 $dir/utt.list $aishell_text > $dir/transcripts.txt
awk '{print $1}' $dir/transcripts.txt > $dir/utt.list
utils/filter_scp.pl -f 1 $dir/utt.list $dir/wav.scp_all | sort -u > $dir/wav.scp
sort -u $dir/transcripts.txt > $dir/text
done
mkdir -p $output_dir/data/train $output_dir/data/dev $output_dir/data/test
for f in wav.scp text; do
cp $train_dir/$f $output_dir/data/train/$f || exit 1;
cp $dev_dir/$f $output_dir/data/dev/$f || exit 1;
cp $test_dir/$f $output_dir/data/test/$f || exit 1;
done
echo "$0: AISHELL data preparation succeeded"
exit 0;

105
egs/aishell/e_branchformer/local/download_and_untar.sh Executable file
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#!/usr/bin/env bash
# Copyright 2014 Johns Hopkins University (author: Daniel Povey)
# 2017 Xingyu Na
# Apache 2.0
remove_archive=false
if [ "$1" == --remove-archive ]; then
remove_archive=true
shift
fi
if [ $# -ne 3 ]; then
echo "Usage: $0 [--remove-archive] <data-base> <url-base> <corpus-part>"
echo "e.g.: $0 /export/a05/xna/data www.openslr.org/resources/33 data_aishell"
echo "With --remove-archive it will remove the archive after successfully un-tarring it."
echo "<corpus-part> can be one of: data_aishell, resource_aishell."
fi
data=$1
url=$2
part=$3
if [ ! -d "$data" ]; then
echo "$0: no such directory $data"
exit 1;
fi
part_ok=false
list="data_aishell resource_aishell"
for x in $list; do
if [ "$part" == $x ]; then part_ok=true; fi
done
if ! $part_ok; then
echo "$0: expected <corpus-part> to be one of $list, but got '$part'"
exit 1;
fi
if [ -z "$url" ]; then
echo "$0: empty URL base."
exit 1;
fi
if [ -f $data/$part/.complete ]; then
echo "$0: data part $part was already successfully extracted, nothing to do."
exit 0;
fi
# sizes of the archive files in bytes.
sizes="15582913665 1246920"
if [ -f $data/$part.tgz ]; then
size=$(/bin/ls -l $data/$part.tgz | awk '{print $5}')
size_ok=false
for s in $sizes; do if [ $s == $size ]; then size_ok=true; fi; done
if ! $size_ok; then
echo "$0: removing existing file $data/$part.tgz because its size in bytes $size"
echo "does not equal the size of one of the archives."
rm $data/$part.tgz
else
echo "$data/$part.tgz exists and appears to be complete."
fi
fi
if [ ! -f $data/$part.tgz ]; then
if ! command -v wget >/dev/null; then
echo "$0: wget is not installed."
exit 1;
fi
full_url=$url/$part.tgz
echo "$0: downloading data from $full_url. This may take some time, please be patient."
cd $data || exit 1
if ! wget --no-check-certificate $full_url; then
echo "$0: error executing wget $full_url"
exit 1;
fi
fi
cd $data || exit 1
if ! tar -xvzf $part.tgz; then
echo "$0: error un-tarring archive $data/$part.tgz"
exit 1;
fi
touch $data/$part/.complete
if [ $part == "data_aishell" ]; then
cd $data/$part/wav || exit 1
for wav in ./*.tar.gz; do
echo "Extracting wav from $wav"
tar -zxf $wav && rm $wav
done
fi
echo "$0: Successfully downloaded and un-tarred $data/$part.tgz"
if $remove_archive; then
echo "$0: removing $data/$part.tgz file since --remove-archive option was supplied."
rm $data/$part.tgz
fi
exit 0;

5
egs/aishell/e_branchformer/path.sh Executable file
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export FUNASR_DIR=$PWD/../../..
# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PATH=$FUNASR_DIR/funasr/bin:$PATH

225
egs/aishell/e_branchformer/run.sh Executable file
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#!/usr/bin/env bash
. ./path.sh || exit 1;
# machines configuration
CUDA_VISIBLE_DEVICES="0,1,2,3"
gpu_num=4
count=1
gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
njob=5
train_cmd=utils/run.pl
infer_cmd=utils/run.pl
# general configuration
feats_dir="../DATA" #feature output dictionary
exp_dir="."
lang=zh
token_type=char
type=sound
scp=wav.scp
speed_perturb="0.9 1.0 1.1"
stage=0
stop_stage=5
# feature configuration
feats_dim=80
nj=64
# data
raw_data=../raw_data
data_url=www.openslr.org/resources/33
# exp tag
tag="exp1"
. utils/parse_options.sh || exit 1;
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail
train_set=train
valid_set=dev
test_sets="dev test"
asr_config=conf/train_asr_e_branchformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
if ${gpu_inference}; then
inference_nj=$[${ngpu}*${njob}]
_ngpu=1
else
inference_nj=$njob
_ngpu=0
fi
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
local/download_and_untar.sh ${raw_data} ${data_url} data_aishell
local/download_and_untar.sh ${raw_data} ${data_url} resource_aishell
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
echo "stage 0: Data preparation"
# Data preparation
local/aishell_data_prep.sh ${raw_data}/data_aishell/wav ${raw_data}/data_aishell/transcript ${feats_dir}
for x in train dev test; do
cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | tr -d " ") \
> ${feats_dir}/data/${x}/text
utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
done
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "stage 1: Feature and CMVN Generation"
utils/compute_cmvn.sh --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$asr_config" --scale 1.0
fi
token_list=${feats_dir}/data/${lang}_token_list/$token_type/tokens.txt
echo "dictionary: ${token_list}"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "stage 2: Dictionary Preparation"
mkdir -p ${feats_dir}/data/${lang}_token_list/$token_type/
echo "make a dictionary"
echo "<blank>" > ${token_list}
echo "<s>" >> ${token_list}
echo "</s>" >> ${token_list}
utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/$train_set/text | cut -f 2- -d" " | tr " " "\n" \
| sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
echo "<unk>" >> ${token_list}
fi
# LM Training Stage
world_size=$gpu_num # run on one machine
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo "stage 3: LM Training"
fi
# ASR Training Stage
world_size=$gpu_num # run on one machine
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "stage 4: ASR Training"
mkdir -p ${exp_dir}/exp/${model_dir}
mkdir -p ${exp_dir}/exp/${model_dir}/log
INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
if [ -f $INIT_FILE ];then
rm -f $INIT_FILE
fi
init_method=file://$(readlink -f $INIT_FILE)
echo "$0: init method is $init_method"
for ((i = 0; i < $gpu_num; ++i)); do
{
rank=$i
local_rank=$i
gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
train.py \
--task_name asr \
--gpu_id $gpu_id \
--use_preprocessor true \
--token_type $token_type \
--token_list $token_list \
--data_dir ${feats_dir}/data \
--train_set ${train_set} \
--valid_set ${valid_set} \
--data_file_names "wav.scp,text" \
--cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.mvn \
--speed_perturb ${speed_perturb} \
--resume true \
--output_dir ${exp_dir}/exp/${model_dir} \
--config $asr_config \
--ngpu $gpu_num \
--num_worker_count $count \
--dist_init_method $init_method \
--dist_world_size $world_size \
--dist_rank $rank \
--local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
} &
done
wait
fi
# Testing Stage
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
echo "stage 5: Inference"
for dset in ${test_sets}; do
asr_exp=${exp_dir}/exp/${model_dir}
inference_tag="$(basename "${inference_config}" .yaml)"
_dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
_logdir="${_dir}/logdir"
if [ -d ${_dir} ]; then
echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
exit 0
fi
mkdir -p "${_logdir}"
_data="${feats_dir}/data/${dset}"
key_file=${_data}/${scp}
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
split_scps=
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/keys.${n}.scp"
done
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
_opts=
if [ -n "${inference_config}" ]; then
_opts+="--config ${inference_config} "
fi
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
python -m funasr.bin.asr_inference_launch \
--batch_size 1 \
--ngpu "${_ngpu}" \
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
--cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.mvn \
--key_file "${_logdir}"/keys.JOB.scp \
--asr_train_config "${asr_exp}"/config.yaml \
--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
--output_dir "${_logdir}"/output.JOB \
--mode asr \
${_opts}
for f in token token_int score text; do
if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
for i in $(seq "${_nj}"); do
cat "${_logdir}/output.${i}/1best_recog/${f}"
done | sort -k1 >"${_dir}/${f}"
fi
done
python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
python utils/proce_text.py ${_data}/text ${_data}/text.proc
python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
cat ${_dir}/text.cer.txt
done
fi
# Prepare files for ModelScope fine-tuning and inference
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
echo "stage 6: ModelScope Preparation"
cp ${feats_dir}/data/${train_set}/cmvn/am.mvn ${exp_dir}/exp/${model_dir}/am.mvn
vocab_size=$(cat ${token_list} | wc -l)
python utils/gen_modelscope_configuration.py \
--am_model_name $inference_asr_model \
--mode asr \
--model_name conformer \
--dataset aishell \
--output_dir $exp_dir/exp/$model_dir \
--vocab_size $vocab_size \
--tag $tag
fi

2
funasr/build_utils/build_asr_model.py
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@ -40,6 +40,7 @@ from funasr.models.encoder.resnet34_encoder import ResNet34Diar
from funasr.models.encoder.rnn_encoder import RNNEncoder
from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
from funasr.models.encoder.branchformer_encoder import BranchformerEncoder
from funasr.models.encoder.e_branchformer_encoder import EBranchformerEncoder
from funasr.models.encoder.transformer_encoder import TransformerEncoder
from funasr.models.frontend.default import DefaultFrontend
from funasr.models.frontend.default import MultiChannelFrontend
@ -115,6 +116,7 @@ encoder_choices = ClassChoices(
sanm_chunk_opt=SANMEncoderChunkOpt,
data2vec_encoder=Data2VecEncoder,
branchformer=BranchformerEncoder,
e_branchformer=EBranchformerEncoder,
mfcca_enc=MFCCAEncoder,
chunk_conformer=ConformerChunkEncoder,
),

467
funasr/models/encoder/e_branchformer_encoder.py Normal file
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# Copyright 2022 Kwangyoun Kim (ASAPP inc.)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""E-Branchformer encoder definition.
Reference:
Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan,
Prashant Sridhar, Kyu J. Han, Shinji Watanabe,
"E-Branchformer: Branchformer with Enhanced merging
for speech recognition," in SLT 2022.
"""
import logging
from typing import List, Optional, Tuple
import torch
from typeguard import check_argument_types
from funasr.models.ctc import CTC
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.modules.cgmlp import ConvolutionalGatingMLP
from funasr.modules.fastformer import FastSelfAttention
from funasr.modules.nets_utils import get_activation, make_pad_mask
from funasr.modules.attention import ( # noqa: H301
LegacyRelPositionMultiHeadedAttention,
MultiHeadedAttention,
RelPositionMultiHeadedAttention,
)
from funasr.modules.embedding import ( # noqa: H301
LegacyRelPositionalEncoding,
PositionalEncoding,
RelPositionalEncoding,
ScaledPositionalEncoding,
)
from funasr.modules.layer_norm import LayerNorm
from funasr.modules.positionwise_feed_forward import (
PositionwiseFeedForward,
)
from funasr.modules.repeat import repeat
from funasr.modules.subsampling import (
Conv2dSubsampling,
Conv2dSubsampling2,
Conv2dSubsampling6,
Conv2dSubsampling8,
TooShortUttError,
check_short_utt,
)
class EBranchformerEncoderLayer(torch.nn.Module):
"""E-Branchformer encoder layer module.
Args:
size (int): model dimension
attn: standard self-attention or efficient attention
cgmlp: ConvolutionalGatingMLP
feed_forward: feed-forward module, optional
feed_forward: macaron-style feed-forward module, optional
dropout_rate (float): dropout probability
merge_conv_kernel (int): kernel size of the depth-wise conv in merge module
"""
def __init__(
self,
size: int,
attn: torch.nn.Module,
cgmlp: torch.nn.Module,
feed_forward: Optional[torch.nn.Module],
feed_forward_macaron: Optional[torch.nn.Module],
dropout_rate: float,
merge_conv_kernel: int = 3,
):
super().__init__()
self.size = size
self.attn = attn
self.cgmlp = cgmlp
self.feed_forward = feed_forward
self.feed_forward_macaron = feed_forward_macaron
self.ff_scale = 1.0
if self.feed_forward is not None:
self.norm_ff = LayerNorm(size)
if self.feed_forward_macaron is not None:
self.ff_scale = 0.5
self.norm_ff_macaron = LayerNorm(size)
self.norm_mha = LayerNorm(size) # for the MHA module
self.norm_mlp = LayerNorm(size) # for the MLP module
self.norm_final = LayerNorm(size) # for the final output of the block
self.dropout = torch.nn.Dropout(dropout_rate)
self.depthwise_conv_fusion = torch.nn.Conv1d(
size + size,
size + size,
kernel_size=merge_conv_kernel,
stride=1,
padding=(merge_conv_kernel - 1) // 2,
groups=size + size,
bias=True,
)
self.merge_proj = torch.nn.Linear(size + size, size)
def forward(self, x_input, mask, cache=None):
"""Compute encoded features.
Args:
x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
- w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
- w/o pos emb: Tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, 1, time).
cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
Returns:
torch.Tensor: Output tensor (#batch, time, size).
torch.Tensor: Mask tensor (#batch, time).
"""
if cache is not None:
raise NotImplementedError("cache is not None, which is not tested")
if isinstance(x_input, tuple):
x, pos_emb = x_input[0], x_input[1]
else:
x, pos_emb = x_input, None
if self.feed_forward_macaron is not None:
residual = x
x = self.norm_ff_macaron(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
# Two branches
x1 = x
x2 = x
# Branch 1: multi-headed attention module
x1 = self.norm_mha(x1)
if isinstance(self.attn, FastSelfAttention):
x_att = self.attn(x1, mask)
else:
if pos_emb is not None:
x_att = self.attn(x1, x1, x1, pos_emb, mask)
else:
x_att = self.attn(x1, x1, x1, mask)
x1 = self.dropout(x_att)
# Branch 2: convolutional gating mlp
x2 = self.norm_mlp(x2)
if pos_emb is not None:
x2 = (x2, pos_emb)
x2 = self.cgmlp(x2, mask)
if isinstance(x2, tuple):
x2 = x2[0]
x2 = self.dropout(x2)
# Merge two branches
x_concat = torch.cat([x1, x2], dim=-1)
x_tmp = x_concat.transpose(1, 2)
x_tmp = self.depthwise_conv_fusion(x_tmp)
x_tmp = x_tmp.transpose(1, 2)
x = x + self.dropout(self.merge_proj(x_concat + x_tmp))
if self.feed_forward is not None:
# feed forward module
residual = x
x = self.norm_ff(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
x = self.norm_final(x)
if pos_emb is not None:
return (x, pos_emb), mask
return x, mask
class EBranchformerEncoder(AbsEncoder):
"""E-Branchformer encoder module."""
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
attention_layer_type: str = "rel_selfattn",
pos_enc_layer_type: str = "rel_pos",
rel_pos_type: str = "latest",
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: Optional[str] = "conv2d",
zero_triu: bool = False,
padding_idx: int = -1,
layer_drop_rate: float = 0.0,
max_pos_emb_len: int = 5000,
use_ffn: bool = False,
macaron_ffn: bool = False,
ffn_activation_type: str = "swish",
linear_units: int = 2048,
positionwise_layer_type: str = "linear",
merge_conv_kernel: int = 3,
interctc_layer_idx=None,
interctc_use_conditioning: bool = False,
):
assert check_argument_types()
super().__init__()
self._output_size = output_size
if rel_pos_type == "legacy":
if pos_enc_layer_type == "rel_pos":
pos_enc_layer_type = "legacy_rel_pos"
if attention_layer_type == "rel_selfattn":
attention_layer_type = "legacy_rel_selfattn"
elif rel_pos_type == "latest":
assert attention_layer_type != "legacy_rel_selfattn"
assert pos_enc_layer_type != "legacy_rel_pos"
else:
raise ValueError("unknown rel_pos_type: " + rel_pos_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 attention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "legacy_rel_pos":
assert attention_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 == "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:
if input_size == output_size:
self.embed = None
else:
self.embed = torch.nn.Linear(input_size, output_size)
else:
raise ValueError("unknown input_layer: " + input_layer)
activation = get_activation(ffn_activation_type)
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
activation,
)
elif positionwise_layer_type is None:
logging.warning("no macaron ffn")
else:
raise ValueError("Support only linear.")
if attention_layer_type == "selfattn":
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
)
elif attention_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 attention_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,
)
elif attention_layer_type == "fast_selfattn":
assert pos_enc_layer_type in ["abs_pos", "scaled_abs_pos"]
encoder_selfattn_layer = FastSelfAttention
encoder_selfattn_layer_args = (
output_size,
attention_heads,
attention_dropout_rate,
)
else:
raise ValueError("unknown encoder_attn_layer: " + attention_layer_type)
cgmlp_layer = ConvolutionalGatingMLP
cgmlp_layer_args = (
output_size,
cgmlp_linear_units,
cgmlp_conv_kernel,
dropout_rate,
use_linear_after_conv,
gate_activation,
)
self.encoders = repeat(
num_blocks,
lambda lnum: EBranchformerEncoderLayer(
output_size,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
cgmlp_layer(*cgmlp_layer_args),
positionwise_layer(*positionwise_layer_args) if use_ffn else None,
positionwise_layer(*positionwise_layer_args)
if use_ffn and macaron_ffn
else None,
dropout_rate,
merge_conv_kernel,
),
layer_drop_rate,
)
self.after_norm = LayerNorm(output_size)
if interctc_layer_idx is None:
interctc_layer_idx = []
self.interctc_layer_idx = interctc_layer_idx
if len(interctc_layer_idx) > 0:
assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
self.interctc_use_conditioning = interctc_use_conditioning
self.conditioning_layer = None
def output_size(self) -> int:
return self._output_size
def forward(
self,
xs_pad: torch.Tensor,
ilens: torch.Tensor,
prev_states: torch.Tensor = None,
ctc: CTC = None,
max_layer: int = None,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
"""Calculate forward propagation.
Args:
xs_pad (torch.Tensor): Input tensor (#batch, L, input_size).
ilens (torch.Tensor): Input length (#batch).
prev_states (torch.Tensor): Not to be used now.
ctc (CTC): Intermediate CTC module.
max_layer (int): Layer depth below which InterCTC is applied.
Returns:
torch.Tensor: Output tensor (#batch, L, output_size).
torch.Tensor: Output length (#batch).
torch.Tensor: Not to be used now.
"""
masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
if (
isinstance(self.embed, Conv2dSubsampling)
or isinstance(self.embed, Conv2dSubsampling2)
or isinstance(self.embed, Conv2dSubsampling6)
or isinstance(self.embed, Conv2dSubsampling8)
):
short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
if short_status:
raise TooShortUttError(
f"has {xs_pad.size(1)} frames and is too short for subsampling "
+ f"(it needs more than {limit_size} frames), return empty results",
xs_pad.size(1),
limit_size,
)
xs_pad, masks = self.embed(xs_pad, masks)
elif self.embed is not None:
xs_pad = self.embed(xs_pad)
intermediate_outs = []
if len(self.interctc_layer_idx) == 0:
if max_layer is not None and 0 <= max_layer < len(self.encoders):
for layer_idx, encoder_layer in enumerate(self.encoders):
xs_pad, masks = encoder_layer(xs_pad, masks)
if layer_idx >= max_layer:
break
else:
xs_pad, masks = self.encoders(xs_pad, masks)
else:
for layer_idx, encoder_layer in enumerate(self.encoders):
xs_pad, masks = encoder_layer(xs_pad, masks)
if layer_idx + 1 in self.interctc_layer_idx:
encoder_out = xs_pad
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
intermediate_outs.append((layer_idx + 1, encoder_out))
if self.interctc_use_conditioning:
ctc_out = ctc.softmax(encoder_out)
if isinstance(xs_pad, tuple):
xs_pad = list(xs_pad)
xs_pad[0] = xs_pad[0] + self.conditioning_layer(ctc_out)
xs_pad = tuple(xs_pad)
else:
xs_pad = xs_pad + self.conditioning_layer(ctc_out)
if isinstance(xs_pad, tuple):
xs_pad = xs_pad[0]
xs_pad = self.after_norm(xs_pad)
olens = masks.squeeze(1).sum(1)
if len(intermediate_outs) > 0:
return (xs_pad, intermediate_outs), olens, None
return xs_pad, olens, None