From ee8c98073234e13a70f9c406dff6c986c5f27fd2 Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 16 一月 2023 18:43:31 +0800
Subject: [PATCH] update version 0.1.6
---
egs/aishell/data2vec_finetune/path.sh | 5
egs/aishell/data2vec_finetune/utils | 1
egs/aishell/data2vec_finetune/conf/decode_asr_transformer.yaml | 6
egs/aishell/data2vec_finetune/local/aishell_data_prep.sh | 66 ++++++++
egs/aishell/data2vec_finetune/run.sh | 252 +++++++++++++++++++++++++++++++
egs/aishell/data2vec_finetune/conf/train_asr_transformer_12e_6d_3072_768.yaml | 95 +++++++++++
egs/aishell/data2vec_finetune/local/prepare_data.sh | 53 ++++++
7 files changed, 478 insertions(+), 0 deletions(-)
diff --git a/egs/aishell/data2vec_finetune/conf/decode_asr_transformer.yaml b/egs/aishell/data2vec_finetune/conf/decode_asr_transformer.yaml
new file mode 100644
index 0000000..61ec03c
--- /dev/null
+++ b/egs/aishell/data2vec_finetune/conf/decode_asr_transformer.yaml
@@ -0,0 +1,6 @@
+beam_size: 10
+penalty: 0.0
+maxlenratio: 0.0
+minlenratio: 0.0
+ctc_weight: 1.0
+lm_weight: 0.7
diff --git a/egs/aishell/data2vec_finetune/conf/train_asr_transformer_12e_6d_3072_768.yaml b/egs/aishell/data2vec_finetune/conf/train_asr_transformer_12e_6d_3072_768.yaml
new file mode 100644
index 0000000..5bc5236
--- /dev/null
+++ b/egs/aishell/data2vec_finetune/conf/train_asr_transformer_12e_6d_3072_768.yaml
@@ -0,0 +1,95 @@
+# network architecture
+# encoder related
+encoder: data2vec_encoder
+encoder_conf:
+ extractor_mode: layer_norm
+ encoder_layerdrop: 0.1
+ dropout_input: 0.0
+ dropout_features: 0.0
+ feature_grad_mult: 0.0
+ encoder_embed_dim: 768
+
+ mask_prob: 0.65
+ mask_length: 10
+
+ loss_beta: 0
+ loss_scale: null
+
+ instance_norm_target_layer: true
+ average_top_k_layers: 8
+
+ pos_conv_depth: 5
+ conv_pos: 95
+
+ ema_decay: 0.999
+ ema_end_decay: 0.9999
+ ema_anneal_end_step: 30000
+ ema_transformer_only: true
+ ema_layers_only: true
+
+ require_same_masks: true
+ mask_dropout: 0
+
+# hybrid CTC/attention
+model_conf:
+ ctc_weight: 1.0
+ lsm_weight: 0.1 # label smoothing option
+ length_normalized_loss: false
+
+# for logger
+log_interval: 50
+
+# minibatch related
+batch_type: length
+batch_bins: 16000
+num_workers: 16
+
+# optimization related
+accum_grad: 1
+grad_clip: 5
+patience: none
+max_epoch: 50
+val_scheduler_criterion:
+ - valid
+ - acc
+best_model_criterion:
+- - valid
+ - cer_ctc
+ - min
+keep_nbest_models: 10
+unused_parameters: true
+normalize: None
+
+# NoamLR is deprecated. Use WarmupLR.
+# The following is equivalent setting for NoamLR:
+#
+# optim: adam
+# optim_conf:
+# lr: 10.
+# scheduler: noamlr
+# scheduler_conf:
+# model_size: 256
+# warmup_steps: 25000
+#
+optim: adam
+optim_conf:
+ lr: 0.00005
+scheduler: warmuplr # pytorch v1.1.0+ required
+scheduler_conf:
+ warmup_steps: 25000
+
+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
+ - 30
+ num_freq_mask: 2
+ apply_time_mask: true
+ time_mask_width_range:
+ - 0
+ - 40
+ num_time_mask: 2
\ No newline at end of file
diff --git a/egs/aishell/data2vec_finetune/local/aishell_data_prep.sh b/egs/aishell/data2vec_finetune/local/aishell_data_prep.sh
new file mode 100755
index 0000000..83f489b
--- /dev/null
+++ b/egs/aishell/data2vec_finetune/local/aishell_data_prep.sh
@@ -0,0 +1,66 @@
+#!/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;
diff --git a/egs/aishell/data2vec_finetune/local/prepare_data.sh b/egs/aishell/data2vec_finetune/local/prepare_data.sh
new file mode 100755
index 0000000..77791f9
--- /dev/null
+++ b/egs/aishell/data2vec_finetune/local/prepare_data.sh
@@ -0,0 +1,53 @@
+#!/usr/bin/env bash
+# Copyright 2018 AIShell-Foundation(Authors:Jiayu DU, Xingyu NA, Bengu WU, Hao ZHENG)
+# 2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)
+# Apache 2.0
+
+# transform raw AISHELL-2 data to kaldi format
+
+. ./path.sh || exit 1;
+
+tmp=
+dir=
+
+if [ $# != 3 ]; then
+ echo "Usage: $0 <corpus-data-dir> <tmp-dir> <output-dir>"
+ echo " $0 /export/AISHELL-2/iOS/train data/local/train data/train"
+ exit 1;
+fi
+
+corpus=$1
+tmp=$2
+dir=$3
+
+echo "prepare_data.sh: Preparing data in $corpus"
+
+mkdir -p $tmp
+mkdir -p $dir
+
+# corpus check
+if [ ! -d $corpus ] || [ ! -f $corpus/wav.scp ] || [ ! -f $corpus/trans.txt ]; then
+ echo "Error: $0 requires wav.scp and trans.txt under $corpus directory."
+ exit 1;
+fi
+
+# validate utt-key list, IC0803W0380 is a bad utterance
+awk '{print $1}' $corpus/wav.scp | grep -v 'IC0803W0380' > $tmp/wav_utt.list
+awk '{print $1}' $corpus/trans.txt > $tmp/trans_utt.list
+utils/filter_scp.pl -f 1 $tmp/wav_utt.list $tmp/trans_utt.list > $tmp/utt.list
+
+# wav.scp
+awk -F'\t' -v path_prefix=$corpus '{printf("%s\t%s/%s\n",$1,path_prefix,$2)}' $corpus/wav.scp > $tmp/tmp_wav.scp
+utils/filter_scp.pl -f 1 $tmp/utt.list $tmp/tmp_wav.scp | sort -k 1 | uniq > $tmp/wav.scp
+
+# text
+utils/filter_scp.pl -f 1 $tmp/utt.list $corpus/trans.txt | sort -k 1 | uniq > $tmp/text
+
+# copy prepared resources from tmp_dir to target dir
+mkdir -p $dir
+for f in wav.scp text; do
+ cp $tmp/$f $dir/$f || exit 1;
+done
+
+echo "local/prepare_data.sh succeeded"
+exit 0;
diff --git a/egs/aishell/data2vec_finetune/path.sh b/egs/aishell/data2vec_finetune/path.sh
new file mode 100755
index 0000000..7972642
--- /dev/null
+++ b/egs/aishell/data2vec_finetune/path.sh
@@ -0,0 +1,5 @@
+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:$PATH
diff --git a/egs/aishell/data2vec_finetune/run.sh b/egs/aishell/data2vec_finetune/run.sh
new file mode 100755
index 0000000..7ab8626
--- /dev/null
+++ b/egs/aishell/data2vec_finetune/run.sh
@@ -0,0 +1,252 @@
+#!/usr/bin/env bash
+
+. ./path.sh || exit 1;
+
+# machines configuration
+CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=2
+count=1
+gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
+# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
+njob=5
+train_cmd=utils/run.pl
+infer_cmd=utils/run.pl
+
+# general configuration
+feats_dir="../DATA" #feature output dictionary, for large data
+exp_dir="."
+lang=zh
+dumpdir=dump/fbank
+feats_type=fbank
+token_type=char
+scp=feats.scp
+type=kaldi_ark
+stage=0
+stop_stage=4
+
+# feature configuration
+feats_dim=80
+sample_frequency=16000
+nj=32
+speed_perturb="0.9,1.0,1.1"
+
+# data
+data_aishell=
+
+# exp tag
+tag=""
+
+model_name=damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch
+init_param="$HOME/.cache/modelscope/hub/$model_name/basemodel.pb"
+
+. utils/parse_options.sh || exit 1;
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+train_set=train
+valid_set=dev
+test_sets="dev test"
+
+asr_config=conf/train_asr_transformer_12e_6d_3072_768.yaml
+model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
+
+inference_config=conf/decode_asr_transformer.yaml
+inference_asr_model=valid.cer_ctc.ave_10best.pth
+
+# you can set gpu num for decoding here
+gpuid_list=$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 0 ] && [ ${stop_stage} -ge 0 ]; then
+ echo "stage 0: Data preparation"
+ # Data preparation
+ local/aishell_data_prep.sh ${data_aishell}/data_aishell/wav ${data_aishell}/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
+
+feat_train_dir=${feats_dir}/${dumpdir}/train; mkdir -p ${feat_train_dir}
+feat_dev_dir=${feats_dir}/${dumpdir}/dev; mkdir -p ${feat_dev_dir}
+feat_test_dir=${feats_dir}/${dumpdir}/test; mkdir -p ${feat_test_dir}
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+ echo "stage 1: Feature Generation"
+ # compute fbank features
+ fbankdir=${feats_dir}/fbank
+ utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \
+ ${feats_dir}/data/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/train
+ utils/fix_data_feat.sh ${fbankdir}/train
+ utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
+ ${feats_dir}/data/dev ${exp_dir}/exp/make_fbank/dev ${fbankdir}/dev
+ utils/fix_data_feat.sh ${fbankdir}/dev
+ utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
+ ${feats_dir}/data/test ${exp_dir}/exp/make_fbank/test ${fbankdir}/test
+ utils/fix_data_feat.sh ${fbankdir}/test
+
+ # compute global cmvn
+ utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} \
+ ${fbankdir}/train ${exp_dir}/exp/make_fbank/train
+
+ # apply cmvn
+ utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
+ ${fbankdir}/train ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/train ${feat_train_dir}
+ utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
+ ${fbankdir}/dev ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/dev ${feat_dev_dir}
+ utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
+ ${fbankdir}/test ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/test ${feat_test_dir}
+
+ cp ${fbankdir}/train/text ${fbankdir}/train/speech_shape ${fbankdir}/train/text_shape ${feat_train_dir}
+ cp ${fbankdir}/dev/text ${fbankdir}/dev/speech_shape ${fbankdir}/dev/text_shape ${feat_dev_dir}
+ cp ${fbankdir}/test/text ${fbankdir}/test/speech_shape ${fbankdir}/test/text_shape ${feat_test_dir}
+
+ utils/fix_data_feat.sh ${feat_train_dir}
+ utils/fix_data_feat.sh ${feat_dev_dir}
+ utils/fix_data_feat.sh ${feat_test_dir}
+
+ #generate ark list
+ utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_train_dir} ${fbankdir}/train ${feat_train_dir}
+ utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_dev_dir} ${fbankdir}/dev ${feat_dev_dir}
+fi
+
+token_list=${feats_dir}/data/${lang}_token_list/char/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/char/
+
+ echo "make a dictionary"
+ echo "<blank>" > ${token_list}
+ echo "<s>" >> ${token_list}
+ echo "</s>" >> ${token_list}
+ utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/train/text | cut -f 2- -d" " | tr " " "\n" \
+ | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
+ num_token=$(cat ${token_list} | wc -l)
+ echo "<unk>" >> ${token_list}
+ vocab_size=$(cat ${token_list} | wc -l)
+ awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
+ awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
+ mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/train
+ mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/dev
+ cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/train
+ cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/dev
+fi
+
+# Training Stage
+world_size=$gpu_num # run on one machine
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+ echo "stage 3: Training"
+ python utils/download_model.py --model_name ${model_name} # download pretrained model on ModelScope
+ 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])
+ asr_train.py \
+ --gpu_id $gpu_id \
+ --use_preprocessor true \
+ --token_type char \
+ --token_list $token_list \
+ --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \
+ --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
+ --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
+ --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
+ --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \
+ --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \
+ --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \
+ --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char \
+ --init_param ${init_param} \
+ --resume true \
+ --output_dir ${exp_dir}/exp/${model_dir} \
+ --config $asr_config \
+ --input_size $feats_dim \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --multiprocessing_distributed true \
+ --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 4 ] && [ ${stop_stage} -ge 4 ]; then
+ echo "stage 4: 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}/${dumpdir}/${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}" \
+ --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
diff --git a/egs/aishell/data2vec_finetune/utils b/egs/aishell/data2vec_finetune/utils
new file mode 120000
index 0000000..f245098
--- /dev/null
+++ b/egs/aishell/data2vec_finetune/utils
@@ -0,0 +1 @@
+../../aishell/tranformer/utils
\ No newline at end of file
--
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