From 1fda62db990156b79d8b393ec64e1c6ad6bd1357 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=98=89=E6=B8=8A?= <wangjiaming.wjm@alibaba-inc.com>
Date: Mon, 15 May 2023 15:52:18 +0800
Subject: [PATCH] update repo

---
 egs/aishell2/paraformer/run.sh | 103 ++++++++++-----------------------
 1 file changed, 30 insertions(+), 73 deletions(-)

diff --git a/egs/aishell2/paraformer/run.sh b/egs/aishell2/paraformer/run.sh
index e1ea4fe73..60aed8bef 100755
--- a/egs/aishell2/paraformer/run.sh
+++ b/egs/aishell2/paraformer/run.sh
@@ -9,31 +9,28 @@ 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=1
-train_cmd=tools/run.pl
+train_cmd=utils/run.pl
 infer_cmd=utils/run.pl
 
 # general configuration
 feats_dir="../DATA" #feature output dictionary
 exp_dir="."
 lang=zh
-dumpdir=dump/fbank
-feats_type=fbank
 token_type=char
+type=sound
+scp=wav.scp
+speed_perturb="0.9 1.0 1.1"
 dataset_type=large
-scp=feats.scp
-type=kaldi_ark
-stage=0
+stage=3
 stop_stage=4
 
 # feature configuration
 feats_dim=80
-sample_frequency=16000
-nj=100
-speed_perturb="0.9,1.0,1.1"
+nj=64
 
 # data
-tr_dir=
-dev_tst_dir=
+tr_dir=/nfs/wangjiaming.wjm/asr_data/aishell2/AISHELL-2/iOS/data
+dev_tst_dir=/nfs/wangjiaming.wjm/asr_data/aishell2/AISHELL-DEV-TEST-SET
 
 # exp tag
 tag="exp1"
@@ -51,7 +48,7 @@ valid_set=dev_ios
 test_sets="dev_ios test_ios"
 
 asr_config=conf/train_asr_paraformer_conformer_20e_1280_320_6d_1280_320.yaml
-model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
+model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
 
 inference_config=conf/decode_asr_transformer_noctc_1best.yaml
 inference_asr_model=valid.acc.ave_10best.pb
@@ -73,61 +70,24 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     # For training set
     local/prepare_data.sh ${tr_dir} ${feats_dir}/data/local/train ${feats_dir}/data/train || exit 1;
     # # For dev and test set
-    for x in Android iOS Mic; do
+    for x in iOS; do
         local/prepare_data.sh ${dev_tst_dir}/${x}/dev ${feats_dir}/data/local/dev_${x,,} ${feats_dir}/data/dev_${x,,} || exit 1;
         local/prepare_data.sh ${dev_tst_dir}/${x}/test ${feats_dir}/data/local/test_${x,,} ${feats_dir}/data/test_${x,,} || exit 1;
-    done 
+    done
     # Normalize text to capital letters
-    for x in train dev_android dev_ios dev_mic test_android test_ios test_mic; do
+    for x in train dev_ios test_ios; do
         mv ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
         paste -d " " <(cut -f 1 ${feats_dir}/data/${x}/text.org) <(cut -f 2- ${feats_dir}/data/${x}/text.org \
              | tr 'A-Z' 'a-z' | tr -d " ") \
             > ${feats_dir}/data/${x}/text
-        tools/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
+        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_set}; mkdir -p ${feat_train_dir}
-feat_dev_dir=${feats_dir}/${dumpdir}/${valid_set}; mkdir -p ${feat_dev_dir}
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    echo "stage 1: Feature Generation"
-    # compute fbank features
-    fbankdir=${feats_dir}/fbank
-    steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj --speed_perturb ${speed_perturb} \
-        ${feats_dir}/data/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/train
-    tools/fix_data_feat.sh ${fbankdir}/train
-    for x in android ios mic; do
-        steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
-            ${feats_dir}/data/dev_${x} ${exp_dir}/exp/make_fbank/dev_${x} ${fbankdir}/dev_${x}
-        tools/fix_data_feat.sh ${fbankdir}/dev_${x}
-        steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
-            ${feats_dir}/data/test_${x} ${exp_dir}/exp/make_fbank/test_${x} ${fbankdir}/test_${x}
-        tools/fix_data_feat.sh ${fbankdir}/test_${x}
-    done
-    
-    # compute global cmvn
-    steps/compute_cmvn.sh --cmd "$train_cmd" --nj $nj \
-        ${fbankdir}/train ${exp_dir}/exp/make_fbank/train
-
-    # apply cmvn 
-    steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
-        ${fbankdir}/${train_set} ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/${train_set} ${feat_train_dir}
-    steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
-        ${fbankdir}/${valid_set} ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/${valid_set} ${feat_dev_dir}
-    for x in android ios mic; do
-        steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
-            ${fbankdir}/test_${x} ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/test_${x} ${feats_dir}/${dumpdir}/test_${x}
-    done
-    
-    cp ${fbankdir}/${train_set}/text ${fbankdir}/${train_set}/speech_shape ${fbankdir}/${train_set}/text_shape ${feat_train_dir}
-    tools/fix_data_feat.sh ${feat_train_dir}
-    cp ${fbankdir}/${valid_set}/text ${fbankdir}/${valid_set}/speech_shape ${fbankdir}/${valid_set}/text_shape ${feat_dev_dir}
-    tools/fix_data_feat.sh ${feat_dev_dir}
-    for x in android ios mic; do
-        cp ${fbankdir}/test_${x}/text ${fbankdir}/test_${x}/speech_shape ${fbankdir}/test_${x}/text_shape ${feats_dir}/${dumpdir}/test_${x}
-        tools/fix_data_feat.sh ${feats_dir}/${dumpdir}/test_${x}
-    done
+    echo "stage 1: Feature and CMVN Generation"
+    utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
 fi
 
 token_list=${feats_dir}/data/${lang}_token_list/char/tokens.txt
@@ -135,23 +95,17 @@ 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}
-    tools/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \
+    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}' >> ${token_list}
-    num_token=$(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_set}
     mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}
-    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_set} 
-    cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}
-fi
+ fi
 
 # Training Stage
 world_size=$gpu_num  # run on one machine
@@ -170,28 +124,30 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
             rank=$i
             local_rank=$i
             gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
-            asr_train_paraformer.py \
+            train.py \
+                --task_name asr \
                 --gpu_id $gpu_id \
                 --use_preprocessor true \
-                --dataset_type $dataset_type \
                 --token_type char \
                 --token_list $token_list \
-                --train_data_file $feats_dir/$dumpdir/${train_set}/data.list \
-                --valid_data_file $feats_dir/$dumpdir/${valid_set}/data.list \
+                --data_dir ${feats_dir}/data \
+                --train_set ${train_set} \
+                --valid_set ${valid_set} \
+                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
+                --speed_perturb ${speed_perturb} \
+                --dataset_type $dataset_type \
                 --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
+        done
+        wait
 fi
 
 # Testing Stage
@@ -207,7 +163,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
             exit 0
         fi
         mkdir -p "${_logdir}"
-        _data="${feats_dir}/${dumpdir}/${dset}"
+        _data="${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")
@@ -228,6 +184,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
                 --njob ${njob} \
                 --gpuid_list ${gpuid_list} \
                 --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
+                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
                 --key_file "${_logdir}"/keys.JOB.scp \
                 --asr_train_config "${asr_exp}"/config.yaml \
                 --asr_model_file "${asr_exp}"/"${inference_asr_model}" \