update

egs/aishell/transformer/run.sh

@@ -3,38 +3,37 @@
 . ./path.sh || exit 1;
 
 # machines configuration
-CUDA_VISIBLE_DEVICES="0,1"
+CUDA_VISIBLE_DEVICES="2,3"
 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=8
+njob=1
 train_cmd=utils/run.pl
 infer_cmd=utils/run.pl
 
 # general configuration
-feats_dir="../DATA" #feature output dictionary, for large data
+feats_dir="../DATA" #feature output dictionary
 exp_dir="."
 lang=zh
 dumpdir=dump/fbank
 feats_type=fbank
 token_type=char
-scp=feats.scp
+scp=wav.scp
-type=kaldi_ark
+type=sound
-stage=0
+stage=3
-stop_stage=4
+stop_stage=3
 
 # feature configuration
 feats_dim=80
-sample_frequency=16000
+nj=64
-nj=32
-speed_perturb="0.9,1.0,1.1"
 
 # data
-data_aishell=
+raw_data=
+data_url=www.openslr.org/resources/33
 
 # exp tag
-tag=""
+tag="exp1"
 
 . utils/parse_options.sh || exit 1;
 
@@ -48,7 +47,7 @@ train_set=train
 valid_set=dev
 test_sets="dev test"
 
-asr_config=conf/train_asr_conformer.yaml
+asr_config=conf/train_asr_transformer.yaml
 model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
 
 inference_config=conf/decode_asr_transformer.yaml
@@ -66,10 +65,16 @@ else
     _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 ${data_aishell}/data_aishell/wav ${data_aishell}/data_aishell/transcript ${feats_dir}
+    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 " ") \
@@ -79,46 +84,9 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     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 ] && [ ${stop_stage} -ge 1 ]; then
-    echo "stage 1: Feature Generation"
+    echo "stage 1: Feature and CMVN Generation"
-    # compute fbank features
+    utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
-    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
@@ -126,22 +94,14 @@ 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" \
+    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 
-    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
@@ -153,7 +113,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
     if [ -f $INIT_FILE ];then
         rm -f $INIT_FILE
-    fi 
+    fi
     init_method=file://$(readlink -f $INIT_FILE)
     echo "$0: init method is $init_method"
     for ((i = 0; i < $gpu_num; ++i)); do
@@ -161,26 +121,21 @@ 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.py \
+            train.py \
+                --task_name asr \
                 --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} \
+                --data_dir ${feats_dir}/data \
-                --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
+                --train_set ${train_set} \
-                --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
+                --valid_set ${valid_set} \
-                --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
+                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
-                --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  \
                 --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 \
@@ -188,60 +143,4 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
         } &
         done
         wait
-fi
+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