#!/usr/bin/env bash
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. ./path.sh || exit 1;
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# machines configuration
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CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
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gpu_num=8
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count=1
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
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njob=5
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train_cmd=tools/run.pl
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infer_cmd=utils/run.pl
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# general configuration
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feats_dir="../DATA" #feature output dictionary
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exp_dir="."
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lang=zh
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token_type=char
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type=sound
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scp=wav.scp
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speed_perturb="0.9 1.0 1.1"
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stage=0
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stop_stage=0
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# feature configuration
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feats_dim=80
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nj=64
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# data
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tr_dir=/nfs/wangjiaming.wjm/asr_data/aishell2/AISHELL-2/iOS/data
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dev_tst_dir=/nfs/wangjiaming.wjm/asr_data/aishell2/AISHELL-DEV-TEST-SET/iOS
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# exp tag
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tag="exp1"
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. utils/parse_options.sh || exit 1;
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# Set bash to 'debug' mode, it will exit on :
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# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
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set -e
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set -u
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set -o pipefail
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train_set=train
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valid_set=dev_ios
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test_sets="dev_ios test_ios"
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asr_config=conf/train_asr_conformer.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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inference_config=conf/decode_asr_transformer.yaml
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inference_asr_model=valid.acc.ave_10best.pb
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# you can set gpu num for decoding here
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gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
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ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
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if ${gpu_inference}; then
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inference_nj=$[${ngpu}*${njob}]
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_ngpu=1
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else
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inference_nj=$njob
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_ngpu=0
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "stage 0: Data preparation"
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# For training set
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local/prepare_data.sh ${tr_dir} ${feats_dir}/data/local/train ${feats_dir}/data/train || exit 1;
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# # For dev and test set
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for x in Android iOS Mic; do
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local/prepare_data.sh ${dev_tst_dir}/${x}/dev ${feats_dir}/data/local/dev_${x,,} ${feats_dir}/data/dev_${x,,} || exit 1;
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local/prepare_data.sh ${dev_tst_dir}/${x}/test ${feats_dir}/data/local/test_${x,,} ${feats_dir}/data/test_${x,,} || exit 1;
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done
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# Normalize text to capital letters
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for x in train dev_ios test_ios; do
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mv ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
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paste -d " " <(cut -f 1 ${feats_dir}/data/${x}/text.org) <(cut -f 2- ${feats_dir}/data/${x}/text.org \
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| tr 'A-Z' 'a-z' | tr -d " ") \
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> ${feats_dir}/data/${x}/text
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utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
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mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
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done
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fi
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feat_train_dir=${feats_dir}/${dumpdir}/${train_set}; mkdir -p ${feat_train_dir}
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feat_dev_dir=${feats_dir}/${dumpdir}/${valid_set}; mkdir -p ${feat_dev_dir}
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Feature Generation"
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# compute fbank features
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fbankdir=${feats_dir}/fbank
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steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj --speed_perturb ${speed_perturb} \
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${feats_dir}/data/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/train
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tools/fix_data_feat.sh ${fbankdir}/train
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for x in android ios mic; do
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steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
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${feats_dir}/data/dev_${x} ${exp_dir}/exp/make_fbank/dev_${x} ${fbankdir}/dev_${x}
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tools/fix_data_feat.sh ${fbankdir}/dev_${x}
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steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
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${feats_dir}/data/test_${x} ${exp_dir}/exp/make_fbank/test_${x} ${fbankdir}/test_${x}
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tools/fix_data_feat.sh ${fbankdir}/test_${x}
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done
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# compute global cmvn
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steps/compute_cmvn.sh --cmd "$train_cmd" --nj $nj \
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${fbankdir}/train ${exp_dir}/exp/make_fbank/train
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# apply cmvn
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steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
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${fbankdir}/${train_set} ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/${train_set} ${feat_train_dir}
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steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
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${fbankdir}/${valid_set} ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/${valid_set} ${feat_dev_dir}
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for x in android ios mic; do
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steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
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${fbankdir}/test_${x} ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/test_${x} ${feats_dir}/${dumpdir}/test_${x}
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done
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cp ${fbankdir}/${train_set}/text ${fbankdir}/${train_set}/speech_shape ${fbankdir}/${train_set}/text_shape ${feat_train_dir}
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tools/fix_data_feat.sh ${feat_train_dir}
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cp ${fbankdir}/${valid_set}/text ${fbankdir}/${valid_set}/speech_shape ${fbankdir}/${valid_set}/text_shape ${feat_dev_dir}
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tools/fix_data_feat.sh ${feat_dev_dir}
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for x in android ios mic; do
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cp ${fbankdir}/test_${x}/text ${fbankdir}/test_${x}/speech_shape ${fbankdir}/test_${x}/text_shape ${feats_dir}/${dumpdir}/test_${x}
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tools/fix_data_feat.sh ${feats_dir}/${dumpdir}/test_${x}
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done
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fi
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token_list=${feats_dir}/data/${lang}_token_list/char/tokens.txt
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echo "dictionary: ${token_list}"
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: Dictionary Preparation"
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mkdir -p ${feats_dir}/data/${lang}_token_list/char/
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echo "make a dictionary"
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echo "<blank>" > ${token_list}
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echo "<s>" >> ${token_list}
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echo "</s>" >> ${token_list}
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tools/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \
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| sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
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num_token=$(cat ${token_list} | wc -l)
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echo "<unk>" >> ${token_list}
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vocab_size=$(cat ${token_list} | wc -l)
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awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
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awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
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mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${train_set}
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mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}
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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}
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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}
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fi
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# Training Stage
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world_size=$gpu_num # run on one machine
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: Training"
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mkdir -p ${exp_dir}/exp/${model_dir}
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mkdir -p ${exp_dir}/exp/${model_dir}/log
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INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
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if [ -f $INIT_FILE ];then
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rm -f $INIT_FILE
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fi
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init_method=file://$(readlink -f $INIT_FILE)
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echo "$0: init method is $init_method"
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for ((i = 0; i < $gpu_num; ++i)); do
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{
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rank=$i
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local_rank=$i
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gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
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asr_train.py \
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--gpu_id $gpu_id \
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--use_preprocessor true \
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--dataset_type $dataset_type \
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--token_type char \
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--token_list $token_list \
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--train_data_file $feats_dir/$dumpdir/${train_set}/data.list \
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--valid_data_file $feats_dir/$dumpdir/${valid_set}/data.list \
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--resume true \
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--output_dir ${exp_dir}/exp/${model_dir} \
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--config $asr_config \
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--input_size $feats_dim \
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--ngpu $gpu_num \
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--num_worker_count $count \
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--multiprocessing_distributed true \
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--dist_init_method $init_method \
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--dist_world_size $world_size \
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--dist_rank $rank \
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--local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
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} &
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done
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wait
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fi
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# Testing Stage
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "stage 4: Inference"
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for dset in ${test_sets}; do
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asr_exp=${exp_dir}/exp/${model_dir}
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inference_tag="$(basename "${inference_config}" .yaml)"
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_dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
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_logdir="${_dir}/logdir"
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if [ -d ${_dir} ]; then
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echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
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exit 0
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fi
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mkdir -p "${_logdir}"
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_data="${feats_dir}/${dumpdir}/${dset}"
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key_file=${_data}/${scp}
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num_scp_file="$(<${key_file} wc -l)"
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_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
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split_scps=
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for n in $(seq "${_nj}"); do
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split_scps+=" ${_logdir}/keys.${n}.scp"
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done
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# shellcheck disable=SC2086
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utils/split_scp.pl "${key_file}" ${split_scps}
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_opts=
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if [ -n "${inference_config}" ]; then
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_opts+="--config ${inference_config} "
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fi
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${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
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python -m funasr.bin.asr_inference_launch \
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--batch_size 1 \
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--ngpu "${_ngpu}" \
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--njob ${njob} \
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--gpuid_list ${gpuid_list} \
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--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
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--key_file "${_logdir}"/keys.JOB.scp \
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--asr_train_config "${asr_exp}"/config.yaml \
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--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
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--output_dir "${_logdir}"/output.JOB \
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--mode asr \
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${_opts}
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for f in token token_int score text; do
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if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
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for i in $(seq "${_nj}"); do
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cat "${_logdir}/output.${i}/1best_recog/${f}"
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done | sort -k1 >"${_dir}/${f}"
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fi
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done
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python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
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python utils/proce_text.py ${_data}/text ${_data}/text.proc
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python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
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tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
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cat ${_dir}/text.cer.txt
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done
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fi
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