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| | | #!/usr/bin/env bash |
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| | | . ./path.sh || exit 1; |
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| | | |
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| | | # machines configuration |
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| | | CUDA_VISIBLE_DEVICES="0,1" |
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| | | gpu_num=2 |
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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=utils/run.pl |
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| | | infer_cmd=utils/run.pl |
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| | | |
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| | | # general configuration |
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| | | feats_dir="../DATA" #feature output dictionary, for large data |
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| | | exp_dir="." |
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| | | lang=zh |
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| | | dumpdir=dump/fbank |
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| | | feats_type=fbank |
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| | | token_type=char |
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| | | scp=feats.scp |
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| | | type=kaldi_ark |
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| | | stage=0 |
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| | | stop_stage=4 |
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| | | |
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| | | # feature configuration |
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| | | feats_dim=80 |
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| | | sample_frequency=16000 |
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| | | nj=32 |
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| | | speed_perturb="0.9,1.0,1.1" |
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| | | |
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| | | # data |
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| | | data_aishell= |
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| | | |
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| | | # exp tag |
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| | | tag="" |
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| | | |
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| | | model_name=damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch |
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| | | init_param="$HOME/.cache/modelscope/hub/$model_name/basemodel.pb" |
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| | | |
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| | | . utils/parse_options.sh || exit 1; |
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| | | |
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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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| | | |
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| | | train_set=train |
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| | | valid_set=dev |
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| | | test_sets="dev test" |
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| | | |
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| | | asr_config=conf/train_asr_paraformer_transformer_12e_6d_3072_768.yaml |
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| | | model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}" |
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| | | |
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| | | inference_config=conf/decode_asr_transformer_noctc_1best.yaml |
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| | | inference_asr_model=valid.acc.ave_10best.pb |
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| | | |
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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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| | | |
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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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| | | |
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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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| | | # Data preparation |
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| | | local/aishell_data_prep.sh ${data_aishell}/data_aishell/wav ${data_aishell}/data_aishell/transcript ${feats_dir} |
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| | | for x in train dev test; do |
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| | | cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org |
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| | | paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | 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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| | | |
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| | | feat_train_dir=${feats_dir}/${dumpdir}/train; mkdir -p ${feat_train_dir} |
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| | | feat_dev_dir=${feats_dir}/${dumpdir}/dev; mkdir -p ${feat_dev_dir} |
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| | | feat_test_dir=${feats_dir}/${dumpdir}/test; mkdir -p ${feat_test_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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| | | utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \ |
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| | | ${feats_dir}/data/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/train |
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| | | utils/fix_data_feat.sh ${fbankdir}/train |
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| | | utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \ |
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| | | ${feats_dir}/data/dev ${exp_dir}/exp/make_fbank/dev ${fbankdir}/dev |
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| | | utils/fix_data_feat.sh ${fbankdir}/dev |
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| | | utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \ |
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| | | ${feats_dir}/data/test ${exp_dir}/exp/make_fbank/test ${fbankdir}/test |
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| | | utils/fix_data_feat.sh ${fbankdir}/test |
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| | | |
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| | | # compute global cmvn |
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| | | utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} \ |
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| | | ${fbankdir}/train ${exp_dir}/exp/make_fbank/train |
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| | | |
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| | | # apply cmvn |
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| | | utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \ |
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| | | ${fbankdir}/train ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/train ${feat_train_dir} |
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| | | utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \ |
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| | | ${fbankdir}/dev ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/dev ${feat_dev_dir} |
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| | | utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \ |
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| | | ${fbankdir}/test ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/test ${feat_test_dir} |
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| | | |
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| | | cp ${fbankdir}/train/text ${fbankdir}/train/speech_shape ${fbankdir}/train/text_shape ${feat_train_dir} |
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| | | cp ${fbankdir}/dev/text ${fbankdir}/dev/speech_shape ${fbankdir}/dev/text_shape ${feat_dev_dir} |
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| | | cp ${fbankdir}/test/text ${fbankdir}/test/speech_shape ${fbankdir}/test/text_shape ${feat_test_dir} |
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| | | |
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| | | utils/fix_data_feat.sh ${feat_train_dir} |
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| | | utils/fix_data_feat.sh ${feat_dev_dir} |
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| | | utils/fix_data_feat.sh ${feat_test_dir} |
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| | | |
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| | | #generate ark list |
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| | | utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_train_dir} ${fbankdir}/train ${feat_train_dir} |
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| | | utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_dev_dir} ${fbankdir}/dev ${feat_dev_dir} |
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| | | fi |
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| | | |
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| | | token_list=${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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| | | |
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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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| | | utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/train/text | cut -f 2- -d" " | tr " " "\n" \ |
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| | | | sort | uniq | grep -a -v -e '^\s*$' | 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 |
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| | | mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/dev |
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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 |
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| | | cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/dev |
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| | | fi |
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| | | |
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| | | # Training Stage |
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| | | world_size=$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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| | | python utils/download_model.py --model_name ${model_name} # download pretrained model on ModelScope |
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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_paraformer.py \ |
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| | | --gpu_id $gpu_id \ |
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| | | --use_preprocessor true \ |
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| | | --token_type char \ |
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| | | --token_list $token_list \ |
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| | | --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \ |
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| | | --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \ |
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| | | --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \ |
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| | | --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \ |
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| | | --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \ |
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| | | --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \ |
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| | | --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \ |
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| | | --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char \ |
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| | | --init_param ${init_param} \ |
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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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| | | |
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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 paraformer \ |
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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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