#!/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=utils/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=en
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dumpdir=dump/fbank
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feats_type=fbank
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token_type=bpe
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dataset_type=large
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scp=feats.scp
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type=kaldi_ark
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stage=3
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stop_stage=4
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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=100
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speed_perturb="0.9,1.0,1.1"
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# data
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data_librispeech=
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# bpe model
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nbpe=5000
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bpemode=unigram
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# exp tag
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tag=""
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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_960
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valid_set=dev
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test_sets="test_clean test_other dev_clean dev_other"
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asr_config=conf/train_asr_conformer.yaml
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#asr_config=conf/train_asr_conformer_uttnorm.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
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inference_config=conf/decode_asr_transformer.yaml
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#inference_config=conf/decode_asr_transformer_beam60_ctc0.3.yaml
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inference_asr_model=valid.acc.ave_10best.pth
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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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# Data preparation
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for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
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local/data_prep_librispeech.sh ${data_librispeech}/LibriSpeech/${x} ${feats_dir}/data/${x//-/_}
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done
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fi
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feat_train_dir=${feats_dir}/${dumpdir}/$train_set; mkdir -p ${feat_train_dir}
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feat_dev_clean_dir=${feats_dir}/${dumpdir}/dev_clean; mkdir -p ${feat_dev_clean_dir}
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feat_dev_other_dir=${feats_dir}/${dumpdir}/dev_other; mkdir -p ${feat_dev_other_dir}
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feat_test_clean_dir=${feats_dir}/${dumpdir}/test_clean; mkdir -p ${feat_test_clean_dir}
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feat_test_other_dir=${feats_dir}/${dumpdir}/test_other; mkdir -p ${feat_test_other_dir}
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feat_dev_dir=${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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for x in dev_clean dev_other test_clean test_other; do
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utils/compute_fbank.sh --cmd "$train_cmd" --nj 1 --max_lengths 3000 --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
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${feats_dir}/data/${x} ${exp_dir}/exp/make_fbank/${x} ${fbankdir}/${x}
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utils/fix_data_feat.sh ${fbankdir}/${x}
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done
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mkdir ${feats_dir}/data/$train_set
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train_sets="train_clean_100 train_clean_360 train_other_500"
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for file in wav.scp text; do
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( for f in $train_sets; do cat $feats_dir/data/$f/$file; done ) | sort -k1 > $feats_dir/data/$train_set/$file || exit 1;
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done
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utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --max_lengths 3000 --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \
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${feats_dir}/data/$train_set ${exp_dir}/exp/make_fbank/$train_set ${fbankdir}/$train_set
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utils/fix_data_feat.sh ${fbankdir}/$train_set
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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_set ${exp_dir}/exp/make_fbank/$train_set
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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_set ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/$train_set ${feat_train_dir}
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
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${fbankdir}/dev_clean ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/dev_clean ${feat_dev_clean_dir}
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1\
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${fbankdir}/dev_other ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/dev_other ${feat_dev_other_dir}
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
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${fbankdir}/test_clean ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/test_clean ${feat_test_clean_dir}
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
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${fbankdir}/test_other ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/test_other ${feat_test_other_dir}
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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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cp ${fbankdir}/dev_clean/text ${fbankdir}/dev_clean/speech_shape ${fbankdir}/dev_clean/text_shape ${feat_dev_clean_dir}
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cp ${fbankdir}/dev_other/text ${fbankdir}/dev_other/speech_shape ${fbankdir}/dev_other/text_shape ${feat_dev_other_dir}
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cp ${fbankdir}/test_clean/text ${fbankdir}/test_clean/speech_shape ${fbankdir}/test_clean/text_shape ${feat_test_clean_dir}
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cp ${fbankdir}/test_other/text ${fbankdir}/test_other/speech_shape ${fbankdir}/test_other/text_shape ${feat_test_other_dir}
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dev_sets="dev_clean dev_other"
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for file in feats.scp text speech_shape text_shape; do
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( for f in $dev_sets; do cat $feats_dir/${dumpdir}/$f/$file; done ) | sort -k1 > $feat_dev_dir/$file || exit 1;
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done
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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_set} ${feat_train_dir}
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utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_dev_dir} ${fbankdir}/${valid_set} ${feat_dev_dir}
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fi
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dict=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
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bpemodel=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}
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echo "dictionary: ${dict}"
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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### Task dependent. You have to check non-linguistic symbols used in the corpus.
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echo "stage 2: Dictionary and Json Data Preparation"
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mkdir -p ${feats_dir}/data/lang_char/
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echo "<blank>" > ${dict}
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echo "<s>" >> ${dict}
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echo "</s>" >> ${dict}
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cut -f 2- -d" " ${feats_dir}/data/${train_set}/text > ${feats_dir}/data/lang_char/input.txt
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spm_train --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
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spm_encode --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${dict}
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echo "<unk>" >> ${dict}
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wc -l ${dict}
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vocab_size=$(cat ${dict} | wc -l)
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awk -v v=,${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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--split_with_space false \
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--bpemodel ${bpemodel}.model \
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--token_type $token_type \
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--dataset_type $dataset_type \
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--token_list $dict \
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--train_data_file $feats_dir/$dumpdir/${train_set}/ark_txt.scp \
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--valid_data_file $feats_dir/$dumpdir/${valid_set}/ark_txt.scp \
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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/compute_wer.py ${_data}/text ${_dir}/text ${_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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