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https://github.com/modelscope/FunASR
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嘉渊
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@ -111,12 +111,12 @@ fi
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world_size=$gpu_num # run on one machine
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: Training"
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# if ! "${skip_extract_embed}"; then
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# echo "extract embeddings..."
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# local/extract_embeds.sh \
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# --bert_model_name ${bert_model_name} \
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# --raw_dataset_path ${feats_dir}
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# fi
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if ! "${skip_extract_embed}"; then
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echo "extract embeddings..."
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local/extract_embeds.sh \
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--bert_model_name ${bert_model_name} \
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--raw_dataset_path ${feats_dir}
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fi
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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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@ -3,20 +3,17 @@
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stage=1
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stop_stage=3
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bert_model_root="../../huggingface_models"
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bert_model_name="bert-base-chinese"
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#bert_model_name="chinese-roberta-wwm-ext"
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#bert_model_name="mengzi-bert-base"
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raw_dataset_path="../DATA"
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model_path=${bert_model_root}/${bert_model_name}
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model_path=${bert_model_name}
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. utils/parse_options.sh || exit 1;
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nj=100
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nj=32
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for data_set in train dev_ios test_ios;do
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scp=$raw_dataset_path/dump/fbank/${data_set}/text
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local_scp_dir_raw=$raw_dataset_path/embeds/$bert_model_name/${data_set}
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for data_set in train dev test;do
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scp=$raw_dataset_path/data/${data_set}/text
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local_scp_dir_raw=${raw_dataset_path}/data/embeds/${data_set}
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local_scp_dir=$local_scp_dir_raw/split$nj
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local_records_dir=$local_scp_dir_raw/ark
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@ -31,7 +28,7 @@ for data_set in train dev_ios test_ios;do
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utils/split_scp.pl $scp ${split_scps}
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for num in {0..24};do
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for num in {0..7};do
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tmp=`expr $num \* 4`
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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@ -41,23 +38,12 @@ for data_set in train dev_ios test_ios;do
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{
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beg=0
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gpu=`expr $beg + $idx`
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echo $local_scp_dir_raw/log/log.${JOB}
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python tools/extract_embeds.py $local_scp_dir/text.$JOB.txt ${local_records_dir}/embeds.${JOB}.ark ${local_records_dir}/embeds.${JOB}.scp ${local_records_dir}/embeds.${JOB}.shape ${gpu} ${model_path} &> $local_scp_dir_raw/log/log.${JOB}
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echo ${local_scp_dir}/log.${JOB}
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python utils/extract_embeds.py $local_scp_dir/data.$JOB.text ${local_records_dir}/embeds.${JOB}.ark ${local_records_dir}/embeds.${JOB}.scp ${local_records_dir}/embeds.${JOB}.shape ${gpu} ${model_path} &> ${local_scp_dir}/log.${JOB}
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} &
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done
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wait
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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for idx in {1..4}; do
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JOB=`expr $tmp + $idx`
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echo "upload jobid=$JOB"
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{
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hadoop fs -put -f ${local_records_dir}/embeds.${JOB}.ark ${odps_des_feature_dir}/embeds.${JOB}.ark
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} &
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done
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wait
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fi
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done
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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@ -17,7 +17,6 @@ if [ $# != 3 ]; then
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fi
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corpus=$1
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#dict_dir=$2
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tmp=$2
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dir=$3
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@ -35,14 +34,14 @@ fi
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# validate utt-key list, IC0803W0380 is a bad utterance
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awk '{print $1}' $corpus/wav.scp | grep -v 'IC0803W0380' > $tmp/wav_utt.list
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awk '{print $1}' $corpus/trans.txt > $tmp/trans_utt.list
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tools/filter_scp.pl -f 1 $tmp/wav_utt.list $tmp/trans_utt.list > $tmp/utt.list
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utils/filter_scp.pl -f 1 $tmp/wav_utt.list $tmp/trans_utt.list > $tmp/utt.list
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# wav.scp
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awk -F'\t' -v path_prefix=$corpus '{printf("%s\t%s/%s\n",$1,path_prefix,$2)}' $corpus/wav.scp > $tmp/tmp_wav.scp
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tools/filter_scp.pl -f 1 $tmp/utt.list $tmp/tmp_wav.scp | sort -k 1 | uniq > $tmp/wav.scp
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utils/filter_scp.pl -f 1 $tmp/utt.list $tmp/tmp_wav.scp | sort -k 1 | uniq > $tmp/wav.scp
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# text
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tools/filter_scp.pl -f 1 $tmp/utt.list $corpus/trans.txt | sort -k 1 | uniq > $tmp/text
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utils/filter_scp.pl -f 1 $tmp/utt.list $corpus/trans.txt | sort -k 1 | uniq > $tmp/text
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# copy prepared resources from tmp_dir to target dir
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mkdir -p $dir
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@ -8,36 +8,32 @@ 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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njob=1
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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, for large data
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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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dumpdir=dump/fbank
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feats_type=fbank
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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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dataset_type=large
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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=5
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stage=3
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stop_stage=4
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skip_extract_embed=false
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bert_model_root="../../huggingface_models"
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bert_model_name="bert-base-chinese"
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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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nj=64
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# data
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tr_dir=
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dev_tst_dir=
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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
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# exp tag
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tag="exp1"
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@ -55,7 +51,7 @@ valid_set=dev_ios
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test_sets="dev_ios test_ios"
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asr_config=conf/train_asr_paraformerbert_conformer_20e_6d_1280_320.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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inference_config=conf/decode_asr_transformer_noctc_1best.yaml
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inference_asr_model=valid.acc.ave_10best.pb
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@ -75,86 +71,44 @@ 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} data/local/train data/train || exit 1;
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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 data/local/dev_${x,,} data/dev_${x,,} || exit 1;
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local/prepare_data.sh ${dev_tst_dir}/${x}/test data/local/test_${x,,} data/test_${x,,} || exit 1;
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done
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for x in iOS; 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_android dev_ios dev_mic test_android test_ios test_mic; do
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mv data/${x}/text data/${x}/text.org
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paste <(cut -f 1 data/${x}/text.org) <(cut -f 2 data/${x}/text.org | tr '[:lower:]' '[:upper:]') \
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> data/${x}/text
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tools/text2token.py -n 1 -s 1 data/${x}/text > data/${x}/text.org
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mv data/${x}/text.org data/${x}/text
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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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data/train 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 $nj \
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data/dev_${x} 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 $nj \
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data/test_${x} 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/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} ${fbankdir}/train/cmvn.json exp/make_fbank/${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} ${fbankdir}/train/cmvn.json exp/make_fbank/${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} ${fbankdir}/train/cmvn.json exp/make_fbank/test_${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_${x}
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done
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echo "stage 1: Feature and CMVN Generation"
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utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
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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 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: Dictionary Preparation"
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mkdir -p data/${lang}_token_list/char/
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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>" >> ${token_list}
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tools/text2token.py -s 1 -n 1 --space "" data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \
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utils/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 > ${feat_dev_dir}/text_shape.char
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mkdir -p asr_stats_fbank_zh_char/${train_set}
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mkdir -p asr_stats_fbank_zh_char/${valid_set}
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cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char asr_stats_fbank_zh_char/${train_set}
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cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char asr_stats_fbank_zh_char/${valid_set}
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fi
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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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fi
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# Training Stage
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world_size=$gpu_num # run on one machine
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@ -37,7 +37,7 @@ def tokenize(data,
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vad = -2
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if bpe_tokenizer is not None:
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text = bpe_tokenizer.text2tokens("".join(text))
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text = bpe_tokenizer.text2tokens(text)
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if seg_dict is not None:
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assert isinstance(seg_dict, dict)
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