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https://github.com/modelscope/FunASR
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嘉渊
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688fb902dd
@ -5,8 +5,6 @@ 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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@ -16,7 +14,7 @@ nj=32
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for data_set in train dev test;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/${data_set}
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local_scp_dir_raw=${raw_dataset_path}/${data_set}
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local_scp_dir=$local_scp_dir_raw/split$nj
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local_records_dir=$local_scp_dir_raw/ark
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@ -16,12 +16,11 @@ infer_cmd=utils/run.pl
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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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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=3
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stop_stage=4
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skip_extract_embed=false
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@ -30,15 +29,14 @@ 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=32
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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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data_aishell=
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raw_data=
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data_url=www.openslr.org/resources/33
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# exp tag
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tag=""
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tag="exp1"
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. utils/parse_options.sh || exit 1;
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@ -53,7 +51,7 @@ valid_set=dev
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test_sets="dev test"
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asr_config=conf/train_asr_paraformerbert_conformer_12e_6d_2048_256.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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@ -70,10 +68,17 @@ else
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_ngpu=0
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fi
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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echo "stage -1: Data Download"
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local/download_and_untar.sh ${raw_data} ${data_url} data_aishell
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local/download_and_untar.sh ${raw_data} ${data_url} resource_aishell
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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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local/aishell_data_prep.sh ${data_aishell}/data_aishell/wav ${data_aishell}/data_aishell/transcript ${feats_dir}
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local/aishell_data_prep.sh ${raw_data}/data_aishell/wav ${raw_data}/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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@ -83,46 +88,9 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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done
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fi
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feat_train_dir=${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 ${fbankdir}/test
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# compute global cmvn
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utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} \
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${fbankdir}/train ${exp_dir}/exp/make_fbank/train
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# apply cmvn
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utils/apply_cmvn.sh --cmd "$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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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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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 ${feat_test_dir}
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#generate ark list
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utils/gen_ark_list.sh --cmd "$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 ${feat_dev_dir}
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echo "stage 1: Feature and CMVN Generation"
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utils/compute_cmvn.sh --cmd "$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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@ -135,17 +103,9 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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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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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 ${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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# Training Stage
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@ -172,31 +132,22 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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rank=$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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train.py \
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--task_name asr \
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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_data_path_and_name_and_type ${feats_dir}/embeds/${bert_model_name}/${train_set}/embeds.scp,embed,${type} \
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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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--train_shape_file ${feats_dir}/embeds/${bert_model_name}/${train_set}/embeds.shape \
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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_data_path_and_name_and_type ${feats_dir}/embeds/${bert_model_name}/${valid_set}/embeds.scp,embed,${type} \
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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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--valid_shape_file ${feats_dir}/embeds/${bert_model_name}/${valid_set}/embeds.shape \
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--data_dir ${feats_dir}/data \
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--train_set ${train_set} \
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--valid_set ${valid_set} \
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--cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
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--speed_perturb ${speed_perturb} \
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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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--allow_variable_data_keys true \
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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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@ -347,6 +347,12 @@ def get_parser():
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default=True,
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help="Apply preprocessing to data or not",
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)
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parser.add_argument(
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"--embed_path",
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type=str,
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default=None,
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help="for model which requires embeds",
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)
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# optimization related
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parser.add_argument(
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@ -181,6 +181,11 @@ def prepare_data(args, distributed_option):
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["{}/{}/wav.scp".format(args.data_dir, args.valid_set), data_names[0], data_types[0]],
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["{}/{}/text".format(args.data_dir, args.valid_set), data_names[1], data_types[1]]
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]
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if args.embed_path is not None:
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args.train_data_path_and_name_and_type[0].append(
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"{}/embed/kaldi_ark".format(os.path.join(args.embed_path, args.train_set, "embeds.scp")))
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args.valid_data_path_and_name_and_type[0].append(
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"{}/embed/kaldi_ark".format(os.path.join(args.embed_path, args.dev_set, "embeds.scp")))
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else:
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args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list")
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args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "data.list")
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