update repo

嘉渊

2023-05-11 6737c14fff2a23cf4cc7d2ae6d5c3bf4a5d12c98

update repo

 egs/aishell/conformer/run.sh

@@ -177,6 +177,7 @@
                --njob ${njob} \
                --gpuid_list ${gpuid_list} \
                --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
                --key_file "${_logdir}"/keys.JOB.scp \
                --asr_train_config "${asr_exp}"/config.yaml \
                --asr_model_file "${asr_exp}"/"${inference_asr_model}" \

 egs/librispeech_100h/conformer/run.sh

@@ -19,8 +19,8 @@
token_type=bpe
type=sound
scp=wav.scp
stage=2
stop_stage=2
stage=3
stop_stage=4

# feature configuration
feats_dim=80
@@ -89,22 +89,21 @@
    utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
fi

dict=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
token_list=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
bpemodel=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}
echo "dictionary: ${dict}"
echo "dictionary: ${token_list}"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    ### Task dependent. You have to check non-linguistic symbols used in the corpus.
    echo "stage 2: Dictionary and Json Data Preparation"
    mkdir -p ${feats_dir}/data/lang_char/
    echo "<blank>" > ${dict}
    echo "<s>" >> ${dict}
    echo "</s>" >> ${dict}
    echo "<blank>" > ${token_list}
    echo "<s>" >> ${token_list}
    echo "</s>" >> ${token_list}
    cut -f 2- -d" " ${feats_dir}/data/${train_set}/text > ${feats_dir}/data/lang_char/input.txt
    local/spm_train.py --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
    local/spm_encode.py --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${dict}
    echo "<unk>" >> ${dict}
    local/spm_encode.py --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${token_list}
    echo "<unk>" >> ${token_list}
fi


# Training Stage
world_size=$gpu_num  # run on one machine
@@ -123,16 +122,17 @@
            rank=$i
            local_rank=$i
            gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
            asr_train.py \
            train.py \
                --task_name asr \
                --gpu_id $gpu_id \
                --use_preprocessor true \
                --split_with_space false \
                --bpemodel ${bpemodel}.model \
                --token_type $token_type \
                --dataset_type $dataset_type \
                --token_list $dict \
                --train_data_file $feats_dir/$dumpdir/${train_set}/ark_txt.scp \
                --valid_data_file $feats_dir/$dumpdir/${valid_set}/ark_txt.scp \
                --token_list $token_list \
                --data_dir ${feats_dir}/data \
                --train_set ${train_set} \
                --valid_set ${valid_set} \
                --resume true \
                --output_dir ${exp_dir}/exp/${model_dir} \
                --config $asr_config \
@@ -183,6 +183,7 @@
                --njob ${njob} \
                --gpuid_list ${gpuid_list} \
                --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
                --key_file "${_logdir}"/keys.JOB.scp \
                --asr_train_config "${asr_exp}"/config.yaml \
                --asr_model_file "${asr_exp}"/"${inference_asr_model}" \