train

游雁

2023-05-16 3d9f094e9652d4b84894c6fd4eae39a4a753b0f0

 egs/aishell/conformer/run.sh

@@ -3,12 +3,12 @@
. ./path.sh || exit 1;

# machines configuration
CUDA_VISIBLE_DEVICES="2,3"
CUDA_VISIBLE_DEVICES="0,1"
gpu_num=2
count=1
gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decoding
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
njob=1
njob=5
train_cmd=utils/run.pl
infer_cmd=utils/run.pl

@@ -16,13 +16,12 @@
feats_dir="../DATA" #feature output dictionary
exp_dir="."
lang=zh
dumpdir=dump/fbank
feats_type=fbank
token_type=char
scp=wav.scp
type=sound
stage=3
stop_stage=3
scp=wav.scp
speed_perturb="0.9 1.0 1.1"
stage=0
stop_stage=5

# feature configuration
feats_dim=80
@@ -48,7 +47,7 @@
test_sets="dev test"

asr_config=conf/train_asr_conformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"

inference_config=conf/decode_asr_transformer.yaml
inference_asr_model=valid.acc.ave_10best.pb
@@ -104,10 +103,16 @@
    echo "<unk>" >> ${token_list}
fi

# Training Stage
# LM Training Stage
world_size=$gpu_num  # run on one machine
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    echo "stage 3: Training"
    echo "stage 3: LM Training"
fi

# ASR Training Stage
world_size=$gpu_num  # run on one machine
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
    echo "stage 4: ASR Training"
    mkdir -p ${exp_dir}/exp/${model_dir}
    mkdir -p ${exp_dir}/exp/${model_dir}/log
    INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
@@ -131,6 +136,7 @@
                --train_set ${train_set} \
                --valid_set ${valid_set} \
                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
                --speed_perturb ${speed_perturb} \
                --resume true \
                --output_dir ${exp_dir}/exp/${model_dir} \
                --config $asr_config \
@@ -143,4 +149,61 @@
        } &
        done
        wait
fi

# Testing Stage
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
    echo "stage 5: Inference"
    for dset in ${test_sets}; do
        asr_exp=${exp_dir}/exp/${model_dir}
        inference_tag="$(basename "${inference_config}" .yaml)"
        _dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
        _logdir="${_dir}/logdir"
        if [ -d ${_dir} ]; then
            echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
            exit 0
        fi
        mkdir -p "${_logdir}"
        _data="${feats_dir}/data/${dset}"
        key_file=${_data}/${scp}
        num_scp_file="$(<${key_file} wc -l)"
        _nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
        split_scps=
        for n in $(seq "${_nj}"); do
            split_scps+=" ${_logdir}/keys.${n}.scp"
        done
        # shellcheck disable=SC2086
        utils/split_scp.pl "${key_file}" ${split_scps}
        _opts=
        if [ -n "${inference_config}" ]; then
            _opts+="--config ${inference_config} "
        fi
        ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
            python -m funasr.bin.asr_inference_launch \
                --batch_size 1 \
                --ngpu "${_ngpu}" \
                --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}" \
                --output_dir "${_logdir}"/output.JOB \
                --mode asr \
                ${_opts}

        for f in token token_int score text; do
            if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
                for i in $(seq "${_nj}"); do
                    cat "${_logdir}/output.${i}/1best_recog/${f}"
                done | sort -k1 >"${_dir}/${f}"
            fi
        done
        python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
        python utils/proce_text.py ${_data}/text ${_data}/text.proc
        python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
        tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
        cat ${_dir}/text.cer.txt
    done
fi