| | |
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| | | valid_set=dev |
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| | | test_sets="dev test" |
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| | | |
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| | | asr_config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml |
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| | | model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}" |
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| | | config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml |
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| | | model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}" |
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| | | |
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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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| | | inference_device="cuda" #"cpu" |
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| | | inference_checkpoint="model.pt" |
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| | | inference_scp="wav.scp" |
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| | | |
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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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| | | # |
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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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| | | |
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| | | |
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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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| | |
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| | | |
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| | | if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then |
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| | | echo "stage 1: Feature and CMVN Generation" |
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| | | # utils/compute_cmvn.sh --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$asr_config" --scale 1.0 |
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| | | # utils/compute_cmvn.sh --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$config" --scale 1.0 |
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| | | python ../../../funasr/bin/compute_audio_cmvn.py \ |
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| | | --config-path "${workspace}" \ |
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| | | --config-name "${asr_config}" \ |
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| | | --config-name "${config}" \ |
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| | | ++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \ |
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| | | ++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \ |
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| | | ++dataset_conf.num_workers=$nj |
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| | |
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| | | |
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| | | # ASR Training Stage |
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| | | if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then |
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| | | echo "stage 4: ASR Training" |
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| | | echo "stage 4: ASR Training" |
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| | | |
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| | | log_file="${exp_dir}/exp/${model_dir}/train.log.txt" |
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| | | echo "log_file: ${log_file}" |
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| | | torchrun \ |
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| | | --nnodes 1 \ |
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| | | --nproc_per_node ${gpu_num} \ |
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| | | ../../../funasr/bin/train.py \ |
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| | | --config-path "${workspace}" \ |
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| | | --config-name "${asr_config}" \ |
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| | | --config-name "${config}" \ |
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| | | ++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \ |
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| | | ++cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \ |
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| | | ++token_list="${token_list}" \ |
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| | | ++output_dir="${exp_dir}/exp/${model_dir}" |
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| | | ++tokenizer_conf.token_list="${token_list}" \ |
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| | | ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \ |
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| | | ++output_dir="${exp_dir}/exp/${model_dir}" &> ${log_file} |
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| | | fi |
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| | | |
|---|
| | | # |
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| | | ## Testing Stage |
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| | | #if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then |
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| | | # echo "stage 5: 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." |
|---|
| | | # exit 0 |
|---|
| | | # fi |
|---|
| | | # mkdir -p "${_logdir}" |
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| | | # _data="${feats_dir}/data/${dset}" |
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| | | # key_file=${_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") |
|---|
| | | # 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 |
|---|
| | | # # shellcheck disable=SC2086 |
|---|
| | | # utils/split_scp.pl "${key_file}" ${split_scps} |
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| | | # _opts= |
|---|
| | | # if [ -n "${inference_config}" ]; then |
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| | | # _opts+="--config ${inference_config} " |
|---|
| | | # fi |
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| | | # ${infer_cmd} --gpu "${_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}" \ |
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| | | # --cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.mvn \ |
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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}" \ |
|---|
| | | # --output_dir "${_logdir}"/output.JOB \ |
|---|
| | | # --mode paraformer \ |
|---|
| | | # ${_opts} |
|---|
| | | # |
|---|
| | | # for f in token token_int score text; do |
|---|
| | | # if [ -f "${_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 |
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| | | # 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 |
|---|
| | | # |
|---|
| | | ## Prepare files for ModelScope fine-tuning and inference |
|---|
| | | #if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then |
|---|
| | | # echo "stage 6: ModelScope Preparation" |
|---|
| | | # cp ${feats_dir}/data/${train_set}/cmvn/am.mvn ${exp_dir}/exp/${model_dir}/am.mvn |
|---|
| | | # vocab_size=$(cat ${token_list} | wc -l) |
|---|
| | | # python utils/gen_modelscope_configuration.py \ |
|---|
| | | # --am_model_name $inference_asr_model \ |
|---|
| | | # --mode paraformer \ |
|---|
| | | # --model_name paraformer \ |
|---|
| | | # --dataset aishell \ |
|---|
| | | # --output_dir $exp_dir/exp/$model_dir \ |
|---|
| | | # --vocab_size $vocab_size \ |
|---|
| | | # --nat _nat \ |
|---|
| | | # --tag $tag |
|---|
| | | #fi |
|---|
| | | |
|---|
| | | |
|---|
| | | # Testing Stage |
|---|
| | | if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then |
|---|
| | | echo "stage 5: Inference" |
|---|
| | | |
|---|
| | | if ${inference_device} == "cuda"; then |
|---|
| | | nj=$(echo CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') |
|---|
| | | else |
|---|
| | | nj=$njob |
|---|
| | | batch_size=1 |
|---|
| | | gpuid_list="" |
|---|
| | | for JOB in $(seq ${nj}); do |
|---|
| | | gpuid_list=CUDA_VISIBLE_DEVICES"-1," |
|---|
| | | done |
|---|
| | | fi |
|---|
| | | |
|---|
| | | for dset in ${test_sets}; do |
|---|
| | | |
|---|
| | | inference_dir="${asr_exp}/${inference_checkpoint}/${dset}" |
|---|
| | | _logdir="${inference_dir}/logdir" |
|---|
| | | |
|---|
| | | mkdir -p "${_logdir}" |
|---|
| | | data_dir="${feats_dir}/data/${dset}" |
|---|
| | | key_file=${data_dir}/${inference_scp} |
|---|
| | | |
|---|
| | | split_scps= |
|---|
| | | for JOB in $(seq "${nj}"); do |
|---|
| | | split_scps+=" ${_logdir}/keys.${JOB}.scp" |
|---|
| | | done |
|---|
| | | utils/split_scp.pl "${key_file}" ${split_scps} |
|---|
| | | |
|---|
| | | for JOB in $(seq ${nj}); do |
|---|
| | | { |
|---|
| | | python ../../../funasr/bin/inference.py \ |
|---|
| | | --config-path="${exp_dir}/exp/${model_dir}" \ |
|---|
| | | --config-name="config.yaml" \ |
|---|
| | | ++init_param="${exp_dir}/exp/${model_dir}/${inference_checkpoint}" \ |
|---|
| | | ++tokenizer_conf.token_list="${token_list}" \ |
|---|
| | | ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \ |
|---|
| | | ++input="${_logdir}/keys.${JOB}.scp" \ |
|---|
| | | ++output_dir="${inference_dir}/${JOB}" \ |
|---|
| | | ++device="${inference_device}" |
|---|
| | | }& |
|---|
| | | |
|---|
| | | done |
|---|
| | | wait |
|---|
| | | |
|---|
| | | mkdir -p ${inference_dir}/1best_recog |
|---|
| | | for f in token score text; do |
|---|
| | | if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then |
|---|
| | | for JOB in $(seq "${nj}"); do |
|---|
| | | cat "${inference_dir}/${JOB}/1best_recog/${f}" |
|---|
| | | done | sort -k1 >"${inference_dir}/1best_recog/${f}" |
|---|
| | | fi |
|---|
| | | done |
|---|
| | | |
|---|
| | | echo "Computing WER ..." |
|---|
| | | cp ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc |
|---|
| | | cp ${data_dir}/text ${inference_dir}/1best_recog/text.ref |
|---|
| | | python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer |
|---|
| | | tail -n 3 ${inference_dir}/1best_recog/text.cer |
|---|
| | | done |
|---|
| | | |
|---|
| | | fi |
|---|
