203 lines
6.6 KiB
Bash
203 lines
6.6 KiB
Bash
#!/usr/bin/env bash
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CUDA_VISIBLE_DEVICES="0,1,2,3"
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# general configuration
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feats_dir="../DATA" #feature output dictionary
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exp_dir=`pwd`
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lang=zh
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token_type=char
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stage=0
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stop_stage=5
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# feature configuration
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nj=32
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inference_device="cuda" #"cpu"
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inference_checkpoint="model.pt.avg10"
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inference_scp="wav.scp"
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inference_batch_size=1
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# data
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raw_data=../raw_data
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data_url=www.openslr.org/resources/33
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# exp tag
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tag="exp1"
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workspace=`pwd`
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master_port=12345
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. utils/parse_options.sh || exit 1;
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# Set bash to 'debug' mode, it will exit on :
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# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
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set -e
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set -u
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set -o pipefail
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train_set=train
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valid_set=dev
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test_sets="dev test"
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config=branchformer_12e_6d_2048_256.yaml
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model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"
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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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mkdir -p ${raw_data}
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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 ${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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> ${feats_dir}/data/${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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# convert wav.scp text to jsonl
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scp_file_list_arg="++scp_file_list='[\"${feats_dir}/data/${x}/wav.scp\",\"${feats_dir}/data/${x}/text\"]'"
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python ../../../funasr/datasets/audio_datasets/scp2jsonl.py \
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++data_type_list='["source", "target"]' \
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++jsonl_file_out=${feats_dir}/data/${x}/audio_datasets.jsonl \
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${scp_file_list_arg}
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done
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Feature and CMVN Generation"
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python ../../../funasr/bin/compute_audio_cmvn.py \
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--config-path "${workspace}/conf" \
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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/${train_set}/cmvn.json" \
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fi
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token_list=${feats_dir}/data/${lang}_token_list/$token_type/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 ${feats_dir}/data/${lang}_token_list/$token_type/
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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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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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echo "<unk>" >> ${token_list}
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fi
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# LM Training Stage
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: LM Training"
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fi
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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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mkdir -p ${exp_dir}/exp/${model_dir}
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current_time=$(date "+%Y-%m-%d_%H-%M")
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log_file="${exp_dir}/exp/${model_dir}/train.log.txt.${current_time}"
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echo "log_file: ${log_file}"
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export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
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gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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torchrun \
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--nnodes 1 \
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--nproc_per_node ${gpu_num} \
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--master_port ${master_port} \
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../../../funasr/bin/train.py \
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--config-path "${workspace}/conf" \
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--config-name "${config}" \
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++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
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++valid_data_set_list="${feats_dir}/data/${valid_set}/audio_datasets.jsonl" \
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++tokenizer_conf.token_list="${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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# 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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if [ ${inference_device} == "cuda" ]; then
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nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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else
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inference_batch_size=1
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CUDA_VISIBLE_DEVICES=""
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for JOB in $(seq ${nj}); do
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CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
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done
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fi
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for dset in ${test_sets}; do
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inference_dir="${exp_dir}/exp/${model_dir}/inference-${inference_checkpoint}/${dset}"
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_logdir="${inference_dir}/logdir"
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echo "inference_dir: ${inference_dir}"
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mkdir -p "${_logdir}"
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data_dir="${feats_dir}/data/${dset}"
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key_file=${data_dir}/${inference_scp}
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split_scps=
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for JOB in $(seq "${nj}"); do
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split_scps+=" ${_logdir}/keys.${JOB}.scp"
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done
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utils/split_scp.pl "${key_file}" ${split_scps}
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gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
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for JOB in $(seq ${nj}); do
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{
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id=$((JOB-1))
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gpuid=${gpuid_list_array[$id]}
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export CUDA_VISIBLE_DEVICES=${gpuid}
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python ../../../funasr/bin/inference.py \
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--config-path="${exp_dir}/exp/${model_dir}" \
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--config-name="config.yaml" \
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++init_param="${exp_dir}/exp/${model_dir}/${inference_checkpoint}" \
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++tokenizer_conf.token_list="${token_list}" \
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++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
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++input="${_logdir}/keys.${JOB}.scp" \
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++output_dir="${inference_dir}/${JOB}" \
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++device="${inference_device}" \
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++ncpu=1 \
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++disable_log=true \
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++batch_size="${inference_batch_size}" &> ${_logdir}/log.${JOB}.txt
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}&
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done
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wait
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mkdir -p ${inference_dir}/1best_recog
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for f in token score text; do
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if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then
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for JOB in $(seq "${nj}"); do
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cat "${inference_dir}/${JOB}/1best_recog/${f}"
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done | sort -k1 >"${inference_dir}/1best_recog/${f}"
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fi
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done
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echo "Computing WER ..."
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python utils/postprocess_text_zh.py ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
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python utils/postprocess_text_zh.py ${data_dir}/text ${inference_dir}/1best_recog/text.ref
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python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer
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tail -n 3 ${inference_dir}/1best_recog/text.cer
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done
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fi |