# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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#!/usr/bin/env bash
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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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. ./path.sh
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workspace=`pwd`
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CUDA_VISIBLE_DEVICES="0,1"
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stage=2
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stop_stage=3
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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=32
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nj=32
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test_sets="test"
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# model_name from model_hub, or model_dir in local path
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## option 1, download model automatically, unsupported currently
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model_name_or_model_dir="iic/speech_charctc_kws_phone-xiaoyun"
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## option 2, download model by git
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local_path_root=${workspace}/modelscope_models
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model_name_or_model_dir=${local_path_root}/${model_name_or_model_dir}
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if [ ! -d $model_name_or_model_dir ]; then
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mkdir -p ${model_name_or_model_dir}
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git clone https://www.modelscope.cn/iic/speech_charctc_kws_phone-xiaoyun.git ${model_name_or_model_dir}
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fi
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config=fsmn_4e_l10r2_250_128_fdim80_t2599_t4.yaml
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token_list=${model_name_or_model_dir}/funasr/tokens_2599.txt
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token_list2=${model_name_or_model_dir}/funasr/tokens_xiaoyun_char.txt
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lexicon_list=${model_name_or_model_dir}/funasr/lexicon.txt
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cmvn_file=${model_name_or_model_dir}/funasr/am.mvn.dim80_l2r2
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init_param="${model_name_or_model_dir}/funasr/basetrain_fsmn_4e_l10r2_250_128_fdim80_t2599.pt"
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# data prepare
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# data dir, which contains: train.json, val.json
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data_dir=../../data
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train_data="${data_dir}/train.jsonl"
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val_data="${data_dir}/val.jsonl"
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Generate audio json list"
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# generate train.jsonl and val.jsonl from wav.scp and text.txt
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python $FUNASR_DIR/funasr/datasets/audio_datasets/scp2jsonl.py \
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++scp_file_list='['''${data_dir}/train_wav.scp''', '''${data_dir}/train_text.txt''']' \
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++data_type_list='["source", "target"]' \
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++jsonl_file_out="${train_data}"
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python $FUNASR_DIR/funasr/datasets/audio_datasets/scp2jsonl.py \
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++scp_file_list='['''${data_dir}/val_wav.scp''', '''${data_dir}/val_text.txt''']' \
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++data_type_list='["source", "target"]' \
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++jsonl_file_out="${val_data}"
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fi
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# exp output dir
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output_dir="${workspace}/exp/finetune_outputs"
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# Training Stage
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: KWS Training"
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mkdir -p ${output_dir}
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current_time=$(date "+%Y-%m-%d_%H-%M")
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log_file="${output_dir}/train.log.txt.${current_time}"
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echo "log_file: ${log_file}"
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echo "finetune use basetrain model: ${init_param}"
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export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
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gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
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../../../funasr/bin/train.py \
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--config-path "${workspace}/conf" \
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--config-name "${config}" \
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++init_param="${init_param}" \
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++token_lists='['''${token_list}''', '''${token_list2}''']' \
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++seg_dicts='['''${lexicon_list}''', '''${lexicon_list}''']' \
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++disable_update=true \
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++train_data_set_list="${train_data}" \
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++valid_data_set_list="${val_data}" \
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++frontend_conf.cmvn_file="${cmvn_file}" \
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++output_dir="${output_dir}" &> ${log_file}
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fi
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# Testing Stage
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: Inference"
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keywords=(小云小云)
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keywords_string=$(IFS=,; echo "${keywords[*]}")
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echo "keywords: $keywords_string"
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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 ${test_sets}; do
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inference_dir="${output_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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test_data_dir="${data_dir}/${dset}"
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key_file=${test_data_dir}/${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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$FUNASR_DIR/examples/aishell/paraformer/utils/split_scp.pl "${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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echo "${output_dir}"
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export CUDA_VISIBLE_DEVICES=${gpuid}
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python ../../../funasr/bin/inference.py \
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--config-path="${output_dir}" \
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--config-name="config.yaml" \
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++init_param="${output_dir}/${inference_checkpoint}" \
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++tokenizer_conf.token_list="${token_list}" \
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++tokenizer_conf.seg_dict="${lexicon_list}" \
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++tokenizer2_conf.token_list="${token_list2}" \
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++tokenizer2_conf.seg_dict="${lexicon_list}" \
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++frontend_conf.cmvn_file="${cmvn_file}" \
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++keywords="\"$keywords_string"\" \
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++input="${_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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for f in detect detect2; do
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if [ -f "${inference_dir}/${JOB}/${f}" ]; then
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for JOB in $(seq "${nj}"); do
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cat "${inference_dir}/${JOB}/${f}"
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done | sort -k1 >"${inference_dir}/${f}"
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fi
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done
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mkdir -p ${inference_dir}/task1
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python funasr/utils/compute_det_ctc.py \
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--keywords ${keywords_string} \
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--test_data ${test_data_dir}/wav.scp \
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--trans_data ${test_data_dir}/text \
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--score_file ${inference_dir}/detect \
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--stats_dir ${inference_dir}/task1
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mkdir -p ${inference_dir}/task2
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python funasr/utils/compute_det_ctc.py \
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--keywords ${keywords_string} \
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--test_data ${test_data_dir}/wav.scp \
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--trans_data ${test_data_dir}/text \
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--score_file ${inference_dir}/detect2 \
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--stats_dir ${inference_dir}/task2
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done
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fi
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