Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.

examples/industrial_data_pretraining/fsmn_kws_mt/finetune.sh

@@ -0,0 +1,184 @@
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+#!/usr/bin/env bash
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+. ./path.sh
+workspace=`pwd`
+
+CUDA_VISIBLE_DEVICES="0,1"
+
+stage=2
+stop_stage=3
+
+inference_device="cuda" #"cpu"
+inference_checkpoint="model.pt.avg10"
+inference_scp="wav.scp"
+inference_batch_size=32
+nj=32
+test_sets="test"
+
+# model_name from model_hub, or model_dir in local path
+
+## option 1, download model automatically, unsupported currently
+model_name_or_model_dir="iic/speech_charctc_kws_phone-xiaoyun_mt"
+
+## option 2, download model by git
+local_path_root=${workspace}/modelscope_models
+model_name_or_model_dir=${local_path_root}/${model_name_or_model_dir}
+if [ ! -d $model_name_or_model_dir ]; then
+  mkdir -p ${model_name_or_model_dir}
+  git clone https://www.modelscope.cn/iic/speech_charctc_kws_phone-xiaoyun_mt.git ${model_name_or_model_dir}
+fi
+
+config=fsmn_4e_l10r2_250_128_fdim80_t2599_t4.yaml
+token_list=${model_name_or_model_dir}/funasr/tokens_2599.txt
+token_list2=${model_name_or_model_dir}/funasr/tokens_xiaoyun.txt
+lexicon_list=${model_name_or_model_dir}/funasr/lexicon.txt
+cmvn_file=${model_name_or_model_dir}/funasr/am.mvn.dim80_l2r2
+init_param="${model_name_or_model_dir}/funasr/basetrain_fsmn_4e_l10r2_250_128_fdim80_t2599.pt"
+
+
+# data prepare
+# data dir, which contains: train.json, val.json
+data_dir=../../data
+
+train_data="${data_dir}/train.jsonl"
+val_data="${data_dir}/val.jsonl"
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+  echo "stage 1: Generate audio json list"
+  # generate train.jsonl and val.jsonl from wav.scp and text.txt
+  python $FUNASR_DIR/funasr/datasets/audio_datasets/scp2jsonl.py \
+  ++scp_file_list='['''${data_dir}/train_wav.scp''', '''${data_dir}/train_text.txt''']' \
+  ++data_type_list='["source", "target"]' \
+  ++jsonl_file_out="${train_data}"
+
+  python $FUNASR_DIR/funasr/datasets/audio_datasets/scp2jsonl.py \
+  ++scp_file_list='['''${data_dir}/val_wav.scp''', '''${data_dir}/val_text.txt''']' \
+  ++data_type_list='["source", "target"]' \
+  ++jsonl_file_out="${val_data}"
+fi
+
+# exp output dir
+output_dir="${workspace}/exp/finetune_outputs"
+
+
+# Training Stage
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+  echo "stage 2: KWS Training"
+
+  mkdir -p ${output_dir}
+  current_time=$(date "+%Y-%m-%d_%H-%M")
+  log_file="${output_dir}/train.log.txt.${current_time}"
+  echo "log_file: ${log_file}"
+  echo "finetune use basetrain model: ${init_param}"
+
+  export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
+  gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
+  ../../../funasr/bin/train.py \
+  --config-path "${workspace}/conf" \
+  --config-name "${config}" \
+  ++init_param="${init_param}" \
+  ++token_lists='['''${token_list}''', '''${token_list2}''']' \
+  ++seg_dicts='['''${lexicon_list}''', '''${lexicon_list}''']' \
+  ++disable_update=true \
+  ++train_data_set_list="${train_data}" \
+  ++valid_data_set_list="${val_data}" \
+  ++frontend_conf.cmvn_file="${cmvn_file}" \
+  ++output_dir="${output_dir}" &> ${log_file}
+fi
+
+
+# Testing Stage
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+  echo "stage 3: Inference"
+  keywords=(小云小云)
+  keywords_string=$(IFS=,; echo "${keywords[*]}")
+  echo "keywords: $keywords_string"
+
+  if [ ${inference_device} == "cuda" ]; then
+      nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  else
+      inference_batch_size=1
+      CUDA_VISIBLE_DEVICES=""
+      for JOB in $(seq ${nj}); do
+          CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
+      done
+  fi
+
+  for dset in ${test_sets}; do
+    inference_dir="${output_dir}/inference-${inference_checkpoint}/${dset}"
+    _logdir="${inference_dir}/logdir"
+    echo "inference_dir: ${inference_dir}"
+
+    mkdir -p "${_logdir}"
+    test_data_dir="${data_dir}/${dset}"
+    key_file=${test_data_dir}/${inference_scp}
+
+    split_scps=
+    for JOB in $(seq "${nj}"); do
+        split_scps+=" ${_logdir}/keys.${JOB}.scp"
+    done
+    $FUNASR_DIR/examples/aishell/paraformer/utils/split_scp.pl "${key_file}" ${split_scps}
+
+    gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
+    for JOB in $(seq ${nj}); do
+        {
+          id=$((JOB-1))
+          gpuid=${gpuid_list_array[$id]}
+
+          echo "${output_dir}"
+
+          export CUDA_VISIBLE_DEVICES=${gpuid}
+          python ../../../funasr/bin/inference.py \
+          --config-path="${output_dir}" \
+          --config-name="config.yaml" \
+          ++init_param="${output_dir}/${inference_checkpoint}" \
+          ++token_lists='['''${token_list}''', '''${token_list2}''']' \
+          ++seg_dicts='['''${lexicon_list}''', '''${lexicon_list}''']' \
+          ++frontend_conf.cmvn_file="${cmvn_file}" \
+          ++keywords="\"$keywords_string"\" \
+          ++input="${_logdir}/keys.${JOB}.scp" \
+          ++output_dir="${inference_dir}/${JOB}" \
+          ++device="${inference_device}" \
+          ++ncpu=1 \
+          ++disable_log=true \
+          ++batch_size="${inference_batch_size}" &> ${_logdir}/log.${JOB}.txt
+        }&
+
+    done
+    wait
+
+    for f in detect detect2; do
+        if [ -f "${inference_dir}/${JOB}/${f}" ]; then
+          for JOB in $(seq "${nj}"); do
+              cat "${inference_dir}/${JOB}/${f}"
+          done | sort -k1 >"${inference_dir}/${f}"
+        fi
+    done
+
+    mkdir -p ${inference_dir}/task1
+    python funasr/utils/compute_det_ctc.py \
+        --keywords ${keywords_string} \
+        --test_data ${test_data_dir}/wav.scp \
+        --trans_data ${test_data_dir}/text \
+        --score_file ${inference_dir}/detect \
+        --stats_dir ${inference_dir}/task1
+
+    mkdir -p ${inference_dir}/task2
+    python funasr/utils/compute_det_ctc.py \
+        --keywords ${keywords_string} \
+        --test_data ${test_data_dir}/wav.scp \
+        --trans_data ${test_data_dir}/text \
+        --score_file ${inference_dir}/detect2 \
+        --stats_dir ${inference_dir}/task2
+  done
+
+fi