FunASR/examples/aishell/paraformer/run.sh

#!/usr/bin/env bash

workspace=`pwd`

# machines configuration
CUDA_VISIBLE_DEVICES="0,1"
gpu_num=2
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

# general configuration
feats_dir="../DATA" #feature output dictionary
exp_dir="."
lang=zh
token_type=char
stage=0
stop_stage=5

# feature configuration
nj=64

# data
raw_data=../raw_data
data_url=www.openslr.org/resources/33

# exp tag
tag="exp1"

. utils/parse_options.sh || exit 1;

# 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

train_set=train
valid_set=dev
test_sets="dev test"

config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"

inference_device="cuda" #"cpu"
inference_checkpoint="model.pt"
inference_scp="wav.scp"



if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
    echo "stage -1: Data Download"
    local/download_and_untar.sh ${raw_data} ${data_url} data_aishell
    local/download_and_untar.sh ${raw_data} ${data_url} resource_aishell
fi

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    echo "stage 0: Data preparation"
    # Data preparation
    local/aishell_data_prep.sh ${raw_data}/data_aishell/wav ${raw_data}/data_aishell/transcript ${feats_dir}
    for x in train dev test; do
        cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
        paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | tr -d " ") \
            > ${feats_dir}/data/${x}/text
        utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
        mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text

        # convert wav.scp text to jsonl
        scp_file_list_arg="++scp_file_list='[\"${feats_dir}/data/${x}/wav.scp\",\"${feats_dir}/data/${x}/text\"]'"
        python ../../../funasr/datasets/audio_datasets/scp2jsonl.py \
        ++data_type_list='["source", "target"]' \
        ++jsonl_file_out=${feats_dir}/data/${x}/audio_datasets.jsonl \
        ${scp_file_list_arg}
    done
fi

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    echo "stage 1: Feature and CMVN Generation"
#    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
    python ../../../funasr/bin/compute_audio_cmvn.py \
    --config-path "${workspace}" \
    --config-name "${config}" \
    ++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
    ++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \
    ++dataset_conf.num_workers=$nj
fi

token_list=${feats_dir}/data/${lang}_token_list/$token_type/tokens.txt
echo "dictionary: ${token_list}"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    echo "stage 2: Dictionary Preparation"
    mkdir -p ${feats_dir}/data/${lang}_token_list/$token_type/
   
    echo "make a dictionary"
    echo "<blank>" > ${token_list}
    echo "<s>" >> ${token_list}
    echo "</s>" >> ${token_list}
    utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/$train_set/text | cut -f 2- -d" " | tr " " "\n" \
        | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
    echo "<unk>" >> ${token_list}
fi

# LM Training Stage
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    echo "stage 3: LM Training"
fi

# ASR Training Stage
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
  echo "stage 4: ASR Training"

  log_file="${exp_dir}/exp/${model_dir}/train.log.txt"
  echo "log_file: ${log_file}"
  torchrun \
  --nnodes 1 \
  --nproc_per_node ${gpu_num} \
  ../../../funasr/bin/train.py \
  --config-path "${workspace}" \
  --config-name "${config}" \
  ++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
  ++tokenizer_conf.token_list="${token_list}" \
  ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
  ++output_dir="${exp_dir}/exp/${model_dir}" &> ${log_file}
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