FunASR/egs/wenetspeech/conformer/run.sh

#!/usr/bin/env bash

. ./path.sh || exit 1;

# machines configuration
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
gpu_num=8
count=1
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=5
train_cmd=utils/run.pl
infer_cmd=utils/run.pl

# general configuration
feats_dir="../DATA" #feature output dictionary
exp_dir="."
lang=zh
token_type=char
type=sound
scp=wav.scp
speed_perturb="0.9 1.0 1.1"
stage=0
stop_stage=5

# feature configuration
feats_dim=80
nj=64

# data
raw_data=/nfs/zhifu.gzf/wenetspeech_proc

# 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

set=L
train_set=train_l
valid_set=dev
test_sets="dev test_net test_meeting"

asr_config=conf/train_asr_conformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"

inference_config=conf/decode_asr_transformer_5beam.yaml
inference_asr_model=valid.acc.ave_10best.pb

# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES  # set gpus for decoding, the same as training stage by default
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')

if ${gpu_inference}; then
    inference_nj=$[${ngpu}*${njob}]
    _ngpu=1
else
    inference_nj=$njob
    _ngpu=0
fi

if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
    echo "For downloading data, please refer to https://github.com/wenet-e2e/WenetSpeech."
    exit 0;
fi

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    echo "stage 0: Data preparation"
    # Data preparation
#    local/data.sh --set ${set} --nj $nj --data_dir $feats_dir --WENETSPEECH $raw_data --train_cmd $train_cmd
    mkdir $feats_dir/data
    mv $feats_dir/$train_set $feats_dir/data/$train_set
    for x in $test_sets; do
        mv $feats_dir/$x $feats_dir/data/
    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 ${feats_dim} --config_file "$asr_config" --scale 0.1
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