#!/usr/bin/env bash
|
|
. ./path.sh || exit 1;
|
|
# machines configuration
|
CUDA_VISIBLE_DEVICES="6,7"
|
gpu_num=2
|
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="." #feature output dictionary
|
exp_dir="."
|
lang=zh
|
dumpdir=dump/raw
|
feats_type=raw
|
token_type=char
|
scp=wav.scp
|
type=kaldi_ark
|
stage=3
|
stop_stage=4
|
|
# feature configuration
|
feats_dim=
|
sample_frequency=16000
|
nj=32
|
speed_perturb=
|
|
# 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"
|
|
asr_config=conf/train_asr_conformer.yaml
|
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
|
|
inference_config=conf/decode_asr_transformer.yaml
|
inference_asr_model=valid.acc.ave_10best.pth
|
|
# 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=$njob
|
_ngpu=0
|
fi
|
|
feat_train_dir=${feats_dir}/${dumpdir}/train; mkdir -p ${feat_train_dir}
|
feat_dev_dir=${feats_dir}/${dumpdir}/dev; mkdir -p ${feat_dev_dir}
|
feat_test_dir=${feats_dir}/${dumpdir}/test; mkdir -p ${feat_test_dir}
|
|
# Training Stage
|
world_size=$gpu_num # run on one machine
|
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
echo "stage 3: Training"
|
mkdir -p ${exp_dir}/exp/${model_dir}
|
mkdir -p ${exp_dir}/exp/${model_dir}/log
|
INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
|
if [ -f $INIT_FILE ];then
|
rm -f $INIT_FILE
|
fi
|
init_method=file://$(readlink -f $INIT_FILE)
|
echo "$0: init method is $init_method"
|
for ((i = 0; i < $gpu_num; ++i)); do
|
{
|
rank=$i
|
local_rank=$i
|
gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
|
asr_train.py \
|
--gpu_id $gpu_id \
|
--use_preprocessor true \
|
--token_type char \
|
--token_list $token_list \
|
--train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \
|
--train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
|
--train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
|
--train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
|
--valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \
|
--valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \
|
--valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \
|
--valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char \
|
--resume true \
|
--output_dir ${exp_dir}/exp/${model_dir} \
|
--config $asr_config \
|
--input_size $feats_dim \
|
--ngpu $gpu_num \
|
--num_worker_count $count \
|
--multiprocessing_distributed true \
|
--dist_init_method $init_method \
|
--dist_world_size $world_size \
|
--dist_rank $rank \
|
--local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
|
} &
|
done
|
wait
|
fi
|
|
# Testing Stage
|
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
echo "stage 4: Inference"
|
for dset in ${test_sets}; do
|
asr_exp=${exp_dir}/exp/${model_dir}
|
inference_tag="$(basename "${inference_config}" .yaml)"
|
_dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
|
_logdir="${_dir}/logdir"
|
if [ -d ${_dir} ]; then
|
echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
|
exit 0
|
fi
|
mkdir -p "${_logdir}"
|
_data="${feats_dir}/${dumpdir}/${dset}"
|
key_file=${_data}/${scp}
|
num_scp_file="$(<${key_file} wc -l)"
|
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
|
split_scps=
|
for n in $(seq "${_nj}"); do
|
split_scps+=" ${_logdir}/keys.${n}.scp"
|
done
|
# shellcheck disable=SC2086
|
utils/split_scp.pl "${key_file}" ${split_scps}
|
_opts=
|
if [ -n "${inference_config}" ]; then
|
_opts+="--config ${inference_config} "
|
fi
|
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1: "${_nj}" "${_logdir}"/asr_inference.JOB.log \
|
python -m funasr.bin.asr_inference_launch \
|
--batch_size 1 \
|
--ngpu "${_ngpu}" \
|
--njob ${njob} \
|
--gpuid_list ${gpuid_list} \
|
--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
|
--key_file "${_logdir}"/keys.JOB.scp \
|
--asr_train_config "${asr_exp}"/config.yaml \
|
--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
|
--output_dir "${_logdir}"/output.JOB \
|
--mode asr \
|
${_opts}
|
|
for f in token token_int score text; do
|
if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
|
for i in $(seq "${_nj}"); do
|
cat "${_logdir}/output.${i}/1best_recog/${f}"
|
done | sort -k1 >"${_dir}/${f}"
|
fi
|
done
|
python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
|
python utils/proce_text.py ${_data}/text ${_data}/text.proc
|
python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
|
tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
|
cat ${_dir}/text.cer.txt
|
done
|
fi
|
