#!/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
|
|
train_cmd=utils/run.pl
|
|
# general configuration
|
feats_dir="../DATA" #feature output dictionary
|
exp_dir="."
|
lang=zh
|
token_type=char
|
speed_perturb="0.9 1.0 1.1"
|
dataset_type=large
|
stage=0
|
stop_stage=3
|
|
# feature configuration
|
feats_dim=80
|
nj=64
|
|
# data
|
tr_dir=
|
dev_tst_dir=
|
|
# 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_ios
|
|
asr_config=conf/train_pretrain_transformer.yaml
|
model_dir="baseline_$(basename "${asr_config}" .yaml) _${lang}_${token_type}_${tag}"
|
|
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
echo "stage 0: Data preparation"
|
# For training set
|
local/prepare_data.sh ${tr_dir} ${feats_dir}/data/local/train ${feats_dir}/data/train || exit 1;
|
# # For dev and test set
|
for x in iOS; do
|
local/prepare_data.sh ${dev_tst_dir}/${x}/dev ${feats_dir}/data/local/dev_${x,,} ${feats_dir}/data/dev_${x,,} || exit 1;
|
local/prepare_data.sh ${dev_tst_dir}/${x}/test ${feats_dir}/data/local/test_${x,,} ${feats_dir}/data/test_${x,,} || exit 1;
|
done
|
# Normalize text to capital letters
|
for x in train dev_ios test_ios; do
|
mv ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
|
paste -d " " <(cut -f 1 ${feats_dir}/data/${x}/text.org) <(cut -f 2- ${feats_dir}/data/${x}/text.org \
|
| tr 'A-Z' 'a-z' | 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
|
done
|
fi
|
|
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
echo "stage 1: Feature and CMVN Generation"
|
utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
|
fi
|
|
token_list=${feats_dir}/data/${lang}_token_list/char/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/char/
|
|
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}
|
mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${train_set}
|
mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}
|
fi
|
|
# 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])
|
train.py \
|
--task_name pretrain \
|
--gpu_id $gpu_id \
|
--use_preprocessor true \
|
--data_dir ${feats_dir}/data \
|
--train_set ${train_set} \
|
--valid_set ${valid_set} \
|
--cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
|
--speed_perturb ${speed_perturb} \
|
--dataset_type $dataset_type \
|
--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
|
