FunASR/egs/aishell2/data2vec_pretrain/run.sh
2023-05-17 16:27:22 +08:00

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#!/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>" >> ${token_list}
fi
# Training Stage
world_size=$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} \
--data_file_names "wav.scp" \
--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