mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
232 lines
9.3 KiB
Bash
Executable File
232 lines
9.3 KiB
Bash
Executable File
#!/usr/bin/env bash
|
|
|
|
. ./path.sh || exit 1;
|
|
|
|
# machines configuration
|
|
CUDA_VISIBLE_DEVICES="0,1" # set gpus, e.g., CUDA_VISIBLE_DEVICES="0,1"
|
|
gpu_num=2
|
|
count=1
|
|
gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
|
|
njob=4 # the number of jobs for each gpu
|
|
train_cmd=utils/run.pl
|
|
|
|
# general configuration
|
|
feats_dir="../DATA" #feature output dictionary, for large data
|
|
exp_dir="."
|
|
lang=zh
|
|
dumpdir=dump/fbank
|
|
feats_type=fbank
|
|
token_type=char
|
|
scp=feats.scp
|
|
type=kaldi_ark
|
|
stage=1
|
|
stop_stage=4
|
|
|
|
# feature configuration
|
|
feats_dim=560
|
|
sample_frequency=16000
|
|
nj=32
|
|
speed_perturb="1.0"
|
|
lfr=True
|
|
lfr_m=7
|
|
lfr_n=6
|
|
|
|
init_model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope during fine-tuning
|
|
model_revision="v1.0.3" # please do not modify the model revision
|
|
cmvn_file=init_model/${init_model_name}/am.mvn
|
|
seg_file=init_model/${init_model_name}/seg_dict
|
|
vocab=init_model/${init_model_name}/tokens.txt
|
|
|
|
# data
|
|
dataset= # dataset (include train/wav.scp, train/text, dev/wav.scp, dev/text, optional test/wav.scp test/text)
|
|
|
|
# exp tag
|
|
tag=""
|
|
|
|
# Set bash to 'debug' mode, it will exit on :
|
|
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
|
|
set -e
|
|
set -u
|
|
set -o pipefail
|
|
|
|
train_set=train
|
|
valid_set=dev
|
|
test_sets="dev test"
|
|
|
|
asr_config=conf/train_asr_paraformer_sanm_50e_16d_2048_512_lfr6.yaml
|
|
init_param="init_model/${init_model_name}/model.pb"
|
|
|
|
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
|
|
inference_asr_model=valid.acc.ave_10best.pth
|
|
|
|
. utils/parse_options.sh || exit 1;
|
|
|
|
# download model from modelscope
|
|
python modelscope_utils/download_model.py --model_name ${init_model_name} --model_revision ${model_revision}
|
|
|
|
if [ ! -d ${HOME}/.cache/modelscope/hub/damo/${init_model_name} ]; then
|
|
echo "${HOME}/.cache/modelscope/hub/damo/${init_model_name} must exist"
|
|
exit 1
|
|
else
|
|
if [ -d init_model/${init_model_name} ]; then
|
|
echo "init_model/${init_model_name} is already exists. if you want to decode again, please delete init_model/${init_model_name} first."
|
|
else
|
|
mkdir -p init_model/${init_model_name}
|
|
cp -r ${HOME}/.cache/modelscope/hub/damo/${init_model_name}/* init_model/${init_model_name}
|
|
fi
|
|
fi
|
|
|
|
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
|
|
|
|
# you can set gpu num for decoding here
|
|
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
|
|
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
|
|
inference_nj=$[${ngpu}*${njob}]
|
|
|
|
[ ! -d ${dataset} ] && echo "$0: Training data is required" && exit 1;
|
|
[ ! -f ${dataset}/train/wav.scp ] && [ ! -f ${dataset}/train/text ] && echo "$0: Training data wav.scp or text is not found" && exit 1;
|
|
|
|
if [ ! -d "${dataset}/dev" ]; then
|
|
utils/fix_data.sh ${dataset}/train
|
|
utils/subset_data_dir_tr_cv.sh --dev-num-utt 1000 ${dataset}/train ${dataset}
|
|
fi
|
|
if [ ! -d "${dataset}/test" ]; then
|
|
test_sets="dev"
|
|
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}
|
|
|
|
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
|
echo "Feature Generation"
|
|
# compute fbank features
|
|
fbankdir=${feats_dir}/fbank
|
|
utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --speed_perturb ${speed_perturb} \
|
|
${dataset}/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/train
|
|
utils/fix_data_feat.sh ${fbankdir}/train
|
|
utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
|
|
${dataset}/dev ${exp_dir}/exp/make_fbank/dev ${fbankdir}/dev
|
|
utils/fix_data_feat.sh ${fbankdir}/dev
|
|
if [ -d "${dataset}/test" ]; then
|
|
utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
|
|
${dataset}/test ${exp_dir}/exp/make_fbank/test ${fbankdir}/test
|
|
utils/fix_data_feat.sh ${fbankdir}/test
|
|
fi
|
|
|
|
echo "apply low_frame_rate and cmvn"
|
|
[ ! -f ${cmvn_file} ] && echo "$0: cmvn file is required" && exit 1;
|
|
utils/apply_lfr_and_cmvn.sh --cmd "$train_cmd" --nj $nj \
|
|
--lfr $lfr --lfr-m $lfr_m --lfr-n $lfr_n \
|
|
${fbankdir}/train ${cmvn_file} ${exp_dir}/exp/make_fbank/train ${feat_train_dir}
|
|
utils/apply_lfr_and_cmvn.sh --cmd "$train_cmd" --nj $nj \
|
|
--lfr $lfr --lfr-m $lfr_m --lfr-n $lfr_n \
|
|
${fbankdir}/dev ${cmvn_file} ${exp_dir}/exp/make_fbank/dev ${feat_dev_dir}
|
|
if [ -d "${dataset}/test" ]; then
|
|
utils/apply_lfr_and_cmvn.sh --cmd "$train_cmd" --nj $nj \
|
|
--lfr $lfr --lfr-m $lfr_m --lfr-n $lfr_n \
|
|
${fbankdir}/test ${cmvn_file} ${exp_dir}/exp/make_fbank/test ${feat_test_dir}
|
|
fi
|
|
|
|
echo "Text Tokenize"
|
|
# 我爱reading->我 爱 read@@ ing
|
|
utils/text_tokenize.sh --cmd "$train_cmd" --nj $nj ${fbankdir}/train ${seg_file} ${feat_train_dir}/log ${feat_train_dir}
|
|
utils/fix_data_feat.sh ${feat_train_dir}
|
|
utils/text_tokenize.sh --cmd "$train_cmd" --nj $nj ${fbankdir}/dev ${seg_file} ${feat_dev_dir}/log ${feat_dev_dir}
|
|
utils/fix_data_feat.sh ${feat_dev_dir}
|
|
if [ -d "${dataset}/test" ]; then
|
|
cp ${fbankdir}/test/text ${feat_test_dir}
|
|
fi
|
|
fi
|
|
|
|
token_list=${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/
|
|
cp $vocab ${token_list}
|
|
|
|
vocab_size=$(wc -l <${token_list})
|
|
awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
|
|
awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
|
|
mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/train
|
|
mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/dev
|
|
cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/train
|
|
cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/dev
|
|
fi
|
|
|
|
# Training Stage
|
|
world_size=$gpu_num # run on one machine
|
|
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
|
# update asr train config.yaml
|
|
python modelscope_utils/update_config.py --modelscope_config init_model/${init_model_name}/finetune.yaml --finetune_config ${asr_config} --output_config init_model/${init_model_name}/asr_finetune_config.yaml
|
|
finetune_config=init_model/${init_model_name}/asr_finetune_config.yaml
|
|
|
|
mkdir -p ${exp_dir}/exp/${model_dir}
|
|
mkdir -p ${exp_dir}/exp/${model_dir}/log
|
|
INIT_FILE=$exp_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_paraformer.py \
|
|
--gpu_id $gpu_id \
|
|
--use_preprocessor true \
|
|
--token_type $token_type \
|
|
--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} \
|
|
--init_param $init_param \
|
|
--config $finetune_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
|
|
./utils/easy_asr_infer.sh \
|
|
--lang zh \
|
|
--datadir ${feats_dir} \
|
|
--feats_type ${feats_type} \
|
|
--feats_dim ${feats_dim} \
|
|
--token_type ${token_type} \
|
|
--gpu_inference ${gpu_inference} \
|
|
--inference_config "${inference_config}" \
|
|
--test_sets "${test_sets}" \
|
|
--token_list $token_list \
|
|
--asr_exp ${exp_dir}/exp/${model_dir} \
|
|
--stage 12 \
|
|
--stop_stage 12 \
|
|
--scp $scp \
|
|
--text text \
|
|
--inference_nj $inference_nj \
|
|
--njob $njob \
|
|
--inference_asr_model $inference_asr_model \
|
|
--gpuid_list $gpuid_list \
|
|
--mode paraformer
|
|
fi
|
|
|