speech_recognition/FunASR
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嘉渊 2023-05-11 14:31:01 +08:00
parent 8d86b121dd
commit 6737c14fff
2 changed files with 17 additions and 15 deletions
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1
egs/aishell/conformer/run.sh
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@ -177,6 +177,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
--cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
--key_file "${_logdir}"/keys.JOB.scp \
--asr_train_config "${asr_exp}"/config.yaml \
--asr_model_file "${asr_exp}"/"${inference_asr_model}" \

31
egs/librispeech_100h/conformer/run.sh
View File

@ -19,8 +19,8 @@ lang=en
token_type=bpe
type=sound
scp=wav.scp
stage=2
stop_stage=2
stage=3
stop_stage=4
# feature configuration
feats_dim=80
@ -89,23 +89,22 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
fi
dict=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
token_list=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
bpemodel=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}
echo "dictionary: ${dict}"
echo "dictionary: ${token_list}"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
### Task dependent. You have to check non-linguistic symbols used in the corpus.
echo "stage 2: Dictionary and Json Data Preparation"
mkdir -p ${feats_dir}/data/lang_char/
echo "<blank>" > ${dict}
echo "<s>" >> ${dict}
echo "</s>" >> ${dict}
echo "<blank>" > ${token_list}
echo "<s>" >> ${token_list}
echo "</s>" >> ${token_list}
cut -f 2- -d" " ${feats_dir}/data/${train_set}/text > ${feats_dir}/data/lang_char/input.txt
local/spm_train.py --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
local/spm_encode.py --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${dict}
echo "<unk>" >> ${dict}
local/spm_encode.py --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | 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
@ -123,16 +122,17 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
rank=$i
local_rank=$i
gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
asr_train.py \
train.py \
--task_name asr \
--gpu_id $gpu_id \
--use_preprocessor true \
--split_with_space false \
--bpemodel ${bpemodel}.model \
--token_type $token_type \
--dataset_type $dataset_type \
--token_list $dict \
--train_data_file $feats_dir/$dumpdir/${train_set}/ark_txt.scp \
--valid_data_file $feats_dir/$dumpdir/${valid_set}/ark_txt.scp \
--token_list $token_list \
--data_dir ${feats_dir}/data \
--train_set ${train_set} \
--valid_set ${valid_set} \
--resume true \
--output_dir ${exp_dir}/exp/${model_dir} \
--config $asr_config \
@ -183,6 +183,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
--cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
--key_file "${_logdir}"/keys.JOB.scp \
--asr_train_config "${asr_exp}"/config.yaml \
--asr_model_file "${asr_exp}"/"${inference_asr_model}" \