| | |
|---|
| | | 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=1 |
|---|
| | | njob=5 |
|---|
| | | train_cmd=utils/run.pl |
|---|
| | | infer_cmd=utils/run.pl |
|---|
| | | |
|---|
| | |
|---|
| | | type=sound |
|---|
| | | scp=wav.scp |
|---|
| | | speed_perturb="0.9 1.0 1.1" |
|---|
| | | stage=3 |
|---|
| | | stop_stage=4 |
|---|
| | | stage=0 |
|---|
| | | stop_stage=5 |
|---|
| | | |
|---|
| | | # feature configuration |
|---|
| | | feats_dim=80 |
|---|
| | |
|---|
| | | asr_config=conf/train_asr_transformer_12e_6d_3072_768.yaml |
|---|
| | | model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}" |
|---|
| | | |
|---|
| | | inference_config=conf/decode_asr_transformer_noctc_1best.yaml |
|---|
| | | inference_asr_model=valid.acc.ave_10best.pb |
|---|
| | | inference_config=conf/decode_asr_transformer.yaml |
|---|
| | | inference_asr_model=valid.cer_ctc.ave_10best.pb |
|---|
| | | |
|---|
| | | # you can set gpu num for decoding here |
|---|
| | | gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default |
|---|
| | |
|---|
| | | echo "<unk>" >> ${token_list} |
|---|
| | | fi |
|---|
| | | |
|---|
| | | # Training Stage |
|---|
| | | # LM Training Stage |
|---|
| | | world_size=$gpu_num # run on one machine |
|---|
| | | if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then |
|---|
| | | echo "stage 3: Training" |
|---|
| | | python utils/download_model.py --model_name ${model_name} # download pretrained model on ModelScope |
|---|
| | | echo "stage 3: LM Training" |
|---|
| | | fi |
|---|
| | | |
|---|
| | | # ASR Training Stage |
|---|
| | | world_size=$gpu_num # run on one machine |
|---|
| | | if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then |
|---|
| | | echo "stage 4: ASR Training" |
|---|
| | | python utils/download_model.py --model_name ${model_name} # download pretrained model on ModelScope |
|---|
| | | mkdir -p ${exp_dir}/exp/${model_dir} |
|---|
| | | mkdir -p ${exp_dir}/exp/${model_dir}/log |
|---|
| | | INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init |
|---|
| | |
|---|
| | | fi |
|---|
| | | |
|---|
| | | # Testing Stage |
|---|
| | | if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then |
|---|
| | | echo "stage 4: Inference" |
|---|
| | | if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then |
|---|
| | | echo "stage 5: Inference" |
|---|
| | | for dset in ${test_sets}; do |
|---|
| | | asr_exp=${exp_dir}/exp/${model_dir} |
|---|
| | | inference_tag="$(basename "${inference_config}" .yaml)" |
|---|
| | |
|---|
| | | --asr_train_config "${asr_exp}"/config.yaml \ |
|---|
| | | --asr_model_file "${asr_exp}"/"${inference_asr_model}" \ |
|---|
| | | --output_dir "${_logdir}"/output.JOB \ |
|---|
| | | --mode paraformer \ |
|---|
| | | --mode asr \ |
|---|
| | | ${_opts} |
|---|
| | | |
|---|
| | | for f in token token_int score text; do |
|---|
