[asr]rename test_hub to test_wav (#1132)

* add the readme, librispeech_asr1

* fix the test_hub

* test=asr

examples/aishell/asr0/local/test_wav.sh

@@ -30,7 +30,7 @@ if [ $? -ne 0 ]; then
    exit 1
 fi
 
-python3 -u ${BIN_DIR}/test_hub.py \
+python3 -u ${BIN_DIR}/test_wav.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
 --result_file ${ckpt_prefix}.rsl \

examples/aishell/asr0/run.sh

@@ -50,5 +50,5 @@ fi
 # Optionally, you can add LM and test it with runtime.
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
     # test a single .wav file
-    CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
 fi

examples/aishell/asr1/README.md

@@ -323,7 +323,7 @@ In some situations, you want to use the trained model to do the inference for th
 ```bash
  if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
      # test a single .wav file
-     CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+     CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
  fi
 ```
 
@@ -341,5 +341,5 @@ wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wa
 You need to prepare an audio file or use the audio demo above, please confirm the sample rate of the audio is 16K. You can get the result by running the script below.
 
 ```bash
-CUDA_VISIBLE_DEVICES= ./local/test_hub.sh conf/transformer.yaml exp/transformer/checkpoints/avg_20 data/demo_01_03.wav
+CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/transformer.yaml exp/transformer/checkpoints/avg_20 data/demo_01_03.wav
 ```

examples/aishell/asr1/local/test_wav.sh

@@ -39,7 +39,7 @@ for type in  attention_rescoring; do
     batch_size=1
     output_dir=${ckpt_prefix}
     mkdir -p ${output_dir}
-    python3 -u ${BIN_DIR}/test_hub.py \
+    python3 -u ${BIN_DIR}/test_wav.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
     --result_file ${output_dir}/${type}.rsl \

examples/aishell/asr1/run.sh

@@ -43,7 +43,7 @@ fi
 # Optionally, you can add LM and test it with runtime.
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # test a single .wav file
-    CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
 fi
 
 # Not supported at now!!!

examples/librispeech/asr0/local/test_wav.sh

@@ -30,7 +30,7 @@ if [ $? -ne 0 ]; then
    exit 1
 fi
 
-python3 -u ${BIN_DIR}/test_hub.py \
+python3 -u ${BIN_DIR}/test_wav.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
 --result_file ${ckpt_prefix}.rsl \

examples/librispeech/asr0/run.sh

@@ -43,5 +43,5 @@ fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # test a single .wav file
-    CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
 fi

examples/librispeech/asr1/README.md

@@ -321,7 +321,7 @@ In some situations, you want to use the trained model to do the inference for th
 ```bash
  if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
      # test a single .wav file
-     CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+     CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
  fi
 ```
 
@@ -338,8 +338,8 @@ You can downloads the audio demo:
 wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/
 ```
 
-You need to prepare an audio file or use the audio demo, please confirm the sample rate of the audio is 16K. You can get the result of audio demo by running the script below.
+You need to prepare an audio file or use the audio demo above, please confirm the sample rate of the audio is 16K. You can get the result of audio demo by running the script below.
 
 ```bash
-CUDA_VISIBLE_DEVICES= ./local/test_hub.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 data/demo_002_en.wav
+CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 data/demo_002_en.wav
 ```

examples/librispeech/asr1/local/test_wav.sh

@@ -46,7 +46,7 @@ for type in attention_rescoring; do
     batch_size=1
     output_dir=${ckpt_prefix}
     mkdir -p ${output_dir}
-    python3 -u ${BIN_DIR}/test_hub.py \
+    python3 -u ${BIN_DIR}/test_wav.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
     --result_file ${output_dir}/${type}.rsl \

examples/librispeech/asr1/run.sh

@@ -44,12 +44,10 @@ fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 6 ]; then
     # test a single .wav file
-    CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
 fi
 
 if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then
      # export ckpt avg_n
      CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
 fi
-
-