fixed the small problems

deepspeech/exps/deepspeech2/bin/export.py

@@ -34,6 +34,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
     if args.model_type is None:
         args.model_type = 'offline'
+    print("model_type:{}".format(args.model_type))
     print_arguments(args)
 
     # https://yaml.org/type/float.html

deepspeech/exps/deepspeech2/bin/test.py

@@ -35,6 +35,7 @@ if __name__ == "__main__":
     print_arguments(args, globals())
     if args.model_type is None:
         args.model_type = 'offline'
+    print("model_type:{}".format(args.model_type))
 
     # https://yaml.org/type/float.html
     config = get_cfg_defaults(args.model_type)

deepspeech/exps/deepspeech2/bin/train.py

@@ -39,6 +39,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
     if args.model_type is None:
         args.model_type = 'offline'
+    print("model_type:{}".format(args.model_type))
     print_arguments(args, globals())
 
     # https://yaml.org/type/float.html

deepspeech/exps/deepspeech2/config.py

@@ -23,26 +23,14 @@ from deepspeech.models.ds2_online import DeepSpeech2ModelOnline
 
 def get_cfg_defaults(model_type='offline'):
     _C = CfgNode()
+    _C.data = ManifestDataset.params()
+    _C.collator = SpeechCollator.params()
+    _C.training = DeepSpeech2Trainer.params()
+    _C.decoding = DeepSpeech2Tester.params()
     if (model_type == 'offline'):
-        _C.data = ManifestDataset.params()
-
-        _C.collator = SpeechCollator.params()
-
         _C.model = DeepSpeech2Model.params()
-
-        _C.training = DeepSpeech2Trainer.params()
-
-        _C.decoding = DeepSpeech2Tester.params()
     else:
-        _C.data = ManifestDataset.params()
-
-        _C.collator = SpeechCollator.params()
-
         _C.model = DeepSpeech2ModelOnline.params()
-
-        _C.training = DeepSpeech2Trainer.params()
-
-        _C.decoding = DeepSpeech2Tester.params()
     """Get a yacs CfgNode object with default values for my_project."""
     # Return a clone so that the defaults will not be altered
     # This is for the "local variable" use pattern

deepspeech/exps/deepspeech2/model.py

@@ -134,7 +134,6 @@ class DeepSpeech2Trainer(Trainer):
                 use_gru=config.model.use_gru,
                 share_rnn_weights=config.model.share_rnn_weights)
         elif self.args.model_type == 'online':
-            print("fc_layers_size_list", config.model.fc_layers_size_list)
             model = DeepSpeech2ModelOnline(
                 feat_size=self.train_loader.collate_fn.feature_size,
                 dict_size=self.train_loader.collate_fn.vocab_size,

deepspeech/models/ds2_online/deepspeech2.py

@@ -174,6 +174,7 @@ class CRNNEncoder(nn.Layer):
         num_chunk = (max_len + padding_len - chunk_size) / chunk_stride + 1
         num_chunk = int(num_chunk)
         chunk_state_list = [None] * self.num_rnn_layers
+        final_chunk_state_list = None
         for i in range(0, num_chunk):
             start = i * chunk_stride
             end = start + chunk_size
@@ -366,4 +367,4 @@ class DeepSpeech2InferModelOnline(DeepSpeech2ModelOnline):
         eouts_chunk, eouts_chunk_lens, final_state_list = self.encoder.forward_chunk(
             audio_chunk, audio_chunk_lens, chunk_state_list)
         probs_chunk = self.decoder.softmax(eouts_chunk)
-        return probs_chunk, final_state_list
+        return probs_chunk, eouts_chunk_lens, final_state_list

examples/tiny/s0/local/export.sh

 #!/bin/bash
 
 if [ $# != 4 ];then
-    echo "usage: $0 config_path ckpt_prefix jit_model_path"
+    echo "usage: $0 config_path ckpt_prefix jit_model_path model_type"
     exit -1
 fi
 

examples/tiny/s0/local/test.sh

 #!/bin/bash
 
 if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+    echo "usage: ${0} config_path ckpt_path_prefix model_type"
     exit -1
 fi
 

examples/tiny/s0/local/train.sh

 #!/bin/bash
 
 if [ $# != 3 ];then
-    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
+    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name model_type"
     exit -1
 fi
 

examples/tiny/s0/run_online.sh

-#!/bin/bash
-set -e
-source path.sh
-
-gpus=7
-stage=1
-stop_stage=1
-conf_path=conf/deepspeech2_online.yaml
-avg_num=1
-model_type=online #online | offline
-
-source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
-
-avg_ckpt=avg_${avg_num}
-ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}') ###ckpt = deepspeech2
-echo "checkpoint name ${ckpt}"
-
-if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    # prepare data
-    bash ./local/data.sh || exit -1
-fi
-
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    # train model, all `ckpt` under `exp` dir
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path}  ${ckpt} ${model_type}
-fi
-
-if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    # avg n best model
-    avg.sh exp/${ckpt}/checkpoints ${avg_num}
-fi
-
-if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
-    # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
-fi
-
-if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
-    # export ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
-fi