merge develop_ds2_online

deepspeech/exps/deepspeech2/bin/export.py

@@ -30,11 +30,13 @@ def main(config, args):
 
 if __name__ == "__main__":
     parser = default_argument_parser()
+    parser.add_argument("--model_type")
     args = parser.parse_args()
+
     print_arguments(args)
 
     # https://yaml.org/type/float.html
-    config = get_cfg_defaults()
+    config = get_cfg_defaults(args.model_type)
     if args.config:
         config.merge_from_file(args.config)
     if args.opts:

deepspeech/exps/deepspeech2/bin/test.py

@@ -30,11 +30,12 @@ def main(config, args):
 
 if __name__ == "__main__":
     parser = default_argument_parser()
+    parser.add_argument("--model_type")
     args = parser.parse_args()
     print_arguments(args, globals())
 
     # https://yaml.org/type/float.html
-    config = get_cfg_defaults()
+    config = get_cfg_defaults(args.model_type)
     if args.config:
         config.merge_from_file(args.config)
     if args.opts:

deepspeech/exps/deepspeech2/bin/train.py

@@ -35,11 +35,12 @@ def main(config, args):
 
 if __name__ == "__main__":
     parser = default_argument_parser()
+    parser.add_argument("--model_type")
     args = parser.parse_args()
     print_arguments(args, globals())
 
     # https://yaml.org/type/float.html
-    config = get_cfg_defaults()
+    config = get_cfg_defaults(args.model_type)
     if args.config:
         config.merge_from_file(args.config)
     if args.opts:

deepspeech/exps/deepspeech2/config.py

@@ -18,9 +18,12 @@ from deepspeech.exps.deepspeech2.model import DeepSpeech2Trainer
 from deepspeech.io.collator import SpeechCollator
 from deepspeech.io.dataset import ManifestDataset
 from deepspeech.models.ds2 import DeepSpeech2Model
+from deepspeech.models.ds2_online import DeepSpeech2ModelOnline
 
-_C = CfgNode()
 
+def get_cfg_defaults(model_type):
+    _C = CfgNode()
+    if (model_type == 'offline'):
         _C.data = ManifestDataset.params()
 
         _C.collator = SpeechCollator.params()
@@ -30,9 +33,16 @@ _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()
 
-def get_cfg_defaults():
+        _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

@@ -29,6 +29,8 @@ from deepspeech.io.sampler import SortagradBatchSampler
 from deepspeech.io.sampler import SortagradDistributedBatchSampler
 from deepspeech.models.ds2 import DeepSpeech2InferModel
 from deepspeech.models.ds2 import DeepSpeech2Model
+from deepspeech.models.ds2_online import DeepSpeech2InferModelOnline
+from deepspeech.models.ds2_online import DeepSpeech2ModelOnline
 from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
 from deepspeech.training.trainer import Trainer
 from deepspeech.utils import error_rate
@@ -122,13 +124,27 @@ class DeepSpeech2Trainer(Trainer):
 
     def setup_model(self):
         config = self.config
+        if self.args.model_type == 'offline':
             model = DeepSpeech2Model(
                 feat_size=self.train_loader.collate_fn.feature_size,
                 dict_size=self.train_loader.collate_fn.vocab_size,
                 num_conv_layers=config.model.num_conv_layers,
                 num_rnn_layers=config.model.num_rnn_layers,
                 rnn_size=config.model.rnn_layer_size,
+                use_gru=config.model.use_gru,
+                share_rnn_weights=config.model.share_rnn_weights)
+        elif self.args.model_type == 'online':
+            model = DeepSpeech2ModelOnline(
+                feat_size=self.train_loader.collate_fn.feature_size,
+                dict_size=self.train_loader.collate_fn.vocab_size,
+                num_conv_layers=config.model.num_conv_layers,
+                num_rnn_layers=config.model.num_rnn_layers,
+                num_fc_layers=config.model.num_fc_layers,
+                fc_layers_size_list=config.model.fc_layers_size_list,
+                rnn_size=config.model.rnn_layer_size,
                 use_gru=config.model.use_gru)
+        else:
+            raise Exception("wrong model type")
         if self.parallel:
             model = paddle.DataParallel(model)
 
@@ -329,8 +345,14 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             exit(-1)
 
     def export(self):
+        if self.args.model_type == 'offline':
             infer_model = DeepSpeech2InferModel.from_pretrained(
                 self.test_loader, self.config, self.args.checkpoint_path)
+        elif self.args.model_type == 'online':
+            infer_model = DeepSpeech2InferModelOnline.from_pretrained(
+                self.test_loader, self.config, self.args.checkpoint_path)
+        else:
+            raise Exception("wrong model tyep")
 
         infer_model.eval()
         feat_dim = self.test_loader.collate_fn.feature_size
@@ -368,13 +390,27 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
 
     def setup_model(self):
         config = self.config
+        if self.args.model_type == 'offline':
             model = DeepSpeech2Model(
                 feat_size=self.test_loader.collate_fn.feature_size,
                 dict_size=self.test_loader.collate_fn.vocab_size,
                 num_conv_layers=config.model.num_conv_layers,
                 num_rnn_layers=config.model.num_rnn_layers,
                 rnn_size=config.model.rnn_layer_size,
+                use_gru=config.model.use_gru,
+                share_rnn_weights=config.model.share_rnn_weights)
+        elif self.args.model_type == 'online':
+            model = DeepSpeech2ModelOnline(
+                feat_size=self.test_loader.collate_fn.feature_size,
+                dict_size=self.test_loader.collate_fn.vocab_size,
+                num_conv_layers=config.model.num_conv_layers,
+                num_rnn_layers=config.model.num_rnn_layers,
+                num_fc_layers=config.model.num_fc_layers,
+                fc_layers_size_list=config.model.fc_layers_size_list,
+                rnn_size=config.model.rnn_layer_size,
                 use_gru=config.model.use_gru)
+        else:
+            raise Exception("Wrong model type")
 
         self.model = model
         logger.info("Setup model!")

examples/tiny/s0/local/export.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-if [ $# != 3 ];then
+if [ $# != 4 ];then
     echo "usage: $0 config_path ckpt_prefix jit_model_path"
     exit -1
 fi
@@ -11,6 +11,7 @@ echo "using $ngpu gpus..."
 config_path=$1
 ckpt_path_prefix=$2
 jit_model_export_path=$3
+model_type=$4
 
 device=gpu
 if [ ${ngpu} == 0 ];then
@@ -22,8 +23,8 @@ python3 -u ${BIN_DIR}/export.py \
 --nproc ${ngpu} \
 --config ${config_path} \
 --checkpoint_path ${ckpt_path_prefix} \
---export_path ${jit_model_export_path}
+--export_path ${jit_model_export_path} \
-
+--model_type ${model_type}
 
 if [ $? -ne 0 ]; then
     echo "Failed in export!"

examples/tiny/s0/local/test.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-if [ $# != 2 ];then
+if [ $# != 3 ];then
     echo "usage: ${0} config_path ckpt_path_prefix"
     exit -1
 fi
@@ -14,6 +14,7 @@ if [ ${ngpu} == 0 ];then
 fi
 config_path=$1
 ckpt_prefix=$2
+model_type=$3
 
 # download language model
 bash local/download_lm_en.sh
@@ -26,7 +27,8 @@ python3 -u ${BIN_DIR}/test.py \
 --nproc 1 \
 --config ${config_path} \
 --result_file ${ckpt_prefix}.rsl \
---checkpoint_path ${ckpt_prefix}
+--checkpoint_path ${ckpt_prefix} \
+--model_type ${model_type}
 
 if [ $? -ne 0 ]; then
     echo "Failed in evaluation!"

examples/tiny/s0/local/train.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-if [ $# != 2 ];then
+if [ $# != 3 ];then
     echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
     exit -1
 fi
@@ -10,6 +10,7 @@ echo "using $ngpu gpus..."
 
 config_path=$1
 ckpt_name=$2
+model_type=$3
 
 device=gpu
 if [ ${ngpu} == 0 ];then
@@ -22,7 +23,8 @@ python3 -u ${BIN_DIR}/train.py \
 --device ${device} \
 --nproc ${ngpu} \
 --config ${config_path} \
---output exp/${ckpt_name}
+--output exp/${ckpt_name} \
+--model_type ${model_type}
 
 if [ $? -ne 0 ]; then
     echo "Failed in training!"

examples/tiny/s0/run.sh

@@ -7,6 +7,7 @@ stage=0
 stop_stage=100
 conf_path=conf/deepspeech2.yaml
 avg_num=1
+model_type=online
 
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
@@ -21,7 +22,7 @@ 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}  ${ckpt}
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path}  ${ckpt} ${model_type}
 fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
@@ -31,10 +32,10 @@ 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/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=${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/${avg_ckpt}.jit
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
 fi

examples/tiny/s0/run_online.sh

@@ -0,0 +1,41 @@
+#!/bin/bash
+set -e
+source path.sh
+
+gpus=7
+stage=1
+stop_stage=100
+conf_path=conf/deepspeech2.yaml
+avg_num=1
+model_type=online
+
+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

tests/deepspeech2_model_test.py

@@ -19,6 +19,7 @@ import paddle
 from deepspeech.models.ds2 import DeepSpeech2Model
 
 
+
 class TestDeepSpeech2Model(unittest.TestCase):
     def setUp(self):
         paddle.set_device('cpu')