ds2 inference as sepearte engine for streaming asr

demos/streaming_asr_server/conf/ws_ds2_application.yaml

@@ -11,7 +11,7 @@ port: 8090
 # protocol = ['websocket'] (only one can be selected).
 # websocket only support online engine type.
 protocol: 'websocket'
-engine_list: ['asr_online']
+engine_list: ['asr_online-inference']
 
 
 #################################################################################
@@ -20,7 +20,7 @@ engine_list: ['asr_online']
 
 ################################### ASR #########################################
 ################### speech task: asr; engine_type: online #######################
-asr_online:
+asr_online-inference:
     model_type: 'deepspeech2online_aishell'
     am_model: # the pdmodel file of am static model [optional]
     am_params:  # the pdiparams file of am static model [optional]

paddlespeech/cli/asr/infer.py

@@ -187,7 +187,7 @@ class ASRExecutor(BaseExecutor):
             elif "conformer" in model_type or "transformer" in model_type:
                 self.config.decode.decoding_method = decode_method
                 if num_decoding_left_chunks:
-                    assert num_decoding_left_chunks == -1 or num_decoding_left_chunks >= 0, f"num_decoding_left_chunks should be -1 or >=0"
+                    assert num_decoding_left_chunks == -1 or num_decoding_left_chunks >= 0, "num_decoding_left_chunks should be -1 or >=0"
                     self.config.num_decoding_left_chunks = num_decoding_left_chunks
 
             else:

paddlespeech/resource/pretrained_models.py

@@ -224,6 +224,26 @@ asr_static_pretrained_models = {
             '29e02312deb2e59b3c8686c7966d4fe3'
         }
     },
+    "deepspeech2online_aishell-zh-16k": {
+        '1.0': {
+            'url':
+            'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_1.0.1.model.tar.gz',
+            'md5':
+            'df5ddeac8b679a470176649ac4b78726',
+            'cfg_path':
+            'model.yaml',
+            'ckpt_path':
+            'exp/deepspeech2_online/checkpoints/avg_1',
+            'model':
+            'exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel',
+            'params':
+            'exp/deepspeech2_online/checkpoints/avg_1.jit.pdiparams',
+            'lm_url':
+            'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
+            'lm_md5':
+            '29e02312deb2e59b3c8686c7966d4fe3'
+        },
+    },
 }
 
 # ---------------------------------

paddlespeech/server/conf/ws_application.yaml → paddlespeech/server/conf/ws_ds2_application.yaml

@@ -11,7 +11,7 @@ port: 8090
 # protocol = ['websocket', 'http'] (only one can be selected).
 # websocket only support online engine type.
 protocol: 'websocket'
-engine_list: ['asr_online']
+engine_list: ['asr_online-inference']
 
 
 #################################################################################
@@ -20,7 +20,7 @@ engine_list: ['asr_online']
 
 ################################### ASR #########################################
 ################### speech task: asr; engine_type: online #######################
-asr_online:
+asr_online-inference:
     model_type: 'deepspeech2online_aishell'
     am_model: # the pdmodel file of am static model [optional]
     am_params:  # the pdiparams file of am static model [optional]

paddlespeech/server/engine/asr/online/python/asr_engine.py

@@ -125,7 +125,6 @@ class PaddleASRConnectionHanddler:
         self.remained_wav = None
         self.cached_feat = None
 
-
         if "deepspeech2" in self.model_type:
             return
 
@@ -698,7 +697,7 @@ class ASRServerExecutor(ASRExecutor):
         self.task_resource = CommonTaskResource(
             task='asr', model_format='dynamic', inference_mode='online')
 
-    def update_config(self)->None:
+    def update_config(self) -> None:
         if "deepspeech2" in self.model_type:
             with UpdateConfig(self.config):
                 # download lm
@@ -720,7 +719,7 @@ class ASRServerExecutor(ASRExecutor):
                     self.config.decode.decoding_method = self.decode_method
                 # update num_decoding_left_chunks
                 if self.num_decoding_left_chunks:
-                    assert self.num_decoding_left_chunks == -1 or self.num_decoding_left_chunks >= 0, f"num_decoding_left_chunks should be -1 or >=0"
+                    assert self.num_decoding_left_chunks == -1 or self.num_decoding_left_chunks >= 0, "num_decoding_left_chunks should be -1 or >=0"
                     self.config.decode.num_decoding_left_chunks = self.num_decoding_left_chunks
                 # we only support ctc_prefix_beam_search and attention_rescoring dedoding method
                 # Generally we set the decoding_method to attention_rescoring
@@ -738,17 +737,17 @@ class ASRServerExecutor(ASRExecutor):
             raise Exception(f"not support: {self.model_type}")
 
     def init_model(self) -> None:
-        if "deepspeech2" in self.model_type :
+        if "deepspeech2" in self.model_type:
             # AM predictor
             logger.info("ASR engine start to init the am predictor")
             self.am_predictor = init_predictor(
                 model_file=self.am_model,
                 params_file=self.am_params,
                 predictor_conf=self.am_predictor_conf)
-        elif "conformer" in self.model_type  or "transformer" in self.model_type :
+        elif "conformer" in self.model_type or "transformer" in self.model_type:
             # load model
             # model_type: {model_name}_{dataset}
-            model_name = self.model_type[:self.model_type.rindex('_')]  
+            model_name = self.model_type[:self.model_type.rindex('_')]
             logger.info(f"model name: {model_name}")
             model_class = self.task_resource.get_model_class(model_name)
             model = model_class.from_config(self.config)
@@ -758,7 +757,6 @@ class ASRServerExecutor(ASRExecutor):
         else:
             raise Exception(f"not support: {self.model_type}")
 
-
     def _init_from_path(self,
                         model_type: str=None,
                         am_model: Optional[os.PathLike]=None,
@@ -786,7 +784,6 @@ class ASRServerExecutor(ASRExecutor):
         self.am_predictor_conf = am_predictor_conf
         logger.info(f"model_type: {self.model_type}")
 
-
         sample_rate_str = '16k' if sample_rate == 16000 else '8k'
         tag = model_type + '-' + lang + '-' + sample_rate_str
         self.task_resource.set_task_model(model_tag=tag)
@@ -831,7 +828,7 @@ class ASRServerExecutor(ASRExecutor):
             spm_model_prefix=self.config.spm_model_prefix)
 
         self.update_config()
-        
+
         # AM predictor
         self.init_model()
 
@@ -850,7 +847,6 @@ class ASREngine(BaseEngine):
         super(ASREngine, self).__init__()
         logger.info("create the online asr engine resource instance")
 
-
     def init_model(self) -> bool:
         if not self.executor._init_from_path(
                 model_type=self.config.model_type,
@@ -865,7 +861,6 @@ class ASREngine(BaseEngine):
             return False
         return True
 
-
     def init(self, config: dict) -> bool:
         """init engine resource
 

paddlespeech/server/engine/engine_factory.py

@@ -28,6 +28,9 @@ class EngineFactory(object):
         elif engine_name == 'asr' and engine_type == 'online':
             from paddlespeech.server.engine.asr.online.python.asr_engine import ASREngine
             return ASREngine()
+        elif engine_name == 'asr' and engine_type == 'online-inference':
+            from paddlespeech.server.engine.asr.online.paddleinference.asr_engine import ASREngine
+            return ASREngine()
         elif engine_name == 'asr' and engine_type == 'online-onnx':
             from paddlespeech.server.engine.asr.online.onnx.asr_engine import ASREngine
             return ASREngine()

paddlespeech/server/ws/asr_api.py

@@ -92,7 +92,7 @@ async def websocket_endpoint(websocket: WebSocket):
                 else:
                     resp = {"status": "ok", "message": "no valid json data"}
                     await websocket.send_json(resp)
-                    
+
             elif "bytes" in message:
                 # bytes for the pcm data
                 message = message["bytes"]

utils/zh_tn.py

@@ -747,7 +747,7 @@ def num2chn(number_string,
                         previous_symbol, (CNU, type(None))):
                     if next_symbol.power != 1 and (
                         (previous_symbol is None) or
-                        (previous_symbol.power != 1)):
+                        (previous_symbol.power != 1)):  # noqa: E129
                         result_symbols[i] = liang
 
     # if big is True, '两' will not be used and `alt_two` has no impact on output