[to #42322933] fix: effect tf warmup, and add model preload to warmup when constructing pipeline

修复tf模型推理时预热无效的问题；
增加在pipeline构造时调用preload接口，以提前加载模型和预热tf
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9719779

modelscope/models/audio/asr/generic_automatic_speech_recognition.py

@@ -4,7 +4,7 @@ from typing import Any, Dict
 from modelscope.metainfo import Models
 from modelscope.models.base import Model
 from modelscope.models.builder import MODELS
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import Frameworks, Tasks
 
 __all__ = ['GenericAutomaticSpeechRecognition']
 
@@ -36,6 +36,29 @@ class GenericAutomaticSpeechRecognition(Model):
         }
 
     def forward(self) -> Dict[str, Any]:
-        """return the info of the model
+        """preload model and return the info of the model
         """
+        if self.model_cfg['model_config']['type'] == Frameworks.tf:
+            from easyasr import asr_inference_paraformer_tf
+            if hasattr(asr_inference_paraformer_tf, 'preload'):
+                model_workspace = self.model_cfg['model_workspace']
+                model_path = os.path.join(model_workspace,
+                                          self.model_cfg['am_model'])
+                vocab_path = os.path.join(
+                    model_workspace,
+                    self.model_cfg['model_config']['vocab_file'])
+                sampled_ids = 'seq2seq/sampled_ids'
+                sampled_lengths = 'seq2seq/sampled_lengths'
+                if 'sampled_ids' in self.model_cfg['model_config']:
+                    sampled_ids = self.model_cfg['model_config']['sampled_ids']
+                if 'sampled_lengths' in self.model_cfg['model_config']:
+                    sampled_lengths = self.model_cfg['model_config'][
+                        'sampled_lengths']
+                asr_inference_paraformer_tf.preload(
+                    ngpu=1,
+                    asr_model_file=model_path,
+                    vocab_file=vocab_path,
+                    sampled_ids=sampled_ids,
+                    sampled_lengths=sampled_lengths)
+
         return self.model_cfg

modelscope/pipelines/audio/asr_inference_pipeline.py

@@ -30,6 +30,7 @@ class AutomaticSpeechRecognitionPipeline(Pipeline):
         """use `model` and `preprocessor` to create an asr pipeline for prediction
         """
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+        self.model_cfg = self.model.forward()
 
     def __call__(self,
                  audio_in: Union[str, bytes],
@@ -49,16 +50,16 @@ class AutomaticSpeechRecognitionPipeline(Pipeline):
                 recog_type=recog_type,
                 audio_format=audio_format)
 
-        if hasattr(asr_utils, 'sample_rate_checking'):
+        if hasattr(asr_utils, 'sample_rate_checking') and audio_fs is None:
             self.audio_fs = asr_utils.sample_rate_checking(
                 self.audio_in, self.audio_format)
 
         if self.preprocessor is None:
             self.preprocessor = WavToScp()
 
-        output = self.preprocessor.forward(self.model.forward(),
-                                           self.recog_type, self.audio_format,
-                                           self.audio_in, self.audio_fs)
+        output = self.preprocessor.forward(self.model_cfg, self.recog_type,
+                                           self.audio_format, self.audio_in,
+                                           self.audio_fs)
         output = self.forward(output)
         rst = self.postprocess(output)
         return rst
@@ -198,8 +199,12 @@ class AutomaticSpeechRecognitionPipeline(Pipeline):
 
             for line in lines:
                 line_item = line.split(None, 1)
-                item = {'key': line_item[0], 'value': line_item[1].strip('\n')}
-                ref_list.append(item)
+                if len(line_item) > 1:
+                    item = {
+                        'key': line_item[0],
+                        'value': line_item[1].strip('\n')
+                    }
+                    ref_list.append(item)
 
         return ref_list