Merge branch 'develop' of https://github.com/jerryuhoo/PaddleSpeech into develop

examples/aishell/asr1/conf/conformer.yaml

@@ -60,7 +60,7 @@ collator:
   batch_frames_in: 0
   batch_frames_out: 0
   batch_frames_inout: 0
-  num_workers: 0
+  num_workers: 8
   subsampling_factor: 1
   num_encs: 1
 

paddlespeech/cli/asr/infer.py

@@ -174,12 +174,12 @@ class ASRExecutor(BaseExecutor):
                 self.config.collator.mean_std_filepath = os.path.join(
                     res_path, self.config.collator.cmvn_path)
                 self.collate_fn_test = SpeechCollator.from_config(self.config)
-                text_feature = TextFeaturizer(
+                self.text_feature = TextFeaturizer(
                     unit_type=self.config.collator.unit_type,
                     vocab=self.config.collator.vocab_filepath,
                     spm_model_prefix=self.config.collator.spm_model_prefix)
                 self.config.model.input_dim = self.collate_fn_test.feature_size
-                self.config.model.output_dim = text_feature.vocab_size
+                self.config.model.output_dim = self.text_feature.vocab_size
             elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
                 self.config.collator.vocab_filepath = os.path.join(
                     res_path, self.config.collator.vocab_filepath)
@@ -187,12 +187,12 @@ class ASRExecutor(BaseExecutor):
                     res_path, self.config.collator.augmentation_config)
                 self.config.collator.spm_model_prefix = os.path.join(
                     res_path, self.config.collator.spm_model_prefix)
-                text_feature = TextFeaturizer(
+                self.text_feature = TextFeaturizer(
                     unit_type=self.config.collator.unit_type,
                     vocab=self.config.collator.vocab_filepath,
                     spm_model_prefix=self.config.collator.spm_model_prefix)
                 self.config.model.input_dim = self.config.collator.feat_dim
-                self.config.model.output_dim = text_feature.vocab_size
+                self.config.model.output_dim = self.text_feature.vocab_size
 
             else:
                 raise Exception("wrong type")
@@ -211,6 +211,7 @@ class ASRExecutor(BaseExecutor):
         model_dict = paddle.load(self.ckpt_path)
         self.model.set_state_dict(model_dict)
 
+
     def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
         """
         Input preprocess and return paddle.Tensor stored in self.input.
@@ -228,7 +229,7 @@ class ASRExecutor(BaseExecutor):
             audio = paddle.to_tensor(audio, dtype='float32')
             audio_len = paddle.to_tensor(audio_len)
             audio = paddle.unsqueeze(audio, axis=0)
-            vocab_list = collate_fn_test.vocab_list
+            # vocab_list = collate_fn_test.vocab_list
             self._inputs["audio"] = audio
             self._inputs["audio_len"] = audio_len
             logger.info(f"audio feat shape: {audio.shape}")
@@ -274,10 +275,7 @@ class ASRExecutor(BaseExecutor):
 
             audio_len = paddle.to_tensor(audio.shape[0])
             audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
-            text_feature = TextFeaturizer(
-                unit_type=self.config.collator.unit_type,
-                vocab=self.config.collator.vocab_filepath,
-                spm_model_prefix=self.config.collator.spm_model_prefix)
+
             self._inputs["audio"] = audio
             self._inputs["audio_len"] = audio_len
             logger.info(f"audio feat shape: {audio.shape}")
@@ -290,10 +288,7 @@ class ASRExecutor(BaseExecutor):
         """
         Model inference and result stored in self.output.
         """
-        text_feature = TextFeaturizer(
-            unit_type=self.config.collator.unit_type,
-            vocab=self.config.collator.vocab_filepath,
-            spm_model_prefix=self.config.collator.spm_model_prefix)
+
         cfg = self.config.decoding
         audio = self._inputs["audio"]
         audio_len = self._inputs["audio_len"]
@@ -301,7 +296,7 @@ class ASRExecutor(BaseExecutor):
             result_transcripts = self.model.decode(
                 audio,
                 audio_len,
-                text_feature.vocab_list,
+                self.text_feature.vocab_list,
                 decoding_method=cfg.decoding_method,
                 lang_model_path=cfg.lang_model_path,
                 beam_alpha=cfg.alpha,
@@ -316,7 +311,7 @@ class ASRExecutor(BaseExecutor):
             result_transcripts = self.model.decode(
                 audio,
                 audio_len,
-                text_feature=text_feature,
+                text_feature=self.text_feature,
                 decoding_method=cfg.decoding_method,
                 beam_size=cfg.beam_size,
                 ctc_weight=cfg.ctc_weight,