support using by __call__

paddlespeech/cli/asr/infer.py

@@ -119,7 +119,8 @@ class ASRExecutor(BaseExecutor):
                         lang: str='zh',
                         model_sample_rate: int=16000,
                         cfg_path: Optional[os.PathLike]=None,
-                        ckpt_path: Optional[os.PathLike]=None):
+                        ckpt_path: Optional[os.PathLike]=None,
+                        device: str='cpu'):
         """
             Init model and other resources from a specific path.
         """
@@ -140,12 +141,8 @@ class ASRExecutor(BaseExecutor):
             res_path = os.path.dirname(
                 os.path.dirname(os.path.abspath(self.cfg_path)))
 
-        # Enter the path of model root
-        os.chdir(res_path)
-
         #Init body.
-        parser_args = self.parser_args
-        paddle.set_device(parser_args.device)
+        paddle.set_device(device)
         self.config = CfgNode(new_allowed=True)
         self.config.merge_from_file(self.cfg_path)
         self.config.decoding.decoding_method = "attention_rescoring"
@@ -153,29 +150,35 @@ class ASRExecutor(BaseExecutor):
         logger.info(model_conf)
 
         with UpdateConfig(model_conf):
-            if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
+            if model_type == "ds2_online" or model_type == "ds2_offline":
                 from paddlespeech.s2t.io.collator import SpeechCollator
                 self.config.collator.vocab_filepath = os.path.join(
                     res_path, self.config.collator.vocab_filepath)
-                self.config.collator.vocab_filepath = os.path.join(
+                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(
+                    unit_type=self.config.collator.unit_type,
+                    vocab_filepath=self.config.collator.vocab_filepath,
+                    spm_model_prefix=self.config.collator.spm_model_prefix)
                 model_conf.input_dim = self.collate_fn_test.feature_size
-                model_conf.output_dim = self.text_feature.vocab_size
-            elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
-
+                model_conf.output_dim = text_feature.vocab_size
+            elif model_type == "conformer" or model_type == "transformer" or model_type == "wenetspeech":
                 self.config.collator.vocab_filepath = os.path.join(
                     res_path, self.config.collator.vocab_filepath)
-                self.text_feature = TextFeaturizer(
+                text_feature = TextFeaturizer(
                     unit_type=self.config.collator.unit_type,
                     vocab_filepath=self.config.collator.vocab_filepath,
                     spm_model_prefix=self.config.collator.spm_model_prefix)
                 model_conf.input_dim = self.config.collator.feat_dim
-                model_conf.output_dim = self.text_feature.vocab_size
+                model_conf.output_dim = text_feature.vocab_size
             else:
                 raise Exception("wrong type")
         self.config.freeze()
-        model_class = dynamic_import(parser_args.model, model_alias)
+        # Enter the path of model root
+        os.chdir(res_path)
+
+        model_class = dynamic_import(model_type, model_alias)
         model = model_class.from_config(model_conf)
         self.model = model
         self.model.eval()
@@ -185,31 +188,31 @@ class ASRExecutor(BaseExecutor):
         model_dict = paddle.load(params_path)
         self.model.set_state_dict(model_dict)
 
-    def preprocess(self, input: Union[str, os.PathLike]):
+    def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
         """
             Input preprocess and return paddle.Tensor stored in self.input.
             Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
         """
 
-        parser_args = self.parser_args
-        config = self.config
         audio_file = input
         logger.info("Preprocess audio_file:" + audio_file)
 
-        self.sr = config.collator.target_sample_rate
+        config_target_sample_rate = self.config.collator.target_sample_rate
 
         # Get the object for feature extraction
-        if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
+        if model_type == "ds2_online" or model_type == "ds2_offline":
             audio, _ = self.collate_fn_test.process_utterance(
                 audio_file=audio_file, transcript=" ")
             audio_len = audio.shape[0]
             audio = paddle.to_tensor(audio, dtype='float32')
-            self.audio_len = paddle.to_tensor(audio_len)
-            self.audio = paddle.unsqueeze(audio, axis=0)
-            self.vocab_list = collate_fn_test.vocab_list
-            logger.info(f"audio feat shape: {self.audio.shape}")
-
-        elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
+            audio_len = paddle.to_tensor(audio_len)
+            audio = paddle.unsqueeze(audio, axis=0)
+            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}")
+
+        elif model_type == "conformer" or model_type == "transformer" or model_type == "wenetspeech":
             logger.info("get the preprocess conf")
             preprocess_conf = os.path.join(
                 os.path.dirname(os.path.abspath(self.cfg_path)),
@@ -235,7 +238,7 @@ class ASRExecutor(BaseExecutor):
             else:
                 audio = audio[:, 0]
 
-            if sample_rate != self.sr:
+            if sample_rate != config_target_sample_rate:
                 logger.error(
                     f"sample rate error: {sample_rate}, need {self.sr} ")
                 sys.exit(-1)
@@ -243,29 +246,36 @@ class ASRExecutor(BaseExecutor):
             # fbank
             audio = preprocessing(audio, **preprocess_args)
 
-            self.audio_len = paddle.to_tensor(audio.shape[0])
-            self.audio = paddle.to_tensor(
-                audio, dtype='float32').unsqueeze(axis=0)
-            logger.info(f"audio feat shape: {self.audio.shape}")
+            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_filepath=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}")
 
         else:
             raise Exception("wrong type")
 
     @paddle.no_grad()
-    def infer(self):
+    def infer(self, model_type: str):
         """
             Model inference and result stored in self.output.
         """
+        text_feature = TextFeaturizer(
+            unit_type=self.config.collator.unit_type,
+            vocab_filepath=self.config.collator.vocab_filepath,
+            spm_model_prefix=self.config.collator.spm_model_prefix)
         cfg = self.config.decoding
-        parser_args = self.parser_args
-        audio = self.audio
-        audio_len = self.audio_len
-        if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
-            vocab_list = self.vocab_list
+        audio = self._inputs["audio"]
+        audio_len = self._inputs["audio_len"]
+        if model_type == "ds2_online" or model_type == "ds2_offline":
             result_transcripts = self.model.decode(
                 audio,
                 audio_len,
-                vocab_list,
+                text_feature.vocab_list,
                 decoding_method=cfg.decoding_method,
                 lang_model_path=cfg.lang_model_path,
                 beam_alpha=cfg.alpha,
@@ -274,14 +284,13 @@ class ASRExecutor(BaseExecutor):
                 cutoff_prob=cfg.cutoff_prob,
                 cutoff_top_n=cfg.cutoff_top_n,
                 num_processes=cfg.num_proc_bsearch)
-            self.result_transcripts = result_transcripts[0]
+            self._outputs["result"] = result_transcripts[0]
 
-        elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
-            text_feature = self.text_feature
+        elif model_type == "conformer" or model_type == "transformer" or model_type == "wenetspeech":
             result_transcripts = self.model.decode(
                 audio,
                 audio_len,
-                text_feature=self.text_feature,
+                text_feature=text_feature,
                 decoding_method=cfg.decoding_method,
                 lang_model_path=cfg.lang_model_path,
                 beam_alpha=cfg.alpha,
@@ -294,23 +303,22 @@ class ASRExecutor(BaseExecutor):
                 decoding_chunk_size=cfg.decoding_chunk_size,
                 num_decoding_left_chunks=cfg.num_decoding_left_chunks,
                 simulate_streaming=cfg.simulate_streaming)
-            self.result_transcripts = result_transcripts[0][0]
+            self._outputs["result"] = result_transcripts[0][0]
         else:
             raise Exception("invalid model name")
 
-        pass
-
     def postprocess(self) -> Union[str, os.PathLike]:
         """
             Output postprocess and return human-readable results such as texts and audio files.
         """
-        return self.result_transcripts
+        return self._outputs["result"]
 
     def _check(self, audio_file: str, model_sample_rate: int):
         self.target_sample_rate = model_sample_rate
         if self.target_sample_rate != 16000 and self.target_sample_rate != 8000:
             logger.error(
-                "please input --model_sample_rate 8000 or --model_sample_rate 16000")
+                "please input --model_sample_rate 8000 or --model_sample_rate 16000"
+            )
             raise Exception("invalid sample rate")
             sys.exit(-1)
 
@@ -336,11 +344,13 @@ class ASRExecutor(BaseExecutor):
             sys.exit(-1)
         logger.info("The sample rate is %d" % sample_rate)
         if sample_rate != self.target_sample_rate:
-            logger.warning("The sample rate of the input file is not {}.\n \
+            logger.warning(
+                "The sample rate of the input file is not {}.\n \
                             The program will resample the wav file to {}.\n \
                             If the result does not meet your expectations，\n \
                             Please input the 16k 16bit 1 channel wav file. \
-                        ".format(self.target_sample_rate, self.target_sample_rate))
+                        "
+                .format(self.target_sample_rate, self.target_sample_rate))
             while (True):
                 logger.info(
                     "Whether to change the sample rate and the channel. Y: change the sample. N: exit the prgream."
@@ -367,34 +377,36 @@ class ASRExecutor(BaseExecutor):
         """
             Command line entry.
         """
-        self.parser_args = self.parser.parse_args(argv)
+        parser_args = self.parser.parse_args(argv)
 
-        model = self.parser_args.model
-        lang = self.parser_args.lang
-        model_sample_rate = self.parser_args.model_sample_rate
-        config = self.parser_args.config
-        ckpt_path = self.parser_args.ckpt_path
-        audio_file = os.path.abspath(self.parser_args.input)
-        device = self.parser_args.device
+        model = parser_args.model
+        lang = parser_args.lang
+        model_sample_rate = parser_args.model_sample_rate
+        config = parser_args.config
+        ckpt_path = parser_args.ckpt_path
+        audio_file = parser_args.input
+        device = parser_args.device
 
         try:
-            res = self(model, lang, model_sample_rate, config, ckpt_path, audio_file,
-                       device)
+            res = self(model, lang, model_sample_rate, config, ckpt_path,
+                       audio_file, device)
             logger.info('ASR Result: {}'.format(res))
             return True
         except Exception as e:
             print(e)
             return False
 
-    def __call__(self, model, lang, model_sample_rate, config, ckpt_path, audio_file,
-                 device):
+    def __call__(self, model, lang, model_sample_rate, config, ckpt_path,
+                 audio_file, device):
         """
             Python API to call an executor.
         """
+        audio_file = os.path.abspath(audio_file)
         self._check(audio_file, model_sample_rate)
-        self._init_from_path(model, lang, model_sample_rate, config, ckpt_path)
-        self.preprocess(audio_file)
-        self.infer()
+        self._init_from_path(model, lang, model_sample_rate, config, ckpt_path,
+                             device)
+        self.preprocess(model, audio_file)
+        self.infer(model)
         res = self.postprocess()  # Retrieve result of asr.
 
         return res