Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into ...

雾聪

2023-05-17 8706e767affc6bdc8cb7a67ca3a20a62779ff048

 funasr/export/export_model.py

@@ -27,15 +27,13 @@
    ):
        assert check_argument_types()
        self.set_all_random_seed(0)
        if cache_dir is None:
            cache_dir = Path.home() / ".cache" / "export"

        self.cache_dir = Path(cache_dir)
        self.cache_dir = cache_dir
        self.export_config = dict(
            feats_dim=560,
            onnx=False,
        )
        print("output dir: {}".format(self.cache_dir))
        
        self.onnx = onnx
        self.device = device
        self.quant = quant
@@ -52,7 +50,7 @@
        verbose: bool = False,
    ):

        export_dir = self.cache_dir / tag_name.replace(' ', '-')
        export_dir = self.cache_dir
        os.makedirs(export_dir, exist_ok=True)

        # export encoder1
@@ -167,31 +165,58 @@
    
    def export(self,
               tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
               mode: str = 'paraformer',
               mode: str = None,
               ):
        
        model_dir = tag_name
        if model_dir.startswith('damo/'):
        if model_dir.startswith('damo'):
            from modelscope.hub.snapshot_download import snapshot_download
            model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir)
        asr_train_config = os.path.join(model_dir, 'config.yaml')
        asr_model_file = os.path.join(model_dir, 'model.pb')
        cmvn_file = os.path.join(model_dir, 'am.mvn')
        json_file = os.path.join(model_dir, 'configuration.json')
        self.cache_dir = model_dir

        if mode is None:
            import json
            json_file = os.path.join(model_dir, 'configuration.json')
            with open(json_file, 'r') as f:
                config_data = json.load(f)
                mode = config_data['model']['model_config']['mode']
                if config_data['task'] == "punctuation":
                    mode = config_data['model']['punc_model_config']['mode']
                else:
                    mode = config_data['model']['model_config']['mode']
        if mode.startswith('paraformer'):
            from funasr.tasks.asr import ASRTaskParaformer as ASRTask
        elif mode.startswith('uniasr'):
            from funasr.tasks.asr import ASRTaskUniASR as ASRTask
            config = os.path.join(model_dir, 'config.yaml')
            model_file = os.path.join(model_dir, 'model.pb')
            cmvn_file = os.path.join(model_dir, 'am.mvn')
            model, asr_train_args = ASRTask.build_model_from_file(
                config, model_file, cmvn_file, 'cpu'
            )
            self.frontend = model.frontend
        elif mode.startswith('offline'):
            from funasr.tasks.vad import VADTask
            config = os.path.join(model_dir, 'vad.yaml')
            model_file = os.path.join(model_dir, 'vad.pb')
            cmvn_file = os.path.join(model_dir, 'vad.mvn')
            
        model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, 'cpu'
        )
        self.frontend = model.frontend
            model, vad_infer_args = VADTask.build_model_from_file(
                config, model_file, cmvn_file=cmvn_file, device='cpu'
            )
            self.export_config["feats_dim"] = 400
            self.frontend = model.frontend
        elif mode.startswith('punc'):
            from funasr.tasks.punctuation import PunctuationTask as PUNCTask
            punc_train_config = os.path.join(model_dir, 'config.yaml')
            punc_model_file = os.path.join(model_dir, 'punc.pb')
            model, punc_train_args = PUNCTask.build_model_from_file(
                punc_train_config, punc_model_file, 'cpu'
            )
        elif mode.startswith('punc_VadRealtime'):
            from funasr.tasks.punctuation import PunctuationTask as PUNCTask
            punc_train_config = os.path.join(model_dir, 'config.yaml')
            punc_model_file = os.path.join(model_dir, 'punc.pb')
            model, punc_train_args = PUNCTask.build_model_from_file(
                punc_train_config, punc_model_file, 'cpu'
            )
        self._export(model, tag_name)