add some vector cli comments, test=doc

paddlespeech/cli/vector/infer.py

@@ -68,12 +68,13 @@ class VectorExecutor(BaseExecutor):
 
         self.parser = argparse.ArgumentParser(
             prog="paddlespeech.vector", add_help=True)
+
         self.parser.add_argument(
             "--model",
             type=str,
             default="ecapatdnn_voxceleb12",
             choices=["ecapatdnn_voxceleb12"],
-            help="Choose model type of asr task.")
+            help="Choose model type of vector task.")
         self.parser.add_argument(
             "--task",
             type=str,
@@ -81,7 +82,7 @@ class VectorExecutor(BaseExecutor):
             choices=["spk"],
             help="task type in vector domain")
         self.parser.add_argument(
-            "--input", type=str, default=None, help="Audio file to recognize.")
+            "--input", type=str, default=None, help="Audio file to extract embedding.")
         self.parser.add_argument(
             "--sample_rate",
             type=int,
@@ -186,7 +187,7 @@ class VectorExecutor(BaseExecutor):
             sample_rate (int, optional): model sample rate. Defaults to 16000.
             config (os.PathLike, optional): yaml config. Defaults to None.
             ckpt_path (os.PathLike, optional): pretrained model path. Defaults to None.
-            device (_type_, optional): paddle running host device. Defaults to paddle.get_device().
+            device (optional): paddle running host device. Defaults to paddle.get_device().
 
         Returns:
             dict: return the audio embedding and the embedding shape
@@ -216,6 +217,7 @@ class VectorExecutor(BaseExecutor):
 
     def _get_pretrained_path(self, tag: str) -> os.PathLike:
         """get the neural network path from the pretrained model list
+           we stored all the pretained mode in the variable `pretrained_models`
 
         Args:
             tag (str): model tag in the pretrained model list
@@ -332,6 +334,7 @@ class VectorExecutor(BaseExecutor):
         logger.info(f"embedding size: {embedding.shape}")
 
         # stage 2: put the embedding and dim info to _outputs property
+        #          the embedding type is numpy.array
         self._outputs["embedding"] = embedding
 
     def postprocess(self) -> Union[str, os.PathLike]:
@@ -356,6 +359,7 @@ class VectorExecutor(BaseExecutor):
             logger.info(f"Preprocess audio file: {audio_file}")
 
         # stage 1: load the audio sample points
+        #    Note: this process must match the training process
         waveform, sr = load_audio(audio_file)
         logger.info(f"load the audio sample points, shape is: {waveform.shape}")