clean dataset doc

PaddlePaddle · Oct 19, 2022 · 8cbcff0 · 8cbcff0
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diff --git a/python/paddle/audio/backends/init_backend.py b/python/paddle/audio/backends/init_backend.py
@@ -134,9 +134,7 @@ def set_backend(backend_name: str):
     """Set the backend by one of the list_audio_backend return.
 
     Args:
-        backend (str): one of the list_audio_backend.
-        "wave_backend" is the default.
-        "soundfile" imported from paddleaudio.
+        backend (str): one of the list_audio_backend. "wave_backend" is the default. "soundfile" imported from paddleaudio.
 
     Examples:
         .. code-block:: python

diff --git a/python/paddle/audio/datasets/esc50.py b/python/paddle/audio/datasets/esc50.py
@@ -34,15 +34,11 @@ class ESC50(AudioClassificationDataset):
         ESC: Dataset for Environmental Sound Classification
         http://dx.doi.org/10.1145/2733373.2806390
 
-    Ags:
-            mode (str, optional):
-                It identifies the dataset mode (train or dev). Default:train.
-            split (int, optional):
-                It specify the fold of dev dataset. Default:1.
-            feat_type (str, optional):
-                It identifies the feature type that user wants to extrace of an audio file. Default:raw.
-            archive(dict, optional):
-                it tells where to download the audio archive. Default:None.
+    Args:
+       mode (str, optional): It identifies the dataset mode (train or dev). Default:train.
+       split (int, optional): It specify the fold of dev dataset. Default:1.
+       feat_type (str, optional): It identifies the feature type that user wants to extrace of an audio file. Default:raw.
+       archive(dict, optional): it tells where to download the audio archive. Default:None.
 
     Returns:
         :ref:`api_paddle_io_Dataset`. An instance of ESC50 dataset.

diff --git a/python/paddle/audio/datasets/tess.py b/python/paddle/audio/datasets/tess.py
@@ -35,17 +35,12 @@ class TESS(AudioClassificationDataset):
         Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487
         https://doi.org/10.5683/SP2/E8H2MF
 
-    Ags:
-            mode (str, optional):
-                It identifies the dataset mode (train or dev). Defaults to train.
-            n_folds (int, optional):
-                Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset. Defaults to 5.
-            split (int, optional):
-                It specify the fold of dev dataset. Defaults to 1.
-            feat_type (str, optional):
-                It identifies the feature type that user wants to extrace of an audio file. Defaults to raw.
-            archive(dict):
-                it tells where to download the audio archive. Defaults to None.
+    Args:
+       mode (str, optional): It identifies the dataset mode (train or dev). Defaults to train.
+       n_folds (int, optional): Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset. Defaults to 5.
+       split (int, optional): It specify the fold of dev dataset. Defaults to 1.
+       feat_type (str, optional): It identifies the feature type that user wants to extrace of an audio file. Defaults to raw.
+       archive(dict): it tells where to download the audio archive. Defaults to None.
 
     Returns:
         :ref:`api_paddle_io_Dataset`. An instance of TESS dataset.