[audio]fix split fold in tess dataset (#47328)

* fix split fold in tess dataset

* check split and n_folds

* fix tess assert log

* fix format

* format

python/paddle/audio/datasets/esc50.py

@@ -143,8 +143,11 @@ class ESC50(AudioClassificationDataset):
         split: int = 1,
         feat_type: str = 'raw',
         archive=None,
-        **kwargs
+        **kwargs,
     ):
+        assert split in range(
+            1, 6
+        ), f'The selected split should be integer, and 1 <= split <= 5, but got {split}'
         if archive is not None:
             self.archive = archive
         files, labels = self._get_data(mode, split)

python/paddle/audio/datasets/tess.py

@@ -91,17 +91,19 @@ class TESS(AudioClassificationDataset):
 
     def __init__(
         self,
-        mode='train',
-        n_folds=5,
-        split=1,
-        feat_type='raw',
+        mode: str = 'train',
+        n_folds: int = 5,
+        split: int = 1,
+        feat_type: str = 'raw',
         archive=None,
         **kwargs,
     ):
-        """ """
-        assert (
-            split <= n_folds
-        ), f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
+        assert isinstance(n_folds, int) and (
+            n_folds >= 1
+        ), f'the n_folds should be integer and n_folds >= 1, but got {n_folds}'
+        assert split in range(
+            1, n_folds + 1
+        ), f'The selected split should be integer and should be 1 <= split <= {n_folds}, but got {split}'
         if archive is not None:
             self.archive = archive
         files, labels = self._get_data(mode, n_folds, split)
@@ -116,7 +118,9 @@ class TESS(AudioClassificationDataset):
             ret.append(self.meta_info(*basename_without_extend.split('_')))
         return ret
 
-    def _get_data(self, mode, n_folds, split) -> Tuple[List[str], List[int]]:
+    def _get_data(
+        self, mode: str, n_folds: int, split: int
+    ) -> Tuple[List[str], List[int]]:
         if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)):
             download.get_path_from_url(
                 self.archive['url'],
@@ -135,11 +139,10 @@ class TESS(AudioClassificationDataset):
 
         files = []
         labels = []
-        n_samples_per_fold = len(meta_info) // n_folds
         for idx, sample in enumerate(meta_info):
             _, _, emotion = sample
             target = self.label_list.index(emotion)
-            fold = idx // n_samples_per_fold + 1
+            fold = idx % n_folds + 1
 
             if mode == 'train' and int(fold) != split:
                 files.append(wav_files[idx])