fix input validation for SafeAudioDataloader

opensoundscape/ml/dataloaders.py

@@ -63,6 +63,18 @@ def __init__(
             "(c) (file,start_time,end_time) as MultiIndex"
         )
 
+        # validate that file paths are correctly placed in the input index or list
+        if len(samples) > 0:
+            if isinstance(samples, pd.DataFrame):  # samples is a pd.DataFrame
+                if isinstance(samples.index, pd.core.indexes.multi.MultiIndex):
+                    # index is (file, start_time, end_time)
+                    first_path = samples.index.values[0][0]
+                else:  # index of df is just file path
+                    first_path = samples.index.values[0]
+            else:  # samples is a list of file path
+                first_path = samples[0]
+            _check_is_path(first_path)
+
         # set up prediction Dataset, considering three possible cases:
         # (c1) user provided multi-index df with file,start_time,end_time of clips
         # (c2) user provided file list and wants clips to be split out automatically
@@ -72,15 +84,11 @@ def __init__(
             and type(samples.index) == pd.core.indexes.multi.MultiIndex
         ):  # c1 user provided multi-index df with file,start_time,end_time of clips
             # raise AssertionError if first item of multi-index is not str or Path
-            if len(samples) > 0:
-                _check_is_path(samples.index.values[0][0])
             dataset = AudioFileDataset(samples=samples, preprocessor=preprocessor)
         elif (
             split_files_into_clips
         ):  # c2 user provided file list; split each file into appropriate length clips
             # raise AssertionError if first item is not str or Path
-            if len(samples) > 0:
-                _check_is_path(samples[0])
             dataset = AudioSplittingDataset(
                 samples=samples,
                 preprocessor=preprocessor,
@@ -90,10 +98,6 @@ def __init__(
         else:  # c3 samples is list of files and
             # split_files_into_clips=False -> one sample & one prediction per file provided
             # eg, each file is a 5 second clips and the model expects 5 second clips
-
-            # raise AssertionError if first item is not str or Path
-            if len(samples) > 0:
-                _check_is_path(samples[0])
             dataset = AudioFileDataset(samples=samples, preprocessor=preprocessor)
 
         dataset.bypass_augmentations = bypass_augmentations