apply random cropping to eval to prevent oom

run_audio_classification.py

@@ -592,21 +592,13 @@ def main():
         output_batch["labels"] = [int(label2id[label]) for label in batch["labels"]]
         return output_batch
 
-    def val_transforms(batch):
-        """Apply val_transforms across a batch."""
-        wavs = [audio["array"] for audio in batch["audio"]]
-        inputs = feature_extractor(wavs, sampling_rate=sampling_rate)
-        output_batch = {model_input_name: inputs.get(model_input_name)}
-        output_batch["labels"] = [int(label2id[label]) for label in batch["labels"]]
-        return output_batch
-
     if training_args.do_train:
         # Set the training transforms
         raw_datasets["train"].set_transform(train_transforms, output_all_columns=False)
 
     if training_args.do_eval:
         # Set the validation transforms
-        raw_datasets["eval"].set_transform(val_transforms, output_all_columns=False)
+        raw_datasets["eval"].set_transform(train_transforms, output_all_columns=False)
 
     # Load the accuracy metric from the datasets package
     metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)