Delete functions.py

functions.py

@@ -1,66 +0,0 @@
-from typing import Tuple, List, Dict, Any
-
-import evaluate
-import numpy as np
-import pandas as pd
-from transformers import ASTForAudioClassification, Trainer, TrainingArguments
-
-from dataset import load_dataset_esc50
-
-
-def squeeze_func(batch: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Remove the extra dimension from input_values.
-    """
-    batch['input_values'] = np.asarray(batch['input_values']).squeeze(0)
-    return batch
-
-
-def prepare_datasets(apply_augmentation: bool) -> Tuple[Any, List[int]]:
-    """
-    Load and preprocess the ESC-50 dataset.
-    """
-    dataset, folds = load_dataset_esc50(apply_augmentation=apply_augmentation)
-    return dataset.map(squeeze_func), folds
-
-
-def get_model(model_name: str) -> ASTForAudioClassification:
-    """
-    Load the AST audio classification model with SDPA.
-    """
-    return ASTForAudioClassification.from_pretrained(
-        model_name,
-        attn_implementation="sdpa"
-    )
-
-
-def make_trainer(
-    model: ASTForAudioClassification,
-    train_dataset: Any,
-    eval_dataset: Any,
-    args: TrainingArguments
-) -> Trainer:
-    """
-    Build and return a HuggingFace Trainer instance.
-    """
-    metric = evaluate.load('accuracy')
-
-    def compute_metrics(pred):
-        logits, labels = pred
-        return metric.compute(predictions=np.argmax(logits, axis=-1), references=labels)
-
-    return Trainer(
-        model=model,
-        args=args,
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-        compute_metrics=compute_metrics,
-    )
-
-
-def save_results(results: List[Dict[str, Any]], csv_path: str) -> None:
-    """
-    Save the experiment results to a CSV file.
-    """
-    pd.DataFrame(results).to_csv(csv_path, index=False)
-    print(f"Results saved to {csv_path}")