Running out of RAM when finetuning model

I am currently trying to finetune Wav2Vec2 model from: https://huggingface.co/dima806/bird_sounds_classification. But my RAM utilisation is running over the free tier on Google Colab.

The following is my code:

from transformers import TrainingArguments, Trainer

# Load model with ignore_mismatched_sizes=True
model = Wav2Vec2ForSequenceClassification.from_pretrained(
    "dima806/bird_sounds_classification",
    num_labels=len(label2id),
    ignore_mismatched_sizes=True
)

# Set up training with gradient accumulation
batch_size = 1  # Reduce batch size to manage memory
accumulation_steps = 4  # Accumulate gradients over 4 steps

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    gradient_accumulation_steps=accumulation_steps,  # Gradient accumulation
    num_train_epochs=3,
    weight_decay=0.01,
    fp16=True,  # Enable mixed precision training
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    tokenizer=feature_extractor,
)

# Train the model
trainer.train()

What could be the reasons the RAM is going past 12.7GB? My dataset only contains 20 items. How can I address this issue?

The sound inputs were too long, after resampling the audio into chunks, the problem was resolved.