我目前正在尝试微调来自https://huggingface.co/dima806/bird_sounds_classification的Wav2Vec2模型。但我在Google Colab的免费版本上使用的内存超过了限制。

以下是我的代码:

from transformers import TrainingArguments, Trainer# Load model with ignore_mismatched_sizes=Truemodel = Wav2Vec2ForSequenceClassification.from_pretrained(    "dima806/bird_sounds_classification",    num_labels=len(label2id),    ignore_mismatched_sizes=True)# Set up training with gradient accumulationbatch_size = 1  # Reduce batch size to manage memoryaccumulation_steps = 4  # Accumulate gradients over 4 stepstraining_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 modeltrainer.train()

为什么内存使用量会超过12.7GB？我的数据集只有20个项目。我该如何解决这个问题？

回答：

声音输入过长，重新采样音频成小段后，问题得到了解决。