Whisper is an Automatic Speech Recognition (ASR) model trained on 680,000 hours of supervised data from the web. However, it has low-performance on low-resource languages like Marathi and Dravidian languages. Fine-tuning Whisper is challenging due to high computational and storage requirements. LoRA is a unique approach to fine-tuning that reduces trainable parameters and GPU memory requirements. It matches or exceeds the performance of traditional methods and offers increased training throughput. Amazon SageMaker is an ideal platform for implementing LoRA fine-tuning of Whisper. The process involves preparing the dataset, training the model, and evaluating its performance. Ultimately, fine-tuning Whisper using LoRA has shown promising results for faster training with comparable performance.
Improve Speech Recognition with LoRA Fine-tuning on Amazon SageMaker
Whisper is an Automatic Speech Recognition (ASR) model trained on a vast amount of supervised data from the web. However, it faces limitations when it comes to low-resource languages. Fine-tuning the Whisper model can help overcome this challenge, but it requires significant computational resources and storage. This can be a hurdle for organizations with limited resources.
Low-Rank Adaptation (LoRA) offers a unique solution to this problem. It reduces the number of trainable parameters and GPU memory requirements by introducing trainable rank decomposition matrices into each layer of the model. Despite these reductions, LoRA maintains or even exceeds the performance of traditional fine-tuning methods. It also increases training throughput and maintains model efficiency during deployment.
Amazon SageMaker is an ideal platform for implementing LoRA fine-tuning of Whisper. It provides fully managed infrastructure, tools, and workflows for building, training, and deploying machine learning models. With SageMaker, you can benefit from lower training costs, distributed training libraries, and more.
Implementing LoRA Fine-tuning in SageMaker
To implement LoRA fine-tuning of Whisper in SageMaker, follow these steps:
- Prepare the dataset for fine-tuning. This involves downloading and splitting the Common Voice dataset, downsampling audio files, and applying Whisper’s feature extractor and tokenizer.
- Upload the processed data to Amazon S3 for easy access during fine-tuning.
- Train the model using the Whisper large pre-trained model and the LoRA implementation from Hugging Face’s peft package.
- Run a SageMaker training job using your own Docker container.
- Evaluate the performance of the fine-tuned Whisper model using word error rate (WER) on a test set.
The implementation of LoRA enables faster and more efficient fine-tuning of the Whisper model compared to traditional methods. It reduces GPU hours by a significant margin.
Conclusion
Fine-tuning Whisper models with LoRA on Amazon SageMaker offers practical solutions for improving speech recognition. It reduces computational and storage requirements while maintaining or exceeding performance. With SageMaker’s infrastructure and tools, organizations can easily implement and scale these solutions.
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