用轻量微调让小模型更准地转录航空通话
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA
- 用低秩适配（LoRA）微调精简版语音模型
- 在美三机场数据上达3.86%的词错误率
- 适合资源有限场景下的航空通信转录
航空通信转录在空管纠错和搜救中具有重要应用。尽管OpenAI的Whisper是领先语音识别模型，但其微调计算成本高。本文采用参数高效微调方法LoRA，对更高效的distil-Whisper模型进行微调。使用来自语言数据联盟的空中交通管制语料库（约70小时），涵盖美国三个主要机场的管制员与飞行员通话。通过5折交叉验证与网格搜索（初始LoRA参数：Alpha=64，Rank=32），优化超参数后，模型在五折平均词错误率达到3.86%。该结果表明模型在机舱环境中具备实际应用潜力。
原文摘要 · Abstract (English)
Transcription of aviation communications has several applications, from assisting air traffic controllers in identifying the accuracy of read-back errors to search and rescue operations. Recent advances in artificial intelligence have provided unprecedented opportunities for improving aviation communication transcription tasks. OpenAI's Whisper is one of the leading automatic speech recognition models. However, fine-tuning Whisper for aviation communication transcription is not computationally efficient. Thus, this paper aims to use a Parameter-Efficient Fine-tuning method called Low-Rank Adaptation to fine-tune a more computationally efficient version of Whisper, distil-Whisper. To perform the fine-tuning, we used the Air Traffic Control Corpus dataset from the Linguistic Data Consortium, which contains approximately 70 hours of controller and pilot transmissions near three major airports in the US. The objective was to reduce the word error rate to enhance accuracy in the transcription of aviation communication. First, starting with an initial set of hyperparameters for LoRA (Alpha = 64 and Rank = 32), we performed a grid search. We applied a 5-fold cross-validation to find the best combination of distil-Whisper hyperparameters. Then, we fine-tuned the model for LoRA hyperparameters, achieving an impressive average word error rate of 3.86% across five folds. This result highlights the model's potential for use in the cockpit.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。