Recent advances in automatic speech recognition (ASR) often rely on large speech foundation models for generating high-quality transcriptions. However, these models can be impractical due to limited computing resources. The situation is even more severe in terms of more realistic or difficult scenarios, such as code-switching ASR (CS-ASR). To address this, we present a framework for developing more efficient models for CS-ASR through knowledge distillation using realistic speech-only data. Our proposed method, Leave No Knowledge Behind During Knowledge Distillation (K$^2$D), leverages both the teacher model's knowledge and additional insights from a small auxiliary model. We evaluate our approach on two in-domain and two out-domain datasets, demonstrating that K$^2$D is effective. By conducting K$^2$D on the unlabeled realistic data, we have successfully obtained a 2-time smaller model with 5-time faster generation speed while outperforming the baseline methods and the teacher model on all the testing sets. We have made our model publicly available on Hugging Face (https://huggingface.co/andybi7676/k2d-whisper.zh-en).

使用现实语音数据进行知识蒸馏，通过 K^2D 方法，成功实现了在生成速度更快的同时，模型参数减少为原来的二分之一，并在所有测试集上均胜过基线方法和教师模型。

Leave No Knowledge Behind During Knowledge Distillation: Towards
  Practical and Effective Knowledge Distillation for Code-Switching ASR Using
  Realistic Data

在知识蒸馏中不留下任何知识：以实际数据为基础的代码切换ASR的实用和有效知识蒸馏