轻量级语音识别框架，低资源下抗噪能力强
DQLoRA: A Lightweight Domain-Aware Denoising ASR via Adapter-guided Distillation
- 用冻结Whisper作教师，Wav2Vec2学生加QLoRA适配器
- 在含噪声FLEURS数据上训练，保持识别准确率
- 适合资源受限场景的鲁棒语音识别应用
我们展示DQLoRA，一种基于适配器引导蒸馏的轻量级领域感知去噪自动语音识别框架，适用于低资源和嘈杂环境。方法采用冻结的Whisper模型作为教师提供语义监督，轻量级Wav2Vec2学生模型配备基于QLoRA的适配器。训练在添加了DNS风格噪声的FLEURS数据集上进行。通过联合最小化CTC损失与基于KL散度的蒸馏损失，学生模型实现高效适应，同时保持识别准确率。
原文摘要 · Abstract (English)
We present a demo of DQLoRA, an Adapter-Guided Distillation framework for robust speech recognition under low-resource and noisy conditions. Our method employs a frozen Whisper model as the teacher to provide semantic supervision, and a lightweight Wav2Vec2 student equipped with QLoRA-based Adapters. Training is conducted on the FLEURS dataset augmented with DNS-style noise. The student is optimized by jointly minimizing CTC loss and KL-based distillation loss, enabling efficient adaptation while preserving recognition accuracy.
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