Distil-Whisper：通过大规模伪标记实现稳健知识蒸馏

摘要

随着预训练语音识别模型的规模增大，在低延迟或资源受限的环境中运行这些大型模型变得具有挑战性。在这项工作中，我们利用伪标记来构建一个大规模开源数据集，用于将Whisper模型提炼成一个更小的变体，称为Distil-Whisper。通过使用简单的词错误率（WER）启发式方法，我们仅选择最高质量的伪标签进行训练。提炼后的模型速度提升了5.8倍，参数数量减少了51%，同时在零次转移设置中，在分布外测试数据上的WER仅相差1%。Distil-Whisper保持了Whisper模型对复杂声学条件的稳健性，同时在长形音频上减少了幻觉错误的倾向。Distil-Whisper旨在与Whisper配对进行推测解码，从而实现2倍速度提升，同时在数学上确保与原始模型相同的输出。为了促进该领域的进一步研究，我们公开了我们的训练代码、推理代码和模型。

English

As the size of pre-trained speech recognition models increases, running these large models in low-latency or resource-constrained environments becomes challenging. In this work, we leverage pseudo-labelling to assemble a large-scale open-source dataset which we use to distill the Whisper model into a smaller variant, called Distil-Whisper. Using a simple word error rate (WER) heuristic, we select only the highest quality pseudo-labels for training. The distilled model is 5.8 times faster with 51% fewer parameters, while performing to within 1% WER on out-of-distribution test data in a zero-shot transfer setting. Distil-Whisper maintains the robustness of the Whisper model to difficult acoustic conditions, while being less prone to hallucination errors on long-form audio. Distil-Whisper is designed to be paired with Whisper for speculative decoding, yielding a 2 times speed-up while mathematically ensuring the same outputs as the original model. To facilitate further research in this domain, we make our training code, inference code and models publicly accessible.

Distil-Whisper：通过大规模伪标记实现稳健知识蒸馏

Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling

摘要

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