  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.
