MURMUR: An Efficient Inference System for Long-Form ASR
Long-form automatic speech recognition (ASR) requires both high accuracy and low latency, but existing systems force a trade-off between the two. Chunk-based pipelines process audio in parallel windows for low latency, but lose cross-chunk context and need brittle heuristics to align speakers and timestamps at boundaries. Long-context ASR models resolve everything in a single pass for better accuracy, but are an order of magnitude slower. We propose Murmur, an inference system that overcomes this trade-off by operating at two levels. At the inter-chunk level, we revisit the chunk-based pipeline for modern long-context ASR, treating chunk size as a tunable hyperparameter, and show that int...
| Authors | Wei-Tzu Lee, Keisuke Kamahori, Baris Kasikci |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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