mstar-project/mstar
A high-performance, universal serving framework for any-to-any models.
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Star counts measured between 09:44 and 09:50 (BKK).
A high-performance, universal serving framework for any-to-any models.
End-to-end spoken language understanding toolkit: a small trainable neural audio codec with RVQ, joint intent-and-slot models over speech features, streaming partial intents, a speech-LLM instruction data pipeline, and turn-level evaluation with reproducible reports. NumPy core with a CPU-only torch extra; synthetic data only, fully offline.
That's all 2 repositories in this period.