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.
python streaming numpy end-to-end pytorch slot-filling intent-detection spoken-language-understanding instruction-data neural-audio-codec speech-language-model
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Updated
Sep 30, 2026 - Python