transformers v5.18.0 — Release 5.18.0

## New Model additions ### Nemotron 3 Diarization Nemotron 3 Diarization is an open-weight streaming speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference, handles up to eight speakers, and orders speaker outputs by each speaker's first arrival in the input audio. The model uses the Arrival-Order Speaker Cache (AOSC) [1](https://huggingface.co/papers/2507.18446) and FIFO queue introduced for Streaming Sortformer [1](https://huggingface.co/papers/2507.18446), [2](https://huggingface.co/papers/2409.06656). A single checkpoint supports configurable latency profiles, from an 80 ms input buffer to a 30.4 s offline-style buffer, and configurable output frame resolution in multiples of 10 ms. With chunked inference, the maximum audio duration is not limited. **Links:** [Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/nemotron3_diarization) * Add Nemotron3Diarization (#49056) by @eustlb in [#49056](https://github.com/huggingface/transformers/pull/49056) ### NemotronH Omni NemotronH Omni is a multimodal reasoning model from NVIDIA that pairs the [NemotronH](https://huggingface.co/docs/transformers/main/en/model_doc/nemotron_h) hybrid Mamba-Transformer language model with a [RADIO](https://huggingface.co/docs/transformers/main/en/model_doc/radio) vision encoder and an optional Parakeet-based sound encoder.…