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Check our open-source repository https://github.com/boson-ai/higgs-audio for more details! > [!TIP] > For **licensing** inquiries (e.g., commercial use) or **collaboration** opportunities, please reach out to us at contact@boson.ai. We introduce a new discretized audio tokenizer that runs at just **25 frames per second** while keeping—or even improving—audio quality compared to tokenizers with twice the bitrate. Our model is the first to train on **24 kHz data** covering speech, music, and sound events in one unified system. It also uses a simple non-diffusion encoder/decoder for fast, batch inference.

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A general-purpose HuBERT-Base checkpoint released by Boson AI, used inside the Higgs Audio Tokenizer as the semantic teacher. - Standard HuBERT-Base architecture (12 transformer layers, hidden size 768, ~95M params) - 16 kHz audio input - Loadable via `AutoModel` with `trust_remote_code=True` - Outputs 768-dim per-layer hidden states (`output_hidden_states=True`) The Higgs Audio Tokenizer distills semantic features from this HuBERT into its semantic branch. From `boson_multimodal/audio_processing/higgs_audio_tokenizer.py` (`semantic_techer="hubert_base_general"`):

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reve-base

brain-bzh

REVE (project page here) is a transformer-based foundation model for EEG signal processing. It was trained on 60k hours of EEG data from various sources and is designed to be adaptable to any electrode configuration and a wide range of EEG-based tasks. REVE (Representation for EEG with Versatile Embeddings), a pretrained encoder explicitly designed to generalize across diverse EEG signals. REVE introduces a novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement. Using a masked autoencoding objective, we pretrain REVE on over 60,000 hours of EEG data from 92 datasets spanning 25,000 subjects. **Developed by** the BRAIN team and UdeM

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Wrapper to provide electrode positions to use the REVE EEG Foundation Model. - **Developed by:** the BRAIN team and UdeM - **Funded by :** AI@IMT, ANR JCJC ENDIVE, Jean Zay (with project numbers), Alliance Canada and Region Bretagne. REVE (Representation for EEG with Versatile Embeddings) is a pretrained model explicitly designed to generalize across diverse EEG signals. REVE introduces a novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement.

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datasets: - UMLS **[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! **[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**! SapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. Please use the mean-pooling of the output as the representation.

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