| --- |
| library_name: maubert |
| license: mit |
| tags: |
| - acoustic-unit-discovery |
| - hubert |
| - maubert |
| - quantisation |
| base_model: |
| - coml/maubert-feat |
| - coml/maubert-phone |
| - facebook/hubert-base-ls960 |
| language: |
| - deu |
| - eng |
| - eus |
| - fra |
| - jpn |
| - swa |
| - tam |
| - tha |
| - tur |
| - ukr |
| - wol |
| - cmn |
| --- |
| |
| # MauBERT quantisers |
|
|
| The quantisation models used to produce the pseudo-labels of |
| the [MauBERT paper](https://aclanthology.org/2026.acl-long.24/) (ACL 2026). Each of them maps the frame-level features |
| of one layer of [`coml/maubert-feat`](https://huggingface.co/coml/maubert-feat), |
| [`coml/maubert-phone`](https://huggingface.co/coml/maubert-phone) or the HuBERT base model to discrete units, and was |
| fitted on 10 hours of one language. |
|
|
| The fine-tuned models themselves are not released: use [`maubert`](https://github.com/bootphon/maubert) to extract the |
| features of a pre-trained model, then one of these quantisers to transcribe them into units. |
|
|
| ## Layout |
|
|
| ``` |
| <model>/<dataset>/<language>/<n_clusters>cl.<ext> |
| ``` |
|
|
| `manifest.tsv` lists every file with its base model, layer, quantisation method, dataset, language and number of |
| clusters. |
|
|
| | model | base model | layer | method | file | |
| |----------------------|---------------|-----------------|----------------|-------| |
| | `fr-feat-simple` | MauBERT-feat | `feat_proj` | `feat-freq` | `.pt` | |
| | `fr-kmeans-feature` | MauBERT-feat | `feat_proj` | `kmeans` | `.km` | |
| | `fr-kmeans-hubert_9` | MauBERT-feat | HuBERT layer 10 | `kmeans` | `.km` | |
| | `fr-phone-ft` | MauBERT-feat | `phone_proj` | `phone-ft` | `.ft` | |
| | `kmeans-hubert_11` | HuBERT base | HuBERT layer 12 | `kmeans` | `.km` | |
| | `pr-kmeans-phone` | MauBERT-phone | `phone_proj` | `kmeans` | `.km` | |
| | `pr-kmeans-ups_proj` | MauBERT-phone | `ups_proj` | `kmeans` | `.km` | |
| | `pr-phone-ft` | MauBERT-phone | `phone_proj` | `phone-ft` | `.ft` | |
| | `pr-phone-simple` | MauBERT-phone | `phone_proj` | `phone-simple` | `.pt` | |
|
|
| The `.km` files are `joblib` pickles of a scikit-learn `MiniBatchKMeans`, the `.ft` files are `torch` state dicts of |
| the adapted phone-prediction layer, and the other `.pt` files are `torch` tensors. <!-- TODO: record the exact |
| scikit-learn version the `.km` pickles were written with, they are version-sensitive. --> |
|
|
| ## Datasets and languages |
|
|
| | dataset | languages | |
| |----------------------------------|------------------------------------------------------------------------------------| |
| | `discophon` | `cmn`, `deu`, `eng`, `eus`, `fra`, `jpn`, `swa`, `tam`, `tha`, `tur`, `ukr`, `wol` | |
| | `dev-languages` | `sw`, `ta`, `th`, `tr`, `uk` | |
| | `zrc2017` (ZeroSpeech 2017 test) | `english`, `french`, `german` (LANG1), `mandarin`, `wolof` (LANG2) | |
| | `casual-languages` | `english`, `french` | |
|
|
| Every quantiser comes in a 100-cluster variant (370 for `pr-phone-simple`, which uses the full phone inventory of |
| MauBERT-phone). The `discophon` languages additionally have a variant with as many clusters as the language has phones: |
| `cmn` 43, `deu` 42, `eng` 40, `eus` 30, `fra` 35, `jpn` 43, `swa` 30, `tam` 30, `tha` 41, `tur` 28, `ukr` 36, |
| `wol` 40. |
|
|
| ## Usage |
|
|
| Extract the features of the corresponding layer with [`maubert`](https://github.com/bootphon/maubert), then transcribe |
| them with the quantiser: |
|
|
| ```bash |
| hf download coml/maubert-quantisers fr-kmeans-hubert_9/discophon/deu/100cl.km --local-dir quantisers |
| |
| python -m maubert.features MANIFEST_FILE FEATURE_DIR \ |
| --type maubert-feat --checkpoint coml/maubert-feat --layer 9 |
| |
| python -m maubert.transcribe FEATURE_DIR \ |
| quantisers/fr-kmeans-hubert_9/discophon/deu/100cl.km units.jsonl kmeans |
| ``` |
|
|
| ## Citing |
|
|
| ```bibtex |
| @inproceedings{ortiztandazo-etal-2026-maubert, |
| title = "{M}au{BERT}: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery", |
| author = "Ortiz Tandazo, Angelo and |
| Khentout, Manel and |
| Benchekroun, Youssef and |
| Hueber, Thomas and |
| Dupoux, Emmanuel", |
| editor = "Liakata, Maria and |
| Moreira, Viviane P. and |
| Zhang, Jiajun and |
| Jurgens, David", |
| booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)", |
| month = jul, |
| year = "2026", |
| address = "San Diego, California, United States", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2026.acl-long.24/", |
| doi = "10.18653/v1/2026.acl-long.24", |
| pages = "568--585", |
| ISBN = "979-8-89176-390-6", |
| } |
| ``` |
|
|
