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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",
}
```
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