README.md

---
license: cc0-1.0
pretty_name: VoxCommunis Artifacts
task_categories:
  - automatic-speech-recognition
tags:
  - phonetics
  - multilingual
source_datasets:
  - mozilla-foundation/common_voice_17_0
  - pacscilab/VoxCommunis
language_bcp47:
  - ab
  - am
  - ba
  - be
  - bg
  - bn
  - ca
  - ckb
  - cs
  - cv
  - dv
  - el
  - eu
  - fr
  - gn
  - ha
  - hi
  - hsb
  - hu
  - hy-AM
  - id
  - it
  - ja
  - ka
  - kk
  - kmr
  - ko
  - ky
  - lij
  - lt
  - ml
  - mn
  - mr
  - mt
  - myv
  - nan-tw
  - nl
  - pa-IN
  - pl
  - pt
  - ro
  - ru
  - rw
  - sk
  - sl
  - sq
  - sr
  - sv-SE
  - sw
  - ta
  - th
  - tk
  - tr
  - tt
  - ug
  - uk
  - ur
  - uz
  - vi
  - yo
  - yue
  - zh-CN
  - zh-HK
---

# VoxCommunis Artifacts

The data artifacts used to train and evaluate the MauBERT models, introduced by
the [MauBERT paper](https://aclanthology.org/2026.acl-long.24/): manifests, frame-level phone alignments, per-language
phone inventories, and the language table.

Companion model repositories:

- [`coml/maubert-feat`](https://huggingface.co/coml/maubert-feat) — articulatory feature prediction.
- [`coml/maubert-phone`](https://huggingface.co/coml/maubert-phone) — IPA phone prediction.

### No audio is included

The recordings come from [Common Voice 16.1](https://commonvoice.mozilla.org/en/datasets), which you must download
yourself; the phone annotations come from
[VoxCommunis](https://huggingface.co/datasets/pacscilab/VoxCommunis). This repository only ships the derived
annotations and the file lists that tie them to the Common Voice audio.

### Language split

At the time of download (August 2024), there were 63 languages, out of which 5 languages were held-out as development
languages (Swahili, Tamil, Thai, Turkish and Ukrainian) and 3 languages were _unused_ due to their presence
in [ZRC2017](https://zerospeech.com/tasks/task_1/benchmarks_datasets/#zrc2017-and-abx17), that is the test languages
(French and two Chinese variants).

## Files

- `vox_communis_languages.tsv`: The 63 VoxCommunis languages, with family, branch, duration in minutes (before and
  after 50h capping), and a `split` column marking the 55 pre-training languages (`train`) against the 8 held-out
  languages used as development (`dev`) or test (`test`) languages.
- `inventories.jsonl`: The IPA phone inventories, one language per line, `{"language": <code>, "name": <name>,
  "phones": [...]}`, exactly as they come out of the VoxCommunis alignments (after character normalisation).
- `canonical_inventories.jsonl`: The same inventories, with every phone replaced by
  its [PanPhon](https://github.com/dmort27/panphon) canonical representative
  (`maubert.data.FeatureDecoder.segment_to_representative`) — these are the segments the models actually index. **This
  is the file the library reads**: it is required to reduce the phone head when extracting features from the phone
  projection layer, for **both** model variants.
- `full_dataset/multilingual-{train,dev,test}.{tsv,align}`: The original multilingual Common Voice splits (**not
  used**), plus the reduced subsets used for validation and testing of the multilingual pre-training, namely
  `multilingual-dev-1h` and `multilingual-test-2h`.
- `full_dataset/{train,dev,test,train-50h}/{manifests,alignments}/<code>.{tsv,align}`: The same data as above, but
  separated by both split and language. **`train-50h` is the duration-capped split actually used during multilingual
  pre-training.**
- `dev_languages/{train-10min,train-1h,train-10h,dev,test}/{manifests,alignments}/`: The extracted splits for the 5
  held-out development languages used for zero- and few-shot acoustic unit discovey
  (see [task 1](https://zerospeech.com/tasks/task_1/tasks_goals/) of ZeroSpeech challenge series), with the
  corresponding ABX `items/` for the `dev` (released but never used due to the high number of speakers) and `test`
  sets.

Manifests (`.tsv`) list audio files: the first line is the corpus root, every subsequent line is
`<path relative to root>\t<number of samples>`. As shipped, the root is the placeholder
`/path/to/cv-corpus-16.1` and every path is a bare filename; see [Re-rooting the manifests](#re-rooting-the-manifests)
to point them at your copy of the corpus. Alignments (`.align`) carry the frame-level phone labels, one line per
utterance, `<utterance id>\t<space-separated phones>`.

## Usage

Install [`maubert`](https://github.com/bootphon/maubert), then download this repository:

```python
from huggingface_hub import snapshot_download

data_dir = snapshot_download("coml/vox-communis-artifacts", repo_type="dataset")
```

### Re-rooting the manifests

Every manifest ships with the placeholder root `/path/to/cv-corpus-16.1` on its first line, and its rows are bare
`.wav` filenames. Point the manifests at your own copy of the corpus before training:

```python
from maubert.data.io import update_manifest

update_manifest(
    f"{data_dir}/full_dataset/multilingual-train.tsv",
    "/data/cv-corpus-16.1",
    file_extension=".mp3",
)
```

Matching is done on the file stem, which is identical between the original `.mp3` and the converted `.wav`, so this
works against an unconverted Common Voice release and against any directory layout under the root you pass — only pass
the extension your copy actually uses. **However, beware that MauBERT models were trained on and accept `.wav` files
only.**

The helper rewrites the manifest **in place** and leaves the previous version alongside it as
`multilingual-train.tsv.bak`; it raises if that backup already exists, so run it once per download. It also raises if
any utterance in the manifest is missing from your tree.

Once re-rooted, the files map directly onto the `data` section of `configs/train_feat.yaml` and
`configs/train_phone.yaml` in the [`maubert`](https://github.com/bootphon/maubert) repository —
`full_dataset/train-50h/{manifests,alignments}`
as `train_manifest` / `train_alignment` and `full_dataset/multilingual-dev-1h.{tsv,align}` as `val_manifest` /
`val_alignment`, while `canonical_inventories.jsonl` fills the `downstream.inventory_file` field used by the
monolingual configs.

### Looking up a language inventory

```python
from maubert.data.phone_dataset import get_language_inventory

# Either the language name or its BCP-47 code
inventory = get_language_inventory(f"{data_dir}/canonical_inventories.jsonl", "italian")
```

## Citing

Please cite MauBERT alongside [Common Voice](https://commonvoice.mozilla.org/en/datasets)
and [VoxCommunis](https://huggingface.co/datasets/pacscilab/VoxCommunis):

```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",
}
```