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license: mit
pretty_name: DiscoPhon artifacts
tags:
- speech
- phoneme-discovery
- discrete-units
- discophon
---
# DiscoPhon artifacts
Discrete units and evaluation scores of the models on the [DiscoPhon](https://benchmarks.cognitive-ml.fr/discophon/)
[leaderboard](https://benchmarks.cognitive-ml.fr/discophon/leaderboard/).
## Baselines
| Key | Model |
|----------------------|-----------------------------------------------------------------------------|
| `spidr-mmsulab` | [coml/spidr-mmsulab](https://huggingface.co/coml/spidr-mmsulab) |
| `spidr-vp20` | [coml/spidr-vp20](https://huggingface.co/coml/spidr-vp20) |
| `hubert-mmsulab-it2` | [coml/hubert-base-mmsulab](https://huggingface.co/coml/hubert-base-mmsulab) |
| `hubert-vp20-it2` | [coml/hubert-base-vp20](https://huggingface.co/coml/hubert-base-vp20) |
The finetuned checkpoints are in [coml/discophon-finetuned-baselines](https://huggingface.co/coml/discophon-finetuned-baselines),
under the same names.
## Layout
One directory per model, then one directory per evaluation run:
```
{key}/
├── info.json
└── {condition}/{folder}/{layer}/
├── units-{language}-{split}.jsonl
└── scores.jsonl
```
- `condition`: `zero-shot`, or `ft-{language}-{duration}` for the model finetuned on `duration` (`10min`, `1h`,
`10h`) of training data in `language`.
- `folder`: how the representations of the layer are evaluated:
- `many_to_one`: 256 units, each mapped to its most frequent phoneme.
- `one_to_one`: as many units as phonemes, plus one, mapped by a bijection. The directory also holds the
K-means models (`km{K}-*.joblib`, with K the number of units).
- `continuous`: ABX on the continuous representations. Scores only, no units.
- `layer`: Transformer layer, starting at 1.
`info.json` gives the step between units (in ms) and the layers reported on the leaderboard, for each track and
condition (`"0"` for zero-shot, `"10h"` for finetuned on 10h):
```json
{"step_units": 20, "layers": {"many_to_one": {"0": 6, "10h": 6}, "one_to_one": {"0": 6, "10h": 6}}}
```
The other runs (other layers, finetuning durations, and languages) are provided for analysis.
## What is included
For the four baselines:
| Folder | Conditions | Layers | Languages evaluated |
|---------------|--------------------------------------------|-------------------------------|---------------------------|
| `many_to_one` | zero-shot, finetuned on 10min, 1h, 10h | SpidR: 5 to 12, HuBERT: 1 to 12 | all 12 |
| `continuous` | zero-shot, finetuned on 10min, 1h, 10h | 1 to 12 | all 12 |
| `one_to_one` | zero-shot, finetuned on 10h | leaderboard layer | all 12 (zero-shot), finetuning language (finetuned) |
SpidR units come from the prediction heads of the layer. HuBERT units come from a K-means on the layer, trained
on the pretraining data (zero-shot) or the finetuning data (finetuned).
Languages: `cmn`, `deu`, `eng`, `eus`, `fra`, `jpn`, `swa`, `tam`, `tha`, `tur`, `ukr`, `wol`. Splits: `dev`, `test`.
## File formats
Units, one utterance per line, as read by the `discophon` evaluation:
```json
{"file": "F07_O_001", "units": [75, 131, 159, 69, 215, 215, ...]}
```
Scores, one score per line, as written by `python -m discophon.benchmark`:
```json
{"language": "deu", "split": "test", "metric": "per", "score": 0.6574108529121169}
```
Metrics: `per`, `r_val`, `f1`, `pnmi` (phoneme discovery), `triphone_abx_discrete_{within,across}_speaker`
(ABX on units), and `triphone_abx_continuous_{within,across}_speaker` (ABX on continuous representations).
## Usage
Download the scores of one model, or the units of one run:
```console
hf download coml/discophon-artifacts --repo-type dataset --include "spidr-vp20/*/scores.jsonl" --local-dir artifacts
hf download coml/discophon-artifacts --repo-type dataset --include "spidr-vp20/zero-shot/many_to_one/6/*" --local-dir artifacts
```
Evaluate the units of a run again with [`discophon`](https://github.com/bootphon/discophon):
```console
python -m discophon.benchmark /path/to/discophon_data artifacts/spidr-vp20/zero-shot/many_to_one/6 scores.jsonl --kind many-to-one
```
Export the leaderboard scores of a model:
```console
python -m discophon.leaderboard export artifacts/spidr-vp20
```
## Submissions
To add a model to the leaderboard, open a pull request on this dataset with a directory following the layout above,
and one on the GitHub repository with the leaderboard scores. See the
[submission guide](https://benchmarks.cognitive-ml.fr/discophon/guide/submission/).
## Citation
```bibtex
@inproceedings{poli2026discophon,
title = {{DiscoPhon: Benchmarking the Unsupervised Discovery of Phoneme Inventories With Discrete Speech Units}},
author = {Maxime Poli and Manel Khentout and Angelo {Ortiz Tandazo} and Ewan Dunbar and Emmanuel Chemla and Emmanuel Dupoux},
year = {2026},
booktitle = {{Interspeech 2026}},
pages = {6664--6669},
doi = {10.21437/Interspeech.2026-2791},
issn = {2958-1796},
}
```
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