| --- |
| 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}, |
| } |
| ``` |
|
|
