Compliance statement — data, models and licences

Google WAXAL ASR Challenge, Phase 2 · user Pricilegangbe · public score 0.761204

This document discloses every data source and pre-trained checkpoint used to build the submitted system, as required by the organisers.

1. Applicable rule on external datasets

The Rules page states "You may use only the datasets specified for this challenge", while the Phase 1 section states that participants "may supplement the provided challenge data with other publicly available open-source speech or language datasets". This inconsistency was raised on the challenge discussion board and clarified by the organisers (discussion 33616, answer by meganomaly (Zindi), 28 June 2026):

"The intended rule is that participants may use additional publicly available open-source speech or language datasets during both Phase 1 and when developing the model they ultimately use for Phase 2. Any external datasets used must: be publicly accessible and appropriately licensed for research or development; comply with the competition rules; and be clearly disclosed in your final solution documentation."

The present section 2 is that disclosure.

2. Data sources used in the submitted system

Dataset	Provenance	Licence	Used for
`google/WaxalNLP` — labelled train split	Official challenge dataset	Challenge dataset (CC-BY 4.0 sharing)	Fine-tuning of all three acoustic models; KenLM text; language-ID statistics; held-out development split
`google/WaxalNLP` — unlabelled Shona split	Official challenge dataset	idem	Self-training: pseudo-labels for the Shona model (labels generated by our own model, no external labels)
`KasuleTrevor/lg_100hrs`	External, public Hugging Face dataset, no gating. ~68.8 k Luganda audio–text pairs. Derived from Mozilla Common Voice (audio identifiers are `common_voice_lg_*`).	Underlying Common Voice corpus is released under CC0-1.0 (public-domain dedication). The Hugging Face mirror carries no separate licence tag.	Additional Luganda training material for the multilingual acoustic model `joint_cont`

Exactly which model used which data

Model	Role in the submission	Training material	Size
`joint_cont_best`	Lingala decoder	WAXAL lin + WAXAL lug + `lg_100hrs` (external) + WAXAL sna, concatenated	95,467 clips / 291.6 h
`sna_ps_best`	Shona decoder	WAXAL sna (labelled) + pseudo-labelled WAXAL sna unlabelled split	61,956 clips / 362.6 h
`sna_r2_best`	Shona n-best rescorer	WAXAL sna (labelled) only	13,799 clips / 79.6 h
KenLM 5-gram	Lingala shallow fusion	13,960 WAXAL Lingala training transcripts only — no external text	—

Note on joint_cont: the external Luganda material is not in either of the two evaluated Phase 2 languages. It was included because multilingual training improves the shared encoder; Luganda was one of the three focus languages when the model was developed.

Not used in the final system

Several exploration scripts kept in code/ for completeness mention datasets that are not part of the submitted system — AfriVoice (referenced in prep_data.py job list and in the train_wbert.py docstring example) and BantuLanguagesInitiative/CSRC. Neither appears in the training manifest of any model used in the final submission; this can be verified from the training manifests listed in the table above.

2-bis. Exact configuration of the submitted system

The submission is sub_LMA06.csv (public score 0.761204, WER 0.3662 / CER 0.1121). Test clips are routed by language using artifacts/test_lang.json (446 Lingala / 446 Shona).

Language	Acoustic model	Decoding
Lingala	`joint_cont_best`	CTC beam search (`pyctcdecode`), beam width 64, shallow fusion with the KenLM 5-gram: alpha = 0.6, beta = 1.0, lm_score_boundary = True. The case of the first character is then copied from the greedy hypothesis.
Shona	`sna_ps_best`	CTC beam search without any language model, beam width 256; the 50-best list is re-scored by `sna_r2_best` (CTC forward score, weight 4.0) and the highest-scoring hypothesis is kept.

alpha = 0.6 was selected on the public leaderboard, not on a held-out split: our offline development sets proved anti-correlated with the leaderboard on the language-model axis. The measured curve is 0.5 → 0.759972, 0.6 → 0.761204, 0.7 → 0.760865, 0.9 → 0.755362, 1.2 → 0.721312.

Explored and not part of the submitted system

For the avoidance of doubt, the following were built and measured but are absent from the submission:

a Shona KenLM (5-gram over WAXAL Shona transcripts) — measured −0.00089, rejected;
a Lingala KenLM enriched with pseudo-labelled text from the WAXAL unlabelled split — measured −0.00444, rejected;
a second round of Shona self-training (sna_ps2) — measured −0.00018, rejected;
a character-level neural rescoring LM — rejected.

The submitted KenLM therefore uses only the 13,960 Lingala training transcripts, as stated in section 2.

3. Data-leakage statement

No Phase 1 test transcriptions were used for training, tuning or pseudo-labelling, at any point, even though they are publicly available.
No Phase 2 test audio or transcriptions were used for training or adaptation.
No pseudo-labels obtained from closed or commercial APIs. The only pseudo-labels used (Shona) were produced by our own model on the challenge's own unlabelled split.
No publicly released checkpoint that had been fine-tuned on the WAXAL corpus was used, in line with the organisers' answer in the same discussion thread.

4. Pre-trained checkpoint

Checkpoint	Role	Licence
`facebook/w2v-bert-2.0`	Initialisation of all three acoustic models (speech encoder of SeamlessM4T v2, self-supervised pre-training by Meta AI, openly available to everyone).	MIT

This is the only pre-trained checkpoint in the system. It was not trained on WAXAL data. The three fine-tuned artifacts are derivatives of it.

5. Third-party software

Component	Role	Licence
`transformers`, `datasets`, `accelerate`	model implementation and training loop	Apache-2.0
`torch`	tensor backend	BSD-3-Clause
`pyctcdecode`	CTC beam search with shallow fusion	Apache-2.0
`kenlm`	n-gram language model	LGPL-2.1
`soundfile` / `libsndfile`	audio I/O	BSD-3-Clause / LGPL-2.1
`jiwer`	our own WER/CER evaluation	Apache-2.0
`ffmpeg`	48 kHz → 16 kHz resampling of the test clips	LGPL-2.1 / GPL-2.0

All packages are open-source, as required. No component carries a non-commercial restriction. facebook/mms-1b-all (CC-BY-NC-4.0) was evaluated during exploration and is not part of the final system.

6. Reproducibility

The development split is deterministic (seed 1234), regenerated by code/build_devhard_linsna.py.
The full pipeline is described step by step in README.md §3.2.
Training used one NVIDIA L40S GPU; inference requires substantially less.

7. Credentials

No access token, key or credential is present in this package. Scripts that need Hugging Face access read the token from the HF_TOKEN environment variable or the local Hugging Face cache; nothing is hard-coded.