Datasets:
license: cc-by-sa-4.0
language:
- ln
tags:
- speech
- speaker-embeddings
- asr
- lingala
pretty_name: Waxal TTIA enrolment gallery
Waxal TTIA enrolment gallery
Data assets for the Test-Time Idiolect Adaptation (TTIA) Lingala lane of the
waxal-asr-solution
pipeline (Google Waxal ASR Challenge on Zindi). Fetched into place by
models/download_assets.py in that repository; every file is verified there
against a recorded SHA-256.
| File | Size | Contents |
|---|---|---|
enrollment.npz |
998 MB | MMS-1B hidden-state voice vectors for 21,566 enrolment clips (layers 4, 6, 8, 12, 16; the pipeline reads 4 and 8), plus their clip ids |
enrollment.parquet |
661 KB | enrolment manifest: clip id, idiolect profile key, derived audio path, language |
train.rows.parquet |
4.5 MB | per-profile Lingala training text the TTIA fusion scores candidates against |
Provenance and licence
Derived from google/WaxalNLP (Lingala labelled training audio and transcripts, and clips from the unlabelled release), © the WaxalNLP authors, licensed CC-BY-SA-4.0 / CC-BY-4.0. These derivatives are published under CC-BY-SA-4.0 with attribution to google/WaxalNLP, as ShareAlike requires. No evaluation/test audio or transcripts are included.
The files are reproducible from google/WaxalNLP with
inference/ttia/build_enrollment.py, embed.py and merge.py in the
solution repository; this upload exists so a reviewer can run the pipeline
without rebuilding them (a ~21,500-clip GPU embedding pass).
| File | SHA-256 |
|---|---|
enrollment.npz |
e112a828ef220fc38eb6ff6e24c49a3e2ec38834f2e806ade0fe3bbdf1fe7086 |
enrollment.parquet |
4fb6005ce024683ea4d5a111615438ba204a37ac7a4a0a57f935046d81f75ce7 |
train.rows.parquet |
b44df5ca8e370df28408bb71ad6628aaa6d316ccf3a4e6b97d11d17d663e53bc |