Add ASR v2 speaker-disjoint split definitions

Adds data/ASR_v2/: the complete v2 split assignment for all 19 ASR languages,
sharded per language, with the v1-vs-v2 audit tables and full provenance.

Purely additive. No existing file is modified and no config is changed, so
every current load_dataset call resolves exactly as it did before.

Eval-set speaker leakage falls from 97-100% (in 11 of 19 languages) to 0% in
every language. The split map reproduces v2 against any copy of v1 without
downloading anything further.

data/ASR_v2/README.md

@@ -0,0 +1,305 @@
+---
+language:
+- ach
+- aka
+- amh
+- dag
+- dga
+- ewe
+- ful
+- kpo
+- lin
+- lug
+- mlg
+- myx
+- nyn
+- orm
+- sid
+- sna
+- tir
+- wal
+- xog
+license:
+- CC-BY-4.0
+- CC-BY-SA-4.0
+task_categories:
+- automatic-speech-recognition
+source_datasets:
+- google/WaxalNLP
+multilinguality:
+- multilingual
+annotation_creators:
+- human-annotated
+size_categories:
+- 100K<n<1M
+pretty_name: WAXAL ASR — speaker-disjoint re-split (v2)
+tags:
+- audio
+- automatic-speech-recognition
+- african-languages
+- speaker-disjoint-splits
+- resplit
+---
+
+# WAXAL ASR — speaker-disjoint re-split (v2)
+
+A speaker-disjoint re-split of the ASR half of this dataset, published by the
+dataset maintainers alongside v1. **v1 is unchanged and stays supported** --
+every existing config resolves to exactly the files it always did.
+
+**The audio is unchanged.** Every payload is the byte-for-byte MP3 from v1;
+this is a re-partitioning of the same recordings, not a re-encode and not a
+quality filter. What changes is *which utterances are in which split*.
+
+- 438,230 labelled utterances, 2,242 hours,
+  19 languages, 308 shards, 122 GB
+- v1 remains untouched and fully usable: `load_dataset("google/WaxalNLP", "ach_asr")`
+- **The repacked audio is not published yet.** What is here is the split
+  definition: every utterance's v1 and v2 split, for all 19 languages. It is
+  complete and final -- the audio is a materialisation of it, not a
+  precondition for using it. Apply it to your existing copy of v1 with the
+  snippet below.
+
+## Why
+
+The v1 ASR splits are not safe to benchmark on. Measured across all
+19 languages:
+
+| Code | Language | v1 eval speakers also in train | v2 | v1 eval transcripts also in train | v2 | v2 leakage vs chance |
+|---|---|---|---|---|---|---|
+| `ach` | Acholi | 99.0 % | **0.0 %** | 0.6 % | 1.5 % | 0.88 (-2.8σ) |
+| `aka` | Akan | 100.0 % | **0.0 %** | 0.0 % | 0.0 % | 0.95 (-0.9σ) |
+| `amh` | Amharic | 0.0 % | **0.0 %** | 0.1 % | 0.4 % | 0.70 (-6.0σ) |
+| `dag` | Dagbani | 97.9 % | **0.0 %** | 0.2 % | 0.1 % | 0.75 (-9.3σ) |
+| `dga` | Dagaare | 100.0 % | **0.0 %** | 19.1 % | 0.2 % | 0.83 (-3.0σ) |
+| `ewe` | Ewe | 99.8 % | **0.0 %** | 0.0 % | 0.0 % | 0.86 (-3.5σ) |
+| `ful` | Fula | 99.4 % | **0.0 %** | 0.0 % | 0.0 % | 0.97 (-0.5σ) |
+| `kpo` | Ikposo | 99.1 % | **0.0 %** | 0.0 % | 0.1 % | 0.76 (-4.4σ) |
+| `lin` | Lingala | 98.7 % | **0.0 %** | 0.1 % | 0.1 % | 1.06 (+1.0σ) |
+| `lug` | Luganda | 99.6 % | **0.0 %** | 0.3 % | 0.3 % | 0.82 (-5.8σ) |
+| `mas` | Masaaba | 98.4 % | **0.0 %** | 0.2 % | 0.2 % | 0.80 (-4.8σ) |
+| `mlg` | Malagasy | 97.7 % | **0.0 %** | 0.0 % | 0.0 % | 0.90 (-1.4σ) |
+| `nyn` | Runyankole | 98.5 % | **0.0 %** | 0.7 % | 0.5 % | 0.78 (-6.1σ) |
+| `orm` | Oromo | 0.0 % | **0.0 %** | 0.0 % | 0.2 % | 0.51 (-7.9σ) |
+| `sid` | Sidama | 0.0 % | **0.0 %** | 0.0 % | 0.2 % | 0.67 (-4.8σ) |
+| `sna` | Shona | 97.0 % | **0.0 %** | 0.1 % | 0.1 % | 1.09 (+1.0σ) |
+| `sog` | Soga | 98.3 % | **0.0 %** | 0.0 % | 0.0 % | 0.87 (-3.8σ) |
+| `tir` | Tigrinya | 0.0 % | **0.0 %** | 2.0 % | 0.1 % | 0.69 (-4.7σ) |
+| `wal` | Wolaytta | 0.0 % | **0.0 %** | 0.0 % | 0.1 % | 0.83 (-3.5σ) |
+
+A model that has heard the test speakers during training reports a score that
+is partly speaker memorisation rather than recognition, and the size of that
+effect is not knowable after the fact. The re-split removes the channel rather
+than trying to correct for it.
+
+## How the splits were built
+
+Speakers are the unit of assignment, so **speaker-disjointness is structural**:
+an utterance cannot cross a split boundary without its speaker, and a speaker
+belongs to exactly one split. The assignment is then chosen to satisfy, in
+order of precedence:
+
+1. **Speaker-disjointness** — a hard constraint, enforced by construction.
+2. **Low train/eval lexical leakage** — a `sqrt(idf)`-weighted affinity over
+   shared sentences, word trigrams and word types is minimised across the
+   train/eval boundary. Because inverse document frequency goes to zero for
+   items nearly everyone uses, *shared common vocabulary is invisible to the
+   objective* and only shared rare content is penalised. This is deliberate:
+   common-word overlap between train and test is coverage, not leakage, and a
+   test set that avoided it would measure a distribution nobody deploys against.
+3. **Gender balance** — the female share of each eval split is matched to the
+   corpus, weighted by reference words (the weighting under which a
+   micro-averaged WER is or is not gender-representative).
+
+Targets are 80/10/10 by duration, but two bounds move that in practice, and
+both are recorded per language in `splits_manifest.json`: the eval share is
+allowed to **grow** to reach a 20,000-reference-word floor in the smallest
+languages, and is **capped** at 12 hours or 2,000 utterances per eval split in
+the largest — so a 220-hour language like Amharic evaluates on about 5.5 % per
+side rather than 10 %, with the surplus going to train. No single speaker may
+exceed 25 % of an eval split. The search is a randomised
+longest-processing-time greedy seed followed by steepest-descent local search,
+seeded from `20260730` and fully deterministic; re-running reproduces
+the assignment hash recorded per language in `splits_manifest.json`.
+
+Every goal is scaled against a **null distribution** of
+200 random size-matched speaker-disjoint splits
+of the same language, so the reported numbers can be read against chance.
+
+## Splits
+
+Train / validation / test, as utterances, hours and distinct speakers:
+
+| Code | train | validation | test |
+|---|---|---|---|
+| `ach` | 4,120 / 26.0 h / 263 | 520 / 3.2 h / 33 | 515 / 3.2 h / 33 |
+| `aka` | 10,196 / 55.6 h / 110 | 1,287 / 7.0 h / 11 | 1,269 / 6.9 h / 13 |
+| `amh` | 39,455 / 196.0 h / 550 | 2,473 / 12.0 h / 34 | 2,426 / 12.0 h / 34 |
+| `dag` | 14,237 / 77.2 h / 884 | 1,798 / 9.7 h / 79 | 1,784 / 9.7 h / 107 |
+| `dga` | 15,091 / 83.8 h / 275 | 1,887 / 10.5 h / 36 | 1,896 / 10.5 h / 38 |
+| `ewe` | 15,092 / 79.8 h / 431 | 1,885 / 10.0 h / 53 | 1,884 / 10.0 h / 54 |
+| `ful` | 19,293 / 100.6 h / 168 | 2,303 / 12.0 h / 21 | 2,285 / 11.9 h / 22 |
+| `kpo` | 14,416 / 82.3 h / 369 | 1,803 / 10.3 h / 46 | 1,800 / 10.3 h / 46 |
+| `lin` | 14,523 / 72.3 h / 74 | 1,778 / 9.1 h / 13 | 1,808 / 9.1 h / 10 |
+| `lug` | 5,366 / 36.7 h / 270 | 692 / 4.7 h / 35 | 699 / 4.7 h / 35 |
+| `mas` | 6,856 / 39.2 h / 290 | 857 / 4.9 h / 36 | 861 / 4.9 h / 36 |
+| `mlg` | 18,467 / 94.5 h / 199 | 2,316 / 11.8 h / 25 | 2,303 / 11.8 h / 27 |
+| `nyn` | 6,752 / 40.8 h / 293 | 861 / 5.1 h / 37 | 859 / 5.2 h / 37 |
+| `orm` | 40,141 / 198.7 h / 361 | 2,473 / 11.9 h / 23 | 2,431 / 12.0 h / 22 |
+| `sid` | 40,547 / 202.0 h / 463 | 2,444 / 11.9 h / 28 | 2,413 / 12.0 h / 28 |
+| `sna` | 14,079 / 79.5 h / 131 | 1,744 / 9.9 h / 20 | 1,762 / 9.9 h / 17 |
+| `sog` | 6,298 / 40.4 h / 260 | 786 / 5.1 h / 33 | 793 / 5.0 h / 32 |
+| `tir` | 44,945 / 194.8 h / 408 | 2,686 / 12.1 h / 25 | 2,684 / 12.1 h / 24 |
+| `wal` | 42,258 / 195.1 h / 381 | 2,572 / 12.2 h / 23 | 2,461 / 12.0 h / 23 |
+
+### Gender balance
+
+Female share of reference words. `n/a` means the source has **no gender labels
+at all** for that language — not that balance was achieved.
+
+| Code | train | validation | test |
+|---|---|---|---|
+| `ach` | 0.268 | 0.267 | 0.264 |
+| `aka` | n/a | n/a | n/a |
+| `amh` | 0.454 | 0.454 | 0.454 |
+| `dag` | n/a | n/a | n/a |
+| `dga` | n/a | n/a | n/a |
+| `ewe` | n/a | n/a | n/a |
+| `ful` | n/a | n/a | n/a |
+| `kpo` | n/a | n/a | n/a |
+| `lin` | 0.550 | 0.548 | 0.539 |
+| `lug` | 0.470 | 0.471 | 0.470 |
+| `mas` | 0.306 | 0.307 | 0.306 |
+| `mlg` | n/a | n/a | n/a |
+| `nyn` | 0.452 | 0.454 | 0.452 |
+| `orm` | 0.562 | 0.561 | 0.559 |
+| `sid` | 0.654 | 0.656 | 0.660 |
+| `sna` | 0.614 | 0.612 | 0.600 |
+| `sog` | 0.479 | 0.477 | 0.482 |
+| `tir` | 0.552 | 0.556 | 0.552 |
+| `wal` | 0.661 | 0.661 | 0.656 |
+
+### Languages needing attention
+
+| Code | Status | Note |
+|---|---|---|
+| `ach` | ok | test: 19,399 reference words against the 20,000 target (WER standard error ~0.36 pp before clustering) |
+| `amh` | ok | eval share reduced 0.100 -> 0.055 by the 12-hour eval cap |
+| `ful` | ok | eval share reduced 0.100 -> 0.096 by the 12-hour eval cap |
+| `lug` | ok | eval share raised 0.100 -> 0.102 to reach the 20,000-reference-word floor; validation: 18,292 reference words against the 20,000 target (WER standard error ~0.37 pp before clustering); test: 18,928 reference words against the 20,000 target (WER standard error ~0.36 pp before clustering) |
+| `mas` | ok | validation: 19,731 reference words against the 20,000 target (WER standard error ~0.35 pp before clustering); test: 14,265 reference words against the 20,000 target (WER standard error ~0.41 pp before clustering) |
+| `nyn` | ok | eval share raised 0.100 -> 0.101 to reach the 20,000-reference-word floor; validation: 17,439 reference words against the 20,000 target (WER standard error ~0.37 pp before clustering); test: 19,041 reference words against the 20,000 target (WER standard error ~0.36 pp before clustering) |
+| `orm` | ok | eval share reduced 0.100 -> 0.054 by the 12-hour eval cap |
+| `sid` | ok | eval share reduced 0.100 -> 0.053 by the 12-hour eval cap |
+| `tir` | ok | eval share reduced 0.100 -> 0.055 by the 12-hour eval cap |
+| `wal` | ok | eval share reduced 0.100 -> 0.055 by the 12-hour eval cap |
+
+## Usage
+
+The split map is itself a loadable config:
+
+```python
+from datasets import load_dataset
+
+splits = load_dataset("google/WaxalNLP", "asr_v2_splits", split="train")
+# columns: language, id, v1_split, v2_split
+```
+
+Apply it to a copy of v1 -- this reproduces v2 exactly, with no download beyond what you already have:
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+mapping = pd.read_parquet(
+    "hf://datasets/google/WaxalNLP/data/ASR_v2/metadata/split_map.parquet"
+)                                   # language, id, v1_split, v2_split
+v2 = mapping.set_index(["language", "id"])["v2_split"]
+
+v1 = load_dataset("google/WaxalNLP", "sna_asr")     # your existing copy
+test = v1["test"].filter(lambda r: v2.get((r["language"], r["id"])) == "test")
+```
+
+
+## Schema
+
+The six v1 columns, unchanged, plus derived fields:
+
+| Column | Notes |
+|---|---|
+| `id`, `speaker_id`, `transcription`, `language`, `gender` | unchanged from v1 |
+| `audio` | `struct<bytes, path>` — **byte-identical** to v1 |
+| `v1_split` | which v1 split this utterance came from |
+| `duration_s`, `n_bytes` | derived; the corpus is 128 kbit/s CBR MP3 |
+| `sample_rate`, `channels` | measured per shard; **heterogeneous** (see limitations) |
+| `provider`, `licence` | the licence travels with the row |
+
+The stray `__index_level_0__` column present in 36 of the v1 shards is dropped,
+and every v2 file uses 100-row row groups.
+
+## Limitations
+
+- **No gender labels at all** for `aka`, `dag`, `dga`, `ewe`, `ful`, `kpo`, `mlg`.
+  The gender goal is undefined there, not satisfied, and is reported as `n/a`.
+- **Heterogeneous audio format.** The corpus mixes 16 kHz mono and 48 kHz
+  stereo MP3 between languages. v1 documents neither; v2 records the measured
+  values per row but does **not** resample, because a silent normalisation
+  cannot be verified against the original.
+- **This is a re-split, not a clean-up.** No quality filtering has been
+  applied. Utterance-level defects present in v1 are present here.
+- **The `unlabeled` split is out of scope** (~816 GB, no transcripts). Take it
+  from the v1 config, which is unchanged.
+- **`speaker_id` is trusted, not verified acoustically** unless the manifest
+  says otherwise. If an id turns out to cover more than one voice, the
+  disjointness guarantee is over ids, not over voices.
+- The `v1 eval transcripts also in train` column is an **exact census** over
+  every eval transcript. A separate fuzzy near-duplicate rate (0.95 similarity)
+  is recorded in `metadata/split_comparison.csv` and is computed on a sample of
+  at most 1,200 eval transcripts per split, with the sample
+  size stored beside it.
+- Rows with an empty or placeholder transcript, or with no `speaker_id`, are
+  excluded from the splits and listed in `metadata/excluded_rows.parquet`.
+  Nothing else is filtered.
+
+## Licensing
+
+The corpus mixes two licences by contributing partner. **Each language is a
+separate config specifically so that aggregating them does not relicense the
+CC-BY-4.0 languages under ShareAlike.** The `licence` column travels with every
+row.
+
+| Provider | Languages | Licence |
+|---|---|---|
+| Digital Umuganda | Amharic (`amh`), Fula (`ful`), Lingala (`lin`), Malagasy (`mlg`), Oromo (`orm`), Shona (`sna`), Sidama (`sid`), Tigrinya (`tir`), Wolaytta (`wal`) | `CC-BY-SA-4.0` |
+| Makerere University | Acholi (`ach`), Luganda (`lug`), Masaaba (`mas`), Runyankole (`nyn`), Soga (`sog`) | `CC-BY-SA-4.0` |
+| University of Ghana | Akan (`aka`), Dagaare (`dga`), Dagbani (`dag`), Ewe (`ewe`), Ikposo (`kpo`) | `CC-BY-4.0` |
+
+Derivatives of the CC-BY-SA-4.0 languages must carry the same terms. Combining
+configs means inheriting ShareAlike for the combined work.
+
+## Attribution and citation
+
+Source corpus: [`google/WaxalNLP`](https://huggingface.co/datasets/google/WaxalNLP),
+revision `e0a62aaebc61`, collected by Makerere
+University, the University of Ghana and Digital Umuganda, funded by Google and
+the Gates Foundation. Upstream sources:
+[UGSpeechData](https://doi.org/10.57760/sciencedb.22298),
+[AfriVoice](https://huggingface.co/datasets/DigitalUmuganda/AfriVoice), and the
+[Yogera Dataset](https://doi.org/10.7910/DVN/BEROE0).
+
+```bibtex
+@article{waxal2026,
+  title={WAXAL: A Large-Scale Multilingual African Language Speech Corpus},
+  author={Anonymous},
+  journal={arXiv preprint arXiv:2602.02734},
+  year={2026}
+}
+```
+
+## Reproducing this
+
+The split is fully determined by the manifest. `splits_manifest.json` records
+the seed, the pinned source revision, every tolerance, the library versions,
+and a hash of the text-canonicalisation function that all leakage figures were
+computed through. `splits_manifest.parquet` carries the per-utterance
+assignment, and `shard_manifest.parquet` the SHA-256 of every written shard.

data/ASR_v2/metadata/split_comparison.csv

@@ -0,0 +1,115 @@
+language,version,split,n_utterances,hours,n_speakers,ref_words,female_share,female_share_words,unknown_gender_share,speaker_leak_from_train,exact_sentence_overlap,near_dup_overlap,near_dup_sample,oov_token_rate,oov_type_rate,unseen_char_rate,max_speaker_share
+ach,v1,train,4108,25.848990885416665,322,160803,0.30331061343719573,0.2632227010690099,0.0,,,,,,,,0.01802406832575798
+ach,v1,validation,519,3.310818142361111,199,20849,0.3140655105973025,0.2894623243321023,0.0,0.9748743718592965,0.0038535645472061657,0.0038535645472061657,519.0,0.030169312676866997,0.22230858919549165,0.0,0.019997138530015945
+ach,v1,test,528,3.2940662977430555,194,20627,0.3181818181818182,0.28196053716003294,0.0,0.9896907216494846,0.005681818181818182,0.005681818181818182,528.0,0.02860328695399234,0.21675845790715972,0.037037037037037035,0.026069996878504753
+ach,v2,train,4120,25.965470920138888,263,162649,0.3048543689320388,0.26844308910598896,0.0,,,,,,,,0.022207921370863914
+ach,v2,validation,520,3.2417279730902777,33,20231,0.26346153846153847,0.2669171074094212,0.0,0.0,0.015384615384615385,0.015384615384615385,520.0,0.03000346003657753,0.22743977133523888,0.0,0.13066771626472473
+ach,v2,test,515,3.2466758897569443,33,19399,0.3572815533980582,0.26372493427496263,0.0,0.0,0.0,0.0,515.0,0.029743801226867365,0.21784666945957268,0.0,0.10795231908559799
+aka,v1,train,10107,55.02978298611111,101,348350,,,1.0,,,,,,,,0.04007843881845474
+aka,v1,validation,1123,6.153727213541667,99,39344,,,1.0,1.0,0.0,0.009795191451469279,1123.0,0.02406974379829199,0.21604330708661418,0.0,0.04804767668247223
+aka,v1,test,1522,8.363488498263889,33,51740,,,1.0,0.0,0.0,0.0033333333333333335,1200.0,0.029010436799381523,0.2822512647554806,0.030303030303030304,0.033697500824928284
+aka,v2,train,10196,55.60658854166667,110,348373,,,1.0,,,,,,,,0.04497991502285004
+aka,v2,validation,1287,7.02416015625,11,48085,,,1.0,0.0,0.0,0.0025,1200.0,0.027347405635853177,0.2653995878177238,0.0,0.14713680744171143
+aka,v2,test,1269,6.91625,13,42976,,,1.0,0.0,0.0,0.015,1200.0,0.04099962769918094,0.3070263740688544,0.058823529411764705,0.11151575297117233
+amh,v1,train,38022,189.7320138888889,566,1007363,0.4307769186260586,0.43713040880000553,0.0,,,,,,,,0.009729607030749321
+amh,v1,validation,2912,13.968602430555556,28,77783,0.5233516483516484,0.5303343918336911,0.0,0.0,0.0010302197802197802,0.0016666666666666668,1200.0,0.05361068613964491,0.2319344571495933,0.0,0.1053454726934433
+amh,v1,test,3420,16.2302734375,24,91866,0.5701754385964912,0.578973722595955,0.0,0.0,0.0011695906432748538,0.008333333333333333,1200.0,0.0595976748742734,0.2655541069100391,0.0,0.0884522795677185
+amh,v2,train,39455,195.99618055555555,550,1060099,0.44795336459257384,0.4544169931298869,0.0,,,,,,,,0.009418642148375511
+amh,v2,validation,2473,11.968304036458333,34,57381,0.47594015365952286,0.4540353078545163,0.0,0.0,0.003234937323089365,0.0033333333333333335,1200.0,0.07385720011850612,0.2842906968467507,0.007575757575757576,0.09336347132921219
+amh,v2,test,2426,11.966408420138889,34,59532,0.41302555647155814,0.4536719747362763,0.0,0.0,0.003709810387469085,0.0025,1200.0,0.06720755224081167,0.24459538893605903,0.0,0.12144400179386139
+dag,v1,train,14231,77.17661458333333,1046,589860,,,1.0,,,,,,,,0.01278792042285204
+dag,v1,validation,1750,9.509487847222223,642,73607,,,1.0,0.9766355140186916,0.0017142857142857142,0.005,1200.0,0.01695490917983344,0.2249464876435104,0.0,0.015343086794018745
+dag,v1,test,1838,9.912782118055556,682,75234,,,1.0,0.9794721407624634,0.000544069640914037,0.0033333333333333335,1200.0,0.017385756439907488,0.22911051212938005,0.023809523809523808,0.00924919918179512
+dag,v2,train,14237,77.18172743055555,884,597247,,,1.0,,,,,,,,0.007015130016952753
+dag,v2,validation,1798,9.744738498263889,79,69418,,,1.0,0.0,0.0005561735261401557,0.0016666666666666668,1200.0,0.017300988216312773,0.23394801155298822,0.05,0.12565946578979492
+dag,v2,test,1784,9.672421875,107,72036,,,1.0,0.0,0.0005605381165919282,0.0025,1200.0,0.025237381309345327,0.2863587152652472,0.05,0.05325062945485115
+dga,v1,train,15071,83.60558159722223,348,584154,,,1.0,,,,,,,,0.03391222283244133
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