AfriSpeech Selector gives you access to 142 languages · 2267.9 hours · 35 countries of African speech for training TTS and ASR models.
Select languages by recorded hours (strength) — a country-balanced top-N or a hand-picked set, sized the way you want — and get the audio + metadata in the format your training pipeline expects. You take it from there; the tool doesn't do any text normalisation or cleaning (that's your framework's job).
It's clean read speech with aligned transcripts, so the natural fit is TTS —
export WAVs + a manifest for LJSpeech, Piper, VITS, or MeloTTS. It works for
ASR too (load_from_disk / Parquet, or stream with stream_dataset(...)),
which is handy for supplementing low-resource languages.
Redistribution: building a local working set for your own training is fine.
Redistributing copies of the audio (e.g. --push to a public repo) is not
recommended, given the permissions of the underlying public data sources.
142 languages · 2267.9 hours · 35 countries. Hours is the strength signal
used for ranking. The --languages value is the name you pass to pick a language
(e.g. --languages twi_twi). List them anytime with afrispeech-select --list-langs, or see the full language catalog at the bottom.
git clone https://github.com/AfriSpeech/afrispeech-selector.git
cd afrispeech-selector
python3 -m venv .venv && source .venv/bin/activate
pip install -e . # gives you the `afrispeech-select` command(Use python3 — the code uses non-ASCII text and won't run under Python 2.)
Most people just want one language for TTS. Pick its name
(afrispeech-select --list-langs, or the catalog at the bottom) and run one
command. Clips are filtered to a 3–15 s window by default, so the result is
training-ready.
# ~5 hours of Twi as an LJSpeech TTS dataset (wavs/ + metadata.csv), 22.05 kHz
afrispeech-select --languages twi_twi --total-hours 5 --out data/twi --format ljspeech
# …for Piper / VITS / MeloTTS instead, just change --format:
afrispeech-select --languages twi_twi --total-hours 5 --out data/twi --format piper(Using it for ASR? See Using it for ASR below.)
That's it — data/twi now holds the audio + metadata in the right layout (omit
--out and it defaults to data/<language>). Want more or less? Change
--total-hours (or use --per-language N for a clip count).
Want longer/shorter clips? Set --min-clip-sec / --max-clip-sec (defaults 3 / 15).
Asking for more than a language has? You get everything available — the hour/clip target is an upper bound; the tool never pads or repeats. When your request can't be met, the CLI tells you how much actually exists and asks you to confirm before downloading, e.g.:
Only ~2.37 h is available vs ~20 h requested. Proceed with all available? [y/N]
Pass -y/--yes to skip the prompt (for scripts/notebooks), or --dry-run to
see the achievable amount up front. (The 3–15 s filter trims a language's usable
hours below its catalog total, so e.g. a 50 h language yields somewhat less.)
TTS data-prep reads WAVs + a manifest from disk. Pick your framework with
--format: ljspeech (generic / Coqui), piper, vits, or melo. Audio is
16-bit mono WAV at --target-sr (default 22050); transcripts are written
verbatim — no normalisation.
# LJSpeech layout (wavs/ + metadata.csv: id|text|text)
afrispeech-select --languages twi_twi --total-hours 5 --out data/twi --format ljspeech
# Piper (metadata.csv: id|speaker|text)
afrispeech-select --languages twi_twi --total-hours 5 --out data/twi --format piper
# VITS (filelist.txt + speakers.txt) and/or MeloTTS (metadata.list)
afrispeech-select --languages twi_twi --total-hours 5 --out data/twi --format vits,meloEach writes <out>/wavs/*.wav plus the manifest. Phonemisation / text cleaning
is left to the framework's own preprocessor.
No install? python3 -m afrispeech_selector … works the same from the repo.
Being clean read speech with transcripts, it's well suited to supplementing low-resource ASR (less so as a stand-alone set for spontaneous/noisy audio). Export an on-disk 🤗 dataset, or stream it straight into a Trainer with no copy:
afrispeech-select --languages twi_twi --total-hours 5 --out data/twi \
--format disk,parquet --target-sr 16000from datasets import load_from_disk
ds = load_from_disk("data/twi").train_test_split(test_size=0.1)
# …or stream (no local copy) — `afrispeech-select … --recipe` prints this:
from afrispeech_selector import stream_dataset
ds = stream_dataset(["twi_twi"], split="train", max_seconds=5 * 3600, target_sampling_rate=16000)
The example notebook is meant to be dropped into your own training notebook:
its cells install the tool and pull your selected language(s) straight into the
running session — write wavs/ + a manifest for a TTS framework, or stream into
ASR training — so your training script has direct access to the speech data needed
for training.
Everything above works for several languages too — the format/output flags are
identical, you just choose which languages. --total-hours is split evenly
across them (so each gets its fair share).
1. Name the languages you want:
# Ghanaian languages, 15 h total (5 h each), LJSpeech
afrispeech-select --languages twi_twi,ewe_ewe,ga_gaa --total-hours 15 \
--out data/gh --format ljspeech2. Or pick across the dataset by strength / balance — top-N by hours, with optional country balancing and pool filters:
# Top 10 languages, at most 2 per country, 24 h total
afrispeech-select --top 10 --max-per-country 2 --total-hours 24 \
--out data/multi --format ljspeech
# Narrow the pool first: only languages >= 20 h, only Ghana & Nigeria
afrispeech-select --top 8 --min-hours 20 --countries GH,NG --total-hours 16 \
--out data/wa --format ljspeechPreview before pulling — list what matches, or dry-run a selection:
afrispeech-select --list-langs --min-hours 20 --countries GH,NG # what matches
afrispeech-select --top 10 --max-per-country 2 --dry-run # what it would pick| flag | meaning |
|---|---|
--top N |
select the top-N languages by hours |
--languages a,b,c |
hand-pick specific subsets instead |
--max-per-country N |
cap languages per country (balance) |
--no-proportional |
pure hours ranking, ignore country balance |
--min-hours / --max-hours / --min-clips |
filter the language pool by strength |
--countries GH,NG |
restrict to these countries |
--total-hours H |
total audio across the selection, split evenly per language |
--per-language N |
max clips per language |
--max-hours-per-lang H |
duration budget per language (decimals OK, e.g. 0.5) |
--min-clip-sec / --max-clip-sec |
per-sample length window, default 3 / 15 s (out-of-range clips skipped; --min-clip-sec 0 --max-clip-sec 9999 to disable) |
--split train|val|test|all |
which split to draw from |
--target-sr HZ |
resample audio (e.g. 16000 for ASR, 22050 for TTS) |
--schema asr|whisper|common_voice |
reshape columns for a training framework |
--recipe |
print a stream_dataset(...) snippet for this selection (no copy) |
--out PATH |
output directory / base name |
--format … |
HF: disk,zip,parquet,csv · TTS: ljspeech,piper,vits,melo |
--push REPO_ID [--public] [--token …] |
push to an HF dataset repo (creates a copy) |
--dry-run / --list-langs |
preview the plan / list matching languages |
-y / --yes |
skip the confirmation prompt when the request exceeds what's available |
Capped pulls stream from the Hub and only transfer the samples you ask for.
An uncapped "full build" downloads whole shards and must be enabled with
--allow-full.
Downloads work anonymously but the Hub rate-limits them; for faster, higher-rate
downloads set a free read token — export HF_TOKEN=hf_… (picked up automatically)
or pass --token.
| column | meaning |
|---|---|
audio |
decoded waveform (HF Audio: array + sampling_rate) |
text |
transcription |
language |
language label |
country |
ISO 3166-1 alpha-2 code |
length |
clip duration in seconds |
iso, subset |
ISO 639-3 code and source config (traceability) |
Load a result later:
from datasets import load_from_disk
ds = load_from_disk("data/twi") # from --format disk
# or: Dataset.from_parquet("data.parquet")
ds = ds.train_test_split(test_size=0.1) # feed your trainerA local browser helper for exploring languages and building the command (it
does not download — it hands you the afrispeech-select line to run):
pip install -e ".[ui]" # adds gradio
python app.py # opens http://127.0.0.1:7860One language:
from afrispeech_selector import build_dataset, export_tts, stream_dataset
# Stream into training — no copy
ds = stream_dataset(["twi_twi"], split="train", max_seconds=5 * 3600, target_sampling_rate=16000)
# …or materialise and export for a TTS framework
copy = build_dataset(["twi_twi"], split="train", max_seconds=5 * 3600, streaming=True)
export_tts(copy, out_dir="./twi", fmt="ljspeech", sampling_rate=22050)Several languages — name them, or select across the dataset:
from afrispeech_selector import filter_catalog, select_top, stream_dataset
langs = select_top(filter_catalog(min_hours=20), 10, proportional=True, max_per_country=2)
ds = stream_dataset(langs, split="train", per_language=200, target_sampling_rate=16000)pip install -e ".[dev]"
pytest tests/ # selection, builder, and CLI tests (mostly offline)afrispeech_selector/data/catalog.tsv is the static strength table (fast,
offline; bundled in the package). Regenerate it
when the source dataset changes:
python3 scripts/refresh_catalog.py --token "$HF_TOKEN"afrispeech_selector/
cli.py the `afrispeech-select` command (workhorse)
catalog.py load the language table; country names
selector.py ranking, filtering, country-proportional top-N, plan
builder.py pull subsets → standard schema; stream_dataset (no copy) + apply_schema
export.py HF (zip/parquet/manifest/push) + TTS (ljspeech/piper/vits/melo)
app.py optional selection UI (emits the CLI command)
data/catalog.tsv strength table (hours, clips, splits) — bundled in the package
scripts/ refresh_catalog.py
tests/ selection, builder, CLI tests
All 142 languages, sorted by hours (strength). The --languages value is what you pass on the CLI.
| # | Language | ISO | Country | Hours | Clips | Train/Val/Test | --languages value |
|---|---|---|---|---|---|---|---|
| 1 | Malagasy | mlg | Madagascar | 61.32 | 20287 | 18900/746/641 | malagasy_mlg |
| 2 | Kabuverdianu | kea | Cabo Verde | 58.07 | 19728 | 18156/699/873 | kabuverdianu_kea |
| 3 | Shona | sna | Zimbabwe | 53.30 | 17996 | 15954/1161/881 | shona_sna |
| 4 | Kabiye | kbp | Togo | 53.19 | 18211 | 16328/825/1058 | kabiye_kbp |
| 5 | Twi | twi | Ghana | 50.32 | 17140 | 15794/828/518 | twi_twi |
| 6 | Bassa (Cameroon) | bas | Cameroon | 48.58 | 16269 | 14981/620/668 | bassa_cameroon_bas |
| 7 | Mauritian Creole | mfe | Mauritius | 43.57 | 15088 | 13115/822/1151 | mauritian_creole_mfe |
| 8 | Nyaneka | nyk | Angola | 42.94 | 14731 | 13430/858/443 | nyaneka_nyk |
| 9 | Gun | guw | Benin | 39.98 | 13340 | 12157/494/689 | gun_guw |
| 10 | Swahili | swa | Tanzania | 38.73 | 13101 | 11781/703/617 | swahili_swa |
| 11 | Kiluba | lub | DR Congo | 38.31 | 12949 | 11553/679/717 | kiluba_lub |
| 12 | Igbo | ibo | Nigeria | 38.07 | 12639 | 11379/556/704 | igbo_ibo |
| 13 | Zulu | zul | South Africa | 36.98 | 12434 | 11540/571/323 | zulu_zul |
| 14 | Changana (Mozambique) | tso | Mozambique | 35.77 | 12394 | 11369/478/547 | changana_mozambique_tso |
| 15 | Kirundi | run | Burundi | 35.62 | 12399 | 11116/550/733 | kirundi_run |
| 16 | Kinyarwanda | kin | Rwanda | 35.57 | 12012 | 10517/606/889 | kinyarwanda_kin |
| 17 | Tsonga | tso | South Africa | 35.25 | 11838 | 10710/682/446 | tsonga_tso |
| 18 | Ga | gaa | Ghana | 35.12 | 11939 | 10703/632/604 | ga_gaa |
| 19 | Amharic | amh | Ethiopia | 33.59 | 11415 | 10340/603/472 | amharic_amh |
| 20 | Fon | fon | Benin | 33.19 | 10912 | 10039/459/414 | fon_fon |
| 21 | Xhosa | xho | South Africa | 33.15 | 11485 | 10073/658/754 | xhosa_xho |
| 22 | Otetela | tll | DR Congo | 31.53 | 11301 | 10316/463/522 | otetela_tll |
| 23 | Lingala | lin | DR Congo | 30.71 | 10080 | 9076/414/590 | lingala_lin |
| 24 | Ewe | ewe | Ghana | 29.41 | 9760 | 8633/611/516 | ewe_ewe |
| 25 | Chitumbuka | tum | Malawi | 29.31 | 10365 | 9303/623/439 | chitumbuka_tum |
| 26 | Ronga | rng | Mozambique | 29.11 | 10285 | 9138/642/505 | ronga_rng |
| 27 | Kamba | kam | Kenya | 28.86 | 9567 | 8603/348/616 | kamba_kam |
| 28 | Kikuyu | kik | Kenya | 28.82 | 9640 | 8808/413/419 | kikuyu_kik |
| 29 | Hausa | hau | Nigeria | 28.37 | 9367 | 8364/509/494 | hausa_hau |
| 30 | Macua | vmw | Mozambique | 28.02 | 10144 | 8966/682/496 | macua_vmw |
| 31 | Kongo | kon | DR Congo | 27.82 | 9980 | 8828/474/678 | kongo_kon |
| 32 | Isoko | iso | Nigeria | 26.84 | 9474 | 8544/447/483 | isoko_iso |
| 33 | Krio | kri | Sierra Leone | 26.16 | 9230 | 8293/553/384 | krio_kri |
| 34 | Edo | bin | Nigeria | 26.04 | 9028 | 8323/346/359 | edo_bin |
| 35 | Urhobo | urh | Nigeria | 25.67 | 8944 | 7946/447/551 | urhobo_urh |
| 36 | Oromo | orm | Ethiopia | 25.66 | 8933 | 7778/557/598 | oromo_orm |
| 37 | Esan | ish | Nigeria | 24.19 | 8499 | 7356/708/435 | esan_ish |
| 38 | Frafra | gur | Ghana | 23.82 | 7960 | 7140/398/422 | frafra_gur |
| 39 | Sesotho (South Africa) | sot | South Africa | 23.32 | 8149 | 7583/332/234 | sesotho_south_africa_sot |
| 40 | Sena | seh | Mozambique | 22.84 | 7849 | 7019/341/489 | sena_seh |
| 41 | Sesotho (Lesotho) | sot | South Africa | 22.76 | 7944 | 7052/390/502 | sesotho_lesotho_sot |
| 42 | Lenje | leh | Zambia | 22.52 | 8099 | 7198/412/489 | lenje_leh |
| 43 | Liberian English | lir | Liberia | 22.06 | 7733 | 7112/335/286 | liberian_english_lir |
| 44 | Fante | fat | Ghana | 22.04 | 7631 | 6942/277/412 | fante_fat |
| 45 | Dagaare | dga | Ghana | 21.53 | 7477 | 6575/375/527 | dagaare_dga |
| 46 | Nzema | nzi | Ghana | 21.14 | 7319 | 6413/532/374 | nzema_nzi |
| 47 | Pidgin (West Africa) | wes | Cameroon | 21.09 | 7405 | 6785/227/393 | pidgin_west_africa_wes |
| 48 | Kisi | kss | Liberia | 20.99 | 7739 | 6967/377/395 | kisi_kss |
| 49 | Moore | mos | Burkina Faso | 20.51 | 6785 | 6293/196/296 | moore_mos |
| 50 | Ahanta | aha | Ghana | 20.30 | 6908 | 6038/441/429 | ahanta_aha |
| 51 | Douala | dua | Cameroon | 20.12 | 6521 | 5832/145/544 | douala_dua |
| 52 | Luo | luo | Kenya | 19.66 | 6829 | 5959/367/503 | luo_luo |
| 53 | Sepedi | nso | South Africa | 19.59 | 7016 | 6435/396/185 | sepedi_nso |
| 54 | Bissau Guinean Creole | pov | Guinea-Bissau | 19.35 | 6573 | 5899/375/299 | bissau_guinean_creole_pov |
| 55 | Swahili (Congo) | swc | DR Congo | 18.67 | 6493 | 5670/503/320 | swahili_congo_swc |
| 56 | Sehwi | sfw | Ghana | 17.48 | 6024 | 5488/199/337 | sehwi_sfw |
| 57 | Runyankore | nyn | Uganda | 17.10 | 5605 | 4736/432/437 | runyankore_nyn |
| 58 | Boulou | bum | Cameroon | 16.93 | 5692 | 5153/231/308 | boulou_bum |
| 59 | Kwanyama | kua | Namibia | 16.66 | 6107 | 5467/239/401 | kwanyama_kua |
| 60 | Jula | dyu | Burkina Faso | 15.85 | 5486 | 5019/258/209 | jula_dyu |
| 61 | Tshwa | tsc | Mozambique | 14.99 | 5221 | 4651/209/361 | tshwa_tsc |
| 62 | Yoruba | yor | Nigeria | 14.19 | 4960 | 4504/216/240 | yoruba_yor |
| 63 | Setswana | tsn | South Africa | 13.84 | 4828 | 4232/344/252 | setswana_tsn |
| 64 | Kikongo ya Leta | ktu | DR Congo | 13.81 | 4754 | 4232/262/260 | kikongo_ya_leta_ktu |
| 65 | Chichewa | nya | Malawi | 12.81 | 3884 | 3407/231/246 | chichewa_nya |
| 66 | Sango | sag | Central African Republic | 12.60 | 4193 | 3645/261/287 | sango_sag |
| 67 | Mashi | shr | DR Congo | 12.41 | 4263 | 3855/273/135 | mashi_shr |
| 68 | Seychelles Creole | crs | Seychelles | 12.16 | 4269 | 3983/72/214 | seychelles_creole_crs |
| 69 | Fang | fan | Equatorial Guinea | 11.68 | 3969 | 3630/184/155 | fang_fan |
| 70 | Luganda | lug | Uganda | 11.25 | 3617 | 3237/75/305 | luganda_lug |
| 71 | Kituba | ktu | DR Congo | 11.24 | 3935 | 3639/195/101 | kituba_ktu |
| 72 | Tshiluba | lua | DR Congo | 11.24 | 3532 | 3234/158/140 | tshiluba_lua |
| 73 | Chopi | cce | Mozambique | 10.80 | 3789 | 3414/144/231 | chopi_cce |
| 74 | Cibemba | bem | Zambia | 10.56 | 3823 | 3333/170/320 | cibemba_bem |
| 75 | Ngangela | nba | Angola | 10.28 | 3638 | 3348/166/124 | ngangela_nba |
| 76 | Ndau (Western) | ndc | Mozambique | 10.22 | 3621 | 3235/239/147 | ndau_western_ndc |
| 77 | Phimbi | phm | Mozambique | 10.00 | 3561 | 3172/121/268 | phimbi_phm |
| 78 | Swati | ssw | South Africa | 9.42 | 3236 | 2767/170/299 | swati_ssw |
| 79 | Toupouri | tui | Cameroon | 8.96 | 3070 | 2588/292/190 | toupouri_tui |
| 80 | Venda | ven | South Africa | 8.65 | 2990 | 2721/135/134 | venda_ven |
| 81 | Nyungwe | nyu | Mozambique | 8.35 | 2976 | 2744/63/169 | nyungwe_nyu |
| 82 | Kimbundu | kmb | Angola | 8.32 | 2894 | 2468/249/177 | kimbundu_kmb |
| 83 | Kinande | nnb | DR Congo | 8.15 | 2871 | 2637/44/190 | kinande_nnb |
| 84 | Nsenga (Mozambique) | nse | Mozambique | 8.09 | 2865 | 2385/313/167 | nsenga_mozambique_nse |
| 85 | Baoule | bci | Côte d'Ivoire | 8.08 | 2657 | 2307/125/225 | baoule_bci |
| 86 | Gokana | gkn | Nigeria | 7.98 | 2808 | 2567/179/62 | gokana_gkn |
| 87 | Ndebele (Zimbabwe) | nde | Zimbabwe | 7.62 | 2552 | 2308/81/163 | ndebele_zimbabwe_nde |
| 88 | Havu | hav | DR Congo | 7.59 | 2619 | 2447/172/0 | havu_hav |
| 89 | Ibinda | yom | DR Congo | 7.46 | 2672 | 2508/59/105 | ibinda_yom |
| 90 | Dinka | din | South Sudan | 6.84 | 2496 | 2178/99/219 | dinka_din |
| 91 | Itsekiri | its | Nigeria | 6.78 | 2387 | 2387/0/0 | itsekiri_its |
| 92 | Kwangali | kwn | Namibia | 6.75 | 2258 | 2139/81/38 | kwangali_kwn |
| 93 | Chitonga | toi | Zambia | 6.61 | 2487 | 2220/195/72 | chitonga_toi |
| 94 | Bassa (Liberia) | bsq | Liberia | 6.07 | 2284 | 2135/12/137 | bassa_liberia_bsq |
| 95 | Cinyanja | nya | Malawi | 5.79 | 1883 | 1765/42/76 | cinyanja_nya |
| 96 | Ndonga | ndo | Namibia | 5.77 | 1920 | 1745/63/112 | ndonga_ndo |
| 97 | Aja | ajg | Benin | 5.71 | 1942 | 1765/105/72 | aja_ajg |
| 98 | Kpelle | xpe | Liberia | 5.70 | 1933 | 1743/112/78 | kpelle_xpe |
| 99 | Réunion Creole | rcf | Réunion | 5.25 | 1803 | 1718/23/62 | r_union_creole_rcf |
| 100 | Ndebele | nbl | South Africa | 5.15 | 1985 | 1819/134/32 | ndebele_nbl |
| 101 | Abbey | aba | Côte d'Ivoire | 5.03 | 1776 | 1574/166/36 | abbey_aba |
| 102 | Yombe | yom | DR Congo | 4.88 | 1634 | 1390/149/95 | yombe_yom |
| 103 | Kikongo | kwy | Angola | 4.87 | 1769 | 1623/103/43 | kikongo_kwy |
| 104 | Umbundu | umb | Angola | 4.70 | 1630 | 1340/227/63 | umbundu_umb |
| 105 | Chiyao | yao | Mozambique | 4.61 | 1647 | 1423/103/121 | chiyao_yao |
| 106 | Loma | lom | Liberia | 4.52 | 1540 | 1350/41/149 | loma_lom |
| 107 | Wolaita | wal | Ethiopia | 4.28 | 1476 | 1250/133/93 | wolaita_wal |
| 108 | Chitonga (Malawi) | tog | Malawi | 4.16 | 1480 | 1363/65/52 | chitonga_malawi_tog |
| 109 | Tiv | tiv | Nigeria | 4.03 | 1374 | 1220/119/35 | tiv_tiv |
| 110 | Lari | ldi | Congo | 3.85 | 1359 | 1284/42/33 | lari_ldi |
| 111 | Meru | mer | Kenya | 3.83 | 1277 | 1139/138/0 | meru_mer |
| 112 | Ewondo | ewo | Cameroon | 3.71 | 1305 | 1134/32/139 | ewondo_ewo |
| 113 | Kabyle | kab | Algeria | 3.65 | 1188 | 1068/64/56 | kabyle_kab |
| 114 | Khana | ogo | Nigeria | 3.62 | 1256 | 921/139/196 | khana_ogo |
| 115 | Gitonga | toh | Mozambique | 3.60 | 1319 | 1228/4/87 | gitonga_toh |
| 116 | Tewe | twx | Mozambique | 3.39 | 1251 | 1076/148/27 | tewe_twx |
| 117 | Dangme | ada | Ghana | 3.37 | 1177 | 941/154/82 | dangme_ada |
| 118 | Ndau | ndc | Mozambique | 3.34 | 1231 | 1147/68/16 | ndau_ndc |
| 119 | Guéré | gxx | Côte d'Ivoire | 3.01 | 1057 | 953/67/37 | gu_r_gxx |
| 120 | Wolof | wol | Senegal | 2.50 | 846 | 803/10/33 | wolof_wol |
| 121 | Damara | naq | Namibia | 2.46 | 789 | 698/62/29 | damara_naq |
| 122 | Swahili (Katanga) | swc | DR Congo | 2.40 | 866 | 798/32/36 | swahili_katanga_swc |
| 123 | Yacouba | daf | Côte d'Ivoire | 2.26 | 787 | 656/0/131 | yacouba_daf |
| 124 | Manyawa | mny | Mozambique | 1.94 | 697 | 697/0/0 | manyawa_mny |
| 125 | Makhuwa-Marrevone | xmc | Mozambique | 1.87 | 704 | 635/41/28 | makhuwa_marrevone_xmc |
| 126 | Makhuwa-Meetto | mgh | Mozambique | 1.83 | 671 | 552/85/34 | makhuwa_meetto_mgh |
| 127 | Cinamwanga | mwn | Zambia | 1.66 | 553 | 500/53/0 | cinamwanga_mwn |
| 128 | Chitonga (Zimbabwe) | toi | Zimbabwe | 1.41 | 486 | 452/0/34 | chitonga_zimbabwe_toi |
| 129 | Attié | ati | Côte d'Ivoire | 1.40 | 482 | 477/5/0 | atti_ati |
| 130 | Lunda | lun | Zambia | 1.11 | 423 | 322/101/0 | lunda_lun |
| 131 | Lomwe | ngl | Mozambique | 1.07 | 378 | 373/5/0 | lomwe_ngl |
| 132 | Chuabo | chw | Mozambique | 1.03 | 390 | 373/0/17 | chuabo_chw |
| 133 | Mambwe-Lungu | mgr | Zambia | 0.93 | 331 | 264/34/33 | mambwe_lungu_mgr |
| 134 | Ijaw | ijc | Nigeria | 0.88 | 312 | 292/0/20 | ijaw_ijc |
| 135 | Ngbandi (Northern) | ngb | DR Congo | 0.78 | 255 | 222/33/0 | ngbandi_northern_ngb |
| 136 | Makhuwa-Shirima | vmk | Mozambique | 0.77 | 277 | 277/0/0 | makhuwa_shirima_vmk |
| 137 | Herero | her | Namibia | 0.61 | 192 | 192/0/0 | herero_her |
| 138 | Chokwe | cjk | Angola | 0.56 | 177 | 169/0/8 | chokwe_cjk |
| 139 | Taabwa | tap | DR Congo | 0.56 | 195 | 163/0/32 | taabwa_tap |
| 140 | Kisonge | sop | DR Congo | 0.40 | 141 | 141/0/0 | kisonge_sop |
| 141 | Kanyok | kny | DR Congo | 0.28 | 109 | 109/0/0 | kanyok_kny |
| 142 | Luvale | lue | Zambia | 0.07 | 20 | 20/0/0 | luvale_lue |
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