[ Model Catalog / v2.1 ]
Language intelligence, production-ready.
Every model in this catalog was trained on community-verified data, quantized for edge deployment, and validated by native speakers before release.
Live Benchmarks
ASR-CoreAfrica
6.2%
Word Error Rate
languages50+
latency<400ms
dialectyes
AfriLION-7B
18.4
Avg. Perplexity
params7B
families38
licenseApache 2
TTS-NaturalVoice
4.6/5
MOS Naturalness
voices32+
tonalyes
formatONNX
DialectID-Africa
92.7%
Dialect Accuracy
variants200+
inputtext+audio
latency<100ms
Modality
Language Family
6 models shown
~/afrilion-7b
import localenlp as lnlp
model = lnlp.load("AfriLION-7B")
out = model.generate(
"Habari za leo", # Swahili
lang="sw_KE",
max_tokens=128
)READY
Multimodal
AfriLION-7B
Foundation model for African languages
SwahiliHausaYorubaAmharicWolof+45 more
Avg. perplexity
18.4
~/asr-coreafrica
result = lnlp.asr.transcribe( audio="path/to/hausa_field.wav", lang="ha_NG", dialect="Kano" ) print(result.transcript) # → "Ina son koyon Hausa..."
READY
Speech
ASR-CoreAfrica
Automatic speech recognition, 50 languages
HausaYorubaIgboAmharicOromo+45 more
Word Error Rate
6.2%
~/tts-naturalvoice
audio = lnlp.tts.synthesize(
text="Bawo ni o ṣe n ṣe?",
lang="yo_NG",
voice="female_v2",
preserve_tone=True
)
audio.save("output.wav")READY
Speech
TTS-NaturalVoice
Neural text-to-speech, tonal language support
YorubaIgboAmharicTwiZulu+20 more
MOS naturalness
4.6 / 5
~/translationsuite
result = lnlp.translate( text="نحن نبني البنية التحتية", source="ar_MSA", target="sw_KE", formality="formal" ) print(result.translation)
READY
Text
TranslationSuite
Neural MT across 80+ language pairs
ArabicSwahiliHausaYorubaAmharic+75 pairs
BLEU score (sw→en)
44.2
~/dialectid-africa
tag = lnlp.detect(
text="Asante sana kwa msaada",
granularity="dialect"
)
# → { lang: "sw", dialect: "sw_TZ",
# confidence: 0.97, region: "Dar es Salaam" }READY
Text
DialectID-Africa
Language + dialect identification at scale
120+ dialects38 countriesVoice + text
Top-1 dialect accuracy
92.7%
~/offlineasr-edge
# Runs 100% on-device (no network) model = lnlp.load( "OfflineASR-Edge", device="arm_cortex_a55", quantization="INT4" ) # Model size: 148MB # Supports: Android 10+, Linux ARM64
READY
Speech
OfflineASR-Edge
INT4-quantized ASR for ARM edge chips
SwahiliHausaAmharicTigrinya+12 more
Inference latency (ARM)
< 4ms
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