African languages are not second-class citizens. Andebet is built from the ground up to understand Ethiopian phonetic nuances, Ge'ez fidel orthography, and regional dialects with measured Word Error Rates under 9%.
Zero configuration language auto-detection · Custom vocabulary support
የኢትዮጵያ ኢኮኖሚ በዲጂታል ቴክኖሎጂ እና በግብርና ምርታማነት ላይ ተመስርቶ እያደገ ነው።
“Ethiopia's economy is expanding based on digital technology and agricultural productivity.”
Most speech recognition technologies from Silicon Valley treat African languages as an afterthought. Their training sets rely on small synthetic readings scraped from the internet, completely disconnected from real Ethiopian speech rhythm, background noises, and colloquial idioms.
When evaluated on spontaneous conversation or field recordings from outside the capital, error rates spike over 45%, rendering them unusable for professional transcription, research, or enterprise automation.
Andebet solves this. By partnering with native speakers, academic institutions, and regional linguists across Ethiopia, our models capture the true phonetic richness and dialectal beauty of Horn of Africa languages.
Trained on authentic conversational speech, broadcast archives, and regional field recordings.
Complete acoustic coverage for over 57 million native and second-language speakers. Handles urban Addis Ababa idioms, formal ministerial language, and regional accents across Shewa, Gojjam, Wollo, and Gondar.
The most widely spoken language in Ethiopia, with over 40 million speakers. Our model faithfully preserves double vowels, gemination, and glottal stops across Central, Western, and Eastern dialects.
Trained on authentic broadcast, legal, and conversational recordings from Tigray and the broader Horn. Accurately maps phonemic distinctions unique to Tigrinya grammar and morphology.
English as spoken in diplomatic, academic, and business circles across East Africa. Understands Ethiopian phonology, stress patterns, and natural code-switching with local languages.
Built to withstand noisy market field recordings, low-bandwidth telephone lines, and multi-speaker settings.
Phone calls in Ethiopia often suffer from low sampling rates and packet loss. Andebet's models are robust to 8kHz and 16kHz audio.
Upload multi-lingual conference recordings without specifying the language upfront. Andebet detects speech dynamically.
Handles variations in fidel homophone spelling while preserving formal legal, religious, and governmental conventions.
Separates spoken dialogue from market background chatter, vehicle noise, and room reverberation in real time.
Continuously updated with contemporary Ethiopian terms, emerging startup vocabulary, and public administrative jargon.
Ethiopic linguistic models hosted on high-performance infrastructure with complete tenant data isolation.
“For the first time, our media network can transcribe both Amharic and Afaan Oromo radio broadcasts in real time with correct fidel punctuation and zero hallucinations. It has cut our publishing cycle by 80%.”
Common questions regarding language support, dialects, and orthography.
We offer full production speech-to-text, speaker diarization, and semantic search for Amharic (አማርኛ), Afaan Oromo, Tigrinya (ትግርኛ), and English as spoken across the Horn of Africa. Additional regional languages, including Somali and Wolaytta, are currently in active research.
Our training corpora include verified speech samples recorded across multiple provinces and administrative regions. For example, in Amharic, our acoustic models handle Addis Ababa, Gojjam, Wollo, and Gondar accents without degradation in word error rate.
Yes. Urban conversations, tech meetings, and financial interviews in Ethiopia frequently switch between Amharic and English. Andebet's bilingual language model automatically switches orthographies mid-sentence, outputting fidel for Amharic and Latin characters for English words.
Our language model includes a contextual normalization layer that infers the intended word meaning based on grammatical structure and context, ensuring standardized, professional spelling in formal transcriptions.
Yes. Our Afaan Oromo models are trained natively on Qubee orthography, accurately transcribing geminated consonants, long vowels, and glottal apostrophes (hudhaa) according to standardized grammatical conventions.
Yes. Enterprise workspaces and Developer API users can supply custom glossaries, medical terminology, legal phrasing, or proprietary product names to bias the acoustic decoder toward specific vocabulary.
Upload audio in Amharic, Afaan Oromo, Tigrinya, or English and see the precision of our tailored speech models.