UBC-NLP/simba

## 📑 Table of Contents - Best-in-Class Multilingual Models - 🗣️✍️ Simba-ASR (Automatic Speech Recognition) - 🔊 Simba-TTS (Text-to-Speech) - 🔍 Simba-SLID (Spoken Language Identification) - SimbaBench Data Release & Benchmarking - How to Use SimbaBench - 📌 ASR Evaluation Configurations - 📌 TTS Evaluation Configurations - 📌 SLID Evaluation - Citation --- ## *Bridging the Digital Divide for African AI* **Voice of a Continent** is a comprehensive open-source ecosystem designed to bring African languages to the forefront of artificial intelligence. By providing a unified suite of benchmarking tools and state-of-the-art models, we ensure that the future of speech technology is inclusive, representative, and accessible to over a billion people. --- ## Best-in-Class Multilingual Models Introduced in our EMNLP 2025 paper *Voice of a Continent*, the **Simba Series** represents the current state-of-the-art for African speech AI. - **Unified Suite:** Models optimized for African languages. - **Superior Accuracy:** Outperforms generic multilingual models by leveraging SimbaBench's high-quality, domain-diverse datasets. - **Multitask Capability:** Designed for high performance in ASR (Automatic Speech Recognition) and TTS (Text-to-Speech). - **Inclusion-First:** Specifically built to mitigate the "digital divide" by empowering speakers of underrepresented languages. The **Simba** family consists of state-of-the-art models fine-tuned using SimbaBench. These models achieve superior performance by leveraging dataset quality, domain diversity, and language family relationships. ### 🗣️✍️ Simba-ASR > **The New Standard for African Speech-to-Text** **🎯 Task** `Automatic Speech Recognition` — Powering high-accuracy transcription across the continent. **🌍 Language Coverage (43 African languages)** > **Amharic** (`amh`), **Arabic** (`ara`), **Asante Twi** (`asanti`), **Bambara** (`bam`), **Baoulé** (`bau`), **Bemba** (`bem`), **Ewe** (`ewe`), **Fanti** (`fat`), **Fon** (`fo …