This is a Swahili text-to-speech (TTS) model fine-tuned on top of the English Chatterbox Multilingual backbone using a LoRA adapter. It was developed by Msingi-AI as part of the Sauti TTS V2 project.

The model was fine-tuned on Google's WaxalNLP swa_tts studio dataset (CC-BY-4.0). The dataset was filtered using a transcript-agreement check, dropping clips where an Automatic Speech Recognition (ASR) judge detected a Character Error Rate (CER) > 10% against the reference text.

The model was evaluated against a held-out evaluation set of 48 Swahili sentences across various stress categories (general, code-switching, named entities, numbers/dates). Intelligibility is measured by Word Error Rate (WER) and Character Error Rate (CER) under two ASR judges: zero-shot openai/whisper-large-v3 and a Swahili-fine-tuned Whisper (Jacaranda-Health/ASR-STT). Swahili's agglutinative morphology tends to inflate WER, making CER the headline metric. UTMOS is used as a proxy for audio quality.

The weights provided here are the LoRA adapter (new_lang_adapter directory). They must be loaded on top of the original Chatterbox backbone using the Chatterbox fine-tuning toolkit.