gabar-tech/chatterbox-afaan-oromo

Afaan Oromo LoRA adapter for Chatterbox Multilingual v3, with inference code, paired demos, evaluation results, and audit files. --- language: - om license: cc-by-sa-4.0 base_model: ResembleAI/chatterbox base_model_relation: adapter library_name: peft pipeline_tag: text-to-speech datasets: - google/WaxalNLP tags: - text-to-speech - oromo - afaan-oromo - ethiopia - lora - voice-cloning - chatterbox --- # Chatterbox Afaan Oromo A LoRA adapter that teaches Chatterbox Multilingual v3 (Resemble AI, MIT) to speak Afaan Oromo, with voice cloning from about ten seconds of reference audio. Trained only on speech that is licensed for it. Stock Chatterbox can read Qubee: it's Latin script, and the tokenizer covers every letter. What it doesn't have is the language. Fed an Afaan Oromo sentence it produces something with the right letters and the wrong sounds: `dh`, `ph`, `ny`, `x`, `q`, the long vowels and the glottal-stop apostrophe (`ta'uu`) all come out as whatever its other languages taught it. The adapter teaches it what those spellings sound like when an Afaan Oromo speaker says them. It also often falls apart near the end of a clip; that goes away too. Qubee needs no new tokens: the base tokenizer already covers a–z and the apostrophe. The repo also has `afaan_oromo_text.py`, the text normalizer the model was trained through. No dependencies, works on its own (below). ## Hear it Each pair uses the same sentence, reference audio, settings and seed. No language tag on either: the adapter was trained without one and the base has no `[om]`. | # | Test case | Stock Chatterbox v3 | + Gabar adapter | |:-:|---|:-:|:-:| | 1 | Ordinary prose | | | | 2 | Long sentence, `'` glottal stop | | | | 3 | Prose, `dh` and long vowels | | | | 4 | Year + large number | | | | 5 | Names, `c` / `q` ejectives | | | | 6 | Question intonation | | | | 7 | Technical prose | | | | 8 | Numbers, percent, two questions | | | Reading this on GitHub? The players only render on Hugging Face. Click a clip in `demo/` to play it, or watch `demo/before_after.mp4` (all eight pairs). …