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README.md

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+---
+language: [ha, yo, ig, en]
+license: mit
+pipeline_tag: text-to-speech
+tags: [text-to-speech, voice-cloning, chatterbox, multilingual, hausa, yoruba, igbo, nigerian-english, african-languages]
+base_model: ResembleAI/chatterbox
+---
+
+# Naija TTS — multilingual Hausa / Yoruba / Igbo / Nigerian English
+
+One Chatterbox model covering four Nigerian languages, with zero-shot voice
+cloning. LoRA fine-tune of Chatterbox Multilingual (MIT), with the grapheme
+vocabulary extended for the characters these languages need.
+
+Base Chatterbox ships 23 languages, of which Swahili is the only African one.
+This model adds Hausa, Yoruba, Igbo and Nigerian English — the last under its
+own `en-ng` tag so base English is left intact.
+
+## Evaluation
+
+| Language | n | CER | CER floor | WER | Speaker SIM | RTF |
+|---|---|---|---|---|---|---|
+| Hausa (`ha`) | 40 | 8.83 % | 4.87 % | 32.35 % | n/a | 0.680 |
+| Yoruba (`yo`) | 40 | 45.02 % | 12.13 % | 77.73 % | n/a | 1.065 |
+| Igbo (`ig`) | 40 | 14.87 % | 8.41 % | 42.57 % | n/a | 0.840 |
+| Nigerian English (`en-ng`) | 40 | 12.27 % | 13.09 % | 31.44 % | n/a | 0.932 |
+
+**Read each CER against its own floor**, not against zero. The floor is what
+MMS-ASR scores on genuine recordings of that language — its own error rate.
+A CER at the floor means the synthesized speech is as intelligible as real
+speech. The floors differ substantially between languages; a raw CER comparison
+across languages is meaningless.
+
+## Usage
+
+```python
+import torchaudio as ta
+import chatterbox.mtl_tts as M
+from chatterbox.mtl_tts import ChatterboxMultilingualTTS
+
+for code, name in [("ha","Hausa"), ("yo","Yoruba"), ("ig","Igbo"),
+                   ("en-ng","Nigerian English")]:
+    M.SUPPORTED_LANGUAGES[code] = name
+
+model = ChatterboxMultilingualTTS.from_local("path/to/repo", device="cuda")
+wav = model.generate("Sannu da zuwa.", language_id="ha",
+                     audio_prompt_path="reference.wav")
+ta.save("out.wav", wav, model.sr)
+```
+
+Normalize text to Unicode NFC and keep the languages' special characters as
+written — for Yoruba this includes the combining tone marks.
+
+## Limitations
+
+- **Hausa tone** is not modelled: Boko orthography marks neither tone nor vowel
+  length, so a character-input model has no signal for it. Yoruba and Igbo write
+  tone with diacritics and are not subject to this.
+- Long utterances can truncate (Chatterbox forces EOS on long tails).
+- The corpus was capped per language for balance, so no language sees its full
+  available data.
+- Outputs carry Resemble's Perth watermark, inherited from the base model.
+
+## Licence
+MIT, from base Chatterbox.