---
license: mit
language:
- zu
- xh
- st
- tn
- ts
- nr
- ve
base_model: dsfsi-anv/za-anv-multilingual-whisper-v3-turbo
library_name: ctranslate2
pipeline_tag: automatic-speech-recognition
tags:
- whisper
- swivuriso
- south-africa
- south-african-languages
- faster-whisper
- ctranslate2
---

# Swivuriso (turbo)

**Swivuriso** is a multilingual South African speech-recognition model covering seven
South African languages: isiZulu, isiXhosa, Sesotho, Setswana, Xitsonga, isiNdebele, Tshivenda.

**This is not our model.** It is `dsfsi-anv/za-anv-multilingual-whisper-v3-turbo`
by the **Data Science for Social Impact (DSFSI)** research group at the University of
Pretoria, built as part of the **African Next Voices** project and named for the
**Swivuriso** dataset it is trained on. It is MIT-licensed. We host it here, under its
own name, only because we converted it to CTranslate2 (int8) so that
[faster-whisper](https://github.com/SYSTRAN/faster-whisper) can load it. No retraining
was done. Full credit to DSFSI / African Next Voices (see `NOTICE`).

## Use (faster-whisper)

```python
from faster_whisper import WhisperModel
model = WhisperModel("digiphyte/swivuriso-turbo", device="cuda", compute_type="int8_float16")  # CPU: device="cpu", compute_type="int8"
# faster-whisper has no language codes for these seven languages, and the source model
# forces none, so run on auto-detect: pass language=None (do NOT force a language).
segments, info = model.transcribe("audio.wav", language=None, beam_size=5)
for s in segments:
    print(s.text)
```

Note on `language=None`: faster-whisper's language set does not include these seven
languages, so you cannot (and should not) force one. The model decodes them correctly
on auto-detect. faster-whisper may *report* the detected language as something else
(e.g. "en") because it has no code to name them, but the transcribed text is the correct
South African language. Use this model only for the seven languages it covers; for
Afrikaans or English, use a Whisper or Fluister model instead.

## Status

Beta. Transcription quality on real-world South African audio is still being verified.

## Licence and attribution

MIT (see `LICENSE`), preserving the copyright of the model's authors. This repository is
a format conversion of a derivative work; the model and its fine-tune (DSFSI / African
Next Voices, MIT), the Swivuriso training data (African Next Voices), and the base model
(OpenAI Whisper, Apache-2.0) are credited in `NOTICE`.