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digiphyte
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Model Details
Model Provider
digiphyte
Model Tree
openai/whisper-large-v3-turbo
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import pipelinepipe = pipeline("automatic-speech-recognition",model="digiphyte/fluister-turbo-transformers",torch_dtype=torch.float16,device="cuda", # or "mps" on Apple silicon, "cpu")print(pipe("audio.wav", generate_kwargs={"language": "af"})["text"]) # or "en"
Tell it the language ("af" or "en") rather than relying on auto-detect. For mixed
Afrikaans/English conversations, "af" handles the code-switch well.
This is a standard Transformers Whisper checkpoint, so it feeds straight into
whisperkittools to produce a WhisperKit Core ML
package for on-device Apple inference.
NCHLT read-speech test sets: Afrikaans WER 0.086, English WER 0.017 (identical weights to the CT2 build). Validated on real SA audio: an Afrikaans physiotherapy intake, an English project meeting, and an Afrikaans/English code-switched conversation. Clean Afrikaans where stock Whisper drifts to Dutch spellings ("gebou" not "gebouw", "mense" not "mensen"), intact code-switching, and accurate SA English.
Same as the CT2 build. Fluister narrows specific failures (Whisper spelling Afrikaans as Dutch; degrading SA English); it does not change the base model size. Language auto-detect can still mislabel audio (tell it the language), and proper nouns, numbers, and rare or technical terms can still be wrong. South African place names and surnames in particular are a known gap we are still improving.
MIT (see LICENSE). This is a derivative work; the base model (OpenAI Whisper, Apache-2.0) and the
training data (andreoosthuizen/afrikaans-30s, CC-BY-4.0; NCHLT afr/eng, CC-BY-3.0) are
credited in NOTICE.