Instructions to use FTruter/za-anv-multilingual-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- WhisperKit
How to use FTruter/za-anv-multilingual-coreml with WhisperKit:
# Install CLI with Homebrew on macOS device brew install whisperkit-cli # View all available inference options whisperkit-cli transcribe --help # Download and run inference using whisper base model whisperkit-cli transcribe --audio-path /path/to/audio.mp3 # Or use your preferred model variant whisperkit-cli transcribe --model "large-v3" --model-prefix "distil" --audio-path /path/to/audio.mp3 --verbose
- Notebooks
- Google Colab
- Kaggle
ZA ANV multilingual (WhisperKit / Core ML)
Core ML / WhisperKit export of xbiglylabs/za-anv-multilingual-whisper-v3-turbo (fine-tune of openai/whisper-large-v3-turbo on the ANV / Swivuriso data).
Apple Silicon / WhisperKit only โ not Transformers, CTranslate2, or faster-whisper.
Uncompressed folder under za-anv-multilingual-v1/ (~1.5 GB).
TextDecoderContextPrefill.mlmodelc is not included (optional; conversion PSNR
failed on this fine-tune โ first tokens may be slower without it).
Converted with argmaxinc/whisperkittools.
See NOTICE for upstream credits.
Intended use
On-device ASR for Southern African languages covered by the source model:
Tswana (tsn), Zulu (zul), Xhosa (xho), Southern Sotho (sot),
Tsonga (tso), Southern Ndebele (nbl), Venda (ven).
Download / load (WhisperKit)
let folder = try await WhisperKit.download(
variant: "za-anv-multilingual-v1",
from: "FTruter/za-anv-multilingual-coreml"
)
let pipe = try await WhisperKit(modelFolder: folder.path, download: false)
Or:
hf download FTruter/za-anv-multilingual-coreml --local-dir ./za-anv-multilingual-coreml
let pipe = try await WhisperKit(
modelFolder: "./za-anv-multilingual-coreml/za-anv-multilingual-v1",
download: false
)
Force the target language code when you know it; do not rely on autodetection for best results.
Size / device notes
~1.5 GB uncompressed. Low-RAM devices can jetsam โ test on target hardware.
Source metrics (from upstream card; not re-measured here)
On the upstream eval set, xbiglylabs report WER 0.1501 / CER 0.0510. Those figures are for the Transformers checkpoint, not this Core ML export.
Limitations
- WhisperKit / Core ML on Apple Silicon only
- Prefill decoder omitted
- Same hallucination / domain limits as the upstream Whisper fine-tune
- This private Hub drop may later move under another namespace
Contents (za-anv-multilingual-v1/)
MelSpectrogram.mlmodelc,AudioEncoder.mlmodelc,TextDecoder.mlmodelcconfig.json,generation_config.json,preprocessor_config.jsontokenizer.json,tokenizer_config.json,manifest.json
Root: this card, LICENSE, LICENSE-whisper-apache-2.0.txt, NOTICE.
Citation / credit
Upstream: xbiglylabs/za-anv-multilingual-whisper-v3-turbo.
Base: OpenAI Whisper large-v3-turbo (Apache-2.0). Training data: ANV / Swivuriso
(dsfsi-anv). See NOTICE.
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Model tree for FTruter/za-anv-multilingual-coreml
Base model
openai/whisper-large-v3