Instructions to use DictionLabs/whisperkit-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- WhisperKit
How to use DictionLabs/whisperkit-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
whisperkit-coreml
OpenAI Whisper in CoreML format, converted and published by Diction Labs for on-device speech-to-text on Apple hardware.
Use with WhisperKit
let config = WhisperKitConfig(
model: "openai_whisper-base",
modelRepo: "DictionLabs/whisperkit-coreml"
)
let pipe = try await WhisperKit(config)
Runs on argmax-oss-swift (MIT). Tokenizers are
resolved separately from the matching openai/whisper-* repo, so a first run still needs network
access even with the weights already on disk.
Variants
| Folder | Base model |
|---|---|
openai_whisper-base |
openai/whisper-base |
openai_whisper-small |
openai/whisper-small |
Provenance
Converted from OpenAI's PyTorch checkpoints with whisperkittools (MIT), not repackaged from another CoreML distribution. Published under our own namespace so the models Diction downloads come from a repository we control: a new upstream release reaches nobody until we have tested it and promoted it deliberately.
MIT licensed, as is Whisper itself.
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Model tree for DictionLabs/whisperkit-coreml
Base model
openai/whisper-base