Instructions to use vipul-modelupload/vani-hinglish-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- WhisperKit
How to use vipul-modelupload/vani-hinglish-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
On-device speech models for Vani (Core ML)
Speech-to-text models converted to Core ML so they run on iPhone, packed as Apple Archives
(.aar) for download by the Vani app.
| File | Model | Runs with | Size | Licence |
|---|---|---|---|---|
hinglish-fast.aar |
Oriserve/Whisper-Hindi2Hinglish-Swift (72.6M params, fp16) | WhisperKit | 128 MB | Apache-2.0 |
hinglish-best.aar |
Oriserve/Whisper-Hindi2Hinglish-Apex (0.8B params, 4-bit palettized) | WhisperKit | 407 MB | Apache-2.0 |
parakeet-en.aar |
nvidia/parakeet-tdt-0.6b-v2, Core ML conversion by FluidInference, unchanged | FluidAudio | 440 MB | CC-BY-4.0 |
parakeet-vocab.aar |
NVIDIA Parakeet CTC 110M, Core ML conversion by FluidInference, unchanged | FluidAudio (vocabulary boosting) | 86 MB | CC-BY-4.0 |
Unpack on a Mac with aa extract -i <file>.aar -d <folder>.
Hinglish models
Oriserve's Hindi2Hinglish Whisper fine-tunes, converted with Argmax's
whisperkittools. They transcribe Hindi-English
speech in English letters ("kal ki meeting mein roadmap discuss karenge"). Each archive is one
WhisperKit model folder (AudioEncoder.mlmodelc, TextDecoder.mlmodelc, MelSpectrogram.mlmodelc,
tokenizer files). Decode with task transcribe and language en (Oriserve's recipe). Token
timestamps are not included: the fine-tunes have no alignment heads.
Parakeet (English)
NVIDIA's Parakeet TDT 0.6B v2, English only, as converted to Core ML by FluidInference. The
archive holds exactly the five files FluidAudio loads for AsrModelVersion.v2
(Preprocessor.mlmodelc, Encoder.mlmodelc, Decoder.mlmodelc, JointDecision.mlmodelc,
parakeet_vocab.json), unpacked into a folder named parakeet-tdt-0.6b-v2 (FluidAudio's cache name).
Dictionary boost (English)
parakeet-vocab.aar holds NVIDIA's Parakeet CTC 110M as converted by FluidInference
(MelSpectrogram.mlmodelc, AudioEncoder.mlmodelc, vocab.json, tokenizer files). FluidAudio's
vocabulary boosting uses it to listen for the user's own words and swap them into Parakeet's text
where the audio supports them. Unpack into parakeet-ctc-110m-coreml, where FluidAudio looks.
Credit and licences
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Model tree for vipul-modelupload/vani-hinglish-coreml
Base model
openai/whisper-large-v3