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A CoreML conversion of bond005/whisper-podlodka-turbo packaged for the WhisperKit runtime. Runs end-to-end on the Apple Neural Engine (ANE) on Apple Silicon Macs, iPhone, and iPad.
Source du modèle
Description de la source
A CoreML conversion of bond005/whisper-podlodka-turbo packaged for the WhisperKit runtime. Runs end-to-end on the Apple Neural Engine (ANE) on Apple Silicon Macs, iPhone, and iPad.
The upstream model is Ivan Bondarenko's Russian-focused fine-tune of , with improved noise robustness and reduced non-speech hallucinations. This repository contains only the converted weights - no architectural or training changes.
Sources
1 sourceVérifié 22 sept.
Artefacts du modèle
1 artefactAudioEncoder.mlmodelc/analytics/coremldata.bin
bin · 243 B · SHA-256 2cf55d7b0256…a9db · Hugging Face
TéléchargerExtraits de sources
2 extraits
openai/whisper-large-v3-turboUsed on-device in Letopis, a local meeting and call recorder and transcriber for macOS and iOS.
| File | Size | Purpose |
|---|---|---|
MelSpectrogram.mlmodelc | ~400 KB | Audio preprocessing (log-mel filterbank) |
AudioEncoder.mlmodelc | ~1.2 GB | 32-layer encoder, FP16 |
TextDecoder.mlmodelc | ~330 MB | 4-layer turbo decoder, FP16 |
config.json | - | Hugging Face Whisper config, inherited from the base model |
generation_config.json | - | Generation defaults, inherited from the base model |
All three .mlmodelc directories are pre-compiled MLProgram assets ready for direct use by WhisperKit. No additional compile step is required.
Inherited from the base model (Whisper Large v3 Turbo):
| Hyperparameter | Value |
|---|---|
| Encoder layers | 32 |
| Decoder layers | 4 |
| Hidden size (d_model) | 1280 |
| Attention heads (enc / dec) | 20 / 20 |
| Mel bins | 128 |
| Vocabulary | 51866 |
| Max source positions | 1500 (30 s @ 16 kHz) |
| Max target positions | 448 |
Converted with argmaxinc/whisperkittools 0.4.2:
whisperkit-generate-model \
--model-version bond005/whisper-podlodka-turbo \
--output-dir ./out \
--generate-decoder-context-prefill-data
torch==2.5.0, coremltools==9.0, transformers==4.53Cat (default)SplitHeadsQ (default)import WhisperKit
let folder = URL(fileURLWithPath: "/path/to/whisper-podlodka-turbo-coreml")
let pipe = try await WhisperKit(modelFolder: folder.path)
let result = try await pipe.transcribe(audioPath: "/path/to/audio.wav")
print(result?.text ?? "")
The tokenizer is the standard Whisper Large v3 tokenizer (vocab 51866) - WhisperKit will fetch it from openai/whisper-large-v3 if it is not present locally. Russian is the recommended decoding language for this model.
End-to-end ANE execution on an M-series Mac yields realtime factors significantly above 1.0x. First inference compiles ANE-specific kernels and may take noticeably longer; subsequent inferences use the cached compilation and are fast.
Primary: Russian. Secondary: English. The base fine-tune preserves the multilingual capability of Whisper Large v3 Turbo but is optimized for Russian ASR and Russian/English speech translation.
Apache 2.0, inherited from the base model.
bond005/whisper-podlodka-turbowhisper-large-v3-turboFor the base fine-tune, cite the upstream model:
@misc{whisper-podlodka-turbo,
author = {Ivan Bondarenko},
title = {Whisper-Podlodka-Turbo: Enhanced Whisper Model for Russian ASR},
year = {2025},
publisher = {Hugging Face},
journal = {Hugging Face Model Hub},
howpublished = {\url{https://huggingface.co/bond005/whisper-podlodka-turbo}}
}
--- license: apache-2.0 language: - ru - en base_model: - bond005/whisper-podlodka-turbo base_model_relation: quantized pipeline_tag: automatic-speech-recognition tags: - whisper - whisperkit - coreml - mlprogram - automatic-speech-recognition - asr - speech-recognition - russian - apple-silicon - ane library_name: whisperkit --- # Whisper-Podlodka-Turbo - CoreML / WhisperKit A CoreML conversion of [bond005/whisper-podlodka-turbo](https://huggingface.co/bond005/whisper-podlodka-turbo) packaged for the [WhisperKit](https://github.com/argmaxinc/whisperkit) runtime. Runs end-to-end on the Apple Neural Engine (ANE) on Apple Silicon Macs, iPhone, and iPad. The upstream model is Ivan Bondarenko's Russian-focused fine-tune of `openai/whisper-large-v3-turbo`, with improved noise robustness and reduced non-speech hallucinations. This repository contains only the converted weights - no architectural or training changes. Used on-device in **[Letopis](https://letopisapp.com/)**, a local meeting and call recorder and transcriber for macOS and iOS. ## Files | File | Size | Purpose | | --- | --- | --- | | `MelSpectrogram.mlmodelc` | ~400 KB | Audio preprocessing (log-mel filterbank) | | `AudioEncoder.mlmodelc` | ~1.2 GB | 32-layer encoder, FP16 | | `TextDecoder.mlmodelc` | ~330 MB | 4-layer turbo decoder, FP16 | | `config.json` | - | Hugging Face Whisper config, inherited from the base model | | `generation_config.json` | - | Generation defaults, inherited from the base model | All three `.mlmodelc` directories are pre-compiled MLProgram assets ready for direct use by WhisperKit. No additional compile step is required. ## Architecture Inherited from the base model (Whisper Large v3 Turbo): | Hyperparameter | Value | | --- | --- | | Encoder layers | 32 | | Decoder layers | 4 | | Hidden size (d_model) | 1280 | | Attention heads (enc / dec) | 20 / 20 | | Mel bins | 128 | | Vocabulary | 51866 | | Max source positions | 1500 (30 s @ 16 kHz) | | Max target positions | 448 | ## Conversion Converted with [argmaxinc/whisperkittools](https://github.com/argmaxinc/whisperkittools) 0.4.2: ```bash whisperkit-generate-model \ --model-version bond005/whisper-podlodka-turbo \ --output-dir ./out \ --generate-decoder-context-prefill-data ``` - Conversion environment: Python 3.12, `torch==2.5.0`, `coremltools==9.0`, `transformers==4.53` - Compute precision: FP16 across all...
Source context: 52 downloads · 0 likes · Pipeline automatic-speech-recognition · Library whisperkit · Repo smkrv/whisper-podlodka-turbo-coreml