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BabaStardust/whisper-large-v3-turbo-swiss-german-MLX

sourceHugging Facecc-by-nc-4.0updated 5mo agoView on Hugging Face

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Model Card

whisper-large-v3-turbo-swiss-german-MLX

This is an MLX-format conversion of Flurin17/whisper-large-v3-turbo-swiss-german for fast, native inference on Apple Silicon using mlx-whisper.

All credit for the original fine-tuning work goes to [Flurin17](https://huggingface.co/Flurin17) — a massive thank you for training and sharing this model. 🙏 (See: Eifach mal es "Dankeschöön")

Model Details

Property	Value
Base model	`openai/whisper-large-v3-turbo`
Fine-tuned model	`Flurin17/whisper-large-v3-turbo-swiss-german`
Format	MLX (converted via mlx-examples/whisper/convert.py)
Dtype	float16
Task	Swiss German speech → Standard German text
Best checkpoint	`checkpoint-750`
Training data	~301 hours curated private Swiss German audio
Training infra	4x A100 80GB GPUs

Why checkpoint-750?

The fine-tune improved steadily up to step 750, then overfit. This checkpoint has the best validation WER:

Step	WER	Normalized WER
250	41.05	40.21
500	39.63	38.86
750	37.96	37.25
1000	42.92	42.20

Improvement over base Whisper Turbo

Evaluated on 16,384 samples from the same training corpus regime:

Model	WER	Normalized WER
`openai/whisper-large-v3-turbo` (base)	45.71	44.52
`Flurin17/whisper-large-v3-turbo-swiss-german`	39.18	38.48

Absolute improvement: -6.54 WER / -6.04 normalized WER

Usage with mlx-whisper

Install

bash

pip install mlx-whisper

Transcribe

python

import mlx_whisper

result = mlx_whisper.transcribe(
    "path/to/audio.wav",
    path_or_hf_repo="BabaStardust/whisper-large-v3-turbo-swiss-german-MLX",
)
print(result["text"])

CLI

bash

mlx_whisper path/to/audio.wav \
  --model BabaStardust/whisper-large-v3-turbo-swiss-german-MLX

Intended Use

This model transcribes Swiss German speech (Schweizerdeutsch) into Standard German text. It works best on:

—Clean conversational or semi-formal speech
—Parliamentary and read/prompted Swiss German
—All major Swiss German dialect regions

It is not intended for High German input — use the base openai/whisper-large-v3-turbo for that.

Limitations

—Training data is private; reported metrics are self-reported from internal evaluation
—Performance varies by dialect, speaker, audio quality, and domain
—The run overfit after checkpoint-750; this is the best available checkpoint

Conversion

Converted from PyTorch safetensors to MLX format using the official mlx-examples/whisper/convert.py script:

bash

python convert.py \
  --torch-name-or-path Flurin17/whisper-large-v3-turbo-swiss-german \
  --mlx-path ./mlx_swiss_german \
  --dtype float16

License

Creative Commons Attribution-NonCommercial 4.0 (cc-by-nc-4.0) — inherited from the original model.