| --- |
| language: he |
| license: apache-2.0 |
| library_name: transformers |
| tags: |
| - whisper |
| - audio |
| - automatic-speech-recognition |
| - hebrew |
| datasets: |
| - ivrit-ai/whisper-training |
| base_model: openai/whisper-tiny |
| pipeline_tag: automatic-speech-recognition |
| --- |
| |
| # whisper-tiny-he |
|
|
| Hebrew fine-tuned [Whisper Tiny](https://huggingface.co/openai/whisper-tiny) for automatic speech recognition. |
|
|
| ## Training |
|
|
| - **Base model**: [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) |
| - **Dataset**: [ivrit-ai/whisper-training](https://huggingface.co/datasets/ivrit-ai/whisper-training) (~400h Hebrew) |
| - **Method**: Supervised fine-tuning with `Seq2SeqTrainer` |
| - **Steps**: 5,000 (streaming, effective batch size 16) |
| - **Hardware**: Apple M4 (MPS), fp32 |
| - **Final eval WER**: 0.659 (on 200-sample test split) |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration |
| |
| processor = WhisperProcessor.from_pretrained("amitkot/whisper-tiny-he") |
| model = WhisperForConditionalGeneration.from_pretrained("amitkot/whisper-tiny-he") |
| |
| model.generation_config.language = "he" |
| model.generation_config.task = "transcribe" |
| ``` |
|
|
| ## Training pipeline |
|
|
| Trained using [whisper-acft-pipeline](https://github.com/amitkot/whisper-acft-pipeline): |
|
|
| ```bash |
| uv run python scripts/finetune.py --config configs/hebrew_tiny_finetune.yaml |
| ``` |
|
|
| ## See also |
|
|
| - [amitkot/whisper-tiny-he-acft](https://huggingface.co/amitkot/whisper-tiny-he-acft) — ACFT-optimized version of this model for short audio (FUTO Keyboard) |
|
|
