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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)
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