Instructions to use Kohn-AI/jem-whisper-yi-2026-05-18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kohn-AI/jem-whisper-yi-2026-05-18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kohn-AI/jem-whisper-yi-2026-05-18")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Kohn-AI/jem-whisper-yi-2026-05-18") model = AutoModelForMultimodalLM.from_pretrained("Kohn-AI/jem-whisper-yi-2026-05-18") - Notebooks
- Google Colab
- Kaggle
jem-whisper-yi-2026-05-18
This model is a fine-tuned version of ivrit-ai/yi-whisper-large-v3 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2645
- Wer Ortho: 0.3364
- Wer: 0.2557
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.4214 | 1.4663 | 100 | 0.3935 | 0.6362 | 0.6136 |
| 0.3033 | 2.9326 | 200 | 0.2645 | 0.3364 | 0.2557 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.4.0
- Datasets 3.6.0
- Tokenizers 0.21.4
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