Instructions to use ruchi01ra/whisper-sinhala-proto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruchi01ra/whisper-sinhala-proto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ruchi01ra/whisper-sinhala-proto")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ruchi01ra/whisper-sinhala-proto") model = AutoModelForSpeechSeq2Seq.from_pretrained("ruchi01ra/whisper-sinhala-proto") - Notebooks
- Google Colab
- Kaggle
whisper-sinhala-proto
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2724
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.1222 | 0.2667 | 100 | 0.8726 |
| 0.6662 | 0.5333 | 200 | 0.5184 |
| 0.3288 | 0.8 | 300 | 0.3103 |
| 0.2772 | 1.0667 | 400 | 0.2857 |
| 0.2638 | 1.3333 | 500 | 0.2724 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.1
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Model tree for ruchi01ra/whisper-sinhala-proto
Base model
openai/whisper-small