Downloads · 30 days
14
7% of all-time downloads
finetuned-whisper-small-nepali
sparshrestha/finetuned-whisper-small-nepali
finetuned-whisper-small-nepali is a automatic speech recognition model from sparshrestha. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
202
Public
Parameters
242M
5.8 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors967 MB · 100%
At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Jun 30, 2026
- Updated
- Jul 1, 2026
- SHA
- b91f3ca1
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Fine-tuned Whisper Small Nepali
This model is a fine-tuned version of openai/whisper-small on the Common Voice 26.0 + Google FLEURS ne_np dataset. It achieves the following results on the evaluation set:
- Loss: 0.4616
- Wer: 63.1340
- Normalized Wer: 62.4060
- Cer: 27.8890
- Avg Pred Words: 10.0194
- Avg Ref Words: 11.3694
- Empty Prediction Rate: 0.0
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Normalized Wer | Cer | Avg Pred Words | Avg Ref Words | Empty Prediction Rate |
|---|---|---|---|---|---|---|---|---|---|
| 13.3546 | 0.8686 | 100 | 0.7885 | 86.2872 | 85.6150 | 39.2049 | 9.7677 | 11.3694 | 0.0 |
| 5.8797 | 1.7296 | 200 | 0.5593 | 70.1075 | 69.7120 | 31.0429 | 9.8871 | 11.3694 | 0.0 |
| 4.4283 | 2.5907 | 300 | 0.4871 | 65.2426 | 64.6191 | 28.6433 | 10.0581 | 11.3694 | 0.0 |
| 3.8697 | 3.4517 | 400 | 0.4643 | 63.5198 | 62.8316 | 28.1000 | 9.9887 | 11.3694 | 0.0 |
| 3.4989 | 4.3127 | 500 | 0.4616 | 63.1340 | 62.4060 | 27.8890 | 10.0194 | 11.3694 | 0.0 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- ne
- Created
- Jun 30, 2026
- Updated
- Jul 1, 2026