Whisper Small Sl - samolego
This model is a fine-tuned version of openai/whisper-small on the
ASR database ARTUR 1.0 (audio) dataset. It was trained on Artur-B-brani and Artur-B-Studio.
It achieves the following results on the evaluation set:
- Loss: 0.1226
- Wer: 11.0097
Model description
Both ggml and safetensors formats are available.
If you're not familiar with ggml, I'd suggest checking out whisper.cpp.
Intended uses & limitations
More information needed
Training and evaluation data
Verdonik, Darinka; et al., 2023, ASR database ARTUR 1.0 (audio), Slovenian language resource repository CLARIN.SI, ISSN 2820-4042, http://hdl.handle.net/11356/1776.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2778 | 0.07 | 500 | 0.2748 | 23.0421 |
| 0.2009 | 0.14 | 1000 | 0.1972 | 17.3073 |
| 0.1643 | 0.21 | 1500 | 0.1658 | 14.5195 |
| 0.1569 | 0.28 | 2000 | 0.1495 | 13.1550 |
| 0.1344 | 0.36 | 2500 | 0.1380 | 12.2945 |
| 0.1295 | 0.43 | 3000 | 0.1302 | 11.6237 |
| 0.1239 | 0.5 | 3500 | 0.1249 | 11.2128 |
| 0.1178 | 0.57 | 4000 | 0.1226 | 11.0097 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2