Whisper small - Denis Musinguzi
This model is a fine-tuned version of openai/whisper-large on the Common Voice 14.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4428
- Wer: 0.2513
- Cer: 0.0983
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|---|---|---|---|---|---|
| 0.9179 | 0.51 | 800 | 0.1412 | 0.5355 | 0.3693 |
| 0.3078 | 1.02 | 1600 | 0.1196 | 0.4343 | 0.3152 |
| 0.1959 | 1.53 | 2400 | 0.1172 | 0.4068 | 0.2822 |
| 0.1737 | 2.04 | 3200 | 0.1145 | 0.3922 | 0.2721 |
| 0.1046 | 2.55 | 4000 | 0.1084 | 0.3958 | 0.2634 |
| 0.1019 | 3.06 | 4800 | 0.1029 | 0.3957 | 0.2578 |
| 0.0588 | 3.57 | 5600 | 0.1132 | 0.4013 | 0.2666 |
| 0.0545 | 4.08 | 6400 | 0.1009 | 0.4112 | 0.2510 |
| 0.0305 | 4.59 | 7200 | 0.0941 | 0.4183 | 0.2442 |
| 0.0275 | 5.1 | 8000 | 0.1005 | 0.4303 | 0.2549 |
| 0.0153 | 5.61 | 8800 | 0.4374 | 0.2407 | 0.0908 |
| 0.014 | 6.12 | 9600 | 0.4428 | 0.2513 | 0.0983 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.0
- Tokenizers 0.15.2