Whisper Small - FutureProofGlitch
This model is a fine-tuned version of openai/whisper-small on the AMI Meeting Corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.4325
- Wer Ortho: 19.5838
- Wer: 19.3832
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.2735 | 0.61 | 500 | 0.3324 | 21.5310 | 21.2081 |
| 0.1235 | 1.22 | 1000 | 0.3473 | 19.6819 | 19.4991 |
| 0.1317 | 1.83 | 1500 | 0.3342 | 19.0920 | 18.7929 |
| 0.0647 | 2.44 | 2000 | 0.3671 | 22.8615 | 22.6949 |
| 0.0294 | 3.05 | 2500 | 0.3842 | 18.5566 | 18.4101 |
| 0.0534 | 3.66 | 3000 | 0.4044 | 20.8094 | 20.5998 |
| 0.0366 | 4.27 | 3500 | 0.4277 | 20.2686 | 20.1372 |
| 0.0328 | 4.88 | 4000 | 0.4325 | 19.5838 | 19.3832 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2