<!-- 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. -->
Whisper Small - Mohammed Rakib
This model is a fine-tuned version of openai/whisper-small on the common-voice-11, the google-fleurs, the openslr53 and the crblp speech corpus datasets. It achieves the following results on the evaluation set:
- Loss: 0.0617
- Cer: 5.4436
- Wer: 9.6538
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: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 8000
- training_steps: 40000
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 0.5361 | 0.13 | 1000 | 0.4043 | 22.6599 | 44.0521 |
| 0.2881 | 0.26 | 2000 | 0.2217 | 16.3939 | 32.4894 |
| 0.2265 | 0.38 | 3000 | 0.1728 | 13.0425 | 25.9637 |
| 0.1974 | 0.51 | 4000 | 0.1430 | 11.3260 | 22.3187 |
| 0.1591 | 0.64 | 5000 | 0.1255 | 10.0167 | 19.5115 |
| 0.1504 | 0.77 | 6000 | 0.1102 | 8.8333 | 17.1919 |
| 0.1259 | 0.89 | 7000 | 0.1003 | 8.1863 | 15.8576 |
| 0.1184 | 1.02 | 8000 | 0.0940 | 7.7868 | 14.9110 |
| 0.1099 | 1.15 | 9000 | 0.0885 | 7.3675 | 13.9444 |
| 0.1075 | 1.28 | 10000 | 0.0830 | 6.9648 | 13.2008 |
| 0.095 | 1.41 | 11000 | 0.0789 | 6.6969 | 12.6776 |
| 0.0943 | 1.53 | 12000 | 0.0766 | 6.3765 | 11.9896 |
| 0.0923 | 1.66 | 13000 | 0.0731 | 6.1784 | 11.7203 |
| 0.0824 | 1.79 | 14000 | 0.0699 | 5.9267 | 11.1632 |
| 0.0756 | 1.92 | 15000 | 0.0683 | 5.6305 | 10.6327 |
| 0.0634 | 2.04 | 16000 | 0.0671 | 5.6905 | 10.6947 |
| 0.0618 | 2.17 | 17000 | 0.0662 | 5.5107 | 10.2926 |
| 0.0679 | 2.3 | 18000 | 0.0643 | 5.4948 | 10.1792 |
| 0.0589 | 2.43 | 19000 | 0.0647 | 5.5201 | 10.1881 |
| 0.0623 | 2.56 | 20000 | 0.0633 | 5.2731 | 9.8449 |
| 0.0558 | 2.68 | 21000 | 0.0623 | 5.4211 | 10.0267 |
| 0.0564 | 2.81 | 22000 | 0.0617 | 5.4553 | 9.9893 |
| 0.0552 | 2.94 | 23000 | 0.0607 | 5.3860 | 9.7778 |
| 0.0403 | 3.07 | 24000 | 0.0621 | 5.7297 | 10.0382 |
| 0.0406 | 3.19 | 25000 | 0.0617 | 5.4436 | 9.6538 |
| 0.041 | 3.32 | 26000 | 0.0611 | 6.0867 | 10.3834 |
| 0.0388 | 3.45 | 27000 | 0.0614 | 6.1641 | 10.3890 |
| 0.0383 | 3.58 | 28000 | 0.0611 | 6.1460 | 10.3537 |
| 0.0401 | 3.71 | 29000 | 0.0603 | 6.9576 | 11.0697 |
| 0.0343 | 3.83 | 30000 | 0.0613 | 7.1918 | 11.2243 |
| 0.0357 | 3.96 | 31000 | 0.0603 | 7.3128 | 11.3313 |
| 0.0313 | 4.09 | 32000 | 0.0624 | 7.3871 | 11.3861 |
| 0.0281 | 4.22 | 33000 | 0.0626 | 7.8705 | 11.8248 |
| 0.0298 | 4.34 | 34000 | 0.0629 | 8.3360 | 12.2368 |
| 0.0282 | 4.47 | 35000 | 0.0627 | 8.7840 | 12.6270 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.2.dev0
- Tokenizers 0.13.2
