End of training

README.md

@@ -26,7 +26,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 20.384831987985734
+      value: 20.157687253613666
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -36,8 +36,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2469
-- Wer: 20.3848
+- Loss: 0.2758
+- Wer: 20.1577
 
 ## Model description
 
@@ -65,23 +65,33 @@ The following hyperparameters were used during training:
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- training_steps: 5000
+- training_steps: 10000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Wer     |
-|:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.5507        | 0.2079 | 500  | 0.3695          | 29.2247 |
-| 0.2802        | 0.4158 | 1000 | 0.3148          | 26.7299 |
-| 0.2408        | 0.6236 | 1500 | 0.2970          | 24.2538 |
-| 0.2208        | 0.8315 | 2000 | 0.2728          | 23.3020 |
-| 0.1811        | 1.0394 | 2500 | 0.2665          | 22.3935 |
-| 0.1096        | 1.2473 | 3000 | 0.2641          | 21.8998 |
-| 0.1068        | 1.4552 | 3500 | 0.2568          | 21.6125 |
-| 0.1042        | 1.6630 | 4000 | 0.2516          | 21.0512 |
-| 0.1001        | 1.8709 | 4500 | 0.2472          | 20.4092 |
-| 0.0827        | 2.0788 | 5000 | 0.2469          | 20.3848 |
+| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
+|:-------------:|:------:|:-----:|:---------------:|:-------:|
+| 0.5507        | 0.2079 | 500   | 0.3695          | 29.2247 |
+| 0.2802        | 0.4158 | 1000  | 0.3148          | 26.7299 |
+| 0.2408        | 0.6236 | 1500  | 0.2970          | 24.2538 |
+| 0.2208        | 0.8315 | 2000  | 0.2728          | 23.3020 |
+| 0.1811        | 1.0394 | 2500  | 0.2665          | 22.3935 |
+| 0.1096        | 1.2473 | 3000  | 0.2641          | 21.8998 |
+| 0.1068        | 1.4552 | 3500  | 0.2568          | 21.6125 |
+| 0.1042        | 1.6630 | 4000  | 0.2516          | 21.0512 |
+| 0.1001        | 1.8709 | 4500  | 0.2472          | 20.4092 |
+| 0.0827        | 2.0788 | 5000  | 0.2469          | 20.3848 |
+| 0.0672        | 2.2869 | 5500  | 0.2665          | 21.1357 |
+| 0.0673        | 2.4948 | 6000  | 0.2674          | 21.5093 |
+| 0.0681        | 2.7026 | 6500  | 0.2635          | 20.6101 |
+| 0.0661        | 2.9105 | 7000  | 0.2602          | 20.5069 |
+| 0.0494        | 3.1184 | 7500  | 0.2708          | 20.5444 |
+| 0.0352        | 3.3263 | 8000  | 0.2688          | 20.5181 |
+| 0.0338        | 3.5341 | 8500  | 0.2717          | 20.2515 |
+| 0.0318        | 3.7420 | 9000  | 0.2723          | 20.2403 |
+| 0.0309        | 3.9499 | 9500  | 0.2711          | 20.1727 |
+| 0.022         | 4.1578 | 10000 | 0.2758          | 20.1577 |
 
 
 ### Framework versions