End of training

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 25.191977931857153
+      value: 24.498620741072095
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [ope100whisper-small](https://huggingface.co/ope100whisper-small) on the kannada voices dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1101
-- Wer: 25.1920
+- Loss: 0.1305
+- Wer: 24.4986
 
 ## Model description
 
@@ -61,18 +61,23 @@ The following hyperparameters were used during training:
 - optimizer: Use 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     |
-|:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.1461        | 0.5869 | 1000 | 0.1511          | 37.9110 |
-| 0.0795        | 1.1737 | 2000 | 0.1172          | 31.0520 |
-| 0.0715        | 1.7613 | 3000 | 0.1090          | 28.1220 |
-| 0.0508        | 2.3486 | 4000 | 0.1033          | 25.7362 |
-| 0.0309        | 2.9356 | 5000 | 0.1101          | 25.1920 |
+| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
+|:-------------:|:------:|:-----:|:---------------:|:-------:|
+| 0.1461        | 0.5869 | 1000  | 0.1511          | 37.9110 |
+| 0.0795        | 1.1737 | 2000  | 0.1172          | 31.0520 |
+| 0.0715        | 1.7613 | 3000  | 0.1090          | 28.1220 |
+| 0.0508        | 2.3486 | 4000  | 0.1033          | 25.7362 |
+| 0.0309        | 2.9356 | 5000  | 0.1101          | 25.1920 |
+| 0.0474        | 3.5230 | 6000  | 0.1105          | 26.1537 |
+| 0.0272        | 4.1098 | 7000  | 0.1169          | 25.4082 |
+| 0.0255        | 4.6967 | 8000  | 0.1195          | 25.0727 |
+| 0.0151        | 5.2835 | 9000  | 0.1285          | 24.7968 |
+| 0.0149        | 5.8704 | 10000 | 0.1305          | 24.4986 |
 
 
 ### Framework versions