End of training

README.md

@@ -24,7 +24,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 33.54106242324475
+      value: 33.449797070760546
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -34,8 +34,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2026
-- Wer: 33.5411
+- Loss: 0.1074
+- Wer: 33.4498
 
 ## Model description
 
@@ -54,38 +54,30 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 3.75e-05
-- train_batch_size: 16
-- eval_batch_size: 8
+- learning_rate: 3e-05
+- train_batch_size: 26
+- eval_batch_size: 46
 - seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 32
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- training_steps: 16000
+- training_steps: 10000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch   | Step  | Validation Loss | Wer     |
-|:-------------:|:-------:|:-----:|:---------------:|:-------:|
-| 0.2369        | 0.6365  | 1000  | 0.2433          | 62.1881 |
-| 0.1242        | 1.2731  | 2000  | 0.1734          | 49.4369 |
-| 0.1022        | 1.9096  | 3000  | 0.1197          | 39.0531 |
-| 0.046         | 2.5461  | 4000  | 0.1067          | 34.5497 |
-| 0.0777        | 3.1827  | 5000  | 0.1440          | 43.2194 |
-| 0.0649        | 3.8192  | 6000  | 0.1266          | 38.6232 |
-| 0.0367        | 4.4558  | 7000  | 0.1288          | 38.0392 |
-| 0.0126        | 5.0923  | 8000  | 0.1382          | 35.0226 |
-| 0.0108        | 5.7288  | 9000  | 0.1416          | 34.5340 |
-| 0.0038        | 6.3654  | 10000 | 0.1611          | 33.3921 |
-| 0.0023        | 7.0019  | 11000 | 0.1744          | 33.4875 |
-| 0.0133        | 7.6384  | 12000 | 0.1625          | 36.0534 |
-| 0.0066        | 8.2750  | 13000 | 0.1801          | 35.3936 |
-| 0.004         | 8.9115  | 14000 | 0.1781          | 34.1577 |
-| 0.0009        | 9.5481  | 15000 | 0.1918          | 33.6939 |
-| 0.0003        | 10.1846 | 16000 | 0.2026          | 33.5411 |
+| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
+|:-------------:|:------:|:-----:|:---------------:|:-------:|
+| 0.2369        | 0.6365 | 1000  | 0.2433          | 62.1881 |
+| 0.1242        | 1.2731 | 2000  | 0.1734          | 49.4369 |
+| 0.1022        | 1.9096 | 3000  | 0.1197          | 39.0531 |
+| 0.046         | 2.5461 | 4000  | 0.1067          | 34.5497 |
+| 0.0702        | 2.6247 | 5000  | 0.1210          | 38.4777 |
+| 0.1028        | 1.5748 | 6000  | 0.1484          | 44.2750 |
+| 0.0772        | 1.8373 | 7000  | 0.1323          | 40.2388 |
+| 0.0648        | 2.0997 | 8000  | 0.1205          | 39.1165 |
+| 0.0367        | 2.3622 | 9000  | 0.1154          | 35.6332 |
+| 0.0249        | 2.6247 | 10000 | 0.1074          | 33.4498 |
 
 
 ### Framework versions