End of training

README.md

@@ -23,7 +23,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 20.273713328965567
+      value: 19.127375087966218
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,8 +33,8 @@ 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: 0.5645
-- Wer: 20.2737
+- Loss: 0.5081
+- Wer: 19.1274
 
 ## Model description
 
@@ -60,26 +60,24 @@ 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: 200
-- training_steps: 1300
+- training_steps: 1100
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss | Wer     |
 |:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.5785        | 0.2222 | 100  | 0.6052          | 21.5622 |
-| 0.5063        | 0.4444 | 200  | 0.5657          | 21.1618 |
-| 0.5192        | 0.6667 | 300  | 0.5506          | 24.1392 |
-| 0.4677        | 0.8889 | 400  | 0.5444          | 22.5595 |
-| 0.2711        | 1.1111 | 500  | 0.5458          | 20.9726 |
-| 0.2687        | 1.3333 | 600  | 0.5454          | 20.3683 |
-| 0.3148        | 1.5556 | 700  | 0.5350          | 20.2519 |
-| 0.248         | 1.7778 | 800  | 0.5367          | 20.2737 |
-| 0.2832        | 2.0    | 900  | 0.5327          | 20.3538 |
-| 0.133         | 2.2222 | 1000 | 0.5632          | 20.1645 |
-| 0.1281        | 2.4444 | 1100 | 0.5651          | 20.1427 |
-| 0.1552        | 2.6667 | 1200 | 0.5622          | 20.1791 |
-| 0.1633        | 2.8889 | 1300 | 0.5645          | 20.2737 |
+| 0.6002        | 0.2222 | 100  | 0.5593          | 21.0556 |
+| 0.4758        | 0.4444 | 200  | 0.5197          | 20.4011 |
+| 0.4916        | 0.6667 | 300  | 0.5082          | 21.7101 |
+| 0.4612        | 0.8889 | 400  | 0.4973          | 19.7467 |
+| 0.2709        | 1.1111 | 500  | 0.4971          | 20.9500 |
+| 0.2823        | 1.3333 | 600  | 0.4974          | 19.4300 |
+| 0.2819        | 1.5556 | 700  | 0.4943          | 19.2892 |
+| 0.2817        | 1.7778 | 800  | 0.4930          | 19.5496 |
+| 0.2752        | 2.0    | 900  | 0.4885          | 19.3878 |
+| 0.1722        | 2.2222 | 1000 | 0.5053          | 19.1414 |
+| 0.1383        | 2.4444 | 1100 | 0.5081          | 19.1274 |
 
 
 ### Framework versions