End of training

README.md

@@ -24,7 +24,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 56.50474595198214
+      value: 58.291457286432156
 ---
 
 <!-- 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 16 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5052
-- Wer: 56.5047
+- Loss: 0.5062
+- Wer: 58.2915
 
 ## Model description
 
@@ -60,24 +60,18 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant_with_warmup
-- lr_scheduler_warmup_steps: 500
-- training_steps: 1000
+- lr_scheduler_warmup_steps: 3000
+- training_steps: 2000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Wer      |
-|:-------------:|:------:|:----:|:---------------:|:--------:|
-| 4.2519        | 0.0991 | 100  | 2.0016          | 176.2144 |
-| 1.9569        | 0.1982 | 200  | 1.0866          | 92.1273  |
-| 1.3814        | 0.2973 | 300  | 0.8375          | 77.2194  |
-| 1.0866        | 0.3964 | 400  | 0.7128          | 69.4026  |
-| 0.8892        | 0.4955 | 500  | 0.6427          | 68.7326  |
-| 0.7668        | 0.5946 | 600  | 0.5942          | 65.7175  |
-| 0.698         | 0.6938 | 700  | 0.5732          | 60.9715  |
-| 0.593         | 0.7929 | 800  | 0.5278          | 57.5656  |
-| 0.5585        | 0.8920 | 900  | 0.5330          | 60.2457  |
-| 0.5199        | 0.9911 | 1000 | 0.5052          | 56.5047  |
+| Training Loss | Epoch  | Step | Validation Loss | Wer     |
+|:-------------:|:------:|:----:|:---------------:|:-------:|
+| 2.9797        | 0.4955 | 500  | 1.0523          | 92.2948 |
+| 1.1502        | 0.9911 | 1000 | 0.6757          | 87.0463 |
+| 0.7171        | 1.4866 | 1500 | 0.5723          | 63.7074 |
+| 0.5485        | 1.9822 | 2000 | 0.5062          | 58.2915 |
 
 
 ### Framework versions