End of training

README.md

@@ -25,7 +25,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 82.95482295482296
+      value: 72.91819291819291
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -35,8 +35,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 de 5% dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7079
-- Wer: 82.9548
+- Loss: 0.7117
+- Wer: 72.9182
 
 ## Model description
 
@@ -55,28 +55,28 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1e-05
+- learning_rate: 1.35e-05
 - train_batch_size: 16
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 500
-- training_steps: 4000
+- lr_scheduler_warmup_steps: 250
+- training_steps: 2000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss | Wer     |
 |:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.6087        | 0.4505 | 500  | 0.8082          | 78.1441 |
-| 0.527         | 0.9009 | 1000 | 0.7504          | 82.5641 |
-| 0.4246        | 1.3514 | 1500 | 0.7281          | 78.5104 |
-| 0.4013        | 1.8018 | 2000 | 0.7131          | 81.7338 |
-| 0.2809        | 2.2523 | 2500 | 0.7136          | 90.3053 |
-| 0.3299        | 2.7027 | 3000 | 0.7078          | 87.2527 |
-| 0.2573        | 3.1532 | 3500 | 0.7070          | 89.4261 |
-| 0.2346        | 3.6036 | 4000 | 0.7079          | 82.9548 |
+| 0.6076        | 0.2252 | 250  | 0.8347          | 76.3126 |
+| 0.5955        | 0.4505 | 500  | 0.7893          | 79.1697 |
+| 0.5179        | 0.6757 | 750  | 0.7593          | 82.1978 |
+| 0.5189        | 0.9009 | 1000 | 0.7370          | 73.0159 |
+| 0.3644        | 1.1261 | 1250 | 0.7254          | 84.1270 |
+| 0.394         | 1.3514 | 1500 | 0.7183          | 73.4066 |
+| 0.3672        | 1.5766 | 1750 | 0.7152          | 73.1136 |
+| 0.3751        | 1.8018 | 2000 | 0.7117          | 72.9182 |
 
 
 ### Framework versions