End of training

README.md

@@ -3,7 +3,7 @@ library_name: transformers
 language:
 - ur
 license: apache-2.0
-base_model: openai/whisper-medium
+base_model: GogetaBlueMUI/whisper-medium-ur-v3
 tags:
 - generated_from_trainer
 datasets:
@@ -25,7 +25,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 26.735436622234133
+      value: 25.0787058744725
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,10 +33,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Whisper Medium Ur - Your Name
 
-This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 19.0 dataset.
+This model is a fine-tuned version of [GogetaBlueMUI/whisper-medium-ur-v3](https://huggingface.co/GogetaBlueMUI/whisper-medium-ur-v3) on the Common Voice 19.0 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3392
-- Wer: 26.7354
+- Loss: 0.3692
+- Wer: 25.0787
 
 ## Model description
 
@@ -55,7 +55,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-06
+- learning_rate: 3e-06
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
@@ -63,17 +63,18 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 16
 - 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: 150
-- training_steps: 1500
+- lr_scheduler_warmup_steps: 100
+- training_steps: 1000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss | Wer     |
 |:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.3835        | 0.6557 | 500  | 0.3813          | 27.1418 |
-| 0.2643        | 1.3108 | 1000 | 0.3522          | 26.4809 |
-| 0.2424        | 1.9666 | 1500 | 0.3392          | 26.7354 |
+| 0.1648        | 0.3279 | 250  | 0.3832          | 28.1711 |
+| 0.1748        | 0.6557 | 500  | 0.3737          | 30.1650 |
+| 0.1887        | 0.9836 | 750  | 0.3587          | 24.8532 |
+| 0.132         | 1.3108 | 1000 | 0.3692          | 25.0787 |
 
 
 ### Framework versions