End of training

README.md

@@ -3,7 +3,7 @@ library_name: transformers
 language:
 - ur
 license: apache-2.0
-base_model: GogetaBlueMUI/whisper-medium-ur-fleurs
+base_model: GogetaBlueMUI/whisper-medium-ur-jalandhary
 tags:
 - generated_from_trainer
 datasets:
@@ -23,7 +23,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 23.71748328784958
+      value: 19.807797769827385
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,10 +31,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Whisper Medium Ur - Jalandhary ASR Fine-Tuned
 
-This model is a fine-tuned version of [GogetaBlueMUI/whisper-medium-ur-fleurs](https://huggingface.co/GogetaBlueMUI/whisper-medium-ur-fleurs) on the Jalandhary ASR dataset.
+This model is a fine-tuned version of [GogetaBlueMUI/whisper-medium-ur-jalandhary](https://huggingface.co/GogetaBlueMUI/whisper-medium-ur-jalandhary) on the Jalandhary ASR dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1728
-- Wer: 23.7175
+- Loss: 0.1012
+- Wer: 19.8078
 
 ## Model description
 
@@ -53,22 +53,24 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-06
+- learning_rate: 1e-06
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
 - 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: 100
-- training_steps: 1000
+- lr_scheduler_warmup_steps: 300
+- training_steps: 2400
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss | Wer     |
 |:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.207         | 0.4859 | 500  | 0.1948          | 25.6679 |
-| 0.1843        | 0.9718 | 1000 | 0.1728          | 23.7175 |
+| 0.1097        | 0.5831 | 600  | 0.1066          | 18.6509 |
+| 0.0664        | 1.1662 | 1200 | 0.1020          | 19.1575 |
+| 0.0821        | 1.7493 | 1800 | 0.1016          | 19.2725 |
+| 0.0567        | 2.3324 | 2400 | 0.1012          | 19.8078 |
 
 
 ### Framework versions