Update README.md

README.md

@@ -1,11 +1,57 @@
 ---
+language: 
+- ur
+
+license: apache-2.0
 tags:
-- generated_from_trainer
+- automatic-speech-recognition
+- robust-speech-event
 datasets:
 - common_voice
+metrics:
+- wer
+- cer
 model-index:
 - name: wav2vec2-large-xlsr-53-urdu
-  results: []
+  results:
+  - task: 
+      type: automatic-speech-recognition  # Required. Example: automatic-speech-recognition
+      name: Urdu Speech Recognition  # Optional. Example: Speech Recognition
+    dataset:
+      type: common_voice  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
+      name: Urdu  # Required. Example: Common Voice zh-CN
+      args: ur         # Optional. Example: zh-CN
+    metrics:
+      - type: wer    # Required. Example: wer
+        value: 66.2  # Required. Example: 20.90
+        name: Test WER    # Optional. Example: Test WER
+        args: 
+        - learning_rate: 0.0003
+        - train_batch_size: 16
+        - eval_batch_size: 8
+        - seed: 42
+        - gradient_accumulation_steps: 2
+        - total_train_batch_size: 32
+        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+        - lr_scheduler_type: linear
+        - lr_scheduler_warmup_steps: 200
+        - num_epochs: 50
+        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
+      - type: cer    # Required. Example: wer
+        value: 31.7  # Required. Example: 20.90
+        name: Test CER    # Optional. Example: Test WER
+        args: 
+        - learning_rate: 0.0003
+        - train_batch_size: 16
+        - eval_batch_size: 8
+        - seed: 42
+        - gradient_accumulation_steps: 2
+        - total_train_batch_size: 32
+        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+        - lr_scheduler_type: linear
+        - lr_scheduler_warmup_steps: 200
+        - num_epochs: 50
+        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -19,19 +65,12 @@ It achieves the following results on the evaluation set:
 - Wer: 0.6620
 - Cer: 0.3166
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
 
 More information needed
+The training and valid dataset is 0.58 hours. It was hard to train any model on lower number of so I decided to take Persian checkpoint and finetune the XLSR model.  
 
 ## Training procedure
+Trained on m3hrdadfi/wav2vec2-large-xlsr-persian-v3 due to lesser number of samples. Persian and Urdu are quite similar. 
 
 ### Training hyperparameters