Update README.md

README.md

@@ -5,6 +5,9 @@ tags:
 model-index:
 - name: urdumodel
   results: []
+metrics:
+- wer
+- cer
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -28,10 +31,11 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used. 
 
 ## Training procedure
-
+All the code is available here
+https://github.com/talhaanwarch/Urdu-ASR
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -46,30 +50,9 @@ The following hyperparameters were used during training:
 - num_epochs: 20
 - mixed_precision_training: Native AMP
 
-### Training results
-
-| Training Loss | Epoch | Step  | Cer    | Validation Loss | Wer    |
-|:-------------:|:-----:|:-----:|:------:|:---------------:|:------:|
-| 2.8998        | 1.0   | 508   | 0.2832 | 1.0261          | 0.7004 |
-| 0.7426        | 2.0   | 1016  | 0.2026 | 0.6532          | 0.5236 |
-| 0.5694        | 3.0   | 1524  | 0.1799 | 0.5495          | 0.4611 |
-| 0.4966        | 4.0   | 2032  | 0.1729 | 0.5361          | 0.4350 |
-| 0.4555        | 5.0   | 2540  | 0.1684 | 0.5335          | 0.4266 |
-| 0.4203        | 6.0   | 3048  | 0.1641 | 0.5040          | 0.4107 |
-| 0.3951        | 7.0   | 3556  | 0.1579 | 0.5213          | 0.4037 |
-| 0.3675        | 8.0   | 4064  | 0.1563 | 0.4949          | 0.3973 |
-| 0.3555        | 9.0   | 4572  | 0.1581 | 0.4968          | 0.3978 |
-| 0.3408        | 10.0  | 5080  | 0.1561 | 0.4827          | 0.3925 |
-| 0.3286        | 11.0  | 5588  | 0.1524 | 0.5011          | 0.3858 |
-| 0.3156        | 12.0  | 6096  | 0.1524 | 0.4871          | 0.3833 |
-| 0.3047        | 13.0  | 6604  | 0.1499 | 0.4835          | 0.3774 |
-| 0.2929        | 14.0  | 7112  | 0.1489 | 0.4844          | 0.3751 |
-| 0.2912        | 15.0  | 7620  | 0.4929 | 0.3763          | 0.1486 |
-| 0.2969        | 16.0  | 8128  | 0.4990 | 0.3749          | 0.1481 |
-| 0.2946        | 17.0  | 8636  | 0.4943 | 0.3735          | 0.1485 |
-| 0.2851        | 18.0  | 9144  | 0.4893 | 0.3717          | 0.1477 |
-| 0.279         | 19.0  | 9652  | 0.4977 | 0.3693          | 0.1464 |
-| 0.2718        | 20.0  | 10160 | 0.4939 | 0.3698          | 0.1465 |
+# Model score on test
+When I train I got different WER and CER score on test set, but when I tested locally
+I got WER of 0.27 without language model and 0.22 with language model.
 
 
 ### Framework versions