Update README.md

README.md

@@ -1,12 +1,56 @@
 ---
+language: 
+- ur
+license: apache-2.0
 tags:
-- generated_from_trainer
+- automatic-speech-recognition
+- robust-speech-event
 datasets:
-- common_voice
+- common_voice_v7
+metrics:
+- wer
+- cer
 model-index:
-- name: wav2vec2-large-xlsr-53-urdu
-  results: []
----
+- name: wav2vec2-60-urdu
+  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: 59.8  # 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: 32.9  # 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
 should probably proofread and complete it, then remove this comment. -->
@@ -15,23 +59,15 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset.
 It achieves the following results on the evaluation set:
-- Loss: 11.1562
 - Wer: 0.5921
 - Cer: 0.3288
 
 ## 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 Urdu-60 checkpoint and finetune the wav2vwc2 model.  
 
 ## Training procedure
+Trained on Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 due to lesser number of samples.
+
 
 ### Training hyperparameters
 
@@ -50,14 +86,14 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
-| 13.83         | 8.33  | 100  | 6.3080          | 0.6611 | 0.3639 |
-| 1.0144        | 16.67 | 200  | 7.7685          | 0.6498 | 0.3731 |
-| 0.5801        | 25.0  | 300  | 7.8429          | 0.6454 | 0.3767 |
-| 0.3344        | 33.33 | 400  | 5.9906          | 0.6349 | 0.3548 |
-| 0.1606        | 41.67 | 500  | 10.8584         | 0.6105 | 0.3348 |
-| 0.0974        | 50.0  | 600  | 11.1562         | 0.5921 | 0.3288 |
+| Training Loss | Epoch | Step | Wer    | Cer    |
+|:-------------:|:-----:|:----:|:------:|:------:|
+| 13.83         | 8.33  | 100  | 0.6611 | 0.3639 |
+| 1.0144        | 16.67 | 200  | 0.6498 | 0.3731 |
+| 0.5801        | 25.0  | 300  | 0.6454 | 0.3767 |
+| 0.3344        | 33.33 | 400  | 0.6349 | 0.3548 |
+| 0.1606        | 41.67 | 500  | 0.6105 | 0.3348 |
+| 0.0974        | 50.0  | 600  | 0.5921 | 0.3288 |
 
 
 ### Framework versions