update model card README.md

README.md

@@ -1,41 +1,11 @@
 ---
-language: 
-- ur
-
-license: apache-2.0
 tags:
-- automatic-speech-recognition
-- robust-speech-event
+- generated_from_trainer
 datasets:
 - common_voice
-metrics:
-- wer
 model-index:
 - name: wav2vec2-large-xlsr-53-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: 100  # 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: 10
-        - num_epochs: 30
-        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,10 +13,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 # wav2vec2-large-xlsr-53-urdu
 
-This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
+This model is a fine-tuned version of [m3hrdadfi/wav2vec2-large-xlsr-persian-v3](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3) on the common_voice dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.6772
-- Wer: 1.0
+- Loss: 1.5727
+- Wer: 0.6620
+- Cer: 0.3166
 
 ## Model description
 
@@ -73,28 +44,25 @@ The following hyperparameters were used during training:
 - 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: 10
-- num_epochs: 30
+- lr_scheduler_warmup_steps: 200
+- num_epochs: 50
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Wer |
-|:-------------:|:-----:|:----:|:---------------:|:---:|
-| 11.1125       | 3.33  | 40   | 3.2875          | 1.0 |
-| 3.2077        | 6.67  | 80   | 3.1499          | 1.0 |
-| 3.1725        | 10.0  | 120  | 3.1484          | 1.0 |
-| 3.148         | 13.33 | 160  | 3.0948          | 1.0 |
-| 3.1098        | 16.67 | 200  | 3.0897          | 1.0 |
-| 3.085         | 20.0  | 240  | 3.0609          | 1.0 |
-| 3.0315        | 23.33 | 280  | 2.9636          | 1.0 |
-| 2.9038        | 26.67 | 320  | 2.7838          | 1.0 |
-| 2.7599        | 30.0  | 360  | 2.6772          | 1.0 |
+| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
+| 2.9707        | 8.33  | 100  | 1.2689          | 0.8463 | 0.4373 |
+| 0.746         | 16.67 | 200  | 1.2370          | 0.7214 | 0.3486 |
+| 0.3719        | 25.0  | 300  | 1.3885          | 0.6908 | 0.3381 |
+| 0.2411        | 33.33 | 400  | 1.4780          | 0.6690 | 0.3186 |
+| 0.1841        | 41.67 | 500  | 1.5557          | 0.6629 | 0.3241 |
+| 0.165         | 50.0  | 600  | 1.5727          | 0.6620 | 0.3166 |
 
 
 ### Framework versions
 
-- Transformers 4.11.3
+- Transformers 4.15.0
 - Pytorch 1.10.0+cu111
 - Datasets 1.17.0
 - Tokenizers 0.10.3