update model card README.md

README.md

@@ -1,39 +1,38 @@
 ---
 license: apache-2.0
 tags:
-- whisper-event
 - generated_from_trainer
 datasets:
-- google/fleurs
+- fleurs
 metrics:
 - wer
 model-index:
-- name: Whisper Small Pashto
+- name: openai/whisper-small
   results:
   - task:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: google/fleurs ps_af
-      type: google/fleurs
+      name: fleurs
+      type: fleurs
       config: ps_af
       split: test
       args: ps_af
     metrics:
     - name: Wer
       type: wer
-      value: 56.651029055690074
+      value: 56.847760290556906
 ---
 
 <!-- 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. -->
 
-# Whisper Small Pashto
+# openai/whisper-small
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs ps_af dataset.
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.2309
-- Wer: 56.6510
+- Loss: 1.2581
+- Wer: 56.8478
 
 ## Model description
 
@@ -53,13 +52,15 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 8
+- train_batch_size: 32
+- eval_batch_size: 16
 - seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- training_steps: 600
+- training_steps: 700
 - mixed_precision_training: Native AMP
 
 ### Training results
@@ -72,6 +73,7 @@ The following hyperparameters were used during training:
 | 0.054         | 14.81 | 400  | 1.0876          | 57.9903 |
 | 0.0159        | 18.52 | 500  | 1.1798          | 57.8768 |
 | 0.0045        | 22.22 | 600  | 1.2309          | 56.6510 |
+| 0.0026        | 100.0 | 700  | 1.2581          | 56.8478 |
 
 
 ### Framework versions