Added rest of the files for 10.1 CER

README.md

@@ -22,48 +22,42 @@ model-index:
     metrics:
     - name: Cer
       type: cer
-      value: 11.463
+      value: 10.11
 ---
 <!-- 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 zh-HK - Alvin
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. This version has a lower CER (by 1%) compared to the previous one. 
 
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
 ## Training and evaluation data
 For training, three datasets were used:
 - Common Voice 11 Canto Train Set
 - CantoMap: Winterstein, Grégoire, Tang, Carmen and Lai, Regine (2020) "CantoMap: a Hong Kong Cantonese MapTask Corpus", in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille: European Language Resources Association, p. 2899-2906.
 - Cantonse-ASR: Yu, Tiezheng, Frieske, Rita, Xu, Peng, Cahyawijaya, Samuel, Yiu, Cheuk Tung, Lovenia, Holy, Dai, Wenliang, Barezi, Elham, Chen, Qifeng, Ma, Xiaojuan, Shi, Bertram, Fung, Pascale (2022) "Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset", 2022. Link: https://arxiv.org/pdf/2201.02419.pdf
 
-## Training procedure
-
 ## Training Hyperparameters
-- learning_rate: 1e-5
-- train_batch_size: 16 (on 2 GPUs)
+- learning_rate: 5e-5
+- train_batch_size: 25 (on 2 GPUs)
 - eval_batch_size: 8
 - gradient_accumulation_steps: 2
 - total_train_batch_size: 16x2x2=64
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- training_steps: 5000
+- training_steps: 14000
 - mixed_precision_training: Native AMP
+- augmentation: SpecAugment
 
 ## Training Results
 
 | Training Loss | Epoch | Step | Validation Loss | Cer    |
 |:-------------:|:-----:|:----:|:---------------:|:------:|
-| 0.1106        | 0.66  | 1000 | 0.3294          | 14.638 |
-| 0.0546        | 1.33  | 2000 | 0.2887          | 12.119 |
-| 0.0293        | 2.01  | 3000 | 0.2727          | 11.646 |
-| 0.0214        | 2.66  | 4000 | 0.2741          | 11.760 |
-| 0.0919        | 3.32  | 5000 | 0.2747          | 11.463 |
-
-
-### Framework versions
+| 0.4610        | 0.55  | 2000 | 0.3106          | 13.08 |
+| 0.3441        | 1.11  | 4000 | 0.2875          | 11.79 |
+| 0.3466        | 1.66  | 6000 | 0.2820          | 11.44 |
+| 0.2539        | 2.22  | 8000 | 0.2777          | 10.59 |
+| 0.2312        | 2.77  | 10000 | 0.2822          | 10.60 |
+| 0.1639        | 3.32  | 12000 | 0.2859          | 10.17 |
+| 0.1569        | 3.88  | 14000 | 0.2866          | 10.11 |

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