End of training

README.md

@@ -7,22 +7,9 @@ tags:
 - generated_from_trainer
 datasets:
 - formospeech/tat_asr_aligned
-metrics:
-- wer
 model-index:
 - name: Whisper Tiny Taiwanese Condenser
-  results:
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: TAT ASR Aligned
-      type: formospeech/tat_asr_aligned
-      args: 'config: taiwanese, split: test'
-    metrics:
-    - name: Wer
-      type: wer
-      value: 50.65108143376739
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +19,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the TAT ASR Aligned dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0814
-- Wer: 50.6511
+- Loss: 0.6252
+- Cer: 11.4109
 
 ## Model description
 
@@ -52,40 +39,30 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.001
+- learning_rate: 0.0001
 - train_batch_size: 64
 - eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 1362
-- training_steps: 13620
+- lr_scheduler_warmup_steps: 681
+- training_steps: 6810
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch   | Step  | Validation Loss | Wer     |
-|:-------------:|:-------:|:-----:|:---------------:|:-------:|
-| 0.4229        | 0.9985  | 681   | 0.6344          | 65.8772 |
-| 0.4529        | 1.9971  | 1362  | 0.7311          | 71.3135 |
-| 0.3271        | 2.9956  | 2043  | 0.6324          | 64.7777 |
-| 0.2257        | 3.9941  | 2724  | 0.5510          | 58.4141 |
-| 0.1554        | 4.9927  | 3405  | 0.5350          | 57.6394 |
-| 0.122         | 5.9912  | 4086  | 0.5911          | 56.8148 |
-| 0.0935        | 6.9897  | 4767  | 0.6122          | 56.1179 |
-| 0.0718        | 7.9883  | 5448  | 0.6492          | 54.7158 |
-| 0.0588        | 8.9868  | 6129  | 0.6623          | 55.5599 |
-| 0.0466        | 9.9853  | 6810  | 0.6883          | 56.9481 |
-| 0.0349        | 10.9839 | 7491  | 0.7069          | 54.6770 |
-| 0.0298        | 11.9824 | 8172  | 0.7441          | 54.2272 |
-| 0.0215        | 12.9809 | 8853  | 0.7937          | 54.9491 |
-| 0.0141        | 13.9795 | 9534  | 0.8062          | 52.9278 |
-| 0.01          | 14.9780 | 10215 | 0.8717          | 53.2804 |
-| 0.0067        | 15.9765 | 10896 | 0.9279          | 52.6029 |
-| 0.0031        | 16.9751 | 11577 | 0.9783          | 52.0393 |
-| 0.0012        | 17.9736 | 12258 | 1.0311          | 50.6622 |
-| 0.0004        | 18.9721 | 12939 | 1.0574          | 50.7566 |
-| 0.0001        | 19.9707 | 13620 | 1.0814          | 50.6511 |
+| Training Loss | Epoch  | Step | Validation Loss | Cer     |
+|:-------------:|:------:|:----:|:---------------:|:-------:|
+| 0.3117        | 0.9985 | 681  | 0.4649          | 17.1248 |
+| 0.1805        | 1.9971 | 1362 | 0.4360          | 13.6667 |
+| 0.108         | 2.9956 | 2043 | 0.4497          | 13.4248 |
+| 0.0648        | 3.9941 | 2724 | 0.4710          | 12.8500 |
+| 0.0349        | 4.9927 | 3405 | 0.5276          | 12.7592 |
+| 0.0192        | 5.9912 | 4086 | 0.5607          | 12.4186 |
+| 0.0089        | 6.9897 | 4767 | 0.5911          | 12.1183 |
+| 0.0035        | 7.9883 | 5448 | 0.6032          | 11.6608 |
+| 0.0009        | 8.9868 | 6129 | 0.6198          | 11.5311 |
+| 0.0005        | 9.9853 | 6810 | 0.6252          | 11.4109 |
 
 
 ### Framework versions