End of training

README.md

@@ -1,18 +1,18 @@
 ---
+license: apache-2.0
 base_model: facebook/wav2vec2-base
+tags:
+- generated_from_trainer
 datasets:
 - vivos
-license: apache-2.0
 metrics:
 - wer
-tags:
-- generated_from_trainer
 model-index:
 - name: wav2vec2-vivos-asr
   results:
   - task:
-      type: automatic-speech-recognition
       name: Automatic Speech Recognition
+      type: automatic-speech-recognition
     dataset:
       name: vivos
       type: vivos
@@ -20,21 +20,21 @@ model-index:
       split: None
       args: default
     metrics:
-    - type: wer
-      value: 0.46007853403141363
-      name: Wer
+    - name: Wer
+      type: wer
+      value: 0.3726759841005257
 ---
 
 <!-- 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. -->
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/khackho01125-CMC-University/Wav2Vec2/runs/abof73b7)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/khackho01125-CMC-University/Wav2Vec2/runs/p3skrhqk)
 # wav2vec2-vivos-asr
 
 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the vivos dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9791
-- Wer: 0.4601
+- Loss: 0.7912
+- Wer: 0.3727
 
 ## Model description
 
@@ -53,14 +53,14 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 8e-05
+- learning_rate: 0.0001
 - train_batch_size: 32
 - eval_batch_size: 8
 - 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_type: cosine
 - lr_scheduler_warmup_steps: 400
 - num_epochs: 30
 - mixed_precision_training: Native AMP
@@ -69,21 +69,21 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Wer    |
 |:-------------:|:-----:|:----:|:---------------:|:------:|
-| 6.0539        | 2.0   | 292  | 3.6334          | 1.0    |
-| 3.4484        | 4.0   | 584  | 3.5348          | 1.0    |
-| 3.2755        | 6.0   | 876  | 2.4805          | 0.9952 |
-| 1.6061        | 8.0   | 1168 | 1.2597          | 0.7021 |
-| 1.0363        | 10.0  | 1460 | 1.0996          | 0.6158 |
-| 0.8403        | 12.0  | 1752 | 0.9858          | 0.5573 |
-| 0.726         | 14.0  | 2044 | 0.9625          | 0.5302 |
-| 0.6721        | 16.0  | 2336 | 0.9326          | 0.5124 |
-| 0.5697        | 18.0  | 2628 | 0.9399          | 0.5012 |
-| 0.5168        | 20.0  | 2920 | 0.9625          | 0.4930 |
-| 0.4663        | 22.0  | 3212 | 0.9432          | 0.4751 |
-| 0.4408        | 24.0  | 3504 | 0.9822          | 0.4723 |
-| 0.4231        | 26.0  | 3796 | 0.9629          | 0.4643 |
-| 0.3855        | 28.0  | 4088 | 0.9744          | 0.4639 |
-| 0.3671        | 30.0  | 4380 | 0.9791          | 0.4601 |
+| 5.8168        | 2.0   | 292  | 3.6240          | 1.0    |
+| 3.4344        | 4.0   | 584  | 3.4785          | 1.0    |
+| 3.0271        | 6.0   | 876  | 1.8947          | 0.9142 |
+| 1.2453        | 8.0   | 1168 | 1.0293          | 0.6091 |
+| 0.7876        | 10.0  | 1460 | 0.8472          | 0.5229 |
+| 0.6062        | 12.0  | 1752 | 0.7675          | 0.4748 |
+| 0.4929        | 14.0  | 2044 | 0.7494          | 0.4303 |
+| 0.4376        | 16.0  | 2336 | 0.7481          | 0.4063 |
+| 0.3523        | 18.0  | 2628 | 0.7580          | 0.4007 |
+| 0.309         | 20.0  | 2920 | 0.7676          | 0.3851 |
+| 0.2694        | 22.0  | 3212 | 0.7631          | 0.3819 |
+| 0.2531        | 24.0  | 3504 | 0.7717          | 0.3761 |
+| 0.2472        | 26.0  | 3796 | 0.7825          | 0.3710 |
+| 0.2223        | 28.0  | 4088 | 0.7905          | 0.3732 |
+| 0.2183        | 30.0  | 4380 | 0.7912          | 0.3727 |
 
 
 ### Framework versions