End of training

README.md

@@ -12,7 +12,7 @@ metrics:
 - recall
 - f1
 model-index:
-- name: miosipof/whisper-small-ft-balbus-sep28k-v1.5
+- name: miosipof/whisper-small-ft-balbus-sep28k-v1.6
   results:
   - task:
       name: Audio Classification
@@ -26,34 +26,31 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value:
-        accuracy: 0.8111877154497023
+      value: 0.8100908806016922
     - name: Precision
       type: precision
-      value:
-        precision: 0.8133174791914387
+      value: 0.8183656957928802
     - name: Recall
       type: recall
-      value:
-        recall: 0.7365398420674802
+      value: 0.7261306532663316
     - name: F1
       type: f1
-      value:
-        f1: 0.7730269353927294
+      value: 0.7694941042221377
 ---
 
 <!-- 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. -->
 
-# miosipof/whisper-small-ft-balbus-sep28k-v1.5
+# miosipof/whisper-small-ft-balbus-sep28k-v1.6
 
 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Apple dataset dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1083
-- Accuracy: {'accuracy': 0.8111877154497023}
-- Precision: {'precision': 0.8133174791914387}
-- Recall: {'recall': 0.7365398420674802}
-- F1: {'f1': 0.7730269353927294}
+- Loss: 0.1091
+- Accuracy: 0.8101
+- Precision: 0.8184
+- Recall: 0.7261
+- F1: 0.7695
+- Roc-auc: 0.8006
 
 ## Model description
 
@@ -72,7 +69,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 3e-06
+- learning_rate: 2e-06
 - train_batch_size: 16
 - eval_batch_size: 8
 - seed: 42
@@ -81,23 +78,19 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.5
-- training_steps: 1000
+- training_steps: 1200
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | Precision                         | Recall                          | F1                           |
-|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:---------------------------------:|:-------------------------------:|:----------------------------:|
-| 0.1718        | 0.1253 | 100  | 0.1705          | {'accuracy': 0.564243183954873}  | {'precision': 0.6190476190476191} | {'recall': 0.00466618808327351} | {'f1': 0.009262557890986818} |
-| 0.1683        | 0.2506 | 200  | 0.1653          | {'accuracy': 0.6118771544970228} | {'precision': 0.7677642980935875} | {'recall': 0.15900933237616655} | {'f1': 0.26345524829021705}  |
-| 0.1595        | 0.3759 | 300  | 0.1494          | {'accuracy': 0.6847383265434033} | {'precision': 0.6486175115207373} | {'recall': 0.6062455132806892}  | {'f1': 0.6267161410018552}   |
-| 0.1299        | 0.5013 | 400  | 0.1266          | {'accuracy': 0.7608900031338138} | {'precision': 0.7008928571428571} | {'recall': 0.7889447236180904}  | {'f1': 0.7423167848699763}   |
-| 0.1174        | 0.6266 | 500  | 0.1140          | {'accuracy': 0.7977123158884363} | {'precision': 0.7800674409891345} | {'recall': 0.747307968413496}   | {'f1': 0.7633363886342804}   |
-| 0.1117        | 0.7519 | 600  | 0.1155          | {'accuracy': 0.7919147602632404} | {'precision': 0.7362281270252754} | {'recall': 0.8155061019382628}  | {'f1': 0.773841961852861}    |
-| 0.1072        | 0.8772 | 700  | 0.1074          | {'accuracy': 0.8096208085239737} | {'precision': 0.8282490597576264} | {'recall': 0.7114142139267767}  | {'f1': 0.765398725622707}    |
-| 0.106         | 1.0025 | 800  | 0.1078          | {'accuracy': 0.8077405202130994} | {'precision': 0.8175152749490835} | {'recall': 0.7203876525484566}  | {'f1': 0.7658843732112193}   |
-| 0.1001        | 1.1278 | 900  | 0.1079          | {'accuracy': 0.810404261986838}  | {'precision': 0.8174858984689767} | {'recall': 0.7282842785355348}  | {'f1': 0.7703113135914958}   |
-| 0.092         | 1.2531 | 1000 | 0.1083          | {'accuracy': 0.8111877154497023} | {'precision': 0.8133174791914387} | {'recall': 0.7365398420674802}  | {'f1': 0.7730269353927294}   |
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc-auc |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
+| 0.1683        | 0.2506 | 200  | 0.1682          | 0.5730   | 0.7364    | 0.0341 | 0.0652 | 0.5123  |
+| 0.1494        | 0.5013 | 400  | 0.1446          | 0.7084   | 0.6603    | 0.6838 | 0.6718 | 0.7056  |
+| 0.1212        | 0.7519 | 600  | 0.1236          | 0.7629   | 0.6917    | 0.8245 | 0.7523 | 0.7699  |
+| 0.1088        | 1.0025 | 800  | 0.1107          | 0.8062   | 0.8337    | 0.6945 | 0.7578 | 0.7936  |
+| 0.0955        | 1.2531 | 1000 | 0.1106          | 0.8081   | 0.8036    | 0.7416 | 0.7713 | 0.8006  |
+| 0.0997        | 1.5038 | 1200 | 0.1091          | 0.8101   | 0.8184    | 0.7261 | 0.7695 | 0.8006  |
 
 
 ### Framework versions