End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
+datasets:
+- balbus-classifier
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: miosipof/whisper-small-ft-balbus-sep28k-v1.5
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: Apple dataset
+      type: balbus-classifier
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value:
+        accuracy: 0.8111877154497023
+    - name: Precision
+      type: precision
+      value:
+        precision: 0.8133174791914387
+    - name: Recall
+      type: recall
+      value:
+        recall: 0.7365398420674802
+    - name: F1
+      type: f1
+      value:
+        f1: 0.7730269353927294
+---
+
+<!-- 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
+
+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}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-06
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- 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
+- 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}   |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.2.0
+- Datasets 3.2.0
+- Tokenizers 0.20.3