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: whisper-small-ft-balbus-sep28k-multiclass_v3
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: Apple dataset
+      type: balbus-classifier
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.37653846153846154
+    - name: Precision
+      type: precision
+      value: 0.1255128205128205
+    - name: Recall
+      type: recall
+      value: 0.3333333333333333
+    - name: F1
+      type: f1
+      value: 0.18236006333240196
+---
+
+<!-- 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-ft-balbus-sep28k-multiclass_v3
+
+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: 0.9882
+- Accuracy: 0.3765
+- Precision: 0.1255
+- Recall: 0.3333
+- F1: 0.1824
+- Roc-auc: None
+
+## 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: 0.001
+- train_batch_size: 8
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 4
+- 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: 1200
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc-auc |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
+| 0.8577        | 0.1368 | 100  | 0.8818          | 0.5219   | 0.5566    | 0.5095 | 0.4750 | None    |
+| 1.0396        | 0.2735 | 200  | 1.0092          | 0.3335   | 0.1112    | 0.3333 | 0.1667 | None    |
+| 1.1409        | 0.4103 | 300  | 1.0101          | 0.3746   | 0.2324    | 0.3353 | 0.2285 | None    |
+| 1.0003        | 0.5470 | 400  | 1.0013          | 0.3765   | 0.1255    | 0.3333 | 0.1824 | None    |
+| 1.0057        | 0.6838 | 500  | 1.0001          | 0.3765   | 0.1255    | 0.3333 | 0.1824 | None    |
+| 0.993         | 0.8205 | 600  | 1.0461          | 0.3765   | 0.1255    | 0.3333 | 0.1824 | None    |
+| 1.0017        | 0.9573 | 700  | 0.9998          | 0.3404   | 0.2501    | 0.3351 | 0.2226 | None    |
+| 0.9992        | 1.0940 | 800  | 0.9927          | 0.3715   | 0.2364    | 0.3344 | 0.2418 | None    |
+| 0.9805        | 1.2308 | 900  | 0.9965          | 0.3765   | 0.1255    | 0.3333 | 0.1824 | None    |
+| 0.9934        | 1.3675 | 1000 | 0.9944          | 0.3804   | 0.2501    | 0.3499 | 0.2848 | None    |
+| 0.9961        | 1.5043 | 1100 | 0.9899          | 0.3792   | 0.3125    | 0.3363 | 0.1928 | None    |
+| 0.9878        | 1.6410 | 1200 | 0.9882          | 0.3765   | 0.1255    | 0.3333 | 0.1824 | None    |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.2.0
+- Datasets 3.6.0
+- Tokenizers 0.20.3