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
+model-index:
+- name: miosipof/whisper-tiny-ft-balbus-sep28k-v1.1
+  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: 0.7718583516139141
+---
+
+<!-- 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-tiny-ft-balbus-sep28k-v1.1
+
+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: 0.4870
+- Accuracy: 0.7719
+
+## 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: 1e-06
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.6991        | 0.1253 | 100  | 0.6929          | 0.4616   |
+| 0.686         | 0.2506 | 200  | 0.6816          | 0.5577   |
+| 0.6776        | 0.3759 | 300  | 0.6726          | 0.5631   |
+| 0.6591        | 0.5013 | 400  | 0.6472          | 0.6244   |
+| 0.6317        | 0.6266 | 500  | 0.6115          | 0.6802   |
+| 0.5836        | 0.7519 | 600  | 0.5672          | 0.7104   |
+| 0.5415        | 0.8772 | 700  | 0.5192          | 0.7499   |
+| 0.4856        | 1.0025 | 800  | 0.4999          | 0.7667   |
+| 0.4886        | 1.1278 | 900  | 0.4894          | 0.7715   |
+| 0.4727        | 1.2531 | 1000 | 0.4870          | 0.7719   |
+
+
+### Framework versions
+
+- Transformers 4.48.0
+- Pytorch 2.2.0
+- Datasets 3.2.0
+- Tokenizers 0.21.0