End of training

README.md

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+---
+language:
+- tr
+license: apache-2.0
+base_model: openai/whisper-large
+tags:
+- generated_from_trainer
+datasets:
+- custom
+metrics:
+- wer
+model-index:
+- name: Whisper large tr - baki
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: custom
+      type: custom
+      args: 'config: tr, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 90.93493367024637
+---
+
+<!-- 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 large tr - baki
+
+This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the custom dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.0105
+- Wer: 90.9349
+
+## 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-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 40
+- training_steps: 300
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Wer      |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 2.1523        | 0.9615 | 100  | 2.1371          | 117.2773 |
+| 1.5102        | 1.9231 | 200  | 1.9995          | 93.6829  |
+| 1.1534        | 2.8846 | 300  | 2.0105          | 90.9349  |
+
+
+### Framework versions
+
+- Transformers 4.42.3
+- Pytorch 2.1.0+cu118
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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+  "transformers_version": "4.42.3"
+}

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