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--- |
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base_model: openai/whisper-base |
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datasets: |
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- fleurs |
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language: |
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- tr |
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license: apache-2.0 |
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metrics: |
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- wer |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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model-index: |
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- name: Whisper Base Turkish Punctuation 4k - Chee Li |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Google Fleurs |
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type: fleurs |
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config: tr_tr |
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split: None |
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args: 'config: tr split: test' |
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metrics: |
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- type: wer |
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value: 37.878198646651626 |
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name: Wer |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Base Turkish Punctuation 4k - Chee Li |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Google Fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6273 |
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- Wer: 37.8782 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-------:|:----:|:---------------:|:-------:| |
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| 0.1116 | 5.5866 | 1000 | 0.4785 | 31.6948 | |
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| 0.0073 | 11.1732 | 2000 | 0.5710 | 34.9615 | |
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| 0.0036 | 16.7598 | 3000 | 0.6137 | 36.7349 | |
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| 0.0027 | 22.3464 | 4000 | 0.6273 | 37.8782 | |
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### Framework versions |
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- Transformers 4.43.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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