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--- |
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language: |
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- tr |
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license: apache-2.0 |
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base_model: openai/whisper-large-v3 |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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datasets: |
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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large V3 tr Fleurs - Chee Li |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Google Fleurs |
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type: google/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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- name: Wer |
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type: wer |
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value: 649.9222153080274 |
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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 Large V3 tr Fleurs - Chee Li |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Google Fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1432 |
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- Wer: 649.9222 |
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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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| 0.0466 | 2.7933 | 500 | 0.1060 | 147.9932 | |
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| 0.006 | 5.5866 | 1000 | 0.1208 | 481.1605 | |
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| 0.0017 | 8.3799 | 1500 | 0.1291 | 602.0769 | |
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| 0.0012 | 11.1732 | 2000 | 0.1288 | 627.3647 | |
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| 0.0002 | 13.9665 | 2500 | 0.1382 | 641.4203 | |
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| 0.0001 | 16.7598 | 3000 | 0.1411 | 647.7520 | |
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| 0.0001 | 19.5531 | 3500 | 0.1426 | 642.9294 | |
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| 0.0001 | 22.3464 | 4000 | 0.1432 | 649.9222 | |
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### Framework versions |
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- Transformers 4.42.4 |
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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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