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--- |
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language: |
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- he |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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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 V2 Hebrew |
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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: he_il |
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split: test |
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args: he_il |
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metrics: |
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- name: Wer |
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type: wer |
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value: 27.250397341424648 |
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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 V2 Hebrew |
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the google/fleurs he_il dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4106 |
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- Wer: 27.2504 |
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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-06 |
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- train_batch_size: 128 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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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: 10 |
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- training_steps: 200 |
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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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| 0.425 | 24.01 | 50 | 0.4106 | 27.2504 | |
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| 0.1906 | 49.01 | 100 | 0.4420 | 29.0131 | |
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| 0.0982 | 74.01 | 150 | 0.4795 | 30.3063 | |
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| 0.0717 | 99.01 | 200 | 0.4945 | 30.8915 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.1.dev0 |
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- Tokenizers 0.13.2 |
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