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--- |
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library_name: transformers |
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language: |
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- gsw |
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license: apache-2.0 |
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base_model: openai/whisper-large-v2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- notebotIE/zh_split_preprocessed |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large V2 - Swiss German |
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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: SwissDialDataset_ETH |
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type: notebotIE/zh_split_preprocessed |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.14542967859585137 |
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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 - Swiss German |
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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 SwissDialDataset_ETH dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2648 |
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- Wer Ortho: 0.2518 |
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- Wer: 0.1454 |
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- Cer: 0.0304 |
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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: 16 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: constant_with_warmup |
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- lr_scheduler_warmup_steps: 50 |
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- training_steps: 500 |
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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 Ortho | Wer | Cer | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:| |
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| 0.1387 | 1.2255 | 250 | 0.2670 | 0.2478 | 0.1523 | 0.0302 | |
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| 0.0781 | 2.4510 | 500 | 0.2648 | 0.2518 | 0.1454 | 0.0304 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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