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--- |
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language: |
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- de |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- rmacek/common_voice_zib2 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small ZIB2 |
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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: ZIB2 Common Voice |
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type: rmacek/common_voice_zib2 |
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args: 'config: de, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 28.93835616438356 |
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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 Small ZIB2 |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ZIB2 Common Voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3366 |
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- Wer: 28.9384 |
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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: 1000 |
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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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| 0.2391 | 10.0 | 100 | 0.2837 | 33.5616 | |
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| 0.0035 | 20.0 | 200 | 0.2701 | 27.7397 | |
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| 0.0012 | 30.0 | 300 | 0.2847 | 27.5685 | |
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| 0.0006 | 40.0 | 400 | 0.2990 | 27.9110 | |
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| 0.0004 | 50.0 | 500 | 0.3118 | 28.5959 | |
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| 0.0003 | 60.0 | 600 | 0.3221 | 28.5959 | |
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| 0.0002 | 70.0 | 700 | 0.3287 | 28.7671 | |
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| 0.0002 | 80.0 | 800 | 0.3333 | 28.9384 | |
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| 0.0002 | 90.0 | 900 | 0.3357 | 28.9384 | |
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| 0.0002 | 100.0 | 1000 | 0.3366 | 28.9384 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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