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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: openai/whisper-medium |
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tags: |
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- generated_from_trainer |
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datasets: |
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- swagen |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-medium-swagen-combined-25hrs-model |
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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: swagen |
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type: swagen |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.25892857142857145 |
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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-medium-swagen-combined-25hrs-model |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the swagen dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3662 |
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- Wer: 0.2589 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 30.0 |
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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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| 2.8233 | 0.0993 | 200 | 0.8047 | 0.4897 | |
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| 1.9329 | 0.1986 | 400 | 0.6191 | 0.4011 | |
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| 1.6927 | 0.2980 | 600 | 0.5421 | 0.3791 | |
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| 1.6183 | 0.3973 | 800 | 0.4889 | 0.3210 | |
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| 1.4431 | 0.4966 | 1000 | 0.4684 | 0.2866 | |
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| 1.4117 | 0.5959 | 1200 | 0.4258 | 0.2650 | |
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| 1.2699 | 0.6952 | 1400 | 0.4222 | 0.2665 | |
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| 1.0532 | 0.7945 | 1600 | 0.4108 | 0.2513 | |
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| 1.0589 | 0.8939 | 1800 | 0.3982 | 0.2291 | |
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| 1.1856 | 0.9932 | 2000 | 0.3853 | 0.2355 | |
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| 0.6692 | 1.0929 | 2200 | 0.4001 | 0.2650 | |
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| 0.6505 | 1.1922 | 2400 | 0.3919 | 0.2389 | |
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| 0.6613 | 1.2915 | 2600 | 0.3809 | 0.2385 | |
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| 0.6194 | 1.3908 | 2800 | 0.3873 | 0.2343 | |
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| 0.6358 | 1.4901 | 3000 | 0.3850 | 0.2142 | |
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| 0.6208 | 1.5894 | 3200 | 0.3779 | 0.2388 | |
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| 0.5932 | 1.6888 | 3400 | 0.3725 | 0.2040 | |
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| 0.5797 | 1.7881 | 3600 | 0.3712 | 0.2092 | |
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| 0.5707 | 1.8874 | 3800 | 0.3738 | 0.2342 | |
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| 0.5928 | 1.9867 | 4000 | 0.3662 | 0.2589 | |
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| 0.2626 | 2.0864 | 4200 | 0.3803 | 0.2697 | |
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| 0.2557 | 2.1857 | 4400 | 0.3853 | 0.2102 | |
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| 0.3342 | 2.2850 | 4600 | 0.3891 | 0.2062 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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