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README.md
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---
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language:
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- ge
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license: apache-2.0
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tags:
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- sbb-asr
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- generated_from_trainer
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datasets:
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- marccgrau/sbbdata
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metrics:
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- wer
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model-index:
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- name: Whisper Small German SBB
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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: SBB Dataset 29.11.2022
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type: marccgrau/sbbdata
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args: 'config: German, split: train, test, val'
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metrics:
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- name: Wer
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type: wer
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value: 0.8658008658008658
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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 German SBB
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 29.11.2022 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0151
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- Wer: 0.8658
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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: 32
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- eval_batch_size: 32
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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: 100
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 0.8659 | 10.0 | 50 | 0.6119 | 6.4935 |
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| 0.2183 | 20.0 | 100 | 0.0727 | 5.1948 |
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| 0.0002 | 30.0 | 150 | 0.0168 | 0.8658 |
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| 0.0001 | 40.0 | 200 | 0.0159 | 0.8658 |
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| 0.0 | 50.0 | 250 | 0.0155 | 0.8658 |
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| 0.0 | 60.0 | 300 | 0.0154 | 0.8658 |
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| 0.0 | 70.0 | 350 | 0.0152 | 0.8658 |
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| 0.0 | 80.0 | 400 | 0.0151 | 0.8658 |
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| 0.0 | 90.0 | 450 | 0.0151 | 0.8658 |
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| 0.0 | 100.0 | 500 | 0.0151 | 0.8658 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.12.1
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- Datasets 2.7.1
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- Tokenizers 0.12.1
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