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README.md
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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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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_allSNR
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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 all SNR - v6
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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 05.01.2023
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type: marccgrau/sbbdata_allSNR
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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: 0.02663284717818643
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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 all SNR - v6
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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 05.01.2023 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0426
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- Wer: 0.0266
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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: 4
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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: 400
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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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| 1.7233 | 0.04 | 100 | 0.4161 | 0.2232 |
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| 0.1932 | 0.09 | 200 | 0.0665 | 0.0361 |
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| 0.0615 | 0.13 | 300 | 0.0666 | 0.0361 |
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| 0.0677 | 0.18 | 400 | 0.0426 | 0.0266 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.13.1
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- Datasets 2.8.0
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- Tokenizers 0.12.1
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