---
language:
- ge
license: apache-2.0
tags:
- sbb-asr
- generated_from_trainer
datasets:
- marccgrau/sbbdata
metrics:
- wer
model-index:
- name: Whisper Small German SBB
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: SBB Dataset 29.11.2022
      type: marccgrau/sbbdata
      args: 'config: German, split: train, test, val'
    metrics:
    - name: Wer
      type: wer
      value: 0.8658008658008658
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Small German SBB

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.
It achieves the following results on the evaluation set:
- Loss: 0.0151
- Wer: 0.8658

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8659        | 10.0  | 50   | 0.6119          | 6.4935 |
| 0.2183        | 20.0  | 100  | 0.0727          | 5.1948 |
| 0.0002        | 30.0  | 150  | 0.0168          | 0.8658 |
| 0.0001        | 40.0  | 200  | 0.0159          | 0.8658 |
| 0.0           | 50.0  | 250  | 0.0155          | 0.8658 |
| 0.0           | 60.0  | 300  | 0.0154          | 0.8658 |
| 0.0           | 70.0  | 350  | 0.0152          | 0.8658 |
| 0.0           | 80.0  | 400  | 0.0151          | 0.8658 |
| 0.0           | 90.0  | 450  | 0.0151          | 0.8658 |
| 0.0           | 100.0 | 500  | 0.0151          | 0.8658 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.12.1