metadata
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
Whisper Small German SBB
This model is a fine-tuned version of 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