metadata

library_name: transformers
language:
  - de
license: apache-2.0
base_model: openai/whisper-small
tags:
  - automatic-speech-recognition
  - generated_from_trainer
datasets:
  - openai/whisper-small
metrics:
  - wer
model-index:
  - name: Whisper Small DE - Maximilian Kenfenheuer
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: openai/whisper-small
          config: de
          split: test
          args: 'config: de, split: validated'
        metrics:
          - name: Wer
            type: wer
            value: 7.5439641517863185

Whisper Small DE - Maximilian Kenfenheuer

This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1231
Wer: 7.5440

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: 16
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1911	0.25	1000	0.1567	9.4088
0.1959	0.5	2000	0.1382	8.5261
0.1843	0.75	3000	0.1285	7.8513
0.2134	1.0	4000	0.1231	7.5440

Framework versions

Transformers 4.48.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0