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---
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
---
<!-- 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 DE - Maximilian Kenfenheuer
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/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
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