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---
library_name: transformers
language:
- de
license: apache-2.0
base_model: mkenfenheuer/whisper-small-de-v2
tags:
- generated_from_trainer
- automatic-speech-recognition
- german
datasets:
- mkenfenheuer/whisper-small-de-v2
metrics:
- wer
model-index:
- name: Whisper Small DE v3.0 - Maximilian Kenfenheuer
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mkenfenheuer/whisper-small-de-v2
config: de
split: test
args: 'config: de, split: validated'
metrics:
- name: Wer
type: wer
value: 2.451703737799017
---
This model is a converted version of [mkenfenheuer/whisper-small-de-v3](https://huggingface.co/mkenfenheuer/whisper-small-de-v3/) converted to ctranslate2 with int8 quantization.
<!-- 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 v3.0 - Maximilian Kenfenheuer
This model is a fine-tuned version of [mkenfenheuer/whisper-small-de-v2](https://huggingface.co/mkenfenheuer/whisper-small-de-v2) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0402
- Wer: 2.4517
## 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
- training_steps: 1001
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0694 | 0.9990 | 1000 | 0.0402 | 2.4517 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0