---
library_name: transformers
language:
- de
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- MR-Eder/GER-TTS-50-Conversations
metrics:
- wer
model-index:
- name: Whisper Large v3 Turbo German - GRAG
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: GER-TTS-50-Conversations
      type: MR-Eder/GER-TTS-50-Conversations
      config: default
      split: None
      args: 'config: de, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 15.170289725316048
---

<!-- 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 - GRAG

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the GER-TTS-50-Conversations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4391
- Wer: 15.1703

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.066         | 8.3333  | 1000 | 0.3653          | 15.4640 |
| 0.0038        | 16.6667 | 2000 | 0.4180          | 15.0235 |
| 0.0006        | 25.0    | 3000 | 0.4340          | 15.1882 |
| 0.0004        | 33.3333 | 4000 | 0.4391          | 15.1703 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3