---
library_name: transformers
language:
- gsw
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
datasets:
- notebotIE/zh_split_preprocessed
metrics:
- wer
model-index:
- name: Whisper Large V2 - Swiss German
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: SwissDialDataset_ETH
      type: notebotIE/zh_split_preprocessed
    metrics:
    - name: Wer
      type: wer
      value: 0.1923865967631639
---

<!-- 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 Large V2 - Swiss German

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the SwissDialDataset_ETH dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2725
- Wer Ortho: 0.2707
- Wer: 0.1924
- Cer: 0.0743

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.1277        | 1.2300 | 250  | 0.2468          | 0.2631    | 0.1855 | 0.0556 |
| 0.0726        | 2.4600 | 500  | 0.2414          | 0.9085    | 0.6793 | 0.5239 |
| 0.0405        | 3.6900 | 750  | 0.2519          | 0.5194    | 0.6558 | 0.4984 |
| 0.0296        | 4.9200 | 1000 | 0.2725          | 0.2707    | 0.1924 | 0.0743 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3