---
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: 0.15773877364941874
---

<!-- 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: 0.2462
- Wer Ortho: 0.2459
- Wer: 0.1577
- Cer: 0.0373

## 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: 5
- training_steps: 250
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.4177        | 0.2460 | 50   | 0.3617          | 0.3915    | 0.3244 | 0.1232 |
| 0.285         | 0.4920 | 100  | 0.3100          | 0.2905    | 0.2013 | 0.0409 |
| 0.2659        | 0.7380 | 150  | 0.2632          | 0.3753    | 0.2909 | 0.4770 |
| 0.2401        | 0.9840 | 200  | 0.2372          | 0.2541    | 0.1568 | 0.0321 |
| 0.1192        | 1.2300 | 250  | 0.2462          | 0.2459    | 0.1577 | 0.0373 |


### Framework versions

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