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---
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: 0.14542967859585137
---
<!-- 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: 0.2648
- Wer Ortho: 0.2518
- Wer: 0.1454
- Cer: 0.0304
## 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: 16
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.1387 | 1.2255 | 250 | 0.2670 | 0.2478 | 0.1523 | 0.0302 |
| 0.0781 | 2.4510 | 500 | 0.2648 | 0.2518 | 0.1454 | 0.0304 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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