metadata
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
Whisper Large V2 - Swiss German
This model is a fine-tuned version of 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