metadata
language:
- nn
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- norwegian-parliament
metrics:
- wer
model-index:
- name: whisper-medium-nn-v3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Stortingskorpuset
type: norwegian-parliament
config: default
split: validation
args: default
metrics:
- name: Wer
type: wer
value: 11.337582785573966
whisper-medium-nn-v3
This model is a fine-tuned version of openai/whisper-medium on the Stortingskorpuset dataset. It achieves the following results on the evaluation set:
- Loss: 0.2116
- Wer: 11.3376
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 8000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4413 | 0.25 | 2000 | 0.4447 | 26.7707 |
| 0.1945 | 1.1 | 4000 | 0.3042 | 17.8344 |
| 0.1013 | 1.35 | 6000 | 0.2421 | 14.2138 |
| 0.0308 | 2.2 | 8000 | 0.2116 | 11.3376 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2