metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ncc_s
metrics:
- wer
model-index:
- name: whisper-large-nob-ncc-s
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: ncc_s
type: ncc_s
config: 'no'
split: validation
args: 'no'
metrics:
- name: Wer
type: wer
value: 12.51522533495737
whisper-large-nob-ncc-s
This model is a fine-tuned version of openai/whisper-large-v2 on the ncc_s dataset. It achieves the following results on the evaluation set:
- Loss: 0.2776
- Wer: 12.5152
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: 12
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.6892 | 0.2 | 1000 | 0.3177 | 15.1035 |
| 0.6782 | 0.4 | 2000 | 0.3033 | 13.4592 |
| 0.6317 | 0.6 | 3000 | 0.2909 | 13.7637 |
| 0.5609 | 0.8 | 4000 | 0.2803 | 12.6675 |
| 0.5726 | 1.0 | 5000 | 0.2776 | 12.5152 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.11.0