metadata
language:
- eng
license: apache-2.0
base_model: openai/whisper-base.en
tags:
- generated_from_trainer
datasets:
- fyp
metrics:
- wer
model-index:
- name: Whisper Fine tuned Base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Fyp Dataset
type: fyp
args: 'config: eng, split: test'
metrics:
- name: Wer
type: wer
value: 15.01856226797165
Whisper Fine tuned Base
This model is a fine-tuned version of openai/whisper-base.en on the Fyp Dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.3550
- Wer: 15.0186
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: 5
- 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: 2
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3123 | 0.5 | 25 | 0.4652 | 20.1147 |
| 0.3282 | 1.0 | 50 | 0.3655 | 16.2673 |
| 0.0376 | 1.5 | 75 | 0.3693 | 15.4573 |
| 0.0468 | 2.0 | 100 | 0.3754 | 20.2497 |
| 0.0067 | 2.5 | 125 | 0.3585 | 15.3898 |
| 0.0098 | 3.0 | 150 | 0.3550 | 15.0186 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1