metadata
language:
- ms
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- clt013/malay-speech-1.6-million-rows-dataset
metrics:
- wer
model-index:
- name: Whisper Medium FT Malay - CLT013
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Malay Speech 1.6 million
type: clt013/malay-speech-1.6-million-rows-dataset
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 39.65666891696403
Whisper Medium FT Malay - CLT013
This model is a fine-tuned version of openai/whisper-medium on the Malay Speech 1.6 million dataset. It achieves the following results on the evaluation set:
- Loss: 0.7057
- Wer: 39.6567
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0434 | 0.1 | 100 | 0.9250 | 53.3417 |
| 0.8131 | 0.2 | 200 | 0.8394 | 46.5908 |
| 0.7852 | 0.3 | 300 | 0.8033 | 45.1635 |
| 0.7643 | 0.4 | 400 | 0.7769 | 53.5732 |
| 0.7424 | 0.5 | 500 | 0.7582 | 46.6969 |
| 0.7406 | 0.6 | 600 | 0.7451 | 39.6760 |
| 0.7913 | 0.7 | 700 | 0.7288 | 39.3866 |
| 0.7452 | 0.8 | 800 | 0.7164 | 37.9979 |
| 0.718 | 0.9 | 900 | 0.7099 | 38.7694 |
| 0.7328 | 1.0 | 1000 | 0.7057 | 39.6567 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1