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: 33.069727071077246
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: 0.5227
- Wer: 33.0697
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 |
|---|---|---|---|---|
| 0.6896 | 0.2 | 1000 | 0.7044 | 40.9683 |
| 0.634 | 0.4 | 2000 | 0.6366 | 40.5439 |
| 0.5836 | 0.6 | 3000 | 0.5821 | 34.3331 |
| 0.5568 | 0.8 | 4000 | 0.5446 | 33.6870 |
| 0.535 | 1.0 | 5000 | 0.5227 | 33.0697 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1