metadata
language:
- my
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- malaysia-ai/malay-conversational-speech-corpus
metrics:
- wer
model-index:
- name: Whisper small Malay (4 batch size) - Gab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: malay-conversational-speech-corpus
type: malaysia-ai/malay-conversational-speech-corpus
args: 'config: malay, split: test'
metrics:
- name: Wer
type: wer
value: 27.394540942928042
Whisper small Malay (4 batch size) - Gab
This model is a fine-tuned version of openai/whisper-small on the malay-conversational-speech-corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.7126
- Wer: 27.3945
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: 4
- 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0217 | 6.1728 | 1000 | 0.5993 | 28.8586 |
| 0.0013 | 12.3457 | 2000 | 0.6816 | 28.0397 |
| 0.0003 | 18.5185 | 3000 | 0.7018 | 27.8660 |
| 0.0002 | 24.6914 | 4000 | 0.7126 | 27.3945 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1