metadata
language:
- eng
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- Svetlana0303/my_eng_texts_6
metrics:
- wer
model-index:
- name: Whisper Small Eng - Three samples
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice MY
type: Svetlana0303/my_eng_texts_6
args: 'split: test'
metrics:
- name: Wer
type: wer
value: 100
Whisper Small Eng - Three samples
This model is a fine-tuned version of openai/whisper-small on the Common Voice MY dataset. It achieves the following results on the evaluation set:
- Loss: 5.1836
- Wer: 100.0
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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0 | 1000.0 | 1000 | 5.0117 | 100.0 |
| 0.0 | 2000.0 | 2000 | 5.0899 | 100.0 |
| 0.0 | 3000.0 | 3000 | 5.1823 | 100.0 |
| 0.0 | 4000.0 | 4000 | 5.1836 | 100.0 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1