metadata
language:
- ml
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Malayalam - Arjun Shaji
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ml_in
split: None
args: 'config: ml, split: test'
metrics:
- name: Wer
type: wer
value: 52.33970351848545
Whisper Small Malayalam - Arjun Shaji
This model is a fine-tuned version of openai/whisper-small on the google/fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.1677
- Wer: 52.3397
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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0239 | 5.1020 | 1000 | 0.1136 | 54.3847 |
| 0.002 | 10.2041 | 2000 | 0.1426 | 52.9827 |
| 0.0003 | 15.3061 | 3000 | 0.1584 | 52.5808 |
| 0.0001 | 20.4082 | 4000 | 0.1643 | 52.3129 |
| 0.0001 | 25.5102 | 5000 | 0.1677 | 52.3397 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1