metadata
language:
- as
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small Assamese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: as
split: None
args: 'config: as, split: test'
metrics:
- name: Wer
type: wer
value: 66.7815299793246
Whisper Small Assamese
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5698
- Wer: 66.7815
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: 200
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2471 | 2.7027 | 200 | 0.3792 | 74.9368 |
| 0.0533 | 5.4054 | 400 | 0.3930 | 68.5275 |
| 0.0128 | 8.1081 | 600 | 0.4844 | 67.5396 |
| 0.0022 | 10.8108 | 800 | 0.5584 | 67.0342 |
| 0.0006 | 13.5135 | 1000 | 0.5698 | 66.7815 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1