metadata
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- whisper-event
- generated_from_trainer
datasets:
- nadsoft/QASR-Speech-Resource
metrics:
- wer
model-index:
- name: Whisper Small Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: nadsoft/QASR-Speech-Resource default
type: nadsoft/QASR-Speech-Resource
metrics:
- name: Wer
type: wer
value: 42.76086285863452
Whisper Small Arabic
This model is a fine-tuned version of openai/whisper-tiny on the nadsoft/QASR-Speech-Resource default dataset. It achieves the following results on the evaluation set:
- Loss: 0.5583
- Wer: 42.7609
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: 32
- eval_batch_size: 32
- 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: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.7005 | 0.2 | 2000 | 0.7135 | 51.5366 |
| 0.6267 | 0.4 | 4000 | 0.6309 | 50.9433 |
| 0.5886 | 0.6 | 6000 | 0.5892 | 50.0225 |
| 0.5627 | 0.8 | 8000 | 0.5679 | 43.9450 |
| 0.5694 | 1.0 | 10000 | 0.5583 | 42.7609 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.1.dev0
- Tokenizers 0.15.1