Whisper Base Lingala - BrainTheos
This model is a fine-tuned version of openai/whisper-base on the Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.7265
- Wer: 25.0509
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0081 | 21.0 | 1000 | 0.6218 | 29.8710 |
| 0.0016 | 42.01 | 2000 | 0.6865 | 25.1188 |
| 0.0009 | 63.01 | 3000 | 0.7152 | 24.9151 |
| 0.0007 | 85.0 | 4000 | 0.7265 | 25.0509 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3
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Dataset used to train BrainTheos/whisper-base-ln
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Evaluation results
- Wer on Fleursvalidation set self-reported25.051