Whisper_small_Yoruba
This model is a fine-tuned version of openai/whisper-small on the google/fleurs yo_ng dataset. It achieves the following results on the evaluation set:
- Loss: 1.6773
- Wer: 67.8866
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: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_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: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.013 | 36.35 | 400 | 1.4068 | 72.9681 |
| 0.0008 | 72.7 | 800 | 1.5546 | 68.4507 |
| 0.0003 | 109.09 | 1200 | 1.6400 | 67.9137 |
| 0.0002 | 145.43 | 1600 | 1.6773 | 67.8866 |
| 0.0002 | 181.78 | 2000 | 1.6901 | 68.1123 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
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Dataset used to train steja/whisper-small-yoruba
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Evaluation results
- Wer on google/fleurs yo_ngtest set self-reported67.887