Speech Translation Models
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whisper-small-FLEURS-GL-EN
This model is a fine-tuned version of openai/whisper-small on juanjucm/FLEURS-SpeechT-GL-EN. It achieves the following results on the evaluation set:
- Loss: 1.6607
- Wer: 67.1683
- Bleu: 22.6201
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.25e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Bleu |
|---|---|---|---|---|---|
| 1.3189 | 1.0 | 86 | 1.6608 | 67.1683 | 22.6201 |
| 0.6613 | 2.0 | 172 | 1.6643 | 68.5990 | 21.1576 |
| 0.3492 | 3.0 | 258 | 1.7873 | 69.7046 | 20.7371 |
| 0.1416 | 4.0 | 344 | 1.9098 | 69.9090 | 20.5952 |
| 0.0974 | 5.0 | 430 | 2.0487 | 70.0948 | 20.6740 |
| 0.061 | 6.0 | 516 | 2.1565 | 73.4578 | 19.2411 |
| 0.0384 | 7.0 | 602 | 2.2107 | 73.6622 | 19.5413 |
| 0.0203 | 8.0 | 688 | 2.2476 | 73.9874 | 19.4512 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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Base model
openai/whisper-small