whisper-small-FLEURS-GL-EN
This model is a fine-tuned version of openai/whisper-small trained on juanjucm/FLEURS-SpeechT-GL-EN for Galician-to-English Text to Speech Translation task. It takes galician speech audios as input and generates the correspondant translated transcription in English.
The motivation behind this work is to increase the visibility of the Galician language, making it more accessible for non-Galician speakers to understand and engage with Galician audio content.
This model was developed during a 3-week Speech Translation workshop organised by Yasmin Moslem.
Performance and training details
Baseline model achieved a BLEU score of 16.0 on the evaluation dataset.
After fine-tuning, it achieves the following results on the evaluation set:
- Loss: 1.6607
- Wer: 67.1683
- BLEU: 22.6201
- ChrF++: 46.11
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
We used BLEU Score as our reference translation metric for selecting the best checkpoint after training.
| 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