Whisper Large GA-EN Speech Translation
This model is a fine-tuned version of openai/whisper-large on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset. It achieves the following results on the evaluation set:
- Loss: 1.1318
- Bleu: 31.26
- Chrf: 50.41
- Wer: 62.3143
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|---|---|---|---|---|---|---|
| 3.1547 | 0.03 | 100 | 3.75 | 18.71 | 2.4173 | 124.0882 |
| 2.6996 | 0.07 | 200 | 8.16 | 25.45 | 2.1329 | 114.1378 |
| 2.4841 | 0.1 | 300 | 6.4 | 23.6 | 2.0262 | 158.1720 |
| 2.4706 | 0.13 | 400 | 9.16 | 27.67 | 1.9688 | 120.0810 |
| 2.3575 | 0.16 | 500 | 13.66 | 31.5 | 1.8284 | 100.8555 |
| 2.1916 | 0.2 | 600 | 12.97 | 31.8 | 1.7486 | 110.1756 |
| 2.1353 | 0.23 | 700 | 16.7 | 33.52 | 1.7568 | 86.8528 |
| 1.9885 | 0.26 | 800 | 19.34 | 35.35 | 1.6395 | 78.7033 |
| 1.9126 | 0.3 | 900 | 20.21 | 36.28 | 1.5658 | 78.2080 |
| 1.6418 | 0.33 | 1000 | 18.61 | 38.49 | 1.4998 | 86.8528 |
| 1.5782 | 0.36 | 1100 | 22.91 | 40.04 | 1.4716 | 71.0941 |
| 1.4899 | 0.39 | 1200 | 21.55 | 40.92 | 1.4444 | 78.7933 |
| 1.3155 | 0.43 | 1300 | 24.95 | 42.05 | 1.3934 | 70.9140 |
| 1.4144 | 0.46 | 1400 | 28.38 | 46.18 | 1.2791 | 65.8262 |
| 1.1949 | 0.49 | 1500 | 26.95 | 45.84 | 1.2879 | 70.6889 |
| 1.0179 | 0.53 | 1600 | 26.12 | 46.4 | 1.2624 | 69.6983 |
| 1.0935 | 0.56 | 1700 | 28.51 | 48.24 | 1.2076 | 67.4021 |
| 1.061 | 0.59 | 1800 | 27.42 | 48.83 | 1.1812 | 71.4543 |
| 1.0955 | 0.62 | 1900 | 31.32 | 49.91 | 1.1503 | 62.9896 |
| 1.0607 | 0.66 | 2000 | 31.26 | 50.41 | 1.1318 | 62.3143 |
| 1.1135 | 0.6897 | 2100 | 1.2135 | 26.57 | 46.18 | 69.7884 |
| 0.9819 | 0.7225 | 2200 | 1.2252 | 26.95 | 49.47 | 65.0158 |
| 0.9909 | 0.7553 | 2300 | 1.2072 | 30.35 | 46.49 | 63.3048 |
| 0.9521 | 0.7882 | 2400 | 1.2130 | 24.76 | 46.44 | 70.6889 |
| 0.8245 | 0.8210 | 2500 | 1.1724 | 24.84 | 47.05 | 78.1630 |
| 0.8303 | 0.8539 | 2600 | 1.1812 | 27.56 | 47.48 | 70.1036 |
| 0.6934 | 0.8867 | 2700 | 1.1716 | 31.61 | 50.4 | 63.8001 |
| 0.7117 | 0.9195 | 2800 | 1.1650 | 30.82 | 49.95 | 65.0158 |
| 0.6944 | 0.9524 | 2900 | 1.1516 | 31.21 | 49.8 | 63.5750 |
| 0.7132 | 0.9852 | 3000 | 1.1390 | 30.16 | 49.77 | 65.6011 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.0.1+cu118
- Datasets 2.18.0
- Tokenizers 0.19.1