Whisper Larget V3 GA-EN Speech Translation
This model is a fine-tuned version of openai/whisper-large-v3 on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset. It achieves the following results on the evaluation set:
- Loss: 1.0552
- Bleu: 11.86
- Chrf: 28.37
- Wer: 127.1049
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|---|---|---|---|---|---|---|
| 2.5918 | 0.0138 | 100 | 0.61 | 8.48 | 2.1791 | 238.2260 |
| 2.476 | 0.0276 | 200 | 0.63 | 10.43 | 2.1702 | 275.7317 |
| 2.2358 | 0.0414 | 300 | 4.76 | 19.98 | 2.0420 | 120.0810 |
| 2.1778 | 0.0552 | 400 | 2.78 | 12.85 | 1.9506 | 86.8528 |
| 1.9779 | 0.0690 | 500 | 4.53 | 18.47 | 1.8609 | 137.1905 |
| 1.9435 | 0.0828 | 600 | 6.67 | 22.37 | 1.7726 | 82.4403 |
| 1.7928 | 0.0966 | 700 | 4.54 | 17.32 | 1.7445 | 133.8586 |
| 1.9004 | 0.1103 | 800 | 1.58 | 12.65 | 1.7290 | 195.2724 |
| 1.7856 | 0.1241 | 900 | 4.84 | 17.5 | 1.6990 | 83.9262 |
| 1.6783 | 0.1379 | 1000 | 8.46 | 24.24 | 1.6329 | 113.5074 |
| 1.6095 | 0.1517 | 1100 | 7.35 | 20.22 | 1.6083 | 102.5214 |
| 1.6328 | 0.1655 | 1200 | 11.46 | 25.29 | 1.5267 | 76.5871 |
| 1.6093 | 0.1793 | 1300 | 6.51 | 17.77 | 1.4947 | 112.4719 |
| 1.5776 | 0.1931 | 1400 | 6.21 | 19.86 | 1.4952 | 90.6348 |
| 1.4767 | 0.2069 | 1500 | 4.86 | 19.57 | 1.4515 | 145.1148 |
| 1.3447 | 0.2207 | 1600 | 6.77 | 19.96 | 1.3974 | 90.5448 |
| 1.3273 | 0.2345 | 1700 | 4.77 | 16.31 | 1.4323 | 152.1837 |
| 1.4253 | 0.2483 | 1800 | 3.95 | 15.66 | 1.3598 | 173.2553 |
| 1.3505 | 0.2621 | 1900 | 11.25 | 23.4 | 1.3517 | 80.3692 |
| 1.2593 | 0.2759 | 2000 | 12.71 | 26.55 | 1.3236 | 77.5777 |
| 1.2483 | 0.2897 | 2100 | 17.88 | 32.0 | 1.2825 | 73.3003 |
| 1.161 | 0.3034 | 2200 | 10.08 | 20.69 | 1.2567 | 115.8937 |
| 1.1597 | 0.3172 | 2300 | 8.61 | 19.54 | 1.2581 | 93.8766 |
| 1.0937 | 0.3310 | 2400 | 12.37 | 25.67 | 1.2577 | 99.0095 |
| 1.0606 | 0.3448 | 2500 | 6.46 | 23.47 | 1.2228 | 172.9401 |
| 1.039 | 0.3586 | 2600 | 9.55 | 21.56 | 1.2186 | 89.7794 |
| 1.0193 | 0.3724 | 2700 | 3.08 | 17.58 | 1.1844 | 281.8100 |
| 1.1153 | 0.3862 | 2800 | 2.69 | 18.38 | 1.1693 | 350.2927 |
| 1.012 | 0.4 | 2900 | 3.56 | 14.74 | 1.1233 | 194.9122 |
| 0.8936 | 0.4138 | 3000 | 5.21 | 17.38 | 1.1161 | 158.3521 |
| 0.8893 | 0.4276 | 3100 | 11.52 | 25.02 | 1.1119 | 80.9095 |
| 0.9491 | 0.4414 | 3200 | 5.93 | 20.91 | 1.1213 | 174.0207 |
| 0.9233 | 0.4552 | 3300 | 5.54 | 20.95 | 1.0656 | 186.2224 |
| 0.8915 | 0.4690 | 3400 | 7.26 | 23.99 | 1.0736 | 155.6506 |
| 0.8296 | 0.4828 | 3500 | 6.74 | 21.46 | 1.0461 | 146.1054 |
| 0.8163 | 0.4966 | 3600 | 11.35 | 24.11 | 1.0706 | 101.8010 |
| 0.8115 | 0.5103 | 3700 | 12.84 | 26.92 | 1.0199 | 115.8487 |
| 0.8245 | 0.5241 | 3800 | 12.47 | 24.29 | 1.0163 | 101.9361 |
| 0.7988 | 0.5379 | 3900 | 15.29 | 28.54 | 0.9891 | 92.7960 |
| 0.769 | 0.5517 | 4000 | 15.23 | 28.15 | 0.9885 | 92.7060 |
| 0.9048 | 0.5655 | 4100 | 1.1588 | 11.58 | 25.38 | 84.6466 |
| 1.015 | 0.5793 | 4200 | 1.1907 | 8.93 | 18.79 | 86.6276 |
| 0.9254 | 0.5931 | 4300 | 1.1832 | 7.96 | 20.76 | 80.2792 |
| 0.9458 | 0.6069 | 4400 | 1.1789 | 12.03 | 25.59 | 82.6204 |
| 0.9783 | 0.6207 | 4500 | 1.1607 | 7.62 | 20.23 | 100.8555 |
| 0.9935 | 0.6345 | 4600 | 1.2477 | 8.89 | 21.49 | 81.7650 |
| 0.9747 | 0.6483 | 4700 | 1.1994 | 14.51 | 28.26 | 76.5421 |
| 0.9794 | 0.6621 | 4800 | 1.1219 | 16.11 | 27.49 | 81.1796 |
| 0.8919 | 0.6759 | 4900 | 1.1540 | 5.19 | 19.48 | 139.9820 |
| 0.8333 | 0.6897 | 5000 | 1.1388 | 9.38 | 20.8 | 84.6015 |
| 0.9083 | 0.7034 | 5100 | 1.1244 | 6.71 | 22.08 | 176.0018 |
| 0.8039 | 0.7172 | 5200 | 1.1072 | 11.42 | 21.77 | 107.2040 |
| 0.8064 | 0.7310 | 5300 | 1.0705 | 8.89 | 17.34 | 122.8276 |
| 0.8319 | 0.7448 | 5400 | 1.0968 | 7.64 | 24.95 | 170.0585 |
| 0.7984 | 0.7586 | 5500 | 1.1110 | 10.44 | 24.66 | 79.2886 |
| 0.7288 | 0.7724 | 5600 | 1.0820 | 10.4 | 23.09 | 82.5754 |
| 0.8128 | 0.7862 | 5700 | 1.1287 | 12.13 | 25.86 | 96.9833 |
| 0.7016 | 0.8 | 5800 | 1.0698 | 4.84 | 21.49 | 207.7893 |
| 0.7456 | 0.8138 | 5900 | 1.0809 | 5.53 | 22.33 | 204.9077 |
| 0.7575 | 0.8276 | 6000 | 1.0611 | 6.24 | 27.03 | 196.4430 |
| 0.6076 | 0.8414 | 6100 | 1.0868 | 7.93 | 22.14 | 134.7591 |
| 0.6913 | 0.8552 | 6200 | 1.0786 | 8.25 | 19.46 | 84.1963 |
| 0.6251 | 0.8690 | 6300 | 1.0372 | 8.69 | 21.0 | 83.4309 |
| 0.6357 | 0.8828 | 6400 | 1.0408 | 13.83 | 25.16 | 83.2508 |
| 0.666 | 0.8966 | 6500 | 1.0528 | 9.45 | 21.12 | 101.8910 |
| 0.6397 | 0.9103 | 6600 | 1.0394 | 8.21 | 20.5 | 118.1450 |
| 0.6475 | 0.9241 | 6700 | 1.0438 | 4.72 | 20.26 | 191.9856 |
| 0.642 | 0.9379 | 6800 | 1.0421 | 4.84 | 21.12 | 200.1801 |
| 0.6867 | 0.9517 | 6900 | 1.0231 | 5.44 | 21.48 | 214.3629 |
| 0.5254 | 0.9655 | 7000 | 1.0436 | 9.96 | 24.2 | 131.6074 |
| 0.599 | 0.9793 | 7100 | 1.0231 | 16.23 | 30.07 | 86.4475 |
| 0.6589 | 0.9931 | 7200 | 1.0365 | 12.51 | 26.46 | 107.5191 |
| 0.3222 | 1.0069 | 7300 | 1.0790 | 9.22 | 24.16 | 131.4723 |
| 0.3309 | 1.0207 | 7400 | 1.1012 | 7.17 | 25.54 | 166.2314 |
| 0.3402 | 1.0345 | 7500 | 1.0839 | 14.56 | 28.4 | 98.1990 |
| 0.3004 | 1.0483 | 7600 | 1.0615 | 15.49 | 29.84 | 104.0522 |
| 0.2561 | 1.0621 | 7700 | 1.0724 | 11.72 | 28.66 | 125.1688 |
| 0.3021 | 1.0759 | 7800 | 1.0592 | 10.85 | 28.55 | 130.3917 |
| 0.2932 | 1.0897 | 7900 | 1.0554 | 11.62 | 28.17 | 123.8631 |
| 0.2619 | 1.1034 | 8000 | 1.0552 | 11.86 | 28.37 | 127.1049 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2+git70dfd51
- Datasets 2.20.0
- Tokenizers 0.19.1