--- language: - ga - en license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer datasets: - ymoslem/IWSLT2023-GA-EN - ymoslem/FLEURS-GA-EN - ymoslem/BitesizeIrish-GA-EN - ymoslem/SpokenWords-GA-EN-MTed - ymoslem/Tatoeba-Speech-Irish - ymoslem/Wikimedia-Speech-Irish - ymoslem/EUbookshop-Speech-Irish metrics: - bleu - wer model-index: - name: Whisper Medium GA-EN Speech Translation results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop type: ymoslem/IWSLT2023-GA-EN metrics: - name: Bleu type: bleu value: 32.0 - name: Wer type: wer value: 66.77172444844665 --- # Whisper Medium GA-EN Speech Translation This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset. It achieves the following results on the evaluation set: - Loss: 1.1067 - Bleu: 32.0 - Chrf: 52.48 - Wer: 66.7717 ## 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 | |:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:| | 2.5219 | 0.0138 | 100 | 0.44 | 10.48 | 2.1106 | 107.2490 | | 2.4608 | 0.0276 | 200 | 3.3 | 20.43 | 2.1816 | 179.1535 | | 2.3008 | 0.0414 | 300 | 3.66 | 21.59 | 2.0587 | 206.4836 | | 2.2095 | 0.0552 | 400 | 8.79 | 27.66 | 1.9459 | 100.3602 | | 2.0454 | 0.0690 | 500 | 8.14 | 27.36 | 1.8681 | 122.1522 | | 1.9937 | 0.0828 | 600 | 11.05 | 30.26 | 1.8717 | 97.2535 | | 1.868 | 0.0966 | 700 | 9.14 | 29.03 | 1.7917 | 129.0410 | | 1.9924 | 0.1103 | 800 | 12.62 | 33.2 | 1.7170 | 89.6443 | | 1.8646 | 0.1241 | 900 | 11.98 | 30.77 | 1.7252 | 97.8838 | | 1.7644 | 0.1379 | 1000 | 10.87 | 31.0 | 1.6832 | 109.1851 | | 1.692 | 0.1517 | 1100 | 13.05 | 34.46 | 1.6837 | 93.3814 | | 1.7044 | 0.1655 | 1200 | 20.95 | 37.42 | 1.5527 | 75.2364 | | 1.6824 | 0.1793 | 1300 | 14.91 | 35.56 | 1.5611 | 92.6159 | | 1.6557 | 0.1931 | 1400 | 14.0 | 36.54 | 1.5554 | 99.8199 | | 1.5456 | 0.2069 | 1500 | 19.72 | 39.81 | 1.5058 | 83.5660 | | 1.3755 | 0.2207 | 1600 | 18.04 | 37.95 | 1.5039 | 82.9806 | | 1.3959 | 0.2345 | 1700 | 17.01 | 39.5 | 1.4374 | 85.2319 | | 1.5012 | 0.2483 | 1800 | 14.93 | 39.24 | 1.4242 | 114.4079 | | 1.4278 | 0.2621 | 1900 | 23.85 | 42.69 | 1.3904 | 73.0302 | | 1.3285 | 0.2759 | 2000 | 17.7 | 37.23 | 1.4493 | 83.8811 | | 1.2655 | 0.2897 | 2100 | 20.1 | 40.32 | 1.3661 | 79.7839 | | 1.2074 | 0.3034 | 2200 | 24.45 | 43.79 | 1.3387 | 72.9851 | | 1.1893 | 0.3172 | 2300 | 21.45 | 42.61 | 1.3308 | 82.3953 | | 1.1236 | 0.3310 | 2400 | 22.77 | 44.17 | 1.3050 | 77.3075 | | 1.0934 | 0.3448 | 2500 | 25.54 | 46.32 | 1.2793 | 72.2647 | | 1.06 | 0.3586 | 2600 | 28.27 | 47.32 | 1.2396 | 65.6911 | | 1.0327 | 0.3724 | 2700 | 28.45 | 47.01 | 1.2577 | 67.3570 | | 1.1623 | 0.3862 | 2800 | 24.54 | 47.43 | 1.2194 | 73.6155 | | 1.0215 | 0.4 | 2900 | 27.4 | 49.6 | 1.2039 | 69.2481 | | 0.9185 | 0.4138 | 3000 | 27.04 | 49.24 | 1.1724 | 67.8973 | | 0.9003 | 0.4276 | 3100 | 31.08 | 50.11 | 1.1674 | 63.8001 | | 0.9839 | 0.4414 | 3200 | 30.24 | 50.63 | 1.1580 | 64.5655 | | 0.9396 | 0.4552 | 3300 | 30.79 | 51.72 | 1.1202 | 64.9257 | | 0.9051 | 0.4690 | 3400 | 30.34 | 53.08 | 1.1180 | 66.4566 | | 0.8621 | 0.4828 | 3500 | 33.3 | 53.86 | 1.1042 | 60.7834 | | 0.8236 | 0.4966 | 3600 | 32.77 | 53.21 | 1.1070 | 62.0441 | | 0.829 | 0.5103 | 3700 | 32.49 | 54.21 | 1.0771 | 62.5844 | | 0.8375 | 0.5241 | 3800 | 32.27 | 53.98 | 1.0780 | 63.0797 | | 0.8206 | 0.5379 | 3900 | 33.26 | 55.07 | 1.0615 | 61.6389 | | 0.8059 | 0.5517 | 4000 | 33.24 | 55.16 | 1.0552 | 61.5038 | | 0.9133 | 0.5655 | 4100 | 1.2218| 29.38 | 49.22 | 66.0964 | | 1.051 | 0.5793 | 4200 | 1.2304| 25.12 | 46.01 | 71.8145 | | 0.954 | 0.5931 | 4300 | 1.2501| 25.47 | 45.88 | 75.3715 | | 0.939 | 0.6069 | 4400 | 1.2204| 29.19 | 47.63 | 66.9068 | | 0.9887 | 0.6207 | 4500 | 1.2099| 27.99 | 47.01 | 67.7172 | | 1.0044 | 0.6345 | 4600 | 1.2080| 23.77 | 45.33 | 73.3904 | | 0.9881 | 0.6483 | 4700 | 1.2188| 26.46 | 47.36 | 68.5277 | | 0.9674 | 0.6621 | 4800 | 1.2296| 26.11 | 45.92 | 68.3026 | | 0.8845 | 0.6759 | 4900 | 1.2347| 27.3 | 46.08 | 68.0324 | | 0.8297 | 0.6897 | 5000 | 1.2108| 29.48 | 48.96 | 64.6105 | | 0.9065 | 0.7034 | 5100 | 1.1873| 29.81 | 49.94 | 64.2503 | | 0.8096 | 0.7172 | 5200 | 1.2122| 28.5 | 46.93 | 66.2314 | | 0.8077 | 0.7310 | 5300 | 1.1945| 29.26 | 48.21 | 64.4755 | | 0.8227 | 0.7448 | 5400 | 1.2310| 26.82 | 48.43 | 71.4093 | | 0.7587 | 0.7586 | 5500 | 1.2067| 29.45 | 49.03 | 65.3309 | | 0.7206 | 0.7724 | 5600 | 1.2114| 29.89 | 49.33 | 65.5561 | | 0.8088 | 0.7862 | 5700 | 1.1689| 31.88 | 51.4 | 64.2954 | | 0.693 | 0.8 | 5800 | 1.1644| 27.23 | 48.11 | 68.7078 | | 0.7099 | 0.8138 | 5900 | 1.1852| 31.01 | 49.42 | 63.3949 | | 0.7564 | 0.8276 | 6000 | 1.1554| 28.3 | 50.34 | 71.0941 | | 0.584 | 0.8414 | 6100 | 1.1566| 34.79 | 51.69 | 59.0725 | | 0.6817 | 0.8552 | 6200 | 1.1245| 34.08 | 51.95 | 59.8829 | | 0.5968 | 0.8690 | 6300 | 1.1475| 32.4 | 51.59 | 62.9896 | | 0.6092 | 0.8828 | 6400 | 1.1250| 32.83 | 52.82 | 62.5844 | | 0.6325 | 0.8966 | 6500 | 1.1108| 29.29 | 51.68 | 69.1130 | | 0.6002 | 0.9103 | 6600 | 1.0993| 27.64 | 52.7 | 71.0941 | | 0.6247 | 0.9241 | 6700 | 1.0898| 28.39 | 52.4 | 68.3026 | | 0.6257 | 0.9379 | 6800 | 1.0863| 28.54 | 52.33 | 70.9140 | | 0.6719 | 0.9517 | 6900 | 1.0891| 31.43 | 53.53 | 66.1414 | | 0.4994 | 0.9655 | 7000 | 1.1066| 33.81 | 52.77 | 61.0986 | | 0.5469 | 0.9793 | 7100 | 1.0891| 30.52 | 53.13 | 67.3570 | | 0.6031 | 0.9931 | 7200 | 1.0933| 33.16 | 54.03 | 62.1792 | | 0.2469 | 1.0069 | 7300 | 1.1426| 33.76 | 52.38 | 62.8546 | | 0.2572 | 1.0207 | 7400 | 1.1292| 33.16 | 51.71 | 64.8807 | | 0.2762 | 1.0345 | 7500 | 1.1090| 34.76 | 54.28 | 60.7384 | | 0.2332 | 1.0483 | 7600 | 1.1073| 30.95 | 52.28 | 66.1864 | | 0.2069 | 1.0621 | 7700 | 1.0999| 32.39 | 53.08 | 65.5561 | | 0.2417 | 1.0759 | 7800 | 1.1008| 31.3 | 53.87 | 65.1058 | | 0.2403 | 1.0897 | 7900 | 1.1053| 32.18 | 53.3 | 66.4566 | | 0.208 | 1.1034 | 8000 | 1.1067| 32.0 | 52.48 | 66.7717 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1