--- language: - ga - en license: apache-2.0 base_model: openai/whisper-small 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 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, and Wikimedia type: ymoslem/IWSLT2023-GA-EN metrics: - name: Bleu type: bleu value: 35.04 - name: Wer type: wer value: 57.90184601530842 --- # Whisper Medium GA-EN Speech Translation This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset. It achieves the following results on the evaluation set: - Loss: 1.2966 - Bleu: 35.04 - Chrf: 55.03 - Wer: 57.9018 ## 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: 7000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer | |:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:| | 2.5164 | 0.0328 | 100 | 2.56 | 17.46 | 2.0060 | 162.9896 | | 2.656 | 0.0657 | 200 | 8.49 | 26.0 | 2.0232 | 99.5498 | | 2.5156 | 0.0985 | 300 | 7.55 | 25.1 | 1.9253 | 141.2877 | | 2.4722 | 0.1314 | 400 | 12.52 | 30.49 | 1.8289 | 90.4548 | | 2.3376 | 0.1642 | 500 | 17.39 | 33.23 | 1.6839 | 81.1796 | | 2.1733 | 0.1970 | 600 | 9.62 | 32.48 | 1.7342 | 137.9559 | | 2.3382 | 0.2299 | 700 | 12.54 | 34.43 | 1.6570 | 112.2467 | | 2.0041 | 0.2627 | 800 | 17.55 | 36.73 | 1.6048 | 85.1418 | | 2.1142 | 0.2956 | 900 | 17.58 | 35.74 | 1.6256 | 82.7105 | | 2.024 | 0.3284 | 1000 | 14.4 | 37.22 | 1.5861 | 86.7177 | | 1.7556 | 0.3612 | 1100 | 17.21 | 38.88 | 1.5415 | 84.5115 | | 1.6904 | 0.3941 | 1200 | 19.6 | 38.84 | 1.4902 | 85.3670 | | 1.674 | 0.4269 | 1300 | 20.33 | 41.3 | 1.4748 | 88.3836 | | 1.6899 | 0.4598 | 1400 | 22.74 | 43.25 | 1.4479 | 80.9995 | | 1.5234 | 0.4926 | 1500 | 20.13 | 42.08 | 1.3763 | 80.6844 | | 1.364 | 0.5255 | 1600 | 23.12 | 41.78 | 1.4164 | 72.9851 | | 1.5267 | 0.5583 | 1700 | 19.94 | 41.63 | 1.3855 | 91.7605 | | 1.4282 | 0.5911 | 1800 | 23.96 | 44.84 | 1.3729 | 74.6961 | | 1.3611 | 0.6240 | 1900 | 23.1 | 45.41 | 1.3562 | 81.8100 | | 1.1396 | 0.6568 | 2000 | 27.9 | 46.89 | 1.3131 | 67.2670 | | 1.1849 | 0.6897 | 2100 | 24.38 | 45.25 | 1.3483 | 75.8667 | | 1.0871 | 0.7225 | 2200 | 28.64 | 48.93 | 1.2848 | 66.6817 | | 1.1822 | 0.7553 | 2300 | 28.41 | 47.25 | 1.2782 | 68.6628 | | 1.1272 | 0.7882 | 2400 | 27.24 | 48.57 | 1.2549 | 75.9568 | | 1.0241 | 0.8210 | 2500 | 25.74 | 47.44 | 1.2922 | 74.4710 | | 0.9629 | 0.8539 | 2600 | 23.93 | 44.61 | 1.3209 | 82.1252 | | 0.8251 | 0.8867 | 2700 | 32.21 | 51.64 | 1.2273 | 65.5110 | | 0.7921 | 0.9195 | 2800 | 26.38 | 48.31 | 1.2881 | 80.2792 | | 0.8873 | 0.9524 | 2900 | 26.57 | 50.09 | 1.2268 | 77.1724 | | 0.7967 | 0.9852 | 3000 | 29.35 | 51.53 | 1.2036 | 69.6533 | | 0.3119 | 1.0181 | 3100 | 31.77 | 51.57 | 1.2231 | 62.3143 | | 0.3009 | 1.0509 | 3200 | 31.8 | 50.44 | 1.2446 | 61.8190 | | 0.2855 | 1.0837 | 3300 | 30.48 | 50.86 | 1.2240 | 66.7717 | | 0.2535 | 1.1166 | 3400 | 31.96 | 52.82 | 1.2287 | 63.3949 | | 0.2162 | 1.1494 | 3500 | 33.91 | 52.17 | 1.2398 | 61.3688 | | 0.2307 | 1.1823 | 3600 | 32.11 | 51.67 | 1.2280 | 64.7456 | | 0.2184 | 1.2151 | 3700 | 34.59 | 53.32 | 1.2149 | 59.9730 | | 0.2365 | 1.2479 | 3800 | 32.51 | 52.98 | 1.2044 | 62.3593 | | 0.1958 | 1.2808 | 3900 | 32.45 | 52.86 | 1.2116 | 63.1697 | | 0.2081 | 1.3136 | 4000 | 32.53 | 52.88 | 1.2087 | 62.8095 | | 0.2768 | 1.3465 | 4100 | 1.3177| 30.73 | 49.53 | 64.3854 | | 0.3241 | 1.3793 | 4200 | 1.3363| 24.44 | 46.88 | 78.2981 | | 0.3326 | 1.4122 | 4300 | 1.3622| 27.77 | 47.05 | 68.7528 | | 0.3623 | 1.4450 | 4400 | 1.3232| 27.0 | 47.25 | 70.4187 | | 0.3114 | 1.4778 | 4500 | 1.3530| 25.64 | 46.53 | 73.7506 | | 0.2933 | 1.5107 | 4600 | 1.3674| 29.95 | 47.77 | 65.3760 | | 0.3162 | 1.5435 | 4700 | 1.4011| 28.58 | 47.12 | 66.2765 | | 0.2687 | 1.5764 | 4800 | 1.2875| 32.67 | 50.02 | 61.7740 | | 0.2733 | 1.6092 | 4900 | 1.3090| 30.86 | 50.51 | 63.2148 | | 0.2552 | 1.6420 | 5000 | 1.2946| 27.95 | 49.41 | 69.8334 | | 0.2781 | 1.6749 | 5100 | 1.2971| 34.16 | 52.07 | 61.5489 | | 0.2367 | 1.7077 | 5200 | 1.2990| 32.3 | 51.69 | 63.3949 | | 0.244 | 1.7406 | 5300 | 1.3185| 32.17 | 50.59 | 62.0891 | | 0.2118 | 1.7734 | 5400 | 1.2813| 32.85 | 52.14 | 60.8735 | | 0.1986 | 1.8062 | 5500 | 1.3007| 30.35 | 50.78 | 64.9707 | | 0.2393 | 1.8391 | 5600 | 1.2729| 34.09 | 53.08 | 59.3426 | | 0.1803 | 1.8719 | 5700 | 1.2481| 33.92 | 53.57 | 59.7929 | | 0.199 | 1.9048 | 5800 | 1.2670| 34.53 | 52.74 | 58.9824 | | 0.2 | 1.9376 | 5900 | 1.2591| 33.57 | 53.24 | 60.0180 | | 0.1585 | 1.9704 | 6000 | 1.2855| 31.51 | 52.67 | 64.0702 | | 0.132 | 2.0033 | 6100 | 1.2915| 30.79 | 51.84 | 66.5466 | | 0.0555 | 2.0361 | 6200 | 1.3077| 34.44 | 51.8 | 61.2337 | | 0.0623 | 2.0690 | 6300 | 1.3224| 35.52 | 53.58 | 59.4327 | | 0.0455 | 2.1018 | 6400 | 1.2942| 35.34 | 53.46 | 58.9824 | | 0.0573 | 2.1346 | 6500 | 1.3020| 34.32 | 53.93 | 59.5227 | | 0.0487 | 2.1675 | 6600 | 1.3091| 35.64 | 54.4 | 58.9824 | | 0.0646 | 2.2003 | 6700 | 1.3184| 34.75 | 53.92 | 59.0725 | | 0.0454 | 2.2332 | 6800 | 1.3062| 35.48 | 55.12 | 58.2620 | | 0.0574 | 2.2660 | 6900 | 1.2996| 34.97 | 55.31 | 58.6673 | | 0.051 | 2.2989 | 7000 | 1.2966| 35.04 | 55.03 | 57.9018 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1