--- 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 - 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: 36.46 - name: Wer type: wer value: 58.26204412426835 --- # 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, Wikimedia, and EUbookshop dataset. It achieves the following results on the evaluation set: - Loss: 1.1121 - Bleu: 36.46 - Chrf: 55.74 - Wer: 58.2620 ## 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: 0.02 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer | |:-------------:|:------:|:-----:|:-----:|:-----:|:---------------:|:--------:| | 2.6534 | 0.0138 | 100 | 1.43 | 15.99 | 2.2446 | 269.1130 | | 2.4519 | 0.0276 | 200 | 2.13 | 18.36 | 2.1941 | 250.5178 | | 2.2928 | 0.0414 | 300 | 7.14 | 25.95 | 2.0086 | 128.3656 | | 2.233 | 0.0552 | 400 | 5.61 | 24.25 | 2.0239 | 134.0837 | | 2.0406 | 0.0690 | 500 | 5.64 | 25.65 | 1.9215 | 183.8361 | | 2.0273 | 0.0828 | 600 | 13.41 | 30.96 | 1.8556 | 83.7010 | | 1.895 | 0.0966 | 700 | 7.02 | 26.82 | 1.8278 | 158.2170 | | 1.9889 | 0.1103 | 800 | 12.22 | 31.62 | 1.7842 | 99.6398 | | 1.8484 | 0.1241 | 900 | 10.97 | 30.45 | 1.7648 | 91.1751 | | 1.7491 | 0.1379 | 1000 | 10.0 | 29.42 | 1.7498 | 109.0050 | | 1.699 | 0.1517 | 1100 | 12.53 | 34.87 | 1.6662 | 109.9054 | | 1.6959 | 0.1655 | 1200 | 14.54 | 34.8 | 1.6287 | 92.3008 | | 1.6682 | 0.1793 | 1300 | 13.26 | 33.5 | 1.5800 | 103.0617 | | 1.6625 | 0.1931 | 1400 | 19.71 | 37.33 | 1.6115 | 75.9118 | | 1.5462 | 0.2069 | 1500 | 18.3 | 39.49 | 1.4993 | 93.7866 | | 1.3834 | 0.2207 | 1600 | 20.32 | 40.87 | 1.4906 | 79.2436 | | 1.39 | 0.2345 | 1700 | 17.3 | 38.16 | 1.4752 | 93.1562 | | 1.5061 | 0.2483 | 1800 | 20.11 | 39.69 | 1.4004 | 81.0446 | | 1.4125 | 0.2621 | 1900 | 23.82 | 42.67 | 1.3854 | 73.3904 | | 1.3181 | 0.2759 | 2000 | 20.57 | 40.87 | 1.3979 | 78.8384 | | 1.283 | 0.2897 | 2100 | 17.97 | 40.47 | 1.3446 | 88.8789 | | 1.2061 | 0.3034 | 2200 | 25.12 | 45.42 | 1.3130 | 73.5254 | | 1.2091 | 0.3172 | 2300 | 22.12 | 43.56 | 1.3274 | 79.8739 | | 1.1264 | 0.3310 | 2400 | 22.94 | 45.96 | 1.2771 | 78.2080 | | 1.0972 | 0.3448 | 2500 | 24.38 | 46.04 | 1.2858 | 75.4615 | | 1.0822 | 0.3586 | 2600 | 27.39 | 48.34 | 1.2376 | 67.6722 | | 1.0316 | 0.3724 | 2700 | 28.0 | 47.61 | 1.2461 | 68.5277 | | 1.165 | 0.3862 | 2800 | 26.05 | 48.13 | 1.1869 | 71.6794 | | 1.025 | 0.4 | 2900 | 27.14 | 47.91 | 1.1716 | 68.7528 | | 0.8978 | 0.4138 | 3000 | 28.34 | 49.15 | 1.1628 | 65.6461 | | 0.9146 | 0.4276 | 3100 | 25.81 | 48.42 | 1.1703 | 71.7244 | | 0.9764 | 0.4414 | 3200 | 29.63 | 51.22 | 1.1526 | 67.3570 | | 0.9455 | 0.4552 | 3300 | 25.31 | 49.73 | 1.1108 | 72.6249 | | 0.9073 | 0.4690 | 3400 | 27.7 | 50.85 | 1.1085 | 72.7150 | | 0.8596 | 0.4828 | 3500 | 28.34 | 52.39 | 1.0927 | 67.9424 | | 0.8241 | 0.4966 | 3600 | 29.95 | 51.37 | 1.1026 | 65.2859 | | 0.8436 | 0.5103 | 3700 | 27.18 | 51.45 | 1.0718 | 71.2292 | | 0.8318 | 0.5241 | 3800 | 30.71 | 53.35 | 1.0678 | 64.3404 | | 0.8262 | 0.5379 | 3900 | 27.05 | 51.94 | 1.0534 | 71.5894 | | 0.8129 | 0.5517 | 4000 | 27.38 | 51.97 | 1.0491 | 72.1747 | | 0.9036 | 0.5655 | 4100 | 14.43 | 40.57 | 1.2250 | 139.3066 | | 1.0314 | 0.5793 | 4200 | 24.27 | 46.97 | 1.2310 | 75.5966 | | 0.9209 | 0.5931 | 4300 | 23.55 | 46.04 | 1.2447 | 76.4070 | | 0.9204 | 0.6069 | 4400 | 25.87 | 45.32 | 1.2891 | 73.0302 | | 0.9843 | 0.6207 | 4500 | 27.2 | 46.36 | 1.2269 | 71.8145 | | 1.0225 | 0.6345 | 4600 | 26.16 | 45.72 | 1.2403 | 69.6983 | | 0.9773 | 0.6483 | 4700 | 26.37 | 45.62 | 1.2464 | 68.4376 | | 0.9794 | 0.6621 | 4800 | 24.77 | 47.11 | 1.2461 | 72.0846 | | 0.8905 | 0.6759 | 4900 | 24.58 | 46.35 | 1.2345 | 71.2742 | | 0.8305 | 0.6897 | 5000 | 27.28 | 48.37 | 1.2239 | 68.1675 | | 0.9019 | 0.7034 | 5100 | 27.04 | 50.28 | 1.1730 | 70.1486 | | 0.7969 | 0.7172 | 5200 | 26.27 | 48.07 | 1.1807 | 69.0230 | | 0.8036 | 0.7310 | 5300 | 23.04 | 48.3 | 1.1632 | 77.5326 | | 0.8195 | 0.7448 | 5400 | 25.58 | 50.29 | 1.1811 | 76.2269 | | 0.7697 | 0.7586 | 5500 | 23.99 | 48.91 | 1.1825 | 81.4948 | | 0.727 | 0.7724 | 5600 | 23.93 | 49.23 | 1.1623 | 79.5137 | | 0.8002 | 0.7862 | 5700 | 26.29 | 50.44 | 1.1503 | 75.6866 | | 0.6909 | 0.8 | 5800 | 29.27 | 50.85 | 1.1338 | 64.0252 | | 0.7146 | 0.8138 | 5900 | 28.24 | 50.82 | 1.1420 | 66.6367 | | 0.7452 | 0.8276 | 6000 | 31.33 | 51.92 | 1.1328 | 62.4944 | | 0.5989 | 0.8414 | 6100 | 31.1 | 52.15 | 1.1455 | 65.1959 | | 0.6818 | 0.8552 | 6200 | 32.56 | 52.46 | 1.1112 | 62.1342 | | 0.6074 | 0.8690 | 6300 | 33.48 | 53.32 | 1.1072 | 60.6033 | | 0.5942 | 0.8828 | 6400 | 31.39 | 51.03 | 1.1462 | 62.8546 | | 0.6341 | 0.8966 | 6500 | 31.55 | 52.15 | 1.1093 | 62.4043 | | 0.5992 | 0.9103 | 6600 | 33.06 | 52.52 | 1.1215 | 61.4588 | | 0.6156 | 0.9241 | 6700 | 32.38 | 52.76 | 1.1031 | 62.9446 | | 0.6169 | 0.9379 | 6800 | 31.46 | 52.96 | 1.1082 | 64.3404 | | 0.6543 | 0.9517 | 6900 | 33.49 | 54.02 | 1.0943 | 63.1247 | | 0.5017 | 0.9655 | 7000 | 30.95 | 52.64 | 1.1141 | 68.6177 | | 0.5583 | 0.9793 | 7100 | 34.39 | 54.03 | 1.1004 | 61.6839 | | 0.5986 | 0.9931 | 7200 | 33.92 | 52.85 | 1.1055 | 62.4944 | | 0.2443 | 1.0069 | 7300 | 34.86 | 53.01 | 1.1442 | 60.1981 | | 0.254 | 1.0207 | 7400 | 33.92 | 53.25 | 1.1458 | 62.1792 | | 0.2827 | 1.0345 | 7500 | 34.49 | 53.43 | 1.1190 | 60.6484 | | 0.2326 | 1.0483 | 7600 | 35.47 | 53.53 | 1.1237 | 59.2076 | | 0.2017 | 1.0621 | 7700 | 34.65 | 53.87 | 1.1179 | 60.0180 | | 0.2367 | 1.0759 | 7800 | 34.23 | 53.67 | 1.1075 | 60.6484 | | 0.2276 | 1.0897 | 7900 | 34.67 | 54.51 | 1.1063 | 60.3332 | | 0.2087 | 1.1034 | 8000 | 34.44 | 54.07 | 1.1090 | 60.6484 | | 0.2514 | 1.1172 | 8100 | 1.1199| 29.85 | 51.91 | 69.6083 | | 0.2692 | 1.1310 | 8200 | 1.1642| 28.05 | 51.94 | 72.1747 | | 0.2784 | 1.1448 | 8300 | 1.1262| 27.26 | 50.77 | 74.8312 | | 0.2539 | 1.1586 | 8400 | 1.1463| 30.7 | 53.1 | 65.0158 | | 0.2599 | 1.1724 | 8500 | 1.1255| 31.64 | 53.71 | 63.2148 | | 0.2419 | 1.1862 | 8600 | 1.1223| 33.2 | 54.15 | 62.4043 | | 0.2583 | 1.2 | 8700 | 1.1304| 33.98 | 53.65 | 61.2787 | | 0.239 | 1.2138 | 8800 | 1.1371| 34.68 | 54.35 | 61.7740 | | 0.2198 | 1.2276 | 8900 | 1.1533| 30.65 | 52.15 | 72.2647 | | 0.248 | 1.2414 | 9000 | 1.1266| 31.98 | 53.68 | 65.4210 | | 0.2377 | 1.2552 | 9100 | 1.1510| 30.9 | 53.6 | 67.9424 | | 0.2183 | 1.2690 | 9200 | 1.1565| 30.35 | 53.04 | 73.1202 | | 0.1999 | 1.2828 | 9300 | 1.1426| 29.48 | 53.0 | 74.2909 | | 0.22 | 1.2966 | 9400 | 1.1332| 31.93 | 53.16 | 66.1414 | | 0.2063 | 1.3103 | 9500 | 1.1144| 32.42 | 53.79 | 63.3949 | | 0.2054 | 1.3241 | 9600 | 1.1146| 33.64 | 54.69 | 61.5038 | | 0.2145 | 1.3379 | 9700 | 1.1123| 36.68 | 55.64 | 57.5867 | | 0.2059 | 1.3517 | 9800 | 1.1102| 36.93 | 56.15 | 57.5416 | | 0.2001 | 1.3655 | 9900 | 1.1143| 36.4 | 56.09 | 57.9469 | | 0.1973 | 1.3793 | 10000 | 1.1121| 36.46 | 55.74 | 58.2620 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1