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Whisper Medium GA-EN Speech Translation

This model is a fine-tuned version of 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: 1.2038
  • Bleu: 34.85
  • Chrf: 54.43
  • Wer: 60.9185

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: 15000
  • 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 29.38 49.22 1.2218 66.0964
1.051 0.5793 4200 25.12 46.01 1.2304 71.8145
0.954 0.5931 4300 25.47 45.88 1.2501 75.3715
0.939 0.6069 4400 29.19 47.63 1.2204 66.9068
0.9887 0.6207 4500 27.99 47.01 1.2099 67.7172
1.0044 0.6345 4600 23.77 45.33 1.2080 73.3904
0.9881 0.6483 4700 26.46 47.36 1.2188 68.5277
0.9674 0.6621 4800 26.11 45.92 1.2296 68.3026
0.8845 0.6759 4900 27.3 46.08 1.2347 68.0324
0.8297 0.6897 5000 29.48 48.96 1.2108 64.6105
0.9065 0.7034 5100 29.81 49.94 1.1873 64.2503
0.8096 0.7172 5200 28.5 46.93 1.2122 66.2314
0.8077 0.7310 5300 29.26 48.21 1.1945 64.4755
0.8227 0.7448 5400 26.82 48.43 1.2310 71.4093
0.7587 0.7586 5500 29.45 49.03 1.2067 65.3309
0.7206 0.7724 5600 29.89 49.33 1.2114 65.5561
0.8088 0.7862 5700 31.88 51.4 1.1689 64.2954
0.693 0.8 5800 27.23 48.11 1.1644 68.7078
0.7099 0.8138 5900 31.01 49.42 1.1852 63.3949
0.7564 0.8276 6000 28.3 50.34 1.1554 71.0941
0.584 0.8414 6100 34.79 51.69 1.1566 59.0725
0.6817 0.8552 6200 34.08 51.95 1.1245 59.8829
0.5968 0.8690 6300 32.4 51.59 1.1475 62.9896
0.6092 0.8828 6400 32.83 52.82 1.1250 62.5844
0.6325 0.8966 6500 29.29 51.68 1.1108 69.1130
0.6002 0.9103 6600 27.64 52.7 1.0993 71.0941
0.6247 0.9241 6700 28.39 52.4 1.0898 68.3026
0.6257 0.9379 6800 28.54 52.33 1.0863 70.9140
0.6719 0.9517 6900 31.43 53.53 1.0891 66.1414
0.4994 0.9655 7000 33.81 52.77 1.1066 61.0986
0.5469 0.9793 7100 30.52 53.13 1.0891 67.3570
0.6031 0.9931 7200 33.16 54.03 1.0933 62.1792
0.2469 1.0069 7300 33.76 52.38 1.1426 62.8546
0.2572 1.0207 7400 33.16 51.71 1.1292 64.8807
0.2762 1.0345 7500 34.76 54.28 1.1090 60.7384
0.2332 1.0483 7600 30.95 52.28 1.1073 66.1864
0.2069 1.0621 7700 32.39 53.08 1.0999 65.5561
0.2417 1.0759 7800 31.3 53.87 1.1008 65.1058
0.2403 1.0897 7900 32.18 53.3 1.1053 66.4566
0.208 1.1034 8000 32.0 52.48 1.1067 66.7717
0.3328 1.1172 8100 28.92 49.12 1.2137 68.4376
0.4045 1.1310 8200 28.47 51.53 1.2165 68.3926
0.4175 1.1448 8300 26.88 47.57 1.2790 74.5160
0.3976 1.1586 8400 21.56 44.64 1.3060 84.1513
0.4026 1.1724 8500 25.22 47.73 1.2476 73.1202
0.4088 1.1862 8600 26.03 48.08 1.2387 72.8050
0.4245 1.2 8700 29.8 49.69 1.2136 67.4021
0.4083 1.2138 8800 26.26 48.23 1.2784 73.4804
0.3832 1.2276 8900 29.06 49.36 1.2527 66.4115
0.4335 1.2414 9000 30.11 49.24 1.2772 67.2670
0.4056 1.2552 9100 32.51 50.18 1.3013 63.3048
0.3877 1.2690 9200 26.91 47.47 1.2897 71.5894
0.3787 1.2828 9300 1.2430 30.16 50.61 65.1058
0.3947 1.2966 9400 1.2318 29.9 50.77 66.0964
0.3908 1.3103 9500 1.1927 30.7 51.62 64.6105
0.405 1.3241 9600 1.2249 26.56 49.05 71.7695
0.3847 1.3379 9700 1.2105 33.22 51.98 61.8640
0.3674 1.3517 9800 1.2545 30.93 50.34 65.6011
0.3642 1.3655 9900 1.2443 25.23 47.97 77.9379
0.3636 1.3793 10000 1.2796 26.78 48.07 73.6155
0.329 1.3931 10100 1.2373 29.06 49.55 66.4566
0.4195 1.4069 10200 1.2187 29.11 50.65 66.2314
0.4244 1.4207 10300 1.2346 27.97 49.86 69.0680
0.3338 1.4345 10400 1.2239 29.96 50.45 66.0063
0.3401 1.4483 10500 1.2501 29.84 51.0 65.6911
0.3792 1.4621 10600 1.2353 28.38 49.19 69.1130
0.3549 1.4759 10700 1.2178 28.63 49.73 68.5727
0.3326 1.4897 10800 1.1936 29.57 51.1 64.4755
0.3418 1.5034 10900 1.1741 33.06 52.86 60.9185
0.3143 1.5172 11000 1.2046 31.49 50.4 63.5750
0.3245 1.5310 11100 1.2145 30.9 50.17 64.6105
0.3268 1.5448 11200 1.2119 33.5 53.0 60.2431
0.2894 1.5586 11300 1.2126 32.01 52.17 61.0986
0.2702 1.5724 11400 1.2213 31.33 50.89 63.7551
0.2876 1.5862 11500 1.2126 31.44 51.28 63.1697
0.2759 1.6 11600 1.2283 30.49 51.02 64.7456
0.2902 1.6138 11700 1.2205 32.33 50.53 63.2148
0.2638 1.6276 11800 1.2097 31.89 51.14 62.6745
0.2605 1.6414 11900 1.2129 31.35 50.63 63.3048
0.2374 1.6552 12000 1.2319 31.48 51.73 63.4849
0.2436 1.6690 12100 1.2219 30.43 50.92 65.5110
0.2366 1.6828 12200 1.2367 31.64 51.14 64.7006
0.218 1.6966 12300 1.2142 30.8 51.63 64.1153
0.2313 1.7103 12400 1.1877 30.8 50.63 64.5655
0.2307 1.7241 12500 1.1817 32.22 51.41 63.3498
0.2638 1.7379 12600 1.1514 33.74 52.11 60.6033
0.2211 1.7517 12700 1.1563 30.71 52.07 64.5655
0.197 1.7655 12800 1.1941 32.22 52.9 62.8546
0.2307 1.7793 12900 1.1771 32.83 52.96 62.7645
0.198 1.7931 13000 1.1908 32.16 51.85 63.9352
0.1716 1.8069 13100 1.2065 31.91 51.37 62.6294
0.2031 1.8207 13200 1.1745 31.83 51.86 64.0252
0.1785 1.8345 13300 1.1607 31.33 52.57 64.7006
0.2013 1.8483 13400 1.1785 33.29 53.34 62.6745
0.1842 1.8621 13500 1.1723 34.41 54.31 60.0630
0.2015 1.8759 13600 1.1859 32.88 53.07 62.2692
0.1848 1.8897 13700 1.1668 33.62 53.75 62.8095
0.1394 1.9034 13800 1.1734 34.33 54.03 61.2787
0.1774 1.9172 13900 1.1735 32.63 53.37 62.8996
0.1506 1.9310 14000 1.1768 35.17 54.34 59.4327
0.1399 1.9448 14100 1.1827 33.68 53.8 62.1792
0.1434 1.9586 14200 1.1721 34.62 54.24 60.9185
0.1203 1.9724 14300 1.1733 34.08 53.75 61.8190
0.1417 1.9862 14400 1.1615 33.98 54.19 62.1792
0.1458 2.0 14500 1.1739 33.65 53.31 62.9896
0.07 2.0138 14600 1.1916 33.98 53.96 61.9090
0.051 2.0276 14700 1.1967 34.13 54.36 61.1887
0.0481 2.0414 14800 1.2024 34.06 54.38 61.4588
0.0574 2.0552 14900 1.2038 34.23 54.08 61.2787
0.0621 2.0690 15000 1.2038 34.85 54.43 60.9185

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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Evaluation results

  • Bleu on IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop
    self-reported
    34.850
  • Wer on IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop
    self-reported
    60.919