|
--- |
|
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: 34.85 |
|
- name: Wer |
|
type: wer |
|
value: 60.91850517784781 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# 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: 1.2038 |
|
- 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 |
|
|
