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--- |
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language: |
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- ga |
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- en |
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license: apache-2.0 |
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base_model: openai/whisper-medium |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ymoslem/IWSLT2023-GA-EN |
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- ymoslem/FLEURS-GA-EN |
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- ymoslem/BitesizeIrish-GA-EN |
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- ymoslem/SpokenWords-GA-EN-MTed |
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- ymoslem/Tatoeba-Speech-Irish |
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- ymoslem/Wikimedia-Speech-Irish |
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metrics: |
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- bleu |
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- wer |
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model-index: |
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- name: Whisper Small GA-EN Speech Translation |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia |
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type: ymoslem/IWSLT2023-GA-EN |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 33.77 |
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- name: Wer |
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type: wer |
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value: 60.828455650607836 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Small GA-EN Speech Translation |
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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, and Wikimedia dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2028 |
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- Bleu: 33.77 |
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- Chrf: 52.79 |
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- Wer: 60.8285 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.03 |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:| |
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| 2.4145 | 0.0109 | 100 | 2.1019 | 2.32 | 16.08 | 170.5088 | |
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| 2.6073 | 0.0219 | 200 | 2.0370 | 5.94 | 23.77 | 158.8924 | |
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| 2.593 | 0.0328 | 300 | 1.8529 | 3.67 | 21.53 | 238.6312 | |
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| 2.3123 | 0.0438 | 400 | 1.8500 | 9.01 | 28.13 | 135.0293 | |
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| 2.3347 | 0.0547 | 500 | 1.7816 | 15.05 | 31.9 | 90.7249 | |
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| 2.1277 | 0.0657 | 600 | 1.6916 | 14.24 | 32.29 | 88.4286 | |
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| 2.1836 | 0.0766 | 700 | 1.6517 | 12.15 | 32.7 | 128.1405 | |
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| 2.0112 | 0.0876 | 800 | 1.6275 | 19.76 | 38.15 | 79.2886 | |
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| 1.8387 | 0.0985 | 900 | 1.6349 | 17.26 | 38.82 | 91.0851 | |
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| 1.8335 | 0.1095 | 1000 | 1.5843 | 20.93 | 38.02 | 75.9118 | |
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| 1.7849 | 0.1204 | 1100 | 1.5863 | 15.98 | 37.5 | 96.5781 | |
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| 1.5698 | 0.1314 | 1200 | 1.5371 | 16.42 | 39.07 | 103.6020 | |
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| 1.4759 | 0.1423 | 1300 | 1.5250 | 18.56 | 38.41 | 96.5781 | |
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| 1.4915 | 0.1533 | 1400 | 1.4862 | 22.05 | 40.15 | 75.1013 | |
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| 1.6583 | 0.1642 | 1500 | 1.4727 | 18.11 | 39.65 | 95.7677 | |
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| 1.3981 | 0.1752 | 1600 | 1.4367 | 27.31 | 44.5 | 66.0513 | |
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| 1.2646 | 0.1861 | 1700 | 1.4574 | 22.85 | 42.19 | 74.4710 | |
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| 1.2172 | 0.1970 | 1800 | 1.3818 | 20.77 | 42.5 | 82.7105 | |
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| 1.183 | 0.2080 | 1900 | 1.4380 | 22.75 | 41.28 | 76.7672 | |
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| 1.1931 | 0.2189 | 2000 | 1.3917 | 23.58 | 41.13 | 77.3075 | |
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| 1.172 | 0.2299 | 2100 | 1.3892 | 24.58 | 44.4 | 74.3809 | |
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| 1.0284 | 0.2408 | 2200 | 1.3806 | 23.34 | 44.1 | 78.0279 | |
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| 0.8507 | 0.2518 | 2300 | 1.3210 | 28.67 | 46.79 | 67.1769 | |
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| 0.9615 | 0.2627 | 2400 | 1.3103 | 27.95 | 46.8 | 70.0135 | |
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| 0.8049 | 0.2737 | 2500 | 1.3141 | 29.92 | 48.9 | 67.2220 | |
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| 0.7639 | 0.2846 | 2600 | 1.3085 | 30.91 | 49.05 | 64.2053 | |
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| 0.8594 | 0.2956 | 2700 | 1.3378 | 27.8 | 47.84 | 68.8879 | |
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| 0.7482 | 0.3065 | 2800 | 1.2978 | 30.6 | 48.62 | 64.9257 | |
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| 0.6941 | 0.3175 | 2900 | 1.3060 | 29.92 | 47.92 | 65.8712 | |
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| 0.7282 | 0.3284 | 3000 | 1.2959 | 31.09 | 48.13 | 65.3309 | |
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| 0.6298 | 0.3394 | 3100 | 1.2893 | 29.76 | 48.8 | 67.1769 | |
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| 0.619 | 0.3503 | 3200 | 1.2388 | 32.61 | 50.27 | 62.0891 | |
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| 0.6252 | 0.3612 | 3300 | 1.2550 | 32.71 | 50.96 | 62.4493 | |
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| 0.4699 | 0.3722 | 3400 | 1.2463 | 32.02 | 51.24 | 65.2409 | |
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| 0.5121 | 0.3831 | 3500 | 1.2214 | 32.26 | 51.29 | 63.7551 | |
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| 0.5092 | 0.3941 | 3600 | 1.2182 | 32.88 | 51.59 | 62.0891 | |
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| 0.4365 | 0.4050 | 3700 | 1.2049 | 32.16 | 51.5 | 62.3143 | |
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| 0.2971 | 0.4160 | 3800 | 1.2201 | 34.45 | 52.78 | 59.7479 | |
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| 0.389 | 0.4269 | 3900 | 1.2007 | 33.86 | 53.28 | 60.6033 | |
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| 0.3879 | 0.4379 | 4000 | 1.2028 | 33.77 | 52.79 | 60.8285 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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