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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: 28.6 |
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- name: Wer |
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type: wer |
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value: 68.52769022962629 |
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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: 1.1842 |
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- Bleu: 28.6 |
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- Chrf: 49.54 |
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- Wer: 68.5277 |
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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 | Bleu | Chrf | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.5585 | 0.0109 | 100 | 2.94 | 16.53 | 2.1737 | 176.2269 | |
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| 2.5748 | 0.0219 | 200 | 6.93 | 24.84 | 2.0289 | 101.8460 | |
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| 2.554 | 0.0328 | 300 | 7.16 | 25.31 | 1.8861 | 142.4584 | |
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| 2.276 | 0.0438 | 400 | 8.9 | 27.72 | 1.8568 | 119.7208 | |
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| 2.3077 | 0.0547 | 500 | 14.51 | 32.58 | 1.7492 | 88.3836 | |
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| 2.0852 | 0.0657 | 600 | 16.71 | 34.1 | 1.6548 | 83.6560 | |
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| 2.1602 | 0.0766 | 700 | 14.93 | 35.2 | 1.6063 | 106.4385 | |
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| 1.9556 | 0.0876 | 800 | 20.64 | 36.74 | 1.6190 | 77.5777 | |
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| 1.7516 | 0.0985 | 900 | 15.44 | 36.67 | 1.5614 | 95.0023 | |
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| 1.7502 | 0.1095 | 1000 | 20.65 | 38.42 | 1.5317 | 81.4948 | |
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| 1.6851 | 0.1204 | 1100 | 19.13 | 37.91 | 1.5289 | 87.7533 | |
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| 1.5154 | 0.1314 | 1200 | 19.79 | 41.21 | 1.4906 | 83.3408 | |
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| 1.3658 | 0.1423 | 1300 | 19.58 | 39.16 | 1.4623 | 96.3980 | |
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| 1.3828 | 0.1533 | 1400 | 22.84 | 42.83 | 1.4069 | 77.5777 | |
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| 1.5339 | 0.1642 | 1500 | 20.91 | 41.62 | 1.3909 | 86.5376 | |
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| 1.2441 | 0.1752 | 1600 | 23.35 | 43.43 | 1.3726 | 75.5966 | |
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| 1.1607 | 0.1861 | 1700 | 20.4 | 42.41 | 1.3471 | 85.4120 | |
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| 1.1043 | 0.1970 | 1800 | 21.13 | 43.4 | 1.3332 | 81.5849 | |
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| 1.0698 | 0.2080 | 1900 | 23.84 | 44.54 | 1.3413 | 73.3904 | |
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| 1.0698 | 0.2189 | 2000 | 28.34 | 47.2 | 1.2848 | 66.9068 | |
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| 1.053 | 0.2299 | 2100 | 25.19 | 46.75 | 1.2951 | 73.1652 | |
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| 0.9139 | 0.2408 | 2200 | 28.43 | 47.11 | 1.2852 | 70.7789 | |
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| 0.742 | 0.2518 | 2300 | 30.5 | 47.62 | 1.2580 | 63.6200 | |
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| 0.8627 | 0.2627 | 2400 | 29.97 | 48.38 | 1.2308 | 66.2314 | |
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| 0.7213 | 0.2737 | 2500 | 22.96 | 46.55 | 1.2176 | 83.7010 | |
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| 0.672 | 0.2846 | 2600 | 27.35 | 48.02 | 1.2272 | 71.7695 | |
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| 0.784 | 0.2956 | 2700 | 31.16 | 50.83 | 1.2010 | 65.3760 | |
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| 0.6463 | 0.3065 | 2800 | 30.67 | 51.24 | 1.1884 | 64.9257 | |
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| 0.6028 | 0.3175 | 2900 | 32.07 | 51.3 | 1.1866 | 61.4588 | |
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| 0.6494 | 0.3284 | 3000 | 32.04 | 50.96 | 1.1768 | 63.3048 | |
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| 0.657 | 0.3394 | 3100 | 1.2126| 30.55 | 50.18 | 66.0964 | |
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| 0.6239 | 0.3503 | 3200 | 1.1836| 33.69 | 52.06 | 60.2431 | |
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| 0.63 | 0.3612 | 3300 | 1.2201| 32.14 | 51.62 | 61.7290 | |
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| 0.5155 | 0.3722 | 3400 | 1.1956| 32.62 | 51.99 | 61.3688 | |
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| 0.5392 | 0.3831 | 3500 | 1.2010| 31.13 | 51.37 | 63.9802 | |
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| 0.5159 | 0.3941 | 3600 | 1.1831| 32.2 | 51.81 | 62.4043 | |
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| 0.4535 | 0.4050 | 3700 | 1.1744| 31.61 | 51.77 | 63.3949 | |
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| 0.3346 | 0.4160 | 3800 | 1.2066| 30.67 | 50.21 | 65.4660 | |
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| 0.3991 | 0.4269 | 3900 | 1.1870| 30.7 | 50.88 | 65.2409 | |
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| 0.395 | 0.4379 | 4000 | 1.1842| 28.6 | 49.54 | 68.5277 | |
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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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