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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-small |
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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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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, and SpokenWords |
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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: 29.51 |
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- name: Wer |
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type: wer |
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value: 67.08689779378658 |
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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-small](https://huggingface.co/openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8231 |
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- Bleu: 29.51 |
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- Chrf: 44.29 |
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- Wer: 67.0869 |
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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: 64 |
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- eval_batch_size: 64 |
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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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- 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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| 1.9416 | 0.2155 | 100 | 13.09 | 26.48 | 1.7899 | 104.4575 | |
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| 1.5186 | 0.4310 | 200 | 18.6 | 35.75 | 1.5696 | 87.5732 | |
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| 1.2884 | 0.6466 | 300 | 17.57 | 37.19 | 1.4751 | 87.2580 | |
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| 1.0729 | 0.8621 | 400 | 17.92 | 38.23 | 1.4345 | 99.2346 | |
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| 0.4574 | 1.0776 | 500 | 22.48 | 39.17 | 1.5585 | 83.1607 | |
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| 0.4517 | 1.2931 | 600 | 22.53 | 38.38 | 1.5763 | 81.7650 | |
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| 0.4385 | 1.5086 | 700 | 20.05 | 39.46 | 1.5852 | 96.8483 | |
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| 0.3934 | 1.7241 | 800 | 26.89 | 42.67 | 1.5332 | 70.6889 | |
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| 0.3587 | 1.9397 | 900 | 28.95 | 44.16 | 1.5025 | 64.9707 | |
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| 0.1528 | 2.1552 | 1000 | 28.32 | 42.36 | 1.5882 | 65.8712 | |
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| 0.1425 | 2.3707 | 1100 | 25.5 | 42.42 | 1.6056 | 75.0113 | |
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| 0.1389 | 2.5862 | 1200 | 26.52 | 42.11 | 1.6236 | 70.6439 | |
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| 0.1532 | 2.8017 | 1300 | 25.78 | 41.61 | 1.6196 | 75.9118 | |
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| 0.1138 | 3.0172 | 1400 | 26.01 | 40.88 | 1.7185 | 69.6983 | |
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| 0.0661 | 3.2328 | 1500 | 28.74 | 43.16 | 1.6626 | 71.2292 | |
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| 0.0625 | 3.4483 | 1600 | 29.16 | 43.6 | 1.6835 | 66.3215 | |
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| 0.0615 | 3.6638 | 1700 | 28.93 | 44.08 | 1.6756 | 68.3476 | |
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| 0.0611 | 3.8793 | 1800 | 27.77 | 43.67 | 1.6648 | 72.1747 | |
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| 0.0344 | 4.0948 | 1900 | 28.33 | 44.18 | 1.7351 | 68.1225 | |
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| 0.0339 | 4.3103 | 2000 | 28.9 | 42.98 | 1.7715 | 67.0869 | |
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| 0.0369 | 4.5259 | 2100 | 29.83 | 44.87 | 1.7200 | 64.8807 | |
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| 0.0326 | 4.7414 | 2200 | 28.23 | 43.75 | 1.7232 | 69.3832 | |
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| 0.0346 | 4.9569 | 2300 | 27.72 | 43.1 | 1.7688 | 72.8050 | |
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| 0.0167 | 5.1724 | 2400 | 28.73 | 43.26 | 1.8072 | 67.4471 | |
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| 0.0146 | 5.3879 | 2500 | 29.91 | 44.24 | 1.7801 | 66.4566 | |
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| 0.0165 | 5.6034 | 2600 | 29.34 | 44.33 | 1.7782 | 68.2125 | |
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| 0.0143 | 5.8190 | 2700 | 27.78 | 43.07 | 1.7675 | 72.5799 | |
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| 0.0106 | 6.0345 | 2800 | 29.45 | 43.31 | 1.7660 | 67.5371 | |
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| 0.0098 | 6.25 | 2900 | 27.89 | 42.67 | 1.7803 | 71.6344 | |
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| 0.0087 | 6.4655 | 3000 | 27.66 | 43.04 | 1.7786 | 72.0396 | |
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| 0.0089 | 6.6810 | 3100 | 1.7661| 29.81 | 44.65 | 67.3120 | |
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| 0.0081 | 6.8966 | 3200 | 1.7744| 29.48 | 44.3 | 68.0324 | |
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| 0.0095 | 7.1121 | 3300 | 1.8197| 29.55 | 44.2 | 67.5371 | |
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| 0.0112 | 7.3276 | 3400 | 1.8102| 29.34 | 43.9 | 66.2765 | |
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| 0.0075 | 7.5431 | 3500 | 1.8004| 29.57 | 44.43 | 67.3570 | |
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| 0.0111 | 7.7586 | 3600 | 1.8015| 29.56 | 44.57 | 66.4566 | |
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| 0.009 | 7.9741 | 3700 | 1.8001| 29.7 | 45.24 | 66.6817 | |
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| 0.005 | 8.1897 | 3800 | 1.8184| 29.21 | 44.4 | 67.4471 | |
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| 0.0055 | 8.4052 | 3900 | 1.8222| 29.67 | 44.35 | 67.1319 | |
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| 0.0042 | 8.6207 | 4000 | 1.8231| 29.51 | 44.29 | 67.0869 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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