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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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- 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 Medium 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: 32.53 |
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- name: Wer |
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type: wer |
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value: 62.809545249887435 |
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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 Medium 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, SpokenWords, Tatoeba, and Wikimedia dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2087 |
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- Bleu: 32.53 |
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- Chrf: 52.88 |
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- Wer: 62.8095 |
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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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| 2.5164 | 0.0328 | 100 | 2.0060 | 2.56 | 17.46 | 162.9896 | |
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| 2.656 | 0.0657 | 200 | 2.0232 | 8.49 | 26.0 | 99.5498 | |
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| 2.5156 | 0.0985 | 300 | 1.9253 | 7.55 | 25.1 | 141.2877 | |
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| 2.4722 | 0.1314 | 400 | 1.8289 | 12.52 | 30.49 | 90.4548 | |
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| 2.3376 | 0.1642 | 500 | 1.6839 | 17.39 | 33.23 | 81.1796 | |
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| 2.1733 | 0.1970 | 600 | 1.7342 | 9.62 | 32.48 | 137.9559 | |
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| 2.3382 | 0.2299 | 700 | 1.6570 | 12.54 | 34.43 | 112.2467 | |
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| 2.0041 | 0.2627 | 800 | 1.6048 | 17.55 | 36.73 | 85.1418 | |
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| 2.1142 | 0.2956 | 900 | 1.6256 | 17.58 | 35.74 | 82.7105 | |
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| 2.024 | 0.3284 | 1000 | 1.5861 | 14.4 | 37.22 | 86.7177 | |
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| 1.7556 | 0.3612 | 1100 | 1.5415 | 17.21 | 38.88 | 84.5115 | |
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| 1.6904 | 0.3941 | 1200 | 1.4902 | 19.6 | 38.84 | 85.3670 | |
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| 1.674 | 0.4269 | 1300 | 1.4748 | 20.33 | 41.3 | 88.3836 | |
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| 1.6899 | 0.4598 | 1400 | 1.4479 | 22.74 | 43.25 | 80.9995 | |
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| 1.5234 | 0.4926 | 1500 | 1.3763 | 20.13 | 42.08 | 80.6844 | |
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| 1.364 | 0.5255 | 1600 | 1.4164 | 23.12 | 41.78 | 72.9851 | |
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| 1.5267 | 0.5583 | 1700 | 1.3855 | 19.94 | 41.63 | 91.7605 | |
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| 1.4282 | 0.5911 | 1800 | 1.3729 | 23.96 | 44.84 | 74.6961 | |
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| 1.3611 | 0.6240 | 1900 | 1.3562 | 23.1 | 45.41 | 81.8100 | |
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| 1.1396 | 0.6568 | 2000 | 1.3131 | 27.9 | 46.89 | 67.2670 | |
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| 1.1849 | 0.6897 | 2100 | 1.3483 | 24.38 | 45.25 | 75.8667 | |
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| 1.0871 | 0.7225 | 2200 | 1.2848 | 28.64 | 48.93 | 66.6817 | |
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| 1.1822 | 0.7553 | 2300 | 1.2782 | 28.41 | 47.25 | 68.6628 | |
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| 1.1272 | 0.7882 | 2400 | 1.2549 | 27.24 | 48.57 | 75.9568 | |
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| 1.0241 | 0.8210 | 2500 | 1.2922 | 25.74 | 47.44 | 74.4710 | |
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| 0.9629 | 0.8539 | 2600 | 1.3209 | 23.93 | 44.61 | 82.1252 | |
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| 0.8251 | 0.8867 | 2700 | 1.2273 | 32.21 | 51.64 | 65.5110 | |
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| 0.7921 | 0.9195 | 2800 | 1.2881 | 26.38 | 48.31 | 80.2792 | |
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| 0.8873 | 0.9524 | 2900 | 1.2268 | 26.57 | 50.09 | 77.1724 | |
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| 0.7967 | 0.9852 | 3000 | 1.2036 | 29.35 | 51.53 | 69.6533 | |
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| 0.3119 | 1.0181 | 3100 | 1.2231 | 31.77 | 51.57 | 62.3143 | |
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| 0.3009 | 1.0509 | 3200 | 1.2446 | 31.8 | 50.44 | 61.8190 | |
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| 0.2855 | 1.0837 | 3300 | 1.2240 | 30.48 | 50.86 | 66.7717 | |
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| 0.2535 | 1.1166 | 3400 | 1.2287 | 31.96 | 52.82 | 63.3949 | |
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| 0.2162 | 1.1494 | 3500 | 1.2398 | 33.91 | 52.17 | 61.3688 | |
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| 0.2307 | 1.1823 | 3600 | 1.2280 | 32.11 | 51.67 | 64.7456 | |
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| 0.2184 | 1.2151 | 3700 | 1.2149 | 34.59 | 53.32 | 59.9730 | |
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| 0.2365 | 1.2479 | 3800 | 1.2044 | 32.51 | 52.98 | 62.3593 | |
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| 0.1958 | 1.2808 | 3900 | 1.2116 | 32.45 | 52.86 | 63.1697 | |
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| 0.2081 | 1.3136 | 4000 | 1.2087 | 32.53 | 52.88 | 62.8095 | |
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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 2.20.0 |
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- Tokenizers 0.19.1 |
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