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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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- ymoslem/EUbookshop-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, Wikimedia, and EUbookshop |
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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: 27.38 |
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- name: Wer |
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type: wer |
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value: 72.17469608284557 |
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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, Wikimedia, and EUbookshop dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0491 |
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- Bleu: 27.38 |
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- Chrf: 51.97 |
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- Wer: 72.1747 |
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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: 0.02 |
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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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| 2.6534 | 0.0138 | 100 | 2.2446 | 1.43 | 15.99 | 269.1130 | |
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| 2.4519 | 0.0276 | 200 | 2.1941 | 2.13 | 18.36 | 250.5178 | |
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| 2.2928 | 0.0414 | 300 | 2.0086 | 7.14 | 25.95 | 128.3656 | |
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| 2.233 | 0.0552 | 400 | 2.0239 | 5.61 | 24.25 | 134.0837 | |
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| 2.0406 | 0.0690 | 500 | 1.9215 | 5.64 | 25.65 | 183.8361 | |
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| 2.0273 | 0.0828 | 600 | 1.8556 | 13.41 | 30.96 | 83.7010 | |
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| 1.895 | 0.0966 | 700 | 1.8278 | 7.02 | 26.82 | 158.2170 | |
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| 1.9889 | 0.1103 | 800 | 1.7842 | 12.22 | 31.62 | 99.6398 | |
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| 1.8484 | 0.1241 | 900 | 1.7648 | 10.97 | 30.45 | 91.1751 | |
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| 1.7491 | 0.1379 | 1000 | 1.7498 | 10.0 | 29.42 | 109.0050 | |
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| 1.699 | 0.1517 | 1100 | 1.6662 | 12.53 | 34.87 | 109.9054 | |
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| 1.6959 | 0.1655 | 1200 | 1.6287 | 14.54 | 34.8 | 92.3008 | |
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| 1.6682 | 0.1793 | 1300 | 1.5800 | 13.26 | 33.5 | 103.0617 | |
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| 1.6625 | 0.1931 | 1400 | 1.6115 | 19.71 | 37.33 | 75.9118 | |
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| 1.5462 | 0.2069 | 1500 | 1.4993 | 18.3 | 39.49 | 93.7866 | |
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| 1.3834 | 0.2207 | 1600 | 1.4906 | 20.32 | 40.87 | 79.2436 | |
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| 1.39 | 0.2345 | 1700 | 1.4752 | 17.3 | 38.16 | 93.1562 | |
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| 1.5061 | 0.2483 | 1800 | 1.4004 | 20.11 | 39.69 | 81.0446 | |
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| 1.4125 | 0.2621 | 1900 | 1.3854 | 23.82 | 42.67 | 73.3904 | |
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| 1.3181 | 0.2759 | 2000 | 1.3979 | 20.57 | 40.87 | 78.8384 | |
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| 1.283 | 0.2897 | 2100 | 1.3446 | 17.97 | 40.47 | 88.8789 | |
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| 1.2061 | 0.3034 | 2200 | 1.3130 | 25.12 | 45.42 | 73.5254 | |
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| 1.2091 | 0.3172 | 2300 | 1.3274 | 22.12 | 43.56 | 79.8739 | |
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| 1.1264 | 0.3310 | 2400 | 1.2771 | 22.94 | 45.96 | 78.2080 | |
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| 1.0972 | 0.3448 | 2500 | 1.2858 | 24.38 | 46.04 | 75.4615 | |
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| 1.0822 | 0.3586 | 2600 | 1.2376 | 27.39 | 48.34 | 67.6722 | |
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| 1.0316 | 0.3724 | 2700 | 1.2461 | 28.0 | 47.61 | 68.5277 | |
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| 1.165 | 0.3862 | 2800 | 1.1869 | 26.05 | 48.13 | 71.6794 | |
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| 1.025 | 0.4 | 2900 | 1.1716 | 27.14 | 47.91 | 68.7528 | |
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| 0.8978 | 0.4138 | 3000 | 1.1628 | 28.34 | 49.15 | 65.6461 | |
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| 0.9146 | 0.4276 | 3100 | 1.1703 | 25.81 | 48.42 | 71.7244 | |
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| 0.9764 | 0.4414 | 3200 | 1.1526 | 29.63 | 51.22 | 67.3570 | |
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| 0.9455 | 0.4552 | 3300 | 1.1108 | 25.31 | 49.73 | 72.6249 | |
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| 0.9073 | 0.4690 | 3400 | 1.1085 | 27.7 | 50.85 | 72.7150 | |
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| 0.8596 | 0.4828 | 3500 | 1.0927 | 28.34 | 52.39 | 67.9424 | |
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| 0.8241 | 0.4966 | 3600 | 1.1026 | 29.95 | 51.37 | 65.2859 | |
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| 0.8436 | 0.5103 | 3700 | 1.0718 | 27.18 | 51.45 | 71.2292 | |
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| 0.8318 | 0.5241 | 3800 | 1.0678 | 30.71 | 53.35 | 64.3404 | |
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| 0.8262 | 0.5379 | 3900 | 1.0534 | 27.05 | 51.94 | 71.5894 | |
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| 0.8129 | 0.5517 | 4000 | 1.0491 | 27.38 | 51.97 | 72.1747 | |
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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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