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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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- chrf |
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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 |
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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: 26.85 |
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- name: Wer |
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type: wer |
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value: 73.52543899144528 |
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library_name: transformers |
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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 datasets. |
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The best model (this version) is at checkpoint 3400, epoch 3.67, and it achieves the following results on the evaluation set: |
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- Loss: 1.5752 |
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- Bleu: 30.77 |
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- Chrf: 46.43 |
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- Wer: 64.6556 |
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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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### Experiment |
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- language=English |
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- +more steps |
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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: 32 |
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- eval_batch_size: 32 |
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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_steps: 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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| 2.4954 | 0.11 | 100 | 3.7 | 18.03 | 2.1286 | 179.7839 | |
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| 2.045 | 0.22 | 200 | 12.65 | 25.53 | 1.8146 | 100.9005 | |
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| 1.7928 | 0.32 | 300 | 13.78 | 30.2 | 1.7253 | 101.9811 | |
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| 1.6615 | 0.43 | 400 | 15.8 | 31.88 | 1.6834 | 92.5259 | |
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| 1.4491 | 0.54 | 500 | 15.61 | 36.27 | 1.5971 | 107.3841 | |
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| 1.2074 | 0.65 | 600 | 19.92 | 36.31 | 1.5939 | 84.3314 | |
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| 1.2308 | 0.76 | 700 | 20.37 | 38.72 | 1.5234 | 84.8267 | |
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| 1.107 | 0.86 | 800 | 21.35 | 37.87 | 1.5460 | 82.8906 | |
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| 0.9491 | 0.97 | 900 | 21.06 | 40.74 | 1.5161 | 82.5754 | |
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| 0.384 | 1.08 | 1000 | 23.24 | 41.98 | 1.4927 | 82.2152 | |
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| 0.362 | 1.19 | 1100 | 23.19 | 42.24 | 1.5567 | 80.2792 | |
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| 0.3756 | 1.29 | 1200 | 27.83 | 43.8 | 1.5265 | 69.2481 | |
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| 0.3401 | 1.4 | 1300 | 21.79 | 41.66 | 1.5522 | 92.3908 | |
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| 0.3346 | 1.51 | 1400 | 24.61 | 42.15 | 1.5085 | 75.4615 | |
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| 0.3101 | 1.62 | 1500 | 26.67 | 43.41 | 1.4933 | 70.7789 | |
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| 0.3231 | 1.73 | 1600 | 27.95 | 42.82 | 1.4979 | 68.3026 | |
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| 0.2665 | 1.83 | 1700 | 28.5 | 43.76 | 1.4977 | 68.1225 | |
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| 0.2704 | 1.94 | 1800 | 28.15 | 43.87 | 1.5063 | 68.8429 | |
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| 0.0769 | 2.05 | 1900 | 25.76 | 43.22 | 1.5162 | 77.6227 | |
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| 0.0597 | 2.16 | 2000 | 25.04 | 43.15 | 1.5216 | 79.0635 | |
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| 0.0743 | 2.27 | 2100 | 27.85 | 44.43 | 1.5313 | 68.3926 | |
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| 0.0878 | 2.37 | 2200 | 27.54 | 43.96 | 1.5495 | 68.3476 | |
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| 0.0712 | 2.48 | 2300 | 28.28 | 44.39 | 1.5355 | 65.8712 | |
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| 0.0789 | 2.59 | 2400 | 28.64 | 44.75 | 1.5277 | 65.7812 | |
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| 0.073 | 2.7 | 2500 | 29.09 | 44.65 | 1.5327 | 65.7812 | |
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| 0.073 | 2.8 | 2600 | 25.26 | 43.44 | 1.5304 | 78.2981 | |
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| 0.0697 | 2.91 | 2700 | 25.71 | 43.02 | 1.5460 | 78.4782 | |
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| 0.0398 | 3.02 | 2800 | 28.26 | 44.71 | 1.5580 | 72.8501 | |
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| 0.0302 | 3.13 | 2900 | 30.25 | 45.46 | 1.5688 | 66.1414 | |
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| 0.0424 | 3.24 | 3000 | 29.88 | 45.21 | 1.5693 | 66.0964 | |
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| 0.0397 | 3.34 | 3100 | 30.01 | 45.85 | 1.5934 | 65.6911 | |
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| 0.0346 | 3.45 | 3200 | 30.2 | 45.8 | 1.5818 | 65.8262 | |
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| 0.032 | 3.56 | 3300 | 29.81 | 46.5 | 1.5823 | 66.7267 | |
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| 0.0348 | 3.67 | 3400 | 30.77 | 46.43 | 1.5752 | 64.6556 | |
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| 0.0277 | 3.78 | 3500 | 30.3 | 46.02 | 1.5791 | 64.6105 | |
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| 0.0364 | 3.88 | 3600 | 29.92 | 45.38 | 1.5895 | 65.0608 | |
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| 0.0398 | 3.99 | 3700 | 27.79 | 44.59 | 1.6167 | 68.2575 | |
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| 0.0152 | 4.1 | 3800 | 28.42 | 44.83 | 1.6241 | 67.5822 | |
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| 0.0201 | 4.21 | 3900 | 29.02 | 45.11 | 1.6243 | 67.4921 | |
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| 0.0168 | 4.31 | 4000 | 26.85 | 44.41 | 1.6195 | 73.5254 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
