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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 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, augmented |
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with noise |
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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: 32.01 |
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- name: Wer |
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type: wer |
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value: 62.76452048626745 |
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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-medium](https://huggingface.co./openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia, augmented with noise dataset. |
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The datasets are augmented in two ways: noise augmentation, and truncating low-magnitude samples. |
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The best model checkpoint (this version) based on ChrF is at step 2900, epoch 0.6349, |
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and it achieves the following results on the evaluation set: |
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- Loss: 1.1883 |
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- Bleu: 32.88 |
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- Chrf: 51.52 |
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- Wer: 62.0441 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 0.02 |
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- training_steps: 3000 |
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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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| 2.4487 | 0.0219 | 100 | 1.9518 | 8.34 | 24.49 | 117.2445 | |
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| 2.11 | 0.0438 | 200 | 1.6630 | 15.32 | 32.12 | 84.0612 | |
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| 1.9757 | 0.0657 | 300 | 1.5366 | 10.86 | 33.42 | 131.7875 | |
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| 1.7964 | 0.0876 | 400 | 1.4825 | 19.81 | 36.71 | 81.9451 | |
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| 1.6422 | 0.1095 | 500 | 1.4432 | 18.83 | 40.4 | 84.0162 | |
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| 1.3839 | 0.1314 | 600 | 1.4169 | 24.91 | 40.87 | 69.0230 | |
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| 1.352 | 0.1533 | 700 | 1.4340 | 25.01 | 41.57 | 71.5894 | |
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| 1.2434 | 0.1752 | 800 | 1.3813 | 24.05 | 41.29 | 73.7506 | |
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| 1.2223 | 0.1970 | 900 | 1.3578 | 25.89 | 41.61 | 70.5988 | |
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| 1.0414 | 0.2189 | 1000 | 1.3075 | 27.45 | 44.17 | 68.2575 | |
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| 0.9199 | 0.2408 | 1100 | 1.3022 | 23.14 | 44.3 | 84.1513 | |
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| 0.8648 | 0.2627 | 1200 | 1.3050 | 23.36 | 43.37 | 72.4448 | |
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| 0.8469 | 0.2846 | 1300 | 1.2853 | 28.37 | 45.97 | 67.1319 | |
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| 0.7649 | 0.3065 | 1400 | 1.2755 | 28.56 | 46.76 | 66.0964 | |
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| 0.7321 | 0.3284 | 1500 | 1.2750 | 27.23 | 46.1 | 69.3381 | |
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| 0.6541 | 0.3503 | 1600 | 1.2557 | 30.02 | 48.06 | 65.6011 | |
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| 0.6107 | 0.3722 | 1700 | 1.2520 | 30.41 | 49.23 | 64.2954 | |
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| 0.5738 | 0.3941 | 1800 | 1.2435 | 32.45 | 50.27 | 63.4399 | |
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| 0.4983 | 0.4160 | 1900 | 1.2007 | 31.17 | 48.58 | 64.0702 | |
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| 0.4439 | 0.4379 | 2000 | 1.2140 | 32.29 | 50.37 | 60.6033 | |
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| 0.367 | 0.4598 | 2100 | 1.2230 | 29.54 | 49.14 | 67.7172 | |
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| 0.2807 | 0.4817 | 2200 | 1.2277 | 33.1 | 51.21 | 62.9446 | |
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| 0.2621 | 0.5036 | 2300 | 1.2441 | 30.59 | 49.49 | 64.8807 | |
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| 0.2965 | 0.5255 | 2400 | 1.1969 | 31.82 | 49.67 | 63.5299 | |
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| 0.236 | 0.5473 | 2500 | 1.2275 | 31.17 | 50.29 | 65.1959 | |
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| 0.229 | 0.5692 | 2600 | 1.2008 | 30.02 | 50.27 | 70.6439 | |
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| 0.164 | 0.5911 | 2700 | 1.2192 | 31.37 | 50.57 | 63.6200 | |
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| 0.1786 | 0.6130 | 2800 | 1.1965 | 31.81 | 50.13 | 62.8546 | |
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| 0.1987 | 0.6349 | 2900 | 1.1883 | 32.88 | 51.52 | 62.0441 | |
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| 0.1633 | 0.6568 | 3000 | 1.1903 | 32.01 | 50.38 | 62.7645 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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