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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 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, 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: 30.91 |
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- name: Wer |
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type: wer |
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value: 65.10580819450698 |
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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, SpokenWords, Tatoeba, and Wikimedia dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2924 |
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- Bleu: 30.91 |
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- Chrf: 47.99 |
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- Wer: 65.1058 |
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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: 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: 0.02 |
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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 | Bleu | Chrf | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.7017 | 0.02 | 100 | 2.83 | 14.96 | 2.4392 | 169.5182 | |
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| 2.6732 | 0.04 | 200 | 7.27 | 22.72 | 1.9552 | 103.2868 | |
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| 2.1622 | 0.07 | 300 | 11.43 | 30.01 | 1.7297 | 108.2395 | |
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| 2.0314 | 0.09 | 400 | 12.96 | 31.0 | 1.6499 | 106.4385 | |
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| 1.7219 | 0.11 | 500 | 12.94 | 33.67 | 1.5543 | 107.6092 | |
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| 1.577 | 0.13 | 600 | 12.84 | 35.03 | 1.4812 | 118.5502 | |
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| 1.3569 | 0.1532 | 700 | 19.94 | 38.08 | 1.4559 | 84.2864 | |
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| 1.3401 | 0.1751 | 800 | 13.39 | 36.11 | 1.3855 | 126.4295 | |
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| 1.2272 | 0.1970 | 900 | 24.39 | 41.75 | 1.3764 | 70.7789 | |
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| 1.2793 | 0.2189 | 1000 | 23.01 | 42.13 | 1.3389 | 80.6844 | |
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| 1.0383 | 0.2408 | 1100 | 23.42 | 43.59 | 1.3125 | 82.3953 | |
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| 1.0485 | 0.2627 | 1200 | 25.42 | 42.99 | 1.2996 | 69.4732 | |
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| 1.0427 | 0.2846 | 1300 | 29.24 | 45.36 | 1.2996 | 65.6461 | |
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| 0.8174 | 0.3065 | 1400 | 27.28 | 45.67 | 1.2522 | 68.3926 | |
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| 0.7345 | 0.3284 | 1500 | 26.35 | 46.78 | 1.2349 | 79.1986 | |
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| 0.7551 | 0.3503 | 1600 | 27.81 | 46.49 | 1.2317 | 70.6439 | |
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| 0.6765 | 0.3722 | 1700 | 27.62 | 47.46 | 1.2062 | 70.9140 | |
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| 0.6613 | 0.3940 | 1800 | 26.56 | 47.12 | 1.2087 | 72.8050 | |
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| 0.6181 | 0.4159 | 1900 | 29.91 | 48.76 | 1.2139 | 65.2859 | |
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| 0.5809 | 0.4378 | 2000 | 30.93 | 49.09 | 1.2119 | 63.1247 | |
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| 0.5898 | 0.4597 | 2100 | 25.91 | 46.24 | 1.2540 | 73.9307 | |
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| 0.5926 | 0.4816 | 2200 | 25.19 | 44.72 | 1.2479 | 78.7933 | |
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| 0.5158 | 0.5035 | 2300 | 28.9 | 46.76 | 1.2532 | 66.3665 | |
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| 0.4511 | 0.5254 | 2400 | 28.89 | 46.83 | 1.2517 | 66.3215 | |
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| 0.4329 | 0.5473 | 2500 | 26.19 | 45.91 | 1.2573 | 72.6700 | |
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| 0.4106 | 0.5692 | 2600 | 26.91 | 46.84 | 1.2615 | 72.4899 | |
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| 0.4002 | 0.5911 | 2700 | 27.77 | 46.93 | 1.2396 | 71.0491 | |
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| 0.4047 | 0.6130 | 2800 | 29.9 | 47.79 | 1.2450 | 66.9968 | |
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| 0.3719 | 0.6349 | 2900 | 30.5 | 48.78 | 1.2522 | 65.1959 | |
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| 0.327 | 0.6567 | 3000 | 31.22 | 49.0 | 1.2493 | 64.1153 | |
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| 0.3138 | 0.6786 | 3100 | 1.2653| 30.1 | 47.82 | 65.1959 | |
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| 0.3349 | 0.7005 | 3200 | 1.2651| 30.37 | 48.64 | 63.9802 | |
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| 0.2807 | 0.7224 | 3300 | 1.2762| 26.02 | 45.46 | 76.8573 | |
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| 0.2648 | 0.7443 | 3400 | 1.2761| 30.65 | 47.58 | 64.6105 | |
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| 0.2633 | 0.7662 | 3500 | 1.2890| 29.73 | 47.74 | 65.5110 | |
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| 0.2316 | 0.7881 | 3600 | 1.2886| 29.94 | 47.33 | 66.4566 | |
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| 0.233 | 0.8100 | 3700 | 1.2905| 27.82 | 48.01 | 73.1202 | |
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| 0.2196 | 0.8319 | 3800 | 1.2994| 31.51 | 48.66 | 63.7100 | |
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| 0.2119 | 0.8538 | 3900 | 1.2910| 30.09 | 48.44 | 65.0158 | |
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| 0.2082 | 0.8757 | 4000 | 1.2924| 30.91 | 47.99 | 65.1058 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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