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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, normalized |
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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: 30.66 |
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- name: Wer |
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type: wer |
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value: 65.46600630346691 |
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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 as well as a copy of the dataset with noise reduction and normalization (for both train and test) dataset. |
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The datasets were processed with noise reduction and normalization (both the train and test splits). |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3339 |
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- Bleu: 30.66 |
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- Chrf: 46.99 |
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- Wer: 65.4660 |
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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: 0.01 |
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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 | Bleu | Chrf | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:| |
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| 1.41 | 0.07 | 100 | 9.78 | 25.23 | 1.8782 | 96.3980 | |
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| 1.2436 | 0.13 | 200 | 10.23 | 28.66 | 1.8301 | 125.9343 | |
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| 1.593 | 0.2 | 300 | 9.53 | 30.7 | 1.7066 | 137.1454 | |
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| 1.9589 | 0.26 | 400 | 12.08 | 32.94 | 1.5629 | 109.3652 | |
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| 1.8174 | 0.33 | 500 | 13.73 | 34.5 | 1.5154 | 123.5930 | |
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| 1.6775 | 0.39 | 600 | 15.8 | 35.68 | 1.5220 | 102.2062 | |
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| 1.7074 | 0.46 | 700 | 16.62 | 37.96 | 1.4570 | 100.5853 | |
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| 1.5793 | 0.53 | 800 | 24.5 | 39.91 | 1.4265 | 71.3643 | |
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| 1.3708 | 0.59 | 900 | 24.35 | 42.26 | 1.3845 | 73.7956 | |
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| 1.3217 | 0.66 | 1000 | 19.34 | 41.3 | 1.3662 | 87.7533 | |
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| 1.2572 | 0.72 | 1100 | 21.59 | 41.35 | 1.3529 | 88.4286 | |
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| 1.1447 | 0.79 | 1200 | 28.39 | 44.99 | 1.3228 | 65.9163 | |
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| 1.1544 | 0.85 | 1300 | 23.69 | 43.07 | 1.2972 | 80.1891 | |
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| 1.0291 | 0.92 | 1400 | 29.36 | 45.45 | 1.2828 | 70.9590 | |
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| 0.9394 | 0.98 | 1500 | 26.44 | 44.0 | 1.2812 | 74.1558 | |
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| 0.3764 | 1.05 | 1600 | 26.95 | 44.82 | 1.3248 | 73.8406 | |
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| 0.3338 | 1.12 | 1700 | 26.5 | 44.96 | 1.3212 | 77.3976 | |
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| 0.3148 | 1.18 | 1800 | 29.57 | 46.31 | 1.3188 | 66.7267 | |
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| 0.3206 | 1.25 | 1900 | 30.87 | 47.21 | 1.3050 | 64.4755 | |
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| 0.3069 | 1.31 | 2000 | 30.15 | 46.19 | 1.3053 | 65.6911 | |
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| 0.3342 | 1.38 | 2100 | 1.3506| 24.14 | 44.12 | 77.2625 | |
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| 0.3125 | 1.44 | 2200 | 1.3369| 30.21 | 46.08 | 63.9802 | |
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| 0.319 | 1.51 | 2300 | 1.3601| 27.71 | 45.45 | 69.9235 | |
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| 0.3067 | 1.58 | 2400 | 1.3473| 26.92 | 45.73 | 69.3381 | |
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| 0.2621 | 1.64 | 2500 | 1.3354| 28.36 | 46.14 | 66.9068 | |
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| 0.2709 | 1.71 | 2600 | 1.3339| 28.75 | 45.47 | 65.2859 | |
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| 0.2644 | 1.77 | 2700 | 1.3100| 28.84 | 47.35 | 65.8262 | |
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| 0.2511 | 1.84 | 2800 | 1.3261| 29.41 | 47.31 | 69.4732 | |
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| 0.2232 | 1.9 | 2900 | 1.3382| 30.79 | 46.63 | 64.1153 | |
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| 0.236 | 1.97 | 3000 | 1.3339| 30.66 | 46.99 | 65.4660 | |
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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 |
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