| --- |
| language: |
| - ga |
| - en |
| license: apache-2.0 |
| base_model: openai/whisper-small |
| tags: |
| - generated_from_trainer |
| datasets: |
| - ymoslem/IWSLT2023-GA-EN |
| - ymoslem/FLEURS-GA-EN |
| - ymoslem/BitesizeIrish-GA-EN |
| - ymoslem/SpokenWords-GA-EN-MTed |
| - ymoslem/Tatoeba-Speech-Irish |
| - ymoslem/Wikimedia-Speech-Irish |
| metrics: |
| - bleu |
| - wer |
| model-index: |
| - name: Whisper Small GA-EN Speech Translation |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia |
| type: ymoslem/IWSLT2023-GA-EN |
| metrics: |
| - name: Bleu |
| type: bleu |
| value: 32.44 |
| - name: Wer |
| type: wer |
| value: 63.259792886087354 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # Whisper Small GA-EN Speech Translation |
|
|
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.3690 |
| - Bleu: 32.44 |
| - Chrf: 48.06 |
| - Wer: 63.2598 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0001 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.01 |
| - training_steps: 3000 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer | |
| |:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:| |
| | 2.3655 | 0.0438 | 100 | 1.7709 | 8.84 | 26.21 | 127.7803 | |
| | 1.8998 | 0.0876 | 200 | 1.5198 | 14.9 | 32.89 | 99.2796 | |
| | 1.5421 | 0.1313 | 300 | 1.3972 | 16.15 | 35.77 | 86.8077 | |
| | 1.3154 | 0.1751 | 400 | 1.3412 | 20.46 | 39.48 | 83.0707 | |
| | 1.1138 | 0.2189 | 500 | 1.3126 | 23.16 | 41.28 | 74.1108 | |
| | 0.9814 | 0.2627 | 600 | 1.3217 | 25.56 | 41.67 | 68.7528 | |
| | 0.8897 | 0.3065 | 700 | 1.2859 | 27.0 | 43.54 | 66.3215 | |
| | 0.7495 | 0.3503 | 800 | 1.2668 | 21.71 | 43.03 | 75.7767 | |
| | 0.7068 | 0.3940 | 900 | 1.2852 | 17.86 | 40.88 | 106.0333 | |
| | 0.6002 | 0.4378 | 1000 | 1.2476 | 24.0 | 44.26 | 78.4331 | |
| | 0.4989 | 0.4816 | 1100 | 1.2756 | 28.88 | 45.57 | 67.2670 | |
| | 0.4464 | 0.5254 | 1200 | 1.2756 | 27.81 | 45.53 | 66.8618 | |
| | 0.3883 | 0.5692 | 1300 | 1.2799 | 29.84 | 46.03 | 64.0702 | |
| | 0.341 | 0.6130 | 1400 | 1.2693 | 26.51 | 43.97 | 75.3715 | |
| | 0.2853 | 0.6567 | 1500 | 1.3310 | 26.99 | 45.58 | 74.0207 | |
| | 0.2611 | 0.7005 | 1600 | 1.3022 | 25.83 | 44.79 | 73.4354 | |
| | 0.2013 | 0.7443 | 1700 | 1.3266 | 30.78 | 46.61 | 63.6650 | |
| | 0.1886 | 0.7881 | 1800 | 1.2943 | 25.56 | 45.46 | 73.7055 | |
| | 0.1517 | 0.8319 | 1900 | 1.3193 | 28.93 | 45.09 | 64.3854 | |
| | 0.1288 | 0.8757 | 2000 | 1.3567 | 28.22 | 44.75 | 67.6722 | |
| | 0.1129 | 0.9194 | 2100 | 1.3431 | 29.55 | 46.22 | 66.2314 | |
| | 0.1 | 0.9632 | 2200 | 1.3365 | 31.46 | 48.14 | 64.9257 | |
| | 0.0505 | 1.0070 | 2300 | 1.3557 | 30.37 | 47.16 | 64.1153 | |
| | 0.0468 | 1.0508 | 2400 | 1.3648 | 31.57 | 48.17 | 62.0891 | |
| | 0.0373 | 1.0946 | 2500 | 1.3661 | 31.56 | 47.76 | 64.7456 | |
| | 0.0297 | 1.1384 | 2600 | 1.3638 | 31.13 | 47.74 | 64.3854 | |
| | 0.0283 | 1.1821 | 2700 | 1.3847 | 29.98 | 47.54 | 65.9613 | |
| | 0.0302 | 1.2259 | 2800 | 1.3730 | 32.32 | 48.28 | 64.0252 | |
| | 0.0229 | 1.2697 | 2900 | 1.3702 | 31.47 | 47.55 | 65.1508 | |
| | 0.0262 | 1.3135 | 3000 | 1.3690 | 32.44 | 48.06 | 63.2598 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.2.0+cu121 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
| |
