| --- |
| 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 |
| - chrf |
| 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 + |
| augmented |
| type: ymoslem/IWSLT2023-GA-EN |
| metrics: |
| - name: Bleu |
| type: bleu |
| value: 28.44 |
| - name: Wer |
| type: wer |
| value: 72.62494371904548 |
| --- |
| |
| <!-- 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 + augmented dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.3641 |
| - Bleu: 28.44 |
| - Chrf: 43.55 |
| - Wer: 72.6249 |
|
|
| ## 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 |
| - warmup_steps: 0 |
| - training_steps: 3000 |
| - mixed_precision_training: Native AMP |
| - generation_max_length: 128 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer | |
| |:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:| |
| | 2.3595 | 0.0438 | 100 | 1.7944 | 9.69 | 26.37 | 114.4529 | |
| | 1.9008 | 0.0876 | 200 | 1.5391 | 14.89 | 32.44 | 93.6065 | |
| | 1.535 | 0.1313 | 300 | 1.3972 | 18.24 | 33.57 | 81.9901 | |
| | 1.3307 | 0.1751 | 400 | 1.3684 | 21.34 | 37.37 | 72.8050 | |
| | 1.1263 | 0.2189 | 500 | 1.3284 | 19.33 | 39.83 | 91.8955 | |
| | 0.9805 | 0.2627 | 600 | 1.3301 | 23.67 | 38.68 | 78.3881 | |
| | 0.8989 | 0.3065 | 700 | 1.3123 | 20.32 | 36.94 | 76.3170 | |
| | 0.7557 | 0.3503 | 800 | 1.2717 | 25.74 | 40.16 | 72.4448 | |
| | 0.7216 | 0.3940 | 900 | 1.3090 | 22.34 | 37.79 | 78.9284 | |
| | 0.6131 | 0.4378 | 1000 | 1.2566 | 24.36 | 41.49 | 74.5160 | |
| | 0.5032 | 0.4816 | 1100 | 1.2742 | 21.69 | 41.12 | 83.3859 | |
| | 0.4567 | 0.5254 | 1200 | 1.2893 | 24.33 | 40.05 | 70.8690 | |
| | 0.3968 | 0.5692 | 1300 | 1.3000 | 26.97 | 41.45 | 69.6083 | |
| | 0.3353 | 0.6130 | 1400 | 1.2784 | 27.51 | 43.97 | 63.9352 | |
| | 0.2826 | 0.6567 | 1500 | 1.3165 | 24.36 | 39.83 | 70.6439 | |
| | 0.2643 | 0.7005 | 1600 | 1.3317 | 24.98 | 40.01 | 68.6628 | |
| | 0.2047 | 0.7443 | 1700 | 1.2905 | 28.01 | 42.72 | 65.8262 | |
| | 0.1946 | 0.7881 | 1800 | 1.2820 | 26.17 | 42.46 | 64.9257 | |
| | 0.1588 | 0.8319 | 1900 | 1.3172 | 26.9 | 43.02 | 63.5299 | |
| | 0.1322 | 0.8757 | 2000 | 1.3248 | 27.78 | 43.53 | 63.8001 | |
| | 0.1134 | 0.9194 | **2100** | 1.3198 | 28.98 | 45.27 | 72.7600 | |
| | 0.1031 | 0.9632 | 2200 | 1.3502 | 29.18 | 44.77 | 68.3476 | |
| | 0.0518 | 1.0070 | 2300 | 1.3433 | 28.6 | 42.96 | 69.0230 | |
| | 0.0481 | 1.0508 | 2400 | 1.3715 | 29.01 | 44.46 | 69.6983 | |
| | 0.0367 | 1.0946 | 2500 | 1.3696 | 26.94 | 42.39 | 73.6605 | |
| | 0.0309 | 1.1384 | 2600 | 1.3665 | 28.12 | 43.32 | 70.3737 | |
| | 0.0302 | 1.1821 | 2700 | 1.3836 | 29.6 | 44.56 | 67.2220 | |
| | 0.0302 | 1.2259 | 2800 | 1.3667 | 29.0 | 44.33 | 67.2220 | |
| | 0.0252 | 1.2697 | 2900 | 1.3633 | 29.07 | 44.09 | 70.6889 | |
| | 0.0257 | 1.3135 | 3000 | 1.3641 | 28.44 | 43.55 | 72.6249 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.40.2 |
| - Pytorch 2.2.0+cu121 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
