Whisper Small GA-EN Speech Translation
This model is a fine-tuned version of openai/whisper-small on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia datasets. The datasets are augmented in two ways: noise augmentation, and truncating low-amplitude samples. The best model checkpoint (this version) based on ChrF is at step 2800, epoch 1.2259, and it achieves the following results on the evaluation set:
- Loss: 1.3547
- Bleu: 32.57
- Chrf: 47.04
- Wer: 62.0891
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Hardware
1 NVIDIA A100-SXM4-80GB
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_steps: 0
- training_steps: 3000
- mixed_precision_training: Native AMP
- generation_max_length: 225
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer |
|---|---|---|---|---|---|---|
| 2.3533 | 0.0438 | 100 | 1.7789 | 6.29 | 25.08 | 148.7618 |
| 1.9035 | 0.0876 | 200 | 1.5122 | 18.21 | 34.02 | 85.6821 |
| 1.5357 | 0.1313 | 300 | 1.3983 | 14.01 | 33.7 | 93.3363 |
| 1.3056 | 0.1751 | 400 | 1.3447 | 18.12 | 37.35 | 95.0023 |
| 1.1177 | 0.2189 | 500 | 1.3168 | 18.47 | 38.44 | 95.3624 |
| 0.984 | 0.2627 | 600 | 1.3202 | 26.82 | 41.23 | 67.3120 |
| 0.8945 | 0.3065 | 700 | 1.2947 | 26.73 | 42.53 | 67.1319 |
| 0.7508 | 0.3503 | 800 | 1.2476 | 25.67 | 42.06 | 74.2008 |
| 0.7127 | 0.3940 | 900 | 1.2630 | 22.59 | 41.05 | 75.7767 |
| 0.5944 | 0.4378 | 1000 | 1.2726 | 22.37 | 40.31 | 82.4854 |
| 0.4972 | 0.4816 | 1100 | 1.2898 | 22.88 | 42.52 | 82.5304 |
| 0.4517 | 0.5254 | 1200 | 1.2509 | 27.99 | 44.42 | 64.1603 |
| 0.3885 | 0.5692 | 1300 | 1.2887 | 29.58 | 44.8 | 63.1247 |
| 0.3337 | 0.6130 | 1400 | 1.2645 | 30.05 | 45.5 | 62.6294 |
| 0.2852 | 0.6567 | 1500 | 1.2972 | 28.2 | 43.57 | 68.6628 |
| 0.2583 | 0.7005 | 1600 | 1.2716 | 28.21 | 45.06 | 73.6155 |
| 0.2016 | 0.7443 | 1700 | 1.3346 | 27.55 | 43.21 | 74.3809 |
| 0.1883 | 0.7881 | 1800 | 1.3124 | 21.45 | 41.83 | 94.1018 |
| 0.1514 | 0.8319 | 1900 | 1.3178 | 28.2 | 44.09 | 63.7551 |
| 0.1311 | 0.8757 | 2000 | 1.3246 | 27.33 | 43.25 | 74.3359 |
| 0.1128 | 0.9194 | 2100 | 1.3464 | 25.21 | 42.93 | 83.2508 |
| 0.0994 | 0.9632 | 2200 | 1.3315 | 30.51 | 45.74 | 64.7456 |
| 0.0512 | 1.0070 | 2300 | 1.3377 | 30.89 | 46.44 | 63.3498 |
| 0.0447 | 1.0508 | 2400 | 1.3587 | 28.72 | 44.36 | 64.3404 |
| 0.0368 | 1.0946 | 2500 | 1.3619 | 31.53 | 46.56 | 61.9541 |
| 0.0281 | 1.1384 | 2600 | 1.3596 | 30.98 | 46.45 | 70.4638 |
| 0.0273 | 1.1821 | 2700 | 1.3656 | 32.09 | 46.85 | 62.1792 |
| 0.0287 | 1.2259 | 2800 | 1.3547 | 32.57 | 47.04 | 62.0891 |
| 0.025 | 1.2697 | 2900 | 1.3539 | 26.94 | 45.43 | 81.1796 |
| 0.0263 | 1.3135 | 3000 | 1.3512 | 30.11 | 46.73 | 71.4993 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Base model
openai/whisper-smallDatasets used to train ymoslem/whisper-small-ga2en-v5.2.1-r
Evaluation results
- Bleu on IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia + augmentedself-reported30.110
- Wer on IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia + augmentedself-reported71.499