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 as well as a copy of the dataset with noise reduction and normalization (for both train and test) dataset. The datasets were processed with noise reduction and normalization (both the train and test splits). It achieves the following results on the evaluation set:
- Loss: 1.3339
- Bleu: 30.66
- Chrf: 46.99
- Wer: 65.4660
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.01
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|---|---|---|---|---|---|---|
| 1.41 | 0.07 | 100 | 9.78 | 25.23 | 1.8782 | 96.3980 |
| 1.2436 | 0.13 | 200 | 10.23 | 28.66 | 1.8301 | 125.9343 |
| 1.593 | 0.2 | 300 | 9.53 | 30.7 | 1.7066 | 137.1454 |
| 1.9589 | 0.26 | 400 | 12.08 | 32.94 | 1.5629 | 109.3652 |
| 1.8174 | 0.33 | 500 | 13.73 | 34.5 | 1.5154 | 123.5930 |
| 1.6775 | 0.39 | 600 | 15.8 | 35.68 | 1.5220 | 102.2062 |
| 1.7074 | 0.46 | 700 | 16.62 | 37.96 | 1.4570 | 100.5853 |
| 1.5793 | 0.53 | 800 | 24.5 | 39.91 | 1.4265 | 71.3643 |
| 1.3708 | 0.59 | 900 | 24.35 | 42.26 | 1.3845 | 73.7956 |
| 1.3217 | 0.66 | 1000 | 19.34 | 41.3 | 1.3662 | 87.7533 |
| 1.2572 | 0.72 | 1100 | 21.59 | 41.35 | 1.3529 | 88.4286 |
| 1.1447 | 0.79 | 1200 | 28.39 | 44.99 | 1.3228 | 65.9163 |
| 1.1544 | 0.85 | 1300 | 23.69 | 43.07 | 1.2972 | 80.1891 |
| 1.0291 | 0.92 | 1400 | 29.36 | 45.45 | 1.2828 | 70.9590 |
| 0.9394 | 0.98 | 1500 | 26.44 | 44.0 | 1.2812 | 74.1558 |
| 0.3764 | 1.05 | 1600 | 26.95 | 44.82 | 1.3248 | 73.8406 |
| 0.3338 | 1.12 | 1700 | 26.5 | 44.96 | 1.3212 | 77.3976 |
| 0.3148 | 1.18 | 1800 | 29.57 | 46.31 | 1.3188 | 66.7267 |
| 0.3206 | 1.25 | 1900 | 30.87 | 47.21 | 1.3050 | 64.4755 |
| 0.3069 | 1.31 | 2000 | 30.15 | 46.19 | 1.3053 | 65.6911 |
| 0.3342 | 1.38 | 2100 | 1.3506 | 24.14 | 44.12 | 77.2625 |
| 0.3125 | 1.44 | 2200 | 1.3369 | 30.21 | 46.08 | 63.9802 |
| 0.319 | 1.51 | 2300 | 1.3601 | 27.71 | 45.45 | 69.9235 |
| 0.3067 | 1.58 | 2400 | 1.3473 | 26.92 | 45.73 | 69.3381 |
| 0.2621 | 1.64 | 2500 | 1.3354 | 28.36 | 46.14 | 66.9068 |
| 0.2709 | 1.71 | 2600 | 1.3339 | 28.75 | 45.47 | 65.2859 |
| 0.2644 | 1.77 | 2700 | 1.3100 | 28.84 | 47.35 | 65.8262 |
| 0.2511 | 1.84 | 2800 | 1.3261 | 29.41 | 47.31 | 69.4732 |
| 0.2232 | 1.9 | 2900 | 1.3382 | 30.79 | 46.63 | 64.1153 |
| 0.236 | 1.97 | 3000 | 1.3339 | 30.66 | 46.99 | 65.4660 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2