Whisper Medium GA-EN Speech Translation Raw
This model is a fine-tuned version of openai/whisper-medium on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset. It achieves the following results on the evaluation set:
- Loss: 1.6246
- Bleu: 27.65
- Chrf: 47.08
- Wer: 71.0941
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer |
|---|---|---|---|---|---|---|
| 2.3743 | 0.0539 | 100 | 2.1064 | 5.67 | 20.91 | 126.9248 |
| 2.3196 | 0.1079 | 200 | 2.1133 | 11.35 | 26.01 | 89.5092 |
| 2.2729 | 0.1618 | 300 | 2.0561 | 6.85 | 25.04 | 156.5061 |
| 2.0887 | 0.2157 | 400 | 1.9701 | 10.46 | 29.21 | 118.6853 |
| 1.9663 | 0.2697 | 500 | 1.9824 | 16.53 | 31.2 | 77.5326 |
| 1.9504 | 0.3236 | 600 | 1.8619 | 7.02 | 27.46 | 193.7416 |
| 1.7843 | 0.3776 | 700 | 1.8683 | 16.6 | 33.6 | 87.7082 |
| 1.8915 | 0.4315 | 800 | 1.7730 | 16.89 | 36.54 | 91.8505 |
| 1.6921 | 0.4854 | 900 | 1.8049 | 13.14 | 34.45 | 114.0477 |
| 1.4761 | 0.5394 | 1000 | 1.8310 | 22.12 | 37.3 | 77.1724 |
| 1.3067 | 0.5933 | 1100 | 1.7911 | 17.21 | 34.34 | 90.5448 |
| 1.3564 | 0.6472 | 1200 | 1.7045 | 20.09 | 39.67 | 85.1869 |
| 1.489 | 0.7012 | 1300 | 1.7601 | 15.3 | 36.53 | 107.8793 |
| 1.3023 | 0.7551 | 1400 | 1.7428 | 18.99 | 39.54 | 89.7794 |
| 1.1744 | 0.8091 | 1500 | 1.7446 | 21.68 | 41.78 | 79.4687 |
| 1.0122 | 0.8630 | 1600 | 1.7180 | 18.28 | 39.27 | 96.7582 |
| 1.0787 | 0.9169 | 1700 | 1.6144 | 16.94 | 39.74 | 98.8744 |
| 0.9561 | 0.9709 | 1800 | 1.6290 | 25.29 | 42.13 | 74.9662 |
| 0.4452 | 1.0248 | 1900 | 1.7223 | 18.95 | 39.14 | 97.0734 |
| 0.4397 | 1.0787 | 2000 | 1.6855 | 23.4 | 40.9 | 77.9379 |
| 0.4382 | 1.1327 | 2100 | 1.6911 | 24.95 | 41.19 | 72.8951 |
| 0.3937 | 1.1866 | 2200 | 1.7127 | 23.33 | 41.09 | 78.4331 |
| 0.4119 | 1.2406 | 2300 | 1.6796 | 23.25 | 42.32 | 83.6560 |
| 0.4139 | 1.2945 | 2400 | 1.6730 | 23.13 | 43.25 | 83.3408 |
| 0.3506 | 1.3484 | 2500 | 1.7361 | 23.37 | 42.31 | 79.9190 |
| 0.4109 | 1.4024 | 2600 | 1.6233 | 23.78 | 44.32 | 82.8005 |
| 0.3563 | 1.4563 | 2700 | 1.6383 | 20.41 | 43.66 | 98.1540 |
| 0.3355 | 1.5102 | 2800 | 1.6675 | 25.27 | 44.91 | 75.6866 |
| 0.2751 | 1.5642 | 2900 | 1.7011 | 24.64 | 43.19 | 74.2008 |
| 0.28 | 1.6181 | 3000 | 1.6308 | 24.76 | 45.49 | 79.4687 |
| 0.3108 | 1.6721 | 3100 | 1.5976 | 28.9 | 47.03 | 68.7978 |
| 0.3231 | 1.7260 | 3200 | 1.6070 | 27.82 | 46.1 | 69.8334 |
| 0.2665 | 1.7799 | 3300 | 1.5853 | 26.0 | 44.51 | 74.9212 |
| 0.2788 | 1.8339 | 3400 | 1.5689 | 26.37 | 46.94 | 75.0113 |
| 0.243 | 1.8878 | 3500 | 1.5885 | 29.12 | 46.94 | 67.4021 |
| 0.2605 | 1.9417 | 3600 | 1.5680 | 28.64 | 46.38 | 67.8523 |
| 0.1664 | 1.9957 | 3700 | 1.5910 | 28.45 | 46.64 | 68.0774 |
| 0.049 | 2.0496 | 3800 | 1.6385 | 27.78 | 46.51 | 69.9235 |
| 0.0635 | 2.1036 | 3900 | 1.6272 | 27.57 | 47.25 | 71.1391 |
| 0.0467 | 2.1575 | 4000 | 1.6246 | 27.65 | 47.08 | 71.0941 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
openai/whisper-mediumDatasets used to train ymoslem/whisper-medium-ga2en-a-v1-r
Evaluation results
- Bleu on IWSLT-2023, FLEURS, BiteSize, and SpokenWordsself-reported27.650
- Wer on IWSLT-2023, FLEURS, BiteSize, and SpokenWordsself-reported71.094