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
- Downloads last month
- 9
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for ymoslem/whisper-small-ga2en-v5.2.1-r
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