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 dataset. It achieves the following results on the evaluation set:
- Loss: 1.3732
- Bleu: 31.78
- Chrf: 47.41
- Wer: 62.3143
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
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
---|---|---|---|---|---|---|
2.4081 | 0.0438 | 100 | 9.25 | 24.92 | 1.8707 | 101.1706 |
1.9316 | 0.0876 | 200 | 13.88 | 32.67 | 1.5419 | 101.5308 |
1.5533 | 0.1313 | 300 | 17.52 | 36.68 | 1.4046 | 83.1607 |
1.3403 | 0.1751 | 400 | 17.71 | 37.97 | 1.3565 | 93.5615 |
1.1303 | 0.2189 | 500 | 19.24 | 40.02 | 1.3224 | 82.0801 |
0.9892 | 0.2627 | 600 | 26.07 | 43.16 | 1.3077 | 72.8050 |
0.9005 | 0.3065 | 700 | 27.37 | 44.24 | 1.2918 | 64.1603 |
0.7547 | 0.3503 | 800 | 27.68 | 44.28 | 1.2754 | 66.0964 |
0.7199 | 0.3940 | 900 | 23.99 | 42.99 | 1.2895 | 78.0729 |
0.6095 | 0.4378 | 1000 | 1.2716 | 16.56 | 41.2 | 116.5691 |
0.5072 | 0.4816 | 1100 | 1.2901 | 25.39 | 44.04 | 75.2364 |
0.4599 | 0.5254 | 1200 | 1.2634 | 28.45 | 45.71 | 67.0419 |
0.3987 | 0.5692 | 1300 | 1.3004 | 25.84 | 45.63 | 75.1013 |
0.3443 | 0.6130 | 1400 | 1.2871 | 29.09 | 46.25 | 65.4210 |
0.2882 | 0.6567 | 1500 | 1.3242 | 29.14 | 44.4 | 66.0063 |
0.2687 | 0.7005 | 1600 | 1.3135 | 22.9 | 43.76 | 92.2557 |
0.2059 | 0.7443 | 1700 | 1.3160 | 31.13 | 47.45 | 63.6650 |
0.1991 | 0.7881 | 1800 | 1.2960 | 31.45 | 47.47 | 63.6650 |
0.1523 | 0.8319 | 1900 | 1.3215 | 31.21 | 47.38 | 64.1153 |
0.1349 | 0.8757 | 2000 | 1.3402 | 30.58 | 46.32 | 63.7551 |
0.111 | 0.9194 | 2100 | 1.3311 | 30.92 | 48.17 | 62.2242 |
0.1055 | 0.9632 | 2200 | 1.3548 | 30.56 | 46.56 | 65.2409 |
0.0525 | 1.0070 | 2300 | 1.3754 | 31.28 | 48.1 | 64.2954 |
0.0498 | 1.0508 | 2400 | 1.3729 | 31.16 | 47.8 | 61.7290 |
0.0372 | 1.0946 | 2500 | 1.3498 | 32.13 | 48.77 | 61.4588 |
0.029 | 1.1384 | 2600 | 1.3723 | 32.04 | 48.32 | 61.8640 |
0.0285 | 1.1821 | 2700 | 1.3748 | 31.91 | 47.58 | 61.8640 |
0.0292 | 1.2259 | 2800 | 1.3764 | 31.92 | 47.96 | 61.1887 |
0.025 | 1.2697 | 2900 | 1.3799 | 31.64 | 47.47 | 62.2242 |
0.0253 | 1.3135 | 3000 | 1.3732 | 31.78 | 47.41 | 62.3143 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Downloads last month
- 3
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-v5.6-r
Base model
openai/whisper-small
Finetuned
this model
Datasets used to train ymoslem/whisper-small-ga2en-v5.6-r
Evaluation results
- Bleu on IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimediaself-reported31.780
- Wer on IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimediaself-reported62.314