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whisper-medium-he-teamim-silsuless
cantillation/whisper-medium-he-teamim-silsuless
whisper-medium-he-teamim-silsuless is a automatic speech recognition model from cantillation. Use it when you need speech turned into text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- Model type
- whisper
- Access
- Public
- Created
- Jan 14, 2024
- Updated
- Jan 30, 2024
- SHA
- c17c0067
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
he
This model is a fine-tuned version of openai/whisper-medium-ori on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0478
- Wer: 7.1707
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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.3738 | 0.02 | 50 | 0.1757 | 6.6641 |
| 0.1077 | 0.03 | 100 | 0.1403 | 33.0086 |
| 0.1118 | 0.05 | 150 | 0.1070 | 40.2962 |
| 0.0848 | 0.07 | 200 | 0.1144 | 16.7966 |
| 0.0879 | 0.08 | 250 | 0.1032 | 21.4341 |
| 0.0786 | 0.1 | 300 | 0.0971 | 7.0538 |
| 0.0665 | 0.11 | 350 | 0.0940 | 15.3157 |
| 0.0631 | 0.13 | 400 | 0.0868 | 14.6142 |
| 0.0591 | 0.15 | 450 | 0.0965 | 27.0460 |
| 0.0574 | 0.16 | 500 | 0.0807 | 19.0959 |
| 0.0536 | 0.18 | 550 | 0.0804 | 12.4318 |
| 0.0443 | 0.2 | 600 | 0.0791 | 3.9751 |
| 0.0455 | 0.21 | 650 | 0.0881 | 12.9774 |
| 0.0435 | 0.23 | 700 | 0.0772 | 6.0016 |
| 0.0439 | 0.25 | 750 | 0.0750 | 13.9906 |
| 0.0317 | 0.26 | 800 | 0.0645 | 9.4310 |
| 0.0352 | 0.28 | 850 | 0.0664 | 5.9626 |
| 0.0328 | 0.29 | 900 | 0.0633 | 8.4178 |
| 0.0287 | 0.31 | 950 | 0.0658 | 15.5105 |
| 0.0266 | 0.33 | 1000 | 0.0570 | 10.2494 |
| 0.0209 | 0.34 | 1050 | 0.0613 | 7.4825 |
| 0.0272 | 0.36 | 1100 | 0.0599 | 6.8589 |
| 0.024 | 0.38 | 1150 | 0.0583 | 10.8730 |
| 0.0203 | 0.39 | 1200 | 0.0538 | 8.0670 |
| 0.0187 | 0.41 | 1250 | 0.0508 | 5.4949 |
| 0.0165 | 0.43 | 1300 | 0.0507 | 6.8200 |
| 0.0106 | 0.44 | 1350 | 0.0504 | 7.3266 |
| 0.0203 | 0.46 | 1400 | 0.0477 | 7.7163 |
| 0.0142 | 0.48 | 1450 | 0.0480 | 7.5994 |
| 0.0194 | 0.49 | 1500 | 0.0478 | 7.1707 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- Languages
- he
- Created
- Jan 14, 2024
- Updated
- Jan 30, 2024