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whisper-small-telugu
steja/whisper-small-telugu
whisper-small-telugu is a automatic speech recognition model from steja. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Dec 8, 2022
- Updated
- Dec 14, 2022
- SHA
- 69590da2
Datasets
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. -->
whisper-small-telugu
This model is a fine-tuned version of openai/whisper-small on the google/fleurs dataset. It achieves the following results on the evaluation set (google/flerus telugu test set):
- Loss: 0.3622
- Wer: 39.6774
openai/whisper-small has the following zero shot performance on google/fleurs test set:
- Wer: 117.91
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2623 | 1.55 | 500 | 0.2733 | 65.9750 |
| 0.0859 | 3.1 | 1000 | 0.2045 | 39.7652 |
| 0.0538 | 4.64 | 1500 | 0.2220 | 42.3811 |
| 0.0265 | 6.19 | 2000 | 0.2526 | 42.3626 |
| 0.0179 | 7.74 | 2500 | 0.2754 | 42.1685 |
| 0.008 | 9.29 | 3000 | 0.2966 | 41.2257 |
| 0.0061 | 10.83 | 3500 | 0.2950 | 40.6202 |
| 0.0034 | 12.38 | 4000 | 0.3049 | 40.3198 |
| 0.004 | 13.93 | 4500 | 0.3106 | 40.5879 |
| 0.0018 | 15.48 | 5000 | 0.3199 | 40.1812 |
| 0.0016 | 17.03 | 5500 | 0.3346 | 39.8345 |
| 0.0006 | 18.57 | 6000 | 0.3337 | 40.2274 |
| 0.0003 | 20.12 | 6500 | 0.3396 | 40.2597 |
| 0.0005 | 21.67 | 7000 | 0.3465 | 40.1072 |
| 0.0002 | 23.22 | 7500 | 0.3485 | 39.7282 |
| 0.0002 | 24.77 | 8000 | 0.3519 | 39.7837 |
| 0.0001 | 26.32 | 8500 | 0.3567 | 39.7560 |
| 0.0001 | 27.86 | 9000 | 0.3614 | 39.8068 |
| 0.0 | 29.41 | 9500 | 0.3609 | 39.4925 |
| 0.0 | 30.96 | 10000 | 0.3622 | 39.6774 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- te
- Created
- Dec 8, 2022
- Updated
- Dec 14, 2022