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whisper-tiny-ta-example
parambharat/whisper-tiny-ta-example
whisper-tiny-ta-example is a automatic speech recognition model from parambharat. 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 15, 2022
- Updated
- Dec 15, 2022
- SHA
- 3ac9e680
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 tiny Ta example - Bharat Ramanathan
This model is a fine-tuned version of parambharat/whisper-tiny-ta on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4016
- Wer: 36.5217
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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 25
- training_steps: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.304 | 12.01 | 25 | 0.3614 | 31.7391 |
| 0.1826 | 24.02 | 50 | 0.3851 | 35.2174 |
| 0.1346 | 37.01 | 75 | 0.3999 | 37.8261 |
| 0.1096 | 49.02 | 100 | 0.4016 | 36.5217 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- ta
- Created
- Dec 15, 2022
- Updated
- Dec 15, 2022