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Whisper_SAE_base
mhlongoke91/Whisper_SAE_base
Whisper_SAE_base is a automatic speech recognition model from mhlongoke91. 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
- May 5, 2025
- Updated
- May 17, 2025
- SHA
- 33cac160
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. -->
Whisper_SAE_base
This model is a fine-tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4485
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: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4014 | 1.3338 | 500 | 0.4619 |
| 0.2756 | 2.6676 | 1000 | 0.4203 |
| 0.1909 | 4.0 | 1500 | 0.4132 |
| 0.1126 | 5.3338 | 2000 | 0.4282 |
| 0.0807 | 6.6676 | 2500 | 0.4341 |
| 0.059 | 8.0 | 3000 | 0.4485 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.1.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Created
- May 5, 2025
- Updated
- May 17, 2025