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STT_15000audio_basev2_0606
gingercake01/STT_15000audio_basev2_0606
STT_15000audio_basev2_0606 is a automatic speech recognition model from gingercake01. 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
- Jun 6, 2024
- Updated
- Jun 7, 2024
- SHA
- 4b81fbeb
Base models
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. -->
baseWhisper_finetune
This model is a fine-tuned version of openai/whisper-base on the gingercake01/0603_15000_freetalk_matched dataset. It achieves the following results on the evaluation set:
- Loss: 0.3428
- Cer: 7.9820
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: 2e-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: 1000
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.0836 | 5.3333 | 1000 | 0.2870 | 8.1564 |
| 0.0068 | 10.6667 | 2000 | 0.3203 | 7.9067 |
| 0.0026 | 16.0 | 3000 | 0.3428 | 7.9820 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- ko
- Created
- Jun 6, 2024
- Updated
- Jun 7, 2024