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Japanese_Fine_Tuned_Whisper_Model
Nikolajvestergaard/Japanese_Fine_Tuned_Whisper_Model
Japanese_Fine_Tuned_Whisper_Model is a automatic speech recognition model from Nikolajvestergaard. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice dataset. It achieves the following results on the evaluation set: - Loss: 0.549100 - Wer: 225.233037
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Mar 14, 2023
- Updated
- Mar 15, 2023
- SHA
- 8fe48d73
Model card
From the Hugging Face model README
Japanese_Fine_Tuned_Whisper_Model
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.549100
- Wer: 225.233037
Model description
The tiny Whisper model is fine-tuned on Japanese speech samples from the Common Voice dataset, based on which users can perform Automatic Speech Recognition in real time in Japanese.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Step | Validation Loss | Wer |
|---|---|---|---|
| 0.8097 | 200 | 0.801917 | 601.560806 |
| 0.7200 | 400 | 0.783436 | 327.335790 |
| 0.6810 | 600 | 0.759281 | 254.064600 |
| 0.7351 | 800 | 0.747759 | 241.426404 |
| 0.5491 | 1000 | 0.747127 | 225.233037 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- ja
- Created
- Mar 14, 2023
- Updated
- Mar 15, 2023