Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use DianaJin/krmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DianaJin/krmodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DianaJin/krmodel")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("DianaJin/krmodel") model = AutoModelForSpeechSeq2Seq.from_pretrained("DianaJin/krmodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
jinkrsmodel
This model is a fine-tuned version of openai/whisper-medium on the DianaJin/krmodel dataset. It achieves the following results on the evaluation set:
- Loss: 1.1290
- Cer: 92.0690
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: 16
- 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: 20
- training_steps: 160
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 2.1899 | 13.33 | 40 | 1.0819 | 153.1034 |
| 0.0027 | 26.67 | 80 | 1.0554 | 24.8276 |
| 0.0006 | 40.0 | 120 | 1.1173 | 39.3103 |
| 0.0004 | 53.33 | 160 | 1.1290 | 92.0690 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for DianaJin/krmodel
Base model
openai/whisper-medium