Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use SubsWay/my_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SubsWay/my_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SubsWay/my_test")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("SubsWay/my_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("SubsWay/my_test") - Notebooks
- Google Colab
- Kaggle
my_test_model
This model is a fine-tuned version of openai/whisper-base on the my_test_dataset dataset.
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.39.1
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 2
Model tree for SubsWay/my_test
Base model
openai/whisper-base