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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("SubsWay/my_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("SubsWay/my_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SubsWay/my_test: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/SubsWay/my_test/resolve/main/README.md
- Command line
-
hf download hf://SubsWay/my_test/README.md
-
curl -L -o README.md https://huggingface.co/SubsWay/my_test/resolve/main/README.md
1.2 kB
metadata
language:
- ko
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
base_model: openai/whisper-base
datasets:
- SubsWay/my_test
model-index:
- name: my_test_model
results: []
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