Automatic Speech Recognition
Transformers
PyTorch
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use wetq423fqsdv/repo_name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wetq423fqsdv/repo_name with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="wetq423fqsdv/repo_name")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("wetq423fqsdv/repo_name") model = AutoModelForSpeechSeq2Seq.from_pretrained("wetq423fqsdv/repo_name", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9ae1c8183708e9b21cb51bd545ba8388108bca4e4aaae1b679231b5f93e5edc
- Size of remote file:
- 4.16 kB
- SHA256:
- 57c4b58ab34943519f249d0e4ad949ab4ac2bb0fbe02d5d4d0c78290a2a8b5f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.