Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Malasar
whisper
Generated from Trainer
Instructions to use kavyamanohar/Malasar_Luke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kavyamanohar/Malasar_Luke with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kavyamanohar/Malasar_Luke")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kavyamanohar/Malasar_Luke") model = AutoModelForSpeechSeq2Seq.from_pretrained("kavyamanohar/Malasar_Luke", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kavyamanohar/Malasar_Luke: direct link, hf CLI and curl.
- Browser
- Download file 4.41 kB
-
https://huggingface.co/kavyamanohar/Malasar_Luke/resolve/main/training_args.bin
- Command line
-
hf download hf://kavyamanohar/Malasar_Luke/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kavyamanohar/Malasar_Luke/resolve/main/training_args.bin
4.41 kB
- Xet hash:
- 59e86874ccecf6bb4b45c13c15ed9885612b7fbcbc04c484c35dc3c41d16ca10
- Size of remote file:
- 4.41 kB
- SHA256:
- e6df14747bdc0273ba411b49cd5bffa5342903b981fd69369bfd24fd979be47d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.