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 pytorch_model.bin from kavyamanohar/Malasar_Luke: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/kavyamanohar/Malasar_Luke/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kavyamanohar/Malasar_Luke/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kavyamanohar/Malasar_Luke/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- dd9bd42b37447182be320dd547f0cbf63b49a7a48432dc0d6c6302776398fe72
- Size of remote file:
- 967 MB
- SHA256:
- 6226796178346aae06f4920088730e5437824d968093d486eecf6bff6861cb8f
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