Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Nepali
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use anuragshas/whisper-large-v2-ne with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anuragshas/whisper-large-v2-ne with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="anuragshas/whisper-large-v2-ne")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("anuragshas/whisper-large-v2-ne") model = AutoModelForSpeechSeq2Seq.from_pretrained("anuragshas/whisper-large-v2-ne", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anuragshas/whisper-large-v2-ne: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/anuragshas/whisper-large-v2-ne/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anuragshas/whisper-large-v2-ne/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anuragshas/whisper-large-v2-ne/resolve/main/pytorch_model.bin
3.09 GB
- Xet hash:
- ffd7b5e7f06257bb4026f395c20079f5d178d92bb330061bc388d864d8ea3f29
- Size of remote file:
- 3.09 GB
- SHA256:
- eef466766805bdf8df108e6f0b00f775cbe7d946bed3add476f88abbe6570059
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