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 training_args.bin from anuragshas/whisper-large-v2-ne: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/anuragshas/whisper-large-v2-ne/resolve/main/training_args.bin
- Command line
-
hf download hf://anuragshas/whisper-large-v2-ne/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/anuragshas/whisper-large-v2-ne/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 8176aefa0855f49ed81eb50782233bd9704d200d7456a5cdc48056fab39fa18c
- Size of remote file:
- 4.73 kB
- SHA256:
- c4a7e5db5e6de0f9dea5ef041a7afd4552a4a5cf4512adaf4f6108ba43b4edd4
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