Instructions to use lichenda/whisper-small-hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lichenda/whisper-small-hi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lichenda/whisper-small-hi")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("lichenda/whisper-small-hi") model = AutoModelForSpeechSeq2Seq.from_pretrained("lichenda/whisper-small-hi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lichenda/whisper-small-hi: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/lichenda/whisper-small-hi/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lichenda/whisper-small-hi/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lichenda/whisper-small-hi/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- 40df53c9e630422d6d328c67a51b826108f8d4f3313438f43d32f4acb8edc57f
- Size of remote file:
- 967 MB
- SHA256:
- 73c02a4afd88b33b04ff575549ce8a5037b2953eaa0ed86d9f72529ffea575d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.