Instructions to use Sjdan/mst_hp5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/mst_hp5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/mst_hp5")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/mst_hp5") model = AutoModelForCTC.from_pretrained("Sjdan/mst_hp5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sjdan/mst_hp5: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Sjdan/mst_hp5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sjdan/mst_hp5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sjdan/mst_hp5/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 600ad3ce921dfb1badd95413b7261aba9032d464295a706af32262e0bc95de1c
- Size of remote file:
- 1.26 GB
- SHA256:
- 9fbd3a85cc0d68ec789c873e27d80c58f51e0d7d4922e2e8d94eed9524c04b70
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.