Instructions to use Gigworks/ASR_id with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gigworks/ASR_id with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Gigworks/ASR_id")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Gigworks/ASR_id") model = AutoModelForCTC.from_pretrained("Gigworks/ASR_id", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Gigworks/ASR_id: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Gigworks/ASR_id/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Gigworks/ASR_id/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Gigworks/ASR_id/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 69d0710a9e1cc7e8c0788ce4e1d0968acfae3d3bfa5e404ac9cf51ae4e2a17a4
- Size of remote file:
- 1.26 GB
- SHA256:
- b82665de92f796180254d221dd442bdee6662bda3a15c436746bc3f9d6759dd4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.