Instructions to use Abkrs1/Hausa-ASR-copy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abkrs1/Hausa-ASR-copy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Abkrs1/Hausa-ASR-copy")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Abkrs1/Hausa-ASR-copy") model = AutoModelForSpeechSeq2Seq.from_pretrained("Abkrs1/Hausa-ASR-copy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Abkrs1/Hausa-ASR-copy: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/Abkrs1/Hausa-ASR-copy/resolve/main/training_args.bin
- Command line
-
hf download hf://Abkrs1/Hausa-ASR-copy/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Abkrs1/Hausa-ASR-copy/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 79e94d49302cb460bf7d91685df1066beee43d8aafbcb8f1fa5409edd84b45ae
- Size of remote file:
- 4.54 kB
- SHA256:
- a0aef3ffea639504c3dce46111a86aef52b93445d92f2c45102bffe1e19294f3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.