Instructions to use hts98/practice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/practice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/practice")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hts98/practice") model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/practice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hts98/practice: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/hts98/practice/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hts98/practice/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hts98/practice/resolve/main/pytorch_model.bin
3.09 GB
- Xet hash:
- 668f99c6dad2bd4815bb9ad62696566f119b54a027f3b3b43dd9a07c3a3bc3d5
- Size of remote file:
- 3.09 GB
- SHA256:
- 7cc2082c8ce080f5639f19f08096b209a9fbad05f354d02e6fa2afc5e2c64229
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