Instructions to use octanove/mosla-whisper-langid-zho with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use octanove/mosla-whisper-langid-zho with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="octanove/mosla-whisper-langid-zho")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("octanove/mosla-whisper-langid-zho") model = AutoModelForAudioClassification.from_pretrained("octanove/mosla-whisper-langid-zho", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from octanove/mosla-whisper-langid-zho: direct link, hf CLI and curl.
- Browser
- Download file 2.55 GB
-
https://huggingface.co/octanove/mosla-whisper-langid-zho/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://octanove/mosla-whisper-langid-zho/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/octanove/mosla-whisper-langid-zho/resolve/main/pytorch_model.bin
2.55 GB
- Xet hash:
- 3e8b52e1c9369079ad2db5b52b869d8b2b0f79b27c5fb093eb6ccc08f9f8f005
- Size of remote file:
- 2.55 GB
- SHA256:
- 59e7076b484161f2aa665eb65471d49eee66f80b0f0db34223abccec4bb68aa6
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