Instructions to use phongdtd/wavLM-VLSP-vi-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phongdtd/wavLM-VLSP-vi-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="phongdtd/wavLM-VLSP-vi-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("phongdtd/wavLM-VLSP-vi-large") model = AutoModelForCTC.from_pretrained("phongdtd/wavLM-VLSP-vi-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from phongdtd/wavLM-VLSP-vi-large: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/phongdtd/wavLM-VLSP-vi-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://phongdtd/wavLM-VLSP-vi-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/phongdtd/wavLM-VLSP-vi-large/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- aa6ba5734f5ac31eb70c0f4512b5bbec9e6178145ed14b9cf1f98d086d224461
- Size of remote file:
- 1.26 GB
- SHA256:
- f4b8b5bf84825ff9c1eaba22dc5dec415e756c860282f2a9966398094b29d88c
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