Instructions to use haohaa/wav2vec2-large-mms-1b-shan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haohaa/wav2vec2-large-mms-1b-shan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="haohaa/wav2vec2-large-mms-1b-shan")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("haohaa/wav2vec2-large-mms-1b-shan") model = AutoModelForCTC.from_pretrained("haohaa/wav2vec2-large-mms-1b-shan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from haohaa/wav2vec2-large-mms-1b-shan: direct link, hf CLI and curl.
- Browser
- Download file 3.86 GB
-
https://huggingface.co/haohaa/wav2vec2-large-mms-1b-shan/resolve/main/model.safetensors
- Command line
-
hf download hf://haohaa/wav2vec2-large-mms-1b-shan/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/haohaa/wav2vec2-large-mms-1b-shan/resolve/main/model.safetensors
3.86 GB
- Xet hash:
- b18b717180dc07a50842776cc008516e2a25d4bae7eaba5910aa63bc2d7bb2c0
- Size of remote file:
- 3.86 GB
- SHA256:
- 5bcfcf67776abf93e4220b3fc48334bb0e05d772692160d62f3e3a693a46ec42
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