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 training_args.bin from haohaa/wav2vec2-large-mms-1b-shan: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/haohaa/wav2vec2-large-mms-1b-shan/resolve/main/training_args.bin
- Command line
-
hf download hf://haohaa/wav2vec2-large-mms-1b-shan/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/haohaa/wav2vec2-large-mms-1b-shan/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 18bdb75879f40f871e6564829d02809d180f3bb7644a5cb4c738d5a831582a0d
- Size of remote file:
- 5.05 kB
- SHA256:
- f94493c57197a3665e0b647944f13c00003b0e95ecc01b291ea28908e51f0d09
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