Instructions to use arbml/wav2vec2-large-xlsr-dialect-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbml/wav2vec2-large-xlsr-dialect-classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("arbml/wav2vec2-large-xlsr-dialect-classification") model = AutoModel.from_pretrained("arbml/wav2vec2-large-xlsr-dialect-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from arbml/wav2vec2-large-xlsr-dialect-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/arbml/wav2vec2-large-xlsr-dialect-classification/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://arbml/wav2vec2-large-xlsr-dialect-classification/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/arbml/wav2vec2-large-xlsr-dialect-classification/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- 6ba1f3a9446e53ead4bf61cd6280397870f190f3b95b40440e5bee6d05efbb0c
- Size of remote file:
- 1.26 GB
- SHA256:
- 6b08668811b5c3755df838abd2b69549a40fc2cf5edeca5ee0db6a04cfecb7e4
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