Instructions to use superb/wav2vec2-base-superb-sid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/wav2vec2-base-superb-sid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-sid")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/wav2vec2-base-superb-sid") model = AutoModelForAudioClassification.from_pretrained("superb/wav2vec2-base-superb-sid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from superb/wav2vec2-base-superb-sid: direct link, hf CLI and curl.
- Browser
- Download file 380 MB
-
https://huggingface.co/superb/wav2vec2-base-superb-sid/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://superb/wav2vec2-base-superb-sid/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/superb/wav2vec2-base-superb-sid/resolve/main/pytorch_model.bin
380 MB
- Xet hash:
- 91615be244a6b7c55b81f167d5c750fb122b2faae0af6e0a4cdc79ffe3c61241
- Size of remote file:
- 380 MB
- SHA256:
- f0618f7154bddbabf66e3ff091d6975d489275657d23b2739c0202c9dbc99be0
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