Instructions to use aibtw/circor_wav2vec_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aibtw/circor_wav2vec_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="aibtw/circor_wav2vec_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("aibtw/circor_wav2vec_model") model = AutoModelForAudioClassification.from_pretrained("aibtw/circor_wav2vec_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aibtw/circor_wav2vec_model: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/aibtw/circor_wav2vec_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aibtw/circor_wav2vec_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aibtw/circor_wav2vec_model/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 1425f2ca5e85de786117cf0064926f8e5f2c288c6d984fb7df0f6d46c6c67412
- Size of remote file:
- 378 MB
- SHA256:
- 0b46687312f97400e2e7bb509d50bb432cce6174f15ec3dd3fd5bf715d234a0e
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