Instructions to use cloudwalkerw/facebook_wav2vec2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwalkerw/facebook_wav2vec2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cloudwalkerw/facebook_wav2vec2-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("cloudwalkerw/facebook_wav2vec2-base") model = AutoModelForAudioClassification.from_pretrained("cloudwalkerw/facebook_wav2vec2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cloudwalkerw/facebook_wav2vec2-base: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/cloudwalkerw/facebook_wav2vec2-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cloudwalkerw/facebook_wav2vec2-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cloudwalkerw/facebook_wav2vec2-base/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- be5fe36c017afc84030213a6b232cc5c1d75c3e93d18ae17abf021ae9733a9b5
- Size of remote file:
- 378 MB
- SHA256:
- 6f37bfd536448cfb3c1afa82a2e00707b182055c157b0c167dea5437d386782d
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