Instructions to use anton-l/wav2vec2-base-ft-common-language with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anton-l/wav2vec2-base-ft-common-language with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anton-l/wav2vec2-base-ft-common-language")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anton-l/wav2vec2-base-ft-common-language") model = AutoModelForAudioClassification.from_pretrained("anton-l/wav2vec2-base-ft-common-language", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anton-l/wav2vec2-base-ft-common-language: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/anton-l/wav2vec2-base-ft-common-language/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anton-l/wav2vec2-base-ft-common-language/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anton-l/wav2vec2-base-ft-common-language/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 1f945aaa75a8c274479b3687d71c3545ffed1d5a78f0474dd1045aae3b4a25c8
- Size of remote file:
- 378 MB
- SHA256:
- 39dbe65b771eecfbc2f591e818c5e5a5d7ae03b92e132c7c03cb13d17f17db6a
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