Instructions to use alkiskoudounas/wav2vec2-large-slurp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alkiskoudounas/wav2vec2-large-slurp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="alkiskoudounas/wav2vec2-large-slurp")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("alkiskoudounas/wav2vec2-large-slurp") model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/wav2vec2-large-slurp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from alkiskoudounas/wav2vec2-large-slurp: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/alkiskoudounas/wav2vec2-large-slurp/resolve/main/training_args.bin
- Command line
-
hf download hf://alkiskoudounas/wav2vec2-large-slurp/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/alkiskoudounas/wav2vec2-large-slurp/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 552887302d66c9af3e73cd6ff33608c2e000c40583a808ff84ed811fbbd230da
- Size of remote file:
- 5.3 kB
- SHA256:
- 93195b6ce37c327e37144d872765300fb413254de9caea3613dca78eed8e3139
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