Instructions to use Ar4ikov/Wav2Vec2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ar4ikov/Wav2Vec2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ar4ikov/Wav2Vec2ForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ar4ikov/Wav2Vec2ForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("Ar4ikov/Wav2Vec2ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ar4ikov/Wav2Vec2ForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/Ar4ikov/Wav2Vec2ForSequenceClassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ar4ikov/Wav2Vec2ForSequenceClassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ar4ikov/Wav2Vec2ForSequenceClassification/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- efff7cbe1ae749c43fc7cc7185daa863b060f38fb5c8a28a37e33e866f294b3d
- Size of remote file:
- 1.27 GB
- SHA256:
- ee8c66ec519295e2f1643e9815853cf972decca568d56e5ab4bcb909dcdc3d1c
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