Audio Classification
Transformers
Safetensors
multilingual
wav2vec2-dual-hypersphere
audio-deepfake
deepfake-detection
deepfake
voice-cloning
anti-spoofing
asvspoof
wav2vec2
speech
audio
synthetic-voice
voice-conversion
tts-detection
trust-and-safety
security
SoTA
Modotte
custom_code
Instructions to use Modotte/AIRealNet-Audio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Modotte/AIRealNet-Audio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Modotte/AIRealNet-Audio", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("Modotte/AIRealNet-Audio", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/banner.jpeg from Modotte/AIRealNet-Audio: direct link, hf CLI and curl.
- Browser
- Download file 531 kB
-
https://huggingface.co/Modotte/AIRealNet-Audio/resolve/main/assets/banner.jpeg
- Command line
-
hf download hf://Modotte/AIRealNet-Audio/assets/banner.jpeg
-
curl -L -o banner.jpeg https://huggingface.co/Modotte/AIRealNet-Audio/resolve/main/assets/banner.jpeg
531 kB

- Xet hash:
- d86462579a57fee481363b893d624b475a7bb99d9fb4983d3575f3bd0a3806ca
- Size of remote file:
- 531 kB
- SHA256:
- 707280e3c0a60c7d82a245cfafc7b0006f75c3130d69e2d09c271b45b77bf38d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.