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)# 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 preprocessor_config.json from Modotte/AIRealNet-Audio: direct link, hf CLI and curl.
- Browser
- Download file 259 Bytes
-
https://huggingface.co/Modotte/AIRealNet-Audio/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Modotte/AIRealNet-Audio/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Modotte/AIRealNet-Audio/resolve/main/preprocessor_config.json
259 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "max_length": 192000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "truncation": true | |
| } |