Audio Classification
Transformers
Safetensors
English
wav2vec2
audio - wav2vec2 - deepfake-detection - synthetic-speech - tts - voice-cloning
Instructions to use Simma7/audio_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simma7/audio_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Simma7/audio_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Simma7/audio_model") model = AutoModelForAudioClassification.from_pretrained("Simma7/audio_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.py from Simma7/audio_model: direct link, hf CLI and curl.
- Browser
- Download file 409 Bytes
-
https://huggingface.co/Simma7/audio_model/resolve/main/model.py
- Command line
-
hf download hf://Simma7/audio_model/model.py
-
curl -L -o model.py https://huggingface.co/Simma7/audio_model/resolve/main/model.py
409 Bytes
| from transformers import AutoModelForAudioClassification, AutoProcessor | |
| import torch | |
| model_name = "Simma7/audio_model" | |
| print("[INFO] Loading model...") | |
| model = AutoModelForAudioClassification.from_pretrained( | |
| model_name, | |
| trust_remote_code=True | |
| ) | |
| processor = AutoProcessor.from_pretrained( | |
| model_name, | |
| trust_remote_code=True | |
| ) | |
| model.eval() | |
| print("[SUCCESS] Model loaded successfully ✅") |