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
File size: 409 Bytes
33442e6 c8e15e2 33442e6 c8e15e2 33442e6 c8e15e2 33442e6 c8e15e2 33442e6 c8e15e2 33442e6 c8e15e2 33442e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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 ✅") |