metadata
license: mit
library_name: pytorch
tags:
- audio-classification
- speech
- music
- pytorch
pipeline_tag: audio-classification
SMAD Audio Classifier
Classifies audio into four labels:
| id | label | meaning |
|---|---|---|
| 0 | speech_noise |
spoken voice over non-music noise |
| 1 | speech_music |
spoken voice over a music bed |
| 2 | singing_music |
sung voice over music |
| 3 | none |
no human voice: instrumental music, noise, or silence |
Install
pip install torch librosa soundfile numpy
Use
from predict_crnn import SmadClassifier
clf = SmadClassifier()
result = clf.predict("audio.mp3")
print(result["overall"])
print(result["share"])
CLI:
python predict_crnn.py audio.mp3 --json
Each 4-second window includes a label, confidence, and per-class probabilities.
The overall label is the class covering the most windows in the file.