panns_inference provides an easy to use Python interface for audio tagging and sound event detection. The audio tagging and sound event detection models are trained from PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition: https://github.com/qiuqiangkong/audioset_tagging_cnn
PyTorch>=1.0 is required.
$ pip install panns-inference
$ python3 example.py
For example:
import librosa
import panns_inference
from panns_inference import AudioTagging, SoundEventDetection, labels
audio_path = 'examples/R9_ZSCveAHg_7s.wav'
(audio, _) = librosa.core.load(audio_path, sr=32000, mono=True)
audio = audio[None, :] # (batch_size, segment_samples)
print('------ Audio tagging ------')
at = AudioTagging(checkpoint_path=None, device='cuda')
(clipwise_output, embedding) = at.inference(audio)
print('------ Sound event detection ------')
sed = SoundEventDetection(checkpoint_path=None, device='cuda')
framewise_output = sed.inference(audio)
------ Audio tagging ------ Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth GPU number: 1 Speech: 0.893 Telephone bell ringing: 0.754 Inside, small room: 0.235 Telephone: 0.183 Music: 0.092 Ringtone: 0.047 Inside, large room or hall: 0.028 Alarm: 0.014 Animal: 0.009 Vehicle: 0.008 ------ Sound event detection ------ Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth GPU number: 1 Save fig to results/sed_result.pdf
Sound event detection plot:
[1] Kong, Qiuqiang, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D. Plumbley. "PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition." arXiv preprint arXiv:1912.10211 (2019).