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  • I want to use VGG16 (or VGG19) for voice clustering task.
  • I read some articles which suggest to use VGG (16 or 19) in order to build the embedding vector for the clustering algorithm.
  • The process is to convert the wav file into mfcc or plot (Amp vs Time) and use this as input to VGG model.
  • I tried it out with VGG19 (and weights='imagenet').
  • I got bad results, and I assumed it because I'm using VGG with wrong weights (weights of images (imagenet))

So:

  1. Are there any audio/voice per-trained weights for VGG ?
  2. If not, are there other per-trained audio /voice models ?
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2 Answers 2

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As far as I know, VVGish is the VGG adapted to audio processing. I can remember using it with mfcc, not Amp-Time input tho.

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Besides VGGish mentioned by @Ubikuity, there are other pre-trained audio models:

  • PANNs by Qiuqiang Kong. As of July 2021 one of the best on general audio classification AudioSet. PANNs @ Github. Based on PyTorch
  • YAMNet, by same team at Google as VGGish. YAMNet @ TfHub. Based on Tensorflow.
  • OpenL3 by Music and Audio Research Laboratory at NYU. Very easy to get started with. OpenL3 @ Github. Based on Tensorflow/Keras.
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