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Looking for maintainers - I no longer have the capacity to maintain this project. If you would like to take over maintenence, please get in touch. I will either forward to your fork, or add you as a maintainer for the project. Thanks.

VGGish

A torch-compatible port of VGGish<sup>[1]</sup>, a feature embedding frontend for audio classification models. The weights are ported directly from the tensorflow model, so embeddings created using torchvggish will be identical.

Usage

import torch

model = torch.hub.load('harritaylor/torchvggish', 'vggish')
model.eval()

# Download an example audio file
import urllib
url, filename = ("http://soundbible.com/grab.php?id=1698&type=wav", "bus_chatter.wav")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)

model.forward(filename)

<hr> [1] S. Hershey et al., ‘CNN Architectures for Large-Scale Audio Classification’,\ in International Conference on Acoustics, Speech and Signal Processing (ICASSP),2017\ Available: https://arxiv.org/abs/1609.09430, https://ai.google/research/pubs/pub45611