Metadata-Version: 2.1
Name: torchvggish
Version: 0.2
Summary: A Pytorch port of Tensorflow's VGGish embedding model.
Home-page: https://github.com/harritaylor/torchvggish
Author: Harri Taylor
Author-email: taylorh23@cardiff.ac.uk
License: Apache-2.0
Description: # Torch VGGish
        A PyTorch port of [VGGish](https://github.com/tensorflow/models/tree/master/research/audioset)<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.
        
        ## Quick start
        There are two options: you can install the last stable version from pypi, or clone this repo and install.
        ```shell script
        # optional: create virtual env
        cd torchvggish && python3 -m venv .env
        source activate .env/bin/activate
        
        pip install -i https://test.pypi.org/simple/ torchvggish==0.1
        
        # OR get the latest version
        git clone git@github.com:harritaylor/torchvggish.git
        pip install -r requirements.txt
        ```
        ## Usage
        Barebones example of creating embeddings from an `example` wav file:
        ```python
        from torchvggish import vggish, vggish_input
        
        # Initialise model and download weights
        embedding_model = vggish()
        embedding_model.eval()
        
        example = vggish_input.wavfile_to_examples("example.wav")
        embeddings = embedding_model.forward(example)
        ```
        
        <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
            
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
