Metadata-Version: 2.1
Name: musicnn
Version: 0.0.4
Summary: Pronounced as "musician", musicnn is a set of pre-trained deep convolutional neural networks for music audio tagging
Home-page: http://github.com/jordipons/musicnn
Author: Jordi Pons
License: ISC
Description: # musicnn
        Pronounced as "musician", `musicnn` is a set of pre-trained musically motivated convolutional neural networks for music audio tagging. This repository also includes some pre-trained [vgg-like](https://github.com/jordipons/musicnn/blob/master/vgg_example.ipynb) baselines.
        
        Check the [documentation](https://github.com/jordipons/musicnn/blob/master/DOCUMENTATION.md) and our [basic](https://github.com/jordipons/musicnn/blob/master/tagging_example.ipynb) / [advanced](https://github.com/jordipons/musicnn/blob/master/musicnn_example.ipynb) examples to understand how to use `musicnn`.
        
        Do you have questions? Check the [FAQs](https://github.com/jordipons/musicnn/blob/master/FAQs.md).
        
        ## Installation
        ```pip install musicnn```
        
        or, to get bigger models and all the documentation (including jupyter notebooks), install from source:
        
        ``` git clone https://github.com/jordipons/musicnn.git```
        
        ``` python setup.py install```
        
        ## Predict tags
        
        From within **python**, you can estimate the topN tags:
        ~~~~python
        from musicnn.tagger import top_tags
        top_tags('./audio/joram-moments_of_clarity-08-solipsism-59-88.mp3', model='MTT_musicnn', topN=10)
        ~~~~
        >['techno', 'electronic', 'synth', 'fast', 'beat', 'drums', 'no vocals', 'no vocal', 'dance', 'ambient']
        
        Let's try another song!
        
        ~~~~python
        top_tags('./audio/TRWJAZW128F42760DD_test.mp3')
        ~~~~
        >['guitar', 'piano', 'fast']
        
        From the **command-line**, you can also print the topN tags on the screen:
        
        ~~~~
        python -m musicnn.tagger file_name.ogg --print
        python -m musicnn.tagger file_name.au --model 'MSD_musicnn' --topN 3 --length 3 --overlap 1.5 --print
        ~~~~~
        
        or save to a file:
        
        ~~~~
        python -m musicnn.tagger file_name.wav --save out.tags
        python -m musicnn.tagger file_name.mp3 --model 'MTT_musicnn' --topN 10 --length 3 --overlap 1 --print --save out.tags
        ~~~~
        
        ## Extract the Taggram
        
        You can also compute the taggram using **python** (see our [basic](https://github.com/jordipons/musicnn/blob/master/tagging_example.ipynb) example for more details on how to depict it):
        
        ~~~~python
        from musicnn.extractor import extractor
        taggram, tags = extractor('./audio/joram-moments_of_clarity-08-solipsism-59-88.mp3', model='MTT_musicnn')
        ~~~~
        ![Taggram](./images/taggram.png "Taggram")
        
        The above analyzed music clips are included in the `./audio/` folder of this repository. 
        
        You can listen to those and evaluate `musicnn` yourself!
        
Keywords: audio music deep learning tagging tensorflow machine listening
Platform: UNKNOWN
Classifier: License :: OSI Approved :: ISC License (ISCL)
Classifier: Programming Language :: Python
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
