Metadata-Version: 2.1
Name: cdpam
Version: 0.0.1
Summary: A pip package for an improved perceptual audio metric
Home-page: https://github.com/pranaymanocha/PerceptualAudio
Author: Pranay Manocha
Author-email: pranaymnch@gmail.com
License: UNKNOWN
Description: # Contrastive learning-based Deep Perceptual Audio Metric (CDPAM) [[Webpage]](https://percepaudio.cs.princeton.edu/Manocha20_CDPAM/)
        
        **Contrastive Learning For Perceptual Audio Similarity**
        
        [Pranay Manocha](https://www.cs.princeton.edu/~pmanocha/), [Zeyu Jin](https://research.adobe.com/person/zeyu-jin/), [Richard Zhang](http://richzhang.github.io/), [Adam Finkelstein](https://www.cs.princeton.edu/~af/)   
        Arxiv 2020()
        
        <img src='https://richzhang.github.io/index_files/audio_teaser.jpg' width=500>
        
        This is a Pytorch implementation of our new and improved audio perceptual metric. It contains (0) minimal code to run our perceptual metric (CDPAM).
        
        ## (0) Usage as a loss function
        
        ### Minimal basic usage as a distance metric
        
        Running the command below takes two audio files as input and gives the perceptual distance between the files. It should return (approx)**distance = 0.1696**. Some GPU's are non-deterministic, and so the distance could vary in the lsb.
        
        Installing the metric (CDPAM - perceptual audio similarity metric)
        ```bash
        pip install cdpam
        ```
        
        Using the metric is as simple as: 
        ```bash
        import cdpam
        loss_fn = cdpam.DPAM()
        wav_ref = cdpam.load_audio('sample_audio/ref.wav')
        wav_out = cdpam.load_audio('sample_audio/2.wav')
        
        dist = loss_fn.forward(wav_ref,wav_out)
        ```
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
