Metadata-Version: 1.1
Name: audiogrep
Version: 0.1.5
Summary: Creates audio supercuts
Home-page: http://antiboredom.github.io/audiogrep
Author: Sam Lavigne
Author-email: splavigne@gmail.com
License: LICENSE
Description: Audiogrep
        =========
        
        Audiogrep transcribes audio files and then creates "audio supercuts" based on search phrases. It uses [CMU Pocketsphinx](http://cmusphinx.sourceforge.net/) for speech-to-text and [pydub](http://pydub.com/) to stitch things together.
        
        Here's some [sample output](http://lav.io/2015/02/audiogrep-automatic-audio-supercuts/).
        
        ## Requirements
        Install using pip
        ```
        pip install audiogrep
        ```
        Install [ffmpeg](http://ffmpeg.org/) with Ogg/Vorbis support. If you're on a mac with [homebrew](http://brew.sh/) you can install ffmpeg with:
        ```
        brew install ffmpeg --with-libvpx --with-libvorbis
        ```
        Finally, install [CMU Pocketsphinx](http://cmusphinx.sourceforge.net/). For mac
        users I followed [these instructions](https://github.com/watsonbox/homebrew-cmu-sphinx) to get it working:
        ```
        brew tap watsonbox/cmu-sphinx
        brew install --HEAD watsonbox/cmu-sphinx/cmu-sphinxbase
        brew install --HEAD watsonbox/cmu-sphinx/cmu-sphinxtrain # optional
        brew install --HEAD watsonbox/cmu-sphinx/cmu-pocketsphinx
        ```
        
        ## How to use it
        First, transcribe the audio (you'll only need to do this once per audio track, but it can take some time)
        ```
        # transcribes all mp3s in the selected folder
        audiogrep --input path/to/*.mp3 --transcribe
        ```
        Then, basic use:
        ```
        # returns all phrases with the word 'word' in them
        audiogrep --input path/to/*.mp3 --search 'word'
        ```
        The previous example will extract phrase chunks containing the search term, but you can also just get individual words:
        ```
        audiogrep --input path/to/*.mp3 --search 'word' --output-mode word
        ```
        If you add the '--regex' flag you can use regular expressions. For example:
        ```
        # creates a supercut of every instance of the words "spectre", "haunting" and "europe"
        audiogrep --input path/to/*.mp3 --search 'spectre|haunting|europe' --output-mode word --regex
        ```
        You can also construct 'frankenstein' sentences (mileage may vary):
        ```
        # stupid joke
        audiogrep --input path/to/*.mp3 --search 'my voice is my passport' --output-mode franken
        ```
        Or you can just extract individual words into files.
        ```
        # extracts each individual word into its own file in a directory called 'extracted_words'
        audiogrep --input path/to/*.mp3 --extract
        
        Exporting to: extracted_words/i.mp3
        Exporting to: extracted_words/am.mp3
        Exporting to: extracted_words/the.mp3
        Exporting to: extracted_words/key.mp3
        Exporting to: extracted_words/master.mp3
        ```
        
        ### Options
        
        audiogrep can take a number of options:
        
        #### --input / -i
        mp3 file or pattern for input
        
        #### --output / -o
        Name of the file to generate. By default this is "supercut.mp3"
        
        #### --search / -s
        Search term
        
        #### --output-mode / -m
        Splice together phrases, single words, fragments with wildcards, or "frankenstein" sentences.
        Options are:
        * sentence: (this is the default)
        * word
        * fragment
        * franken
        
        #### --padding / -p
        Time in milliseconds to add between audio segments. Default is 0.
        
        #### --crossfade / -c
        Time in milliseconds to crossfade audio segments. Default is 0.
        
        #### --extract / -x
        
        #### --demo / -d
        Show the results of the search without outputing a file
        
Keywords: audio supercut pydub transcribe transcription
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Multimedia :: Sound/Audio
Classifier: Topic :: Multimedia :: Sound/Audio :: Editors
Classifier: Topic :: Multimedia :: Sound/Audio :: Speech
