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https://github.com/correl/dejavu.git
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Merge branch 'next_version'
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Saleem Ansari
commit
6c1ef735cd
6 changed files with 109 additions and 70 deletions
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@ -1,31 +0,0 @@
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# Dependencies required by dejavu
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* [`pyaudio`](http://people.csail.mit.edu/hubert/pyaudio/)
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* [`ffmpeg`](https://github.com/FFmpeg/FFmpeg)
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* [`pydub`](http://pydub.com/)
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* [`numpy`](http://www.numpy.org/)
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* [`scipy`](http://www.scipy.org/)
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* [`matplotlib`](http://matplotlib.org/)
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* [`MySQLdb`](http://mysql-python.sourceforge.net/MySQLdb.html)
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## Dependency installation for Mac OS X
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Tested on OS X Mavericks. An option is to install [Homebrew](http://brew.sh) and do the following:
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```
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brew install portaudio
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brew install ffmpeg
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sudo easy_install pyaudio
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sudo easy_install pydub
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sudo easy_install numpy
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sudo easy_install scipy
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sudo easy_install matplotlib
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sudo easy_install pip
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sudo pip install MySQL-python
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sudo ln -s /usr/local/mysql/lib/libmysqlclient.18.dylib /usr/lib/libmysqlclient.18.dylib
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```
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However installing `portaudio` and/or `ffmpeg` from source is also doable.
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63
INSTALLATION.md
Normal file
63
INSTALLATION.md
Normal file
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@ -0,0 +1,63 @@
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# Installation of dejavu
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So far dejavu has only been tested on Unix systems.
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* [`pyaudio`](http://people.csail.mit.edu/hubert/pyaudio/) for grabbing audio from microphone
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* [`ffmpeg`](https://github.com/FFmpeg/FFmpeg) for converting audio files to .wav format
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* [`pydub`](http://pydub.com/), a Python `ffmpeg` wrapper
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* [`numpy`](http://www.numpy.org/) for taking the FFT of audio signals
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* [`scipy`](http://www.scipy.org/), used in peak finding algorithms
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* [`matplotlib`](http://matplotlib.org/), used for spectrograms and plotting
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* [`MySQLdb`](http://mysql-python.sourceforge.net/MySQLdb.html) for interfacing with MySQL databases
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For installing `ffmpeg` on Mac OS X, I highly recommend [this post](http://jungels.net/articles/ffmpeg-howto.html).
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## Fedora 20+
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### Dependency installation for Mac OS X
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Install the dependencies
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sudo yum install numpy scipy python-matplotlib ffmpeg portaudio-devel
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pip install PyAudio
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pip install pydub
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Now setup virtualenv ([howto?](http://www.pythoncentral.io/how-to-install-virtualenv-python/))
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pip install virtualenv
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virtualenv --system-site-packages env_with_system
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Install from PyPI
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source env_with_system/bin/activate
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pip install PyDejavu
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You can also install the latest code from GitHub:
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source env_with_system/bin/activate
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pip install https://github.com/tuxdna/dejavu/zipball/master
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## Max OS X
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### Dependency installation for Mac OS X
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Tested on OS X Mavericks. An option is to install [Homebrew](http://brew.sh) and do the following:
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```
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brew install portaudio
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brew install ffmpeg
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sudo easy_install pyaudio
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sudo easy_install pydub
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sudo easy_install numpy
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sudo easy_install scipy
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sudo easy_install matplotlib
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sudo easy_install pip
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sudo pip install MySQL-python
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sudo ln -s /usr/local/mysql/lib/libmysqlclient.18.dylib /usr/lib/libmysqlclient.18.dylib
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```
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However installing `portaudio` and/or `ffmpeg` from source is also doable.
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16
README.md
16
README.md
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@ -6,19 +6,9 @@ Audio fingerprinting and recognition algorithm implemented in Python, see the ex
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Dejavu can memorize audio by listening to it once and fingerprinting it. Then by playing a song and recording microphone input, Dejavu attempts to match the audio against the fingerprints held in the database, returning the song being played.
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## Dependencies:
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## Installation and Dependencies:
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I've only tested this on Unix systems.
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* [`pyaudio`](http://people.csail.mit.edu/hubert/pyaudio/) for grabbing audio from microphone
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* [`ffmpeg`](https://github.com/FFmpeg/FFmpeg) for converting audio files to .wav format
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* [`pydub`](http://pydub.com/), a Python `ffmpeg` wrapper
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* [`numpy`](http://www.numpy.org/) for taking the FFT of audio signals
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* [`scipy`](http://www.scipy.org/), used in peak finding algorithms
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* [`matplotlib`](http://matplotlib.org/), used for spectrograms and plotting
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* [`MySQLdb`](http://mysql-python.sourceforge.net/MySQLdb.html) for interfacing with MySQL databases
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For installing `ffmpeg` on Mac OS X, I highly recommend [this post](http://jungels.net/articles/ffmpeg-howto.html).
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Read [INSTALLATION.md](INSTALLATION.md)
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## Setup
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@ -148,7 +138,7 @@ and with the command line script, you specify the number of seconds to listen:
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$ python dejavu.py --recognize mic 10
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```
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## Testing (New!)
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## Testing
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Testing out different parameterizations of the fingerprinting algorithm is often useful as the corpus becomes larger and larger, and inevitable tradeoffs between speed and accuracy come into play.
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@ -3,6 +3,9 @@ import dejavu.decoder as decoder
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import fingerprint
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import multiprocessing
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import os
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import traceback
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import sys
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class Dejavu(object):
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@ -27,7 +30,7 @@ class Dejavu(object):
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# if we should limit seconds fingerprinted,
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# None|-1 means use entire track
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self.limit = self.config.get("fingerprint_limit", None)
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if self.limit == -1: # for JSON compatibility
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if self.limit == -1: # for JSON compatibility
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self.limit = None
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self.get_fingerprinted_songs()
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@ -79,9 +82,7 @@ class Dejavu(object):
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break
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except:
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print("Failed fingerprinting")
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# Print traceback because we can't reraise it here
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import traceback, sys
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traceback.print_exc(file=sys.stdout)
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else:
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sid = self.db.insert_song(song_name)
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@ -94,13 +95,12 @@ class Dejavu(object):
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pool.join()
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def fingerprint_file(self, filepath, song_name=None):
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songname = decoder.path_to_songname(filepath)
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song_name = song_name or songname
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# don't refingerprint already fingerprinted files
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songname = decoder.path_to_songname(filepath)
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song_name = song_name or songname
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# don't refingerprint already fingerprinted files
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if song_name in self.songnames_set:
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print "%s already fingerprinted, continuing..." % song_name
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else:
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else:
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song_name, hashes = _fingerprint_worker(filepath,
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self.limit,
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song_name=song_name)
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@ -129,9 +129,9 @@ class Dejavu(object):
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song_id = -1
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for tup in matches:
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sid, diff = tup
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if not diff in diff_counter:
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if diff not in diff_counter:
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diff_counter[diff] = {}
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if not sid in diff_counter[diff]:
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if sid not in diff_counter[diff]:
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diff_counter[diff][sid] = 0
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diff_counter[diff][sid] += 1
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@ -149,15 +149,16 @@ class Dejavu(object):
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return None
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# return match info
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nseconds = round(float(largest) / fingerprint.DEFAULT_FS * \
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fingerprint.DEFAULT_WINDOW_SIZE * \
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fingerprint.DEFAULT_OVERLAP_RATIO, 5)
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nseconds = round(float(largest) / fingerprint.DEFAULT_FS *
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fingerprint.DEFAULT_WINDOW_SIZE *
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fingerprint.DEFAULT_OVERLAP_RATIO, 5)
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song = {
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Dejavu.SONG_ID : song_id,
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Dejavu.SONG_NAME : songname,
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Dejavu.CONFIDENCE : largest_count,
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Dejavu.OFFSET : largest,
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Dejavu.OFFSET_SECS : nseconds }
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Dejavu.SONG_ID: song_id,
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Dejavu.SONG_NAME: songname,
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Dejavu.CONFIDENCE: largest_count,
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Dejavu.OFFSET: largest,
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Dejavu.OFFSET_SECS: nseconds
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}
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return song
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@ -2,7 +2,7 @@
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### BEGIN ###
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pydub==0.9.4
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PyAudio==0.2.8
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PyAudio>=0.2.7
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numpy==1.8.2
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scipy==0.12.1
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matplotlib==1.3.1
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30
setup.py
30
setup.py
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return reqs
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PACKAGE_NAME = "PyDejavu"
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PACKAGE_VERSION = "0.1.1"
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SUMMARY = 'Dejavu Audio Fingerprinting'
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DESCRIPTION = """Dejavu Audio Fingerprinting"""
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PACKAGE_VERSION = "0.1.2"
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SUMMARY = 'Dejavu: Audio Fingerprinting in Python'
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DESCRIPTION = """
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Audio fingerprinting and recognition algorithm implemented in Python
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See the explanation here:
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`http://willdrevo.com/fingerprinting-and-audio-recognition-with-python/`__
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Dejavu can memorize recorded audio by listening to it once and fingerprinting
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it. Then by playing a song and recording microphone input or on disk file,
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Dejavu attempts to match the audio against the fingerprints held in the
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database, returning the song or recording being played.
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__ http://willdrevo.com/fingerprinting-and-audio-recognition-with-python/
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"""
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REQUIREMENTS = parse_requirements("requirements.txt")
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setup(
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version=PACKAGE_VERSION,
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description=SUMMARY,
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long_description=DESCRIPTION,
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author='worldveil',
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author='Will Drevo',
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author_email='will.drevo@gmail.com',
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maintainer="Saleem Ansari",
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maintainer_email="tuxdna@gmail.com",
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url='http://github.com/tuxdna/dejavu',
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license='Apache 2.0',
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license='MIT License',
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include_package_data=True,
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packages=find_packages(),
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platforms=['Any'],
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platforms=['Unix'],
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install_requires=REQUIREMENTS,
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classifiers=[
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'Development Status :: 4 - Beta',
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'Environment :: Console',
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'Intended Audience :: Developers',
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'License :: OSI Approved :: Apache Software License',
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'License :: OSI Approved :: MIT License',
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'Operating System :: OS Independent',
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'Topic :: Software Development :: Libraries :: Python Modules',
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],
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keywords="python, audio, fingerprinting, music, numpy, landmark",
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)
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