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Merge pull request #205 from mauriciorepetto/dejavu_python_3.6.6

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Dejavu on python 3.6.6
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Will Drevo 2020-06-02 16:49:59 -07:00 committed by GitHub
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28
README.md
View file

@ -24,6 +24,8 @@ Second, you'll need to create a MySQL database where Dejavu can store fingerprin
Now you're ready to start fingerprinting your audio collection!
Obs: The same from above goes for postgres database if you want to use it.
## Quickstart
```bash
@ -44,8 +46,8 @@ Start by creating a Dejavu object with your configurations settings (Dejavu take
... "database": {
... "host": "127.0.0.1",
... "user": "root",
... "passwd": <password above>,
... "db": <name of the database you created above>,
... "password": <password above>,
... "database": <name of the database you created above>,
... }
... }
>>> djv = Dejavu(config)
@ -81,7 +83,7 @@ The following keys are mandatory:
The following keys are optional:
* `fingerprint_limit`: allows you to control how many seconds of each audio file to fingerprint. Leaving out this key, or alternatively using `-1` and `None` will cause Dejavu to fingerprint the entire audio file. Default value is `None`.
* `database_type`: as of now, only `mysql` (the default value) is supported. If you'd like to subclass `Database` and add another, please fork and send a pull request!
* `database_type`: `mysql` (the default value) and `postgres` are supported. If you'd like to add another subclass for `BaseDatabase` and implement a new type of database, please fork and send a pull request!
An example configuration is as follows:
@ -91,8 +93,8 @@ An example configuration is as follows:
... "database": {
... "host": "127.0.0.1",
... "user": "root",
... "passwd": "Password123",
... "db": "dejavu_db",
... "password": "Password123",
... "database": "dejavu_db",
... },
... "database_type" : "mysql",
... "fingerprint_limit" : 10
@ -102,16 +104,16 @@ An example configuration is as follows:
## Tuning
Inside `fingerprint.py`, you may want to adjust following parameters (some values are given below).
Inside `config/settings.py`, you may want to adjust following parameters (some values are given below).
FINGERPRINT_REDUCTION = 30
PEAK_SORT = False
DEFAULT_OVERLAP_RATIO = 0.4
DEFAULT_FAN_VALUE = 10
DEFAULT_AMP_MIN = 15
PEAK_NEIGHBORHOOD_SIZE = 30
DEFAULT_FAN_VALUE = 5
DEFAULT_AMP_MIN = 10
PEAK_NEIGHBORHOOD_SIZE = 10
These parameters are described in the `fingerprint.py` in detail. Read that in-order to understand the impact of changing these values.
These parameters are described within the file in detail. Read that in-order to understand the impact of changing these values.
## Recognizing
@ -123,13 +125,13 @@ Through the terminal:
```bash
$ python dejavu.py --recognize file sometrack.wav
{'song_id': 1, 'song_name': 'Taylor Swift - Shake It Off', 'confidence': 3948, 'offset_seconds': 30.00018, 'match_time': 0.7159781455993652, 'offset': 646L}
{'total_time': 2.863781690597534, 'fingerprint_time': 2.4306554794311523, 'query_time': 0.4067542552947998, 'align_time': 0.007731199264526367, 'results': [{'song_id': 1, 'song_name': 'Taylor Swift - Shake It Off', 'input_total_hashes': 76168, 'fingerprinted_hashes_in_db': 4919, 'hashes_matched_in_input': 794, 'input_confidence': 0.01, 'fingerprinted_confidence': 0.16, 'offset': -924, 'offset_seconds': -30.00018, 'file_sha1': b'3DC269DF7B8DB9B30D2604DA80783155912593E8'}, {...}, ...]}
```
or in scripting, assuming you've already instantiated a Dejavu object:
```python
>>> from dejavu.recognize import FileRecognizer
>>> from dejavu.logic.recognizer.file_recognizer import FileRecognizer
>>> song = djv.recognize(FileRecognizer, "va_us_top_40/wav/Mirrors - Justin Timberlake.wav")
```
@ -138,7 +140,7 @@ or in scripting, assuming you've already instantiated a Dejavu object:
With scripting:
```python
>>> from dejavu.recognize import MicrophoneRecognizer
>>> from dejavu.logic.recognizer.microphone_recognizer import MicrophoneRecognizer
>>> song = djv.recognize(MicrophoneRecognizer, seconds=10) # Defaults to 10 seconds.
```

9
dejavu.cnf.SAMPLE
View file

@ -2,7 +2,8 @@
"database": {
"host": "127.0.0.1",
"user": "root",
"passwd": "12345678",
"db": "dejavu"
}
}
"password": "rootpass",
"database": "dejavu"
},
"database_type": "mysql"
}

40
dejavu.py
View file

@ -1,30 +1,25 @@
#!/usr/bin/python
import os
import sys
import json
import warnings
import argparse
import json
import sys
from argparse import RawTextHelpFormatter
from os.path import isdir
from dejavu import Dejavu
from dejavu.recognize import FileRecognizer
from dejavu.recognize import MicrophoneRecognizer
from argparse import RawTextHelpFormatter
warnings.filterwarnings("ignore")
from dejavu.logic.recognizer.file_recognizer import FileRecognizer
from dejavu.logic.recognizer.microphone_recognizer import MicrophoneRecognizer
DEFAULT_CONFIG_FILE = "dejavu.cnf.SAMPLE"
def init(configpath):
"""
"""
Load config from a JSON file
"""
try:
with open(configpath) as f:
config = json.load(f)
except IOError as err:
print("Cannot open configuration: %s. Exiting" % (str(err)))
print(f"Cannot open configuration: {str(err)}. Exiting")
sys.exit(1)
# create a Dejavu instance
@ -46,7 +41,7 @@ if __name__ == '__main__':
'--fingerprint /path/to/directory')
parser.add_argument('-r', '--recognize', nargs=2,
help='Recognize what is '
'playing through the microphone\n'
'playing through the microphone or in a file.\n'
'Usage: \n'
'--recognize mic number_of_seconds \n'
'--recognize file path/to/file \n')
@ -59,7 +54,6 @@ if __name__ == '__main__':
config_file = args.config
if config_file is None:
config_file = DEFAULT_CONFIG_FILE
# print "Using default config file: %s" % (config_file)
djv = init(config_file)
if args.fingerprint:
@ -67,28 +61,24 @@ if __name__ == '__main__':
if len(args.fingerprint) == 2:
directory = args.fingerprint[0]
extension = args.fingerprint[1]
print("Fingerprinting all .%s files in the %s directory"
% (extension, directory))
print(f"Fingerprinting all .{extension} files in the {directory} directory")
djv.fingerprint_directory(directory, ["." + extension], 4)
elif len(args.fingerprint) == 1:
filepath = args.fingerprint[0]
if os.path.isdir(filepath):
if isdir(filepath):
print("Please specify an extension if you'd like to fingerprint a directory!")
sys.exit(1)
djv.fingerprint_file(filepath)
elif args.recognize:
# Recognize audio source
song = None
songs = None
source = args.recognize[0]
opt_arg = args.recognize[1]
if source in ('mic', 'microphone'):
song = djv.recognize(MicrophoneRecognizer, seconds=opt_arg)
songs = djv.recognize(MicrophoneRecognizer, seconds=opt_arg)
elif source == 'file':
song = djv.recognize(FileRecognizer, opt_arg)
decoded_song = repr(song).decode('string_escape')
print(decoded_song)
sys.exit(0)
songs = djv.recognize(FileRecognizer, opt_arg)
print(songs)

275
dejavu/__init__.py
View file

@ -1,28 +1,29 @@
from dejavu.database import get_database, Database
import dejavu.decoder as decoder
import fingerprint
import multiprocessing
import os
import traceback
import sys
import traceback
from itertools import groupby
from time import time
from typing import Dict, List, Tuple
import dejavu.logic.decoder as decoder
from dejavu.base_classes.base_database import get_database
from dejavu.config.settings import (DEFAULT_FS, DEFAULT_OVERLAP_RATIO,
DEFAULT_WINDOW_SIZE, FIELD_FILE_SHA1,
FIELD_TOTAL_HASHES,
FINGERPRINTED_CONFIDENCE,
FINGERPRINTED_HASHES, HASHES_MATCHED,
INPUT_CONFIDENCE, INPUT_HASHES, OFFSET,
OFFSET_SECS, SONG_ID, SONG_NAME, TOPN)
from dejavu.logic.fingerprint import fingerprint
class Dejavu(object):
SONG_ID = "song_id"
SONG_NAME = 'song_name'
CONFIDENCE = 'confidence'
MATCH_TIME = 'match_time'
OFFSET = 'offset'
OFFSET_SECS = 'offset_seconds'
class Dejavu:
def __init__(self, config):
super(Dejavu, self).__init__()
self.config = config
# initialize db
db_cls = get_database(config.get("database_type", None))
db_cls = get_database(config.get("database_type", "mysql").lower())
self.db = db_cls(**config.get("database", {}))
self.db.setup()
@ -32,17 +33,44 @@ class Dejavu(object):
self.limit = self.config.get("fingerprint_limit", None)
if self.limit == -1: # for JSON compatibility
self.limit = None
self.get_fingerprinted_songs()
self.__load_fingerprinted_audio_hashes()
def get_fingerprinted_songs(self):
def __load_fingerprinted_audio_hashes(self) -> None:
"""
Keeps a dictionary with the hashes of the fingerprinted songs, in that way is possible to check
whether or not an audio file was already processed.
"""
# get songs previously indexed
self.songs = self.db.get_songs()
self.songhashes_set = set() # to know which ones we've computed before
for song in self.songs:
song_hash = song[Database.FIELD_FILE_SHA1]
song_hash = song[FIELD_FILE_SHA1]
self.songhashes_set.add(song_hash)
def fingerprint_directory(self, path, extensions, nprocesses=None):
def get_fingerprinted_songs(self) -> List[Dict[str, any]]:
"""
To pull all fingerprinted songs from the database.
:return: a list of fingerprinted audios from the database.
"""
return self.db.get_songs()
def delete_songs_by_id(self, song_ids: List[int]) -> None:
"""
Deletes all audios given their ids.
:param song_ids: song ids to delete from the database.
"""
self.db.delete_songs_by_id(song_ids)
def fingerprint_directory(self, path: str, extensions: str, nprocesses: int = None) -> None:
"""
Given a directory and a set of extensions it fingerprints all files that match each extension specified.
:param path: path to the directory.
:param extensions: list of file extensions to consider.
:param nprocesses: amount of processes to fingerprint the files within the directory.
"""
# Try to use the maximum amount of processes if not given.
try:
nprocesses = nprocesses or multiprocessing.cpu_count()
@ -55,54 +83,58 @@ class Dejavu(object):
filenames_to_fingerprint = []
for filename, _ in decoder.find_files(path, extensions):
# don't refingerprint already fingerprinted files
if decoder.unique_hash(filename) in self.songhashes_set:
print "%s already fingerprinted, continuing..." % filename
print(f"{filename} already fingerprinted, continuing...")
continue
filenames_to_fingerprint.append(filename)
# Prepare _fingerprint_worker input
worker_input = zip(filenames_to_fingerprint,
[self.limit] * len(filenames_to_fingerprint))
worker_input = list(zip(filenames_to_fingerprint, [self.limit] * len(filenames_to_fingerprint)))
# Send off our tasks
iterator = pool.imap_unordered(_fingerprint_worker,
worker_input)
iterator = pool.imap_unordered(Dejavu._fingerprint_worker, worker_input)
# Loop till we have all of them
while True:
try:
song_name, hashes, file_hash = iterator.next()
song_name, hashes, file_hash = next(iterator)
except multiprocessing.TimeoutError:
continue
except StopIteration:
break
except:
except Exception:
print("Failed fingerprinting")
# Print traceback because we can't reraise it here
traceback.print_exc(file=sys.stdout)
else:
sid = self.db.insert_song(song_name, file_hash)
sid = self.db.insert_song(song_name, file_hash, len(hashes))
self.db.insert_hashes(sid, hashes)
self.db.set_song_fingerprinted(sid)
self.get_fingerprinted_songs()
self.__load_fingerprinted_audio_hashes()
pool.close()
pool.join()
def fingerprint_file(self, filepath, song_name=None):
songname = decoder.path_to_songname(filepath)
song_hash = decoder.unique_hash(filepath)
song_name = song_name or songname
def fingerprint_file(self, file_path: str, song_name: str = None) -> None:
"""
Given a path to a file the method generates hashes for it and stores them in the database
for later be queried.
:param file_path: path to the file.
:param song_name: song name associated to the audio file.
"""
song_name_from_path = decoder.get_audio_name_from_path(file_path)
song_hash = decoder.unique_hash(file_path)
song_name = song_name or song_name_from_path
# don't refingerprint already fingerprinted files
if song_hash in self.songhashes_set:
print "%s already fingerprinted, continuing..." % song_name
print(f"{song_name} already fingerprinted, continuing...")
else:
song_name, hashes, file_hash = _fingerprint_worker(
filepath,
song_name, hashes, file_hash = Dejavu._fingerprint_worker(
file_path,
self.limit,
song_name=song_name
)
@ -110,93 +142,118 @@ class Dejavu(object):
self.db.insert_hashes(sid, hashes)
self.db.set_song_fingerprinted(sid)
self.get_fingerprinted_songs()
self.__load_fingerprinted_audio_hashes()
def find_matches(self, samples, Fs=fingerprint.DEFAULT_FS):
hashes = fingerprint.fingerprint(samples, Fs=Fs)
return self.db.return_matches(hashes)
def generate_fingerprints(self, samples: List[int], Fs=DEFAULT_FS) -> Tuple[List[Tuple[str, int]], float]:
f"""
Generate the fingerprints for the given sample data (channel).
def align_matches(self, matches):
:param samples: list of ints which represents the channel info of the given audio file.
:param Fs: sampling rate which defaults to {DEFAULT_FS}.
:return: a list of tuples for hash and its corresponding offset, together with the generation time.
"""
Finds hash matches that align in time with other matches and finds
consensus about which hashes are "true" signal from the audio.
t = time()
hashes = fingerprint(samples, Fs=Fs)
fingerprint_time = time() - t
return hashes, fingerprint_time
Returns a dictionary with match information.
def find_matches(self, hashes: List[Tuple[str, int]]) -> Tuple[List[Tuple[int, int]], Dict[str, int], float]:
"""
# align by diffs
diff_counter = {}
largest = 0
largest_count = 0
song_id = -1
for tup in matches:
sid, diff = tup
if diff not in diff_counter:
diff_counter[diff] = {}
if sid not in diff_counter[diff]:
diff_counter[diff][sid] = 0
diff_counter[diff][sid] += 1
Finds the corresponding matches on the fingerprinted audios for the given hashes.
if diff_counter[diff][sid] > largest_count:
largest = diff
largest_count = diff_counter[diff][sid]
song_id = sid
:param hashes: list of tuples for hashes and their corresponding offsets
:return: a tuple containing the matches found against the db, a dictionary which counts the different
hashes matched for each song (with the song id as key), and the time that the query took.
# extract idenfication
song = self.db.get_song_by_id(song_id)
if song:
# TODO: Clarify what `get_song_by_id` should return.
songname = song.get(Dejavu.SONG_NAME, None)
else:
return None
"""
t = time()
matches, dedup_hashes = self.db.return_matches(hashes)
query_time = time() - t
# return match info
nseconds = round(float(largest) / fingerprint.DEFAULT_FS *
fingerprint.DEFAULT_WINDOW_SIZE *
fingerprint.DEFAULT_OVERLAP_RATIO, 5)
song = {
Dejavu.SONG_ID : song_id,
Dejavu.SONG_NAME : songname.encode("utf8"),
Dejavu.CONFIDENCE : largest_count,
Dejavu.OFFSET : int(largest),
Dejavu.OFFSET_SECS : nseconds,
Database.FIELD_FILE_SHA1 : song.get(Database.FIELD_FILE_SHA1, None).encode("utf8"),}
return song
return matches, dedup_hashes, query_time
def recognize(self, recognizer, *options, **kwoptions):
def align_matches(self, matches: List[Tuple[int, int]], dedup_hashes: Dict[str, int], queried_hashes: int,
topn: int = TOPN) -> List[Dict[str, any]]:
"""
Finds hash matches that align in time with other matches and finds
consensus about which hashes are "true" signal from the audio.
:param matches: matches from the database
:param dedup_hashes: dictionary containing the hashes matched without duplicates for each song
(key is the song id).
:param queried_hashes: amount of hashes sent for matching against the db
:param topn: number of results being returned back.
:return: a list of dictionaries (based on topn) with match information.
"""
# count offset occurrences per song and keep only the maximum ones.
sorted_matches = sorted(matches, key=lambda m: (m[0], m[1]))
counts = [(*key, len(list(group))) for key, group in groupby(sorted_matches, key=lambda m: (m[0], m[1]))]
songs_matches = sorted(
[max(list(group), key=lambda g: g[2]) for key, group in groupby(counts, key=lambda count: count[0])],
key=lambda count: count[2], reverse=True
)
songs_result = []
for song_id, offset, _ in songs_matches[0:topn]: # consider topn elements in the result
song = self.db.get_song_by_id(song_id)
song_name = song.get(SONG_NAME, None)
song_hashes = song.get(FIELD_TOTAL_HASHES, None)
nseconds = round(float(offset) / DEFAULT_FS * DEFAULT_WINDOW_SIZE * DEFAULT_OVERLAP_RATIO, 5)
hashes_matched = dedup_hashes[song_id]
song = {
SONG_ID: song_id,
SONG_NAME: song_name.encode("utf8"),
INPUT_HASHES: queried_hashes,
FINGERPRINTED_HASHES: song_hashes,
HASHES_MATCHED: hashes_matched,
# Percentage regarding hashes matched vs hashes from the input.
INPUT_CONFIDENCE: round(hashes_matched / queried_hashes, 2),
# Percentage regarding hashes matched vs hashes fingerprinted in the db.
FINGERPRINTED_CONFIDENCE: round(hashes_matched / song_hashes, 2),
OFFSET: offset,
OFFSET_SECS: nseconds,
FIELD_FILE_SHA1: song.get(FIELD_FILE_SHA1, None).encode("utf8")
}
songs_result.append(song)
return songs_result
def recognize(self, recognizer, *options, **kwoptions) -> Dict[str, any]:
r = recognizer(self)
return r.recognize(*options, **kwoptions)
@staticmethod
def _fingerprint_worker(arguments):
# Pool.imap sends arguments as tuples so we have to unpack
# them ourself.
try:
file_name, limit = arguments
except ValueError:
pass
def _fingerprint_worker(filename, limit=None, song_name=None):
# Pool.imap sends arguments as tuples so we have to unpack
# them ourself.
try:
filename, limit = filename
except ValueError:
pass
song_name, extension = os.path.splitext(os.path.basename(file_name))
songname, extension = os.path.splitext(os.path.basename(filename))
song_name = song_name or songname
channels, Fs, file_hash = decoder.read(filename, limit)
result = set()
channel_amount = len(channels)
fingerprints, file_hash = Dejavu.get_file_fingerprints(file_name, limit, print_output=True)
for channeln, channel in enumerate(channels):
# TODO: Remove prints or change them into optional logging.
print("Fingerprinting channel %d/%d for %s" % (channeln + 1,
channel_amount,
filename))
hashes = fingerprint.fingerprint(channel, Fs=Fs)
print("Finished channel %d/%d for %s" % (channeln + 1, channel_amount,
filename))
result |= set(hashes)
return song_name, fingerprints, file_hash
return song_name, result, file_hash
@staticmethod
def get_file_fingerprints(file_name: str, limit: int, print_output: bool = False):
channels, fs, file_hash = decoder.read(file_name, limit)
fingerprints = set()
channel_amount = len(channels)
for channeln, channel in enumerate(channels, start=1):
if print_output:
print(f"Fingerprinting channel {channeln}/{channel_amount} for {file_name}")
hashes = fingerprint(channel, Fs=fs)
def chunkify(lst, n):
"""
Splits a list into roughly n equal parts.
http://stackoverflow.com/questions/2130016/splitting-a-list-of-arbitrary-size-into-only-roughly-n-equal-parts
"""
return [lst[i::n] for i in xrange(n)]
if print_output:
print(f"Finished channel {channeln}/{channel_amount} for {file_name}")
fingerprints |= set(hashes)
return fingerprints, file_hash

0
dejavu/base_classes/__init__.py Normal file
View file

195
dejavu/base_classes/base_database.py Executable file
View file

@ -0,0 +1,195 @@
import abc
import importlib
from typing import Dict, List, Tuple
from dejavu.config.settings import DATABASES
class BaseDatabase(object, metaclass=abc.ABCMeta):
# Name of your Database subclass, this is used in configuration
# to refer to your class
type = None
def __init__(self):
super().__init__()
def before_fork(self) -> None:
"""
Called before the database instance is given to the new process
"""
pass
def after_fork(self) -> None:
"""
Called after the database instance has been given to the new process
This will be called in the new process.
"""
pass
def setup(self) -> None:
"""
Called on creation or shortly afterwards.
"""
pass
@abc.abstractmethod
def empty(self) -> None:
"""
Called when the database should be cleared of all data.
"""
pass
@abc.abstractmethod
def delete_unfingerprinted_songs(self) -> None:
"""
Called to remove any song entries that do not have any fingerprints
associated with them.
"""
pass
@abc.abstractmethod
def get_num_songs(self) -> int:
"""
Returns the song's count stored.
:return: the amount of songs in the database.
"""
pass
@abc.abstractmethod
def get_num_fingerprints(self) -> int:
"""
Returns the fingerprints' count stored.
:return: the number of fingerprints in the database.
"""
pass
@abc.abstractmethod
def set_song_fingerprinted(self, song_id: int):
"""
Sets a specific song as having all fingerprints in the database.
:param song_id: song identifier.
"""
pass
@abc.abstractmethod
def get_songs(self) -> List[Dict[str, str]]:
"""
Returns all fully fingerprinted songs in the database
:return: a dictionary with the songs info.
"""
pass
@abc.abstractmethod
def get_song_by_id(self, song_id: int) -> Dict[str, str]:
"""
Brings the song info from the database.
:param song_id: song identifier.
:return: a song by its identifier. Result must be a Dictionary.
"""
pass
@abc.abstractmethod
def insert(self, fingerprint: str, song_id: int, offset: int):
"""
Inserts a single fingerprint into the database.
:param fingerprint: Part of a sha1 hash, in hexadecimal format
:param song_id: Song identifier this fingerprint is off
:param offset: The offset this fingerprint is from.
"""
pass
@abc.abstractmethod
def insert_song(self, song_name: str, file_hash: str, total_hashes: int) -> int:
"""
Inserts a song name into the database, returns the new
identifier of the song.
:param song_name: The name of the song.
:param file_hash: Hash from the fingerprinted file.
:param total_hashes: amount of hashes to be inserted on fingerprint table.
:return: the inserted id.
"""
pass
@abc.abstractmethod
def query(self, fingerprint: str = None) -> List[Tuple]:
"""
Returns all matching fingerprint entries associated with
the given hash as parameter, if None is passed it returns all entries.
:param fingerprint: part of a sha1 hash, in hexadecimal format
:return: a list of fingerprint records stored in the db.
"""
pass
@abc.abstractmethod
def get_iterable_kv_pairs(self) -> List[Tuple]:
"""
Returns all fingerprints in the database.
:return: a list containing all fingerprints stored in the db.
"""
pass
@abc.abstractmethod
def insert_hashes(self, song_id: int, hashes: List[Tuple[str, int]], batch_size: int = 1000) -> None:
"""
Insert a multitude of fingerprints.
:param song_id: Song identifier the fingerprints belong to
:param hashes: A sequence of tuples in the format (hash, offset)
- hash: Part of a sha1 hash, in hexadecimal format
- offset: Offset this hash was created from/at.
:param batch_size: insert batches.
"""
@abc.abstractmethod
def return_matches(self, hashes: List[Tuple[str, int]], batch_size: int = 1000) \
-> Tuple[List[Tuple[int, int]], Dict[int, int]]:
"""
Searches the database for pairs of (hash, offset) values.
:param hashes: A sequence of tuples in the format (hash, offset)
- hash: Part of a sha1 hash, in hexadecimal format
- offset: Offset this hash was created from/at.
:param batch_size: number of query's batches.
:return: a list of (sid, offset_difference) tuples and a
dictionary with the amount of hashes matched (not considering
duplicated hashes) in each song.
- song id: Song identifier
- offset_difference: (database_offset - sampled_offset)
"""
pass
@abc.abstractmethod
def delete_songs_by_id(self, song_ids: List[int], batch_size: int = 1000) -> None:
"""
Given a list of song ids it deletes all songs specified and their corresponding fingerprints.
:param song_ids: song ids to be deleted from the database.
:param batch_size: number of query's batches.
"""
pass
def get_database(database_type: str = "mysql") -> BaseDatabase:
"""
Given a database type it returns a database instance for that type.
:param database_type: type of the database.
:return: an instance of BaseDatabase depending on given database_type.
"""
try:
path, db_class_name = DATABASES[database_type]
db_module = importlib.import_module(path)
db_class = getattr(db_module, db_class_name)
return db_class
except (ImportError, KeyError):
raise TypeError("Unsupported database type supplied.")

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import abc
from time import time
from typing import Dict, List, Tuple
import numpy as np
from dejavu.config.settings import DEFAULT_FS
class BaseRecognizer(object, metaclass=abc.ABCMeta):
def __init__(self, dejavu):
self.dejavu = dejavu
self.Fs = DEFAULT_FS
def _recognize(self, *data) -> Tuple[List[Dict[str, any]], int, int, int]:
fingerprint_times = []
hashes = set() # to remove possible duplicated fingerprints we built a set.
for channel in data:
fingerprints, fingerprint_time = self.dejavu.generate_fingerprints(channel, Fs=self.Fs)
fingerprint_times.append(fingerprint_time)
hashes |= set(fingerprints)
matches, dedup_hashes, query_time = self.dejavu.find_matches(hashes)
t = time()
final_results = self.dejavu.align_matches(matches, dedup_hashes, len(hashes))
align_time = time() - t
return final_results, np.sum(fingerprint_times), query_time, align_time
@abc.abstractmethod
def recognize(self) -> Dict[str, any]:
pass # base class does nothing

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import abc
from typing import Dict, List, Tuple
from dejavu.base_classes.base_database import BaseDatabase
class CommonDatabase(BaseDatabase, metaclass=abc.ABCMeta):
# Since several methods across different databases are actually just the same
# I've built this class with the idea to reuse that logic instead of copy pasting
# over and over the same code.
def __init__(self):
super().__init__()
def before_fork(self) -> None:
"""
Called before the database instance is given to the new process
"""
pass
def after_fork(self) -> None:
"""
Called after the database instance has been given to the new process
This will be called in the new process.
"""
pass
def setup(self) -> None:
"""
Called on creation or shortly afterwards.
"""
with self.cursor() as cur:
cur.execute(self.CREATE_SONGS_TABLE)
cur.execute(self.CREATE_FINGERPRINTS_TABLE)
cur.execute(self.DELETE_UNFINGERPRINTED)
def empty(self) -> None:
"""
Called when the database should be cleared of all data.
"""
with self.cursor() as cur:
cur.execute(self.DROP_FINGERPRINTS)
cur.execute(self.DROP_SONGS)
self.setup()
def delete_unfingerprinted_songs(self) -> None:
"""
Called to remove any song entries that do not have any fingerprints
associated with them.
"""
with self.cursor() as cur:
cur.execute(self.DELETE_UNFINGERPRINTED)
def get_num_songs(self) -> int:
"""
Returns the song's count stored.
:return: the amount of songs in the database.
"""
with self.cursor(buffered=True) as cur:
cur.execute(self.SELECT_UNIQUE_SONG_IDS)
count = cur.fetchone()[0] if cur.rowcount != 0 else 0
return count
def get_num_fingerprints(self) -> int:
"""
Returns the fingerprints' count stored.
:return: the number of fingerprints in the database.
"""
with self.cursor(buffered=True) as cur:
cur.execute(self.SELECT_NUM_FINGERPRINTS)
count = cur.fetchone()[0] if cur.rowcount != 0 else 0
return count
def set_song_fingerprinted(self, song_id):
"""
Sets a specific song as having all fingerprints in the database.
:param song_id: song identifier.
"""
with self.cursor() as cur:
cur.execute(self.UPDATE_SONG_FINGERPRINTED, (song_id,))
def get_songs(self) -> List[Dict[str, str]]:
"""
Returns all fully fingerprinted songs in the database
:return: a dictionary with the songs info.
"""
with self.cursor(dictionary=True) as cur:
cur.execute(self.SELECT_SONGS)
return list(cur)
def get_song_by_id(self, song_id: int) -> Dict[str, str]:
"""
Brings the song info from the database.
:param song_id: song identifier.
:return: a song by its identifier. Result must be a Dictionary.
"""
with self.cursor(dictionary=True) as cur:
cur.execute(self.SELECT_SONG, (song_id,))
return cur.fetchone()
def insert(self, fingerprint: str, song_id: int, offset: int):
"""
Inserts a single fingerprint into the database.
:param fingerprint: Part of a sha1 hash, in hexadecimal format
:param song_id: Song identifier this fingerprint is off
:param offset: The offset this fingerprint is from.
"""
with self.cursor() as cur:
cur.execute(self.INSERT_FINGERPRINT, (fingerprint, song_id, offset))
@abc.abstractmethod
def insert_song(self, song_name: str, file_hash: str, total_hashes: int) -> int:
"""
Inserts a song name into the database, returns the new
identifier of the song.
:param song_name: The name of the song.
:param file_hash: Hash from the fingerprinted file.
:param total_hashes: amount of hashes to be inserted on fingerprint table.
:return: the inserted id.
"""
pass
def query(self, fingerprint: str = None) -> List[Tuple]:
"""
Returns all matching fingerprint entries associated with
the given hash as parameter, if None is passed it returns all entries.
:param fingerprint: part of a sha1 hash, in hexadecimal format
:return: a list of fingerprint records stored in the db.
"""
with self.cursor() as cur:
if fingerprint:
cur.execute(self.SELECT, (fingerprint,))
else: # select all if no key
cur.execute(self.SELECT_ALL)
return list(cur)
def get_iterable_kv_pairs(self) -> List[Tuple]:
"""
Returns all fingerprints in the database.
:return: a list containing all fingerprints stored in the db.
"""
return self.query(None)
def insert_hashes(self, song_id: int, hashes: List[Tuple[str, int]], batch_size: int = 1000) -> None:
"""
Insert a multitude of fingerprints.
:param song_id: Song identifier the fingerprints belong to
:param hashes: A sequence of tuples in the format (hash, offset)
- hash: Part of a sha1 hash, in hexadecimal format
- offset: Offset this hash was created from/at.
:param batch_size: insert batches.
"""
values = [(song_id, hsh, int(offset)) for hsh, offset in hashes]
with self.cursor() as cur:
for index in range(0, len(hashes), batch_size):
cur.executemany(self.INSERT_FINGERPRINT, values[index: index + batch_size])
def return_matches(self, hashes: List[Tuple[str, int]],
batch_size: int = 1000) -> Tuple[List[Tuple[int, int]], Dict[int, int]]:
"""
Searches the database for pairs of (hash, offset) values.
:param hashes: A sequence of tuples in the format (hash, offset)
- hash: Part of a sha1 hash, in hexadecimal format
- offset: Offset this hash was created from/at.
:param batch_size: number of query's batches.
:return: a list of (sid, offset_difference) tuples and a
dictionary with the amount of hashes matched (not considering
duplicated hashes) in each song.
- song id: Song identifier
- offset_difference: (database_offset - sampled_offset)
"""
# Create a dictionary of hash => offset pairs for later lookups
mapper = {}
for hsh, offset in hashes:
if hsh.upper() in mapper.keys():
mapper[hsh.upper()].append(offset)
else:
mapper[hsh.upper()] = [offset]
values = list(mapper.keys())
# in order to count each hash only once per db offset we use the dic below
dedup_hashes = {}
results = []
with self.cursor() as cur:
for index in range(0, len(values), batch_size):
# Create our IN part of the query
query = self.SELECT_MULTIPLE % ', '.join([self.IN_MATCH] * len(values[index: index + batch_size]))
cur.execute(query, values[index: index + batch_size])
for hsh, sid, offset in cur:
if sid not in dedup_hashes.keys():
dedup_hashes[sid] = 1
else:
dedup_hashes[sid] += 1
# we now evaluate all offset for each hash matched
for song_sampled_offset in mapper[hsh]:
results.append((sid, offset - song_sampled_offset))
return results, dedup_hashes
def delete_songs_by_id(self, song_ids: List[int], batch_size: int = 1000) -> None:
"""
Given a list of song ids it deletes all songs specified and their corresponding fingerprints.
:param song_ids: song ids to be deleted from the database.
:param batch_size: number of query's batches.
"""
with self.cursor() as cur:
for index in range(0, len(song_ids), batch_size):
# Create our IN part of the query
query = self.DELETE_SONGS % ', '.join(['%s'] * len(song_ids[index: index + batch_size]))
cur.execute(query, song_ids[index: index + batch_size])

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# Dejavu
# DEJAVU JSON RESPONSE
SONG_ID = "song_id"
SONG_NAME = 'song_name'
RESULTS = 'results'
HASHES_MATCHED = 'hashes_matched_in_input'
# Hashes fingerprinted in the db.
FINGERPRINTED_HASHES = 'fingerprinted_hashes_in_db'
# Percentage regarding hashes matched vs hashes fingerprinted in the db.
FINGERPRINTED_CONFIDENCE = 'fingerprinted_confidence'
# Hashes generated from the input.
INPUT_HASHES = 'input_total_hashes'
# Percentage regarding hashes matched vs hashes from the input.
INPUT_CONFIDENCE = 'input_confidence'
TOTAL_TIME = 'total_time'
FINGERPRINT_TIME = 'fingerprint_time'
QUERY_TIME = 'query_time'
ALIGN_TIME = 'align_time'
OFFSET = 'offset'
OFFSET_SECS = 'offset_seconds'
# DATABASE CLASS INSTANCES:
DATABASES = {
'mysql': ("dejavu.database_handler.mysql_database", "MySQLDatabase"),
'postgres': ("dejavu.database_handler.postgres_database", "PostgreSQLDatabase")
}
# TABLE SONGS
SONGS_TABLENAME = "songs"
# SONGS FIELDS
FIELD_SONG_ID = 'song_id'
FIELD_SONGNAME = 'song_name'
FIELD_FINGERPRINTED = "fingerprinted"
FIELD_FILE_SHA1 = 'file_sha1'
FIELD_TOTAL_HASHES = 'total_hashes'
# TABLE FINGERPRINTS
FINGERPRINTS_TABLENAME = "fingerprints"
# FINGERPRINTS FIELDS
FIELD_HASH = 'hash'
FIELD_OFFSET = 'offset'
# FINGERPRINTS CONFIG:
# This is used as connectivity parameter for scipy.generate_binary_structure function. This parameter
# changes the morphology mask when looking for maximum peaks on the spectrogram matrix.
# Possible values are: [1, 2]
# Where 1 sets a diamond morphology which implies that diagonal elements are not considered as neighbors (this
# is the value used in the original dejavu code).
# And 2 sets a square mask, i.e. all elements are considered neighbors.
CONNECTIVITY_MASK = 2
# Sampling rate, related to the Nyquist conditions, which affects
# the range frequencies we can detect.
DEFAULT_FS = 44100
# Size of the FFT window, affects frequency granularity
DEFAULT_WINDOW_SIZE = 4096
# Ratio by which each sequential window overlaps the last and the
# next window. Higher overlap will allow a higher granularity of offset
# matching, but potentially more fingerprints.
DEFAULT_OVERLAP_RATIO = 0.5
# Degree to which a fingerprint can be paired with its neighbors. Higher values will
# cause more fingerprints, but potentially better accuracy.
DEFAULT_FAN_VALUE = 5 # 15 was the original value.
# Minimum amplitude in spectrogram in order to be considered a peak.
# This can be raised to reduce number of fingerprints, but can negatively
# affect accuracy.
DEFAULT_AMP_MIN = 10
# Number of cells around an amplitude peak in the spectrogram in order
# for Dejavu to consider it a spectral peak. Higher values mean less
# fingerprints and faster matching, but can potentially affect accuracy.
PEAK_NEIGHBORHOOD_SIZE = 10 # 20 was the original value.
# Thresholds on how close or far fingerprints can be in time in order
# to be paired as a fingerprint. If your max is too low, higher values of
# DEFAULT_FAN_VALUE may not perform as expected.
MIN_HASH_TIME_DELTA = 0
MAX_HASH_TIME_DELTA = 200
# If True, will sort peaks temporally for fingerprinting;
# not sorting will cut down number of fingerprints, but potentially
# affect performance.
PEAK_SORT = True
# Number of bits to grab from the front of the SHA1 hash in the
# fingerprint calculation. The more you grab, the more memory storage,
# with potentially lesser collisions of matches.
FINGERPRINT_REDUCTION = 20
# Number of results being returned for file recognition
TOPN = 2

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from __future__ import absolute_import
import abc
class Database(object):
__metaclass__ = abc.ABCMeta
FIELD_FILE_SHA1 = 'file_sha1'
FIELD_SONG_ID = 'song_id'
FIELD_SONGNAME = 'song_name'
FIELD_OFFSET = 'offset'
FIELD_HASH = 'hash'
# Name of your Database subclass, this is used in configuration
# to refer to your class
type = None
def __init__(self):
super(Database, self).__init__()
def before_fork(self):
"""
Called before the database instance is given to the new process
"""
pass
def after_fork(self):
"""
Called after the database instance has been given to the new process
This will be called in the new process.
"""
pass
def setup(self):
"""
Called on creation or shortly afterwards.
"""
pass
@abc.abstractmethod
def empty(self):
"""
Called when the database should be cleared of all data.
"""
pass
@abc.abstractmethod
def delete_unfingerprinted_songs(self):
"""
Called to remove any song entries that do not have any fingerprints
associated with them.
"""
pass
@abc.abstractmethod
def get_num_songs(self):
"""
Returns the amount of songs in the database.
"""
pass
@abc.abstractmethod
def get_num_fingerprints(self):
"""
Returns the number of fingerprints in the database.
"""
pass
@abc.abstractmethod
def set_song_fingerprinted(self, sid):
"""
Sets a specific song as having all fingerprints in the database.
sid: Song identifier
"""
pass
@abc.abstractmethod
def get_songs(self):
"""
Returns all fully fingerprinted songs in the database.
"""
pass
@abc.abstractmethod
def get_song_by_id(self, sid):
"""
Return a song by its identifier
sid: Song identifier
"""
pass
@abc.abstractmethod
def insert(self, hash, sid, offset):
"""
Inserts a single fingerprint into the database.
hash: Part of a sha1 hash, in hexadecimal format
sid: Song identifier this fingerprint is off
offset: The offset this hash is from
"""
pass
@abc.abstractmethod
def insert_song(self, song_name):
"""
Inserts a song name into the database, returns the new
identifier of the song.
song_name: The name of the song.
"""
pass
@abc.abstractmethod
def query(self, hash):
"""
Returns all matching fingerprint entries associated with
the given hash as parameter.
hash: Part of a sha1 hash, in hexadecimal format
"""
pass
@abc.abstractmethod
def get_iterable_kv_pairs(self):
"""
Returns all fingerprints in the database.
"""
pass
@abc.abstractmethod
def insert_hashes(self, sid, hashes):
"""
Insert a multitude of fingerprints.
sid: Song identifier the fingerprints belong to
hashes: A sequence of tuples in the format (hash, offset)
- hash: Part of a sha1 hash, in hexadecimal format
- offset: Offset this hash was created from/at.
"""
pass
@abc.abstractmethod
def return_matches(self, hashes):
"""
Searches the database for pairs of (hash, offset) values.
hashes: A sequence of tuples in the format (hash, offset)
- hash: Part of a sha1 hash, in hexadecimal format
- offset: Offset this hash was created from/at.
Returns a sequence of (sid, offset_difference) tuples.
sid: Song identifier
offset_difference: (offset - database_offset)
"""
pass
def get_database(database_type=None):
# Default to using the mysql database
database_type = database_type or "mysql"
# Lower all the input.
database_type = database_type.lower()
for db_cls in Database.__subclasses__():
if db_cls.type == database_type:
return db_cls
raise TypeError("Unsupported database type supplied.")
# Import our default database handler
import dejavu.database_sql

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import queue
import mysql.connector
from mysql.connector.errors import DatabaseError
from dejavu.base_classes.common_database import CommonDatabase
from dejavu.config.settings import (FIELD_FILE_SHA1, FIELD_FINGERPRINTED,
FIELD_HASH, FIELD_OFFSET, FIELD_SONG_ID,
FIELD_SONGNAME, FIELD_TOTAL_HASHES,
FINGERPRINTS_TABLENAME, SONGS_TABLENAME)
class MySQLDatabase(CommonDatabase):
type = "mysql"
# CREATES
CREATE_SONGS_TABLE = f"""
CREATE TABLE IF NOT EXISTS `{SONGS_TABLENAME}` (
`{FIELD_SONG_ID}` MEDIUMINT UNSIGNED NOT NULL AUTO_INCREMENT
, `{FIELD_SONGNAME}` VARCHAR(250) NOT NULL
, `{FIELD_FINGERPRINTED}` TINYINT DEFAULT 0
, `{FIELD_FILE_SHA1}` BINARY(20) NOT NULL
, `{FIELD_TOTAL_HASHES}` INT NOT NULL DEFAULT 0
, `date_created` DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP
, `date_modified` DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
, CONSTRAINT `pk_{SONGS_TABLENAME}_{FIELD_SONG_ID}` PRIMARY KEY (`{FIELD_SONG_ID}`)
, CONSTRAINT `uq_{SONGS_TABLENAME}_{FIELD_SONG_ID}` UNIQUE KEY (`{FIELD_SONG_ID}`)
) ENGINE=INNODB;
"""
CREATE_FINGERPRINTS_TABLE = f"""
CREATE TABLE IF NOT EXISTS `{FINGERPRINTS_TABLENAME}` (
`{FIELD_HASH}` BINARY(10) NOT NULL
, `{FIELD_SONG_ID}` MEDIUMINT UNSIGNED NOT NULL
, `{FIELD_OFFSET}` INT UNSIGNED NOT NULL
, `date_created` DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP
, `date_modified` DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
, INDEX `ix_{FINGERPRINTS_TABLENAME}_{FIELD_HASH}` (`{FIELD_HASH}`)
, CONSTRAINT `uq_{FINGERPRINTS_TABLENAME}_{FIELD_SONG_ID}_{FIELD_OFFSET}_{FIELD_HASH}`
UNIQUE KEY (`{FIELD_SONG_ID}`, `{FIELD_OFFSET}`, `{FIELD_HASH}`)
, CONSTRAINT `fk_{FINGERPRINTS_TABLENAME}_{FIELD_SONG_ID}` FOREIGN KEY (`{FIELD_SONG_ID}`)
REFERENCES `{SONGS_TABLENAME}`(`{FIELD_SONG_ID}`) ON DELETE CASCADE
) ENGINE=INNODB;
"""
# INSERTS (IGNORES DUPLICATES)
INSERT_FINGERPRINT = f"""
INSERT IGNORE INTO `{FINGERPRINTS_TABLENAME}` (
`{FIELD_SONG_ID}`
, `{FIELD_HASH}`
, `{FIELD_OFFSET}`)
VALUES (%s, UNHEX(%s), %s);
"""
INSERT_SONG = f"""
INSERT INTO `{SONGS_TABLENAME}` (`{FIELD_SONGNAME}`,`{FIELD_FILE_SHA1}`,`{FIELD_TOTAL_HASHES}`)
VALUES (%s, UNHEX(%s), %s);
"""
# SELECTS
SELECT = f"""
SELECT `{FIELD_SONG_ID}`, `{FIELD_OFFSET}`
FROM `{FINGERPRINTS_TABLENAME}`
WHERE `{FIELD_HASH}` = UNHEX(%s);
"""
SELECT_MULTIPLE = f"""
SELECT HEX(`{FIELD_HASH}`), `{FIELD_SONG_ID}`, `{FIELD_OFFSET}`
FROM `{FINGERPRINTS_TABLENAME}`
WHERE `{FIELD_HASH}` IN (%s);
"""
SELECT_ALL = f"SELECT `{FIELD_SONG_ID}`, `{FIELD_OFFSET}` FROM `{FINGERPRINTS_TABLENAME}`;"
SELECT_SONG = f"""
SELECT `{FIELD_SONGNAME}`, HEX(`{FIELD_FILE_SHA1}`) AS `{FIELD_FILE_SHA1}`, `{FIELD_TOTAL_HASHES}`
FROM `{SONGS_TABLENAME}`
WHERE `{FIELD_SONG_ID}` = %s;
"""
SELECT_NUM_FINGERPRINTS = f"SELECT COUNT(*) AS n FROM `{FINGERPRINTS_TABLENAME}`;"
SELECT_UNIQUE_SONG_IDS = f"""
SELECT COUNT(`{FIELD_SONG_ID}`) AS n
FROM `{SONGS_TABLENAME}`
WHERE `{FIELD_FINGERPRINTED}` = 1;
"""
SELECT_SONGS = f"""
SELECT
`{FIELD_SONG_ID}`
, `{FIELD_SONGNAME}`
, HEX(`{FIELD_FILE_SHA1}`) AS `{FIELD_FILE_SHA1}`
, `{FIELD_TOTAL_HASHES}`
, `date_created`
FROM `{SONGS_TABLENAME}`
WHERE `{FIELD_FINGERPRINTED}` = 1;
"""
# DROPS
DROP_FINGERPRINTS = f"DROP TABLE IF EXISTS `{FINGERPRINTS_TABLENAME}`;"
DROP_SONGS = f"DROP TABLE IF EXISTS `{SONGS_TABLENAME}`;"
# UPDATE
UPDATE_SONG_FINGERPRINTED = f"""
UPDATE `{SONGS_TABLENAME}` SET `{FIELD_FINGERPRINTED}` = 1 WHERE `{FIELD_SONG_ID}` = %s;
"""
# DELETES
DELETE_UNFINGERPRINTED = f"""
DELETE FROM `{SONGS_TABLENAME}` WHERE `{FIELD_FINGERPRINTED}` = 0;
"""
DELETE_SONGS = f"""
DELETE FROM `{SONGS_TABLENAME}` WHERE `{FIELD_SONG_ID}` IN (%s);
"""
# IN
IN_MATCH = f"UNHEX(%s)"
def __init__(self, **options):
super().__init__()
self.cursor = cursor_factory(**options)
self._options = options
def after_fork(self) -> None:
# Clear the cursor cache, we don't want any stale connections from
# the previous process.
Cursor.clear_cache()
def insert_song(self, song_name: str, file_hash: str, total_hashes: int) -> int:
"""
Inserts a song name into the database, returns the new
identifier of the song.
:param song_name: The name of the song.
:param file_hash: Hash from the fingerprinted file.
:param total_hashes: amount of hashes to be inserted on fingerprint table.
:return: the inserted id.
"""
with self.cursor() as cur:
cur.execute(self.INSERT_SONG, (song_name, file_hash, total_hashes))
return cur.lastrowid
def __getstate__(self):
return self._options,
def __setstate__(self, state):
self._options, = state
self.cursor = cursor_factory(**self._options)
def cursor_factory(**factory_options):
def cursor(**options):
options.update(factory_options)
return Cursor(**options)
return cursor
class Cursor(object):
"""
Establishes a connection to the database and returns an open cursor.
# Use as context manager
with Cursor() as cur:
cur.execute(query)
...
"""
def __init__(self, dictionary=False, **options):
super().__init__()
self._cache = queue.Queue(maxsize=5)
try:
conn = self._cache.get_nowait()
# Ping the connection before using it from the cache.
conn.ping(True)
except queue.Empty:
conn = mysql.connector.connect(**options)
self.conn = conn
self.dictionary = dictionary
@classmethod
def clear_cache(cls):
cls._cache = queue.Queue(maxsize=5)
def __enter__(self):
self.cursor = self.conn.cursor(dictionary=self.dictionary)
return self.cursor
def __exit__(self, extype, exvalue, traceback):
# if we had a MySQL related error we try to rollback the cursor.
if extype is DatabaseError:
self.cursor.rollback()
self.cursor.close()
self.conn.commit()
# Put it back on the queue
try:
self._cache.put_nowait(self.conn)
except queue.Full:
self.conn.close()

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import queue
import psycopg2
from psycopg2.extras import DictCursor
from dejavu.base_classes.common_database import CommonDatabase
from dejavu.config.settings import (FIELD_FILE_SHA1, FIELD_FINGERPRINTED,
FIELD_HASH, FIELD_OFFSET, FIELD_SONG_ID,
FIELD_SONGNAME, FIELD_TOTAL_HASHES,
FINGERPRINTS_TABLENAME, SONGS_TABLENAME)
class PostgreSQLDatabase(CommonDatabase):
type = "postgres"
# CREATES
CREATE_SONGS_TABLE = f"""
CREATE TABLE IF NOT EXISTS "{SONGS_TABLENAME}" (
"{FIELD_SONG_ID}" SERIAL
, "{FIELD_SONGNAME}" VARCHAR(250) NOT NULL
, "{FIELD_FINGERPRINTED}" SMALLINT DEFAULT 0
, "{FIELD_FILE_SHA1}" BYTEA
, "{FIELD_TOTAL_HASHES}" INT NOT NULL DEFAULT 0
, "date_created" TIMESTAMP NOT NULL DEFAULT now()
, "date_modified" TIMESTAMP NOT NULL DEFAULT now()
, CONSTRAINT "pk_{SONGS_TABLENAME}_{FIELD_SONG_ID}" PRIMARY KEY ("{FIELD_SONG_ID}")
, CONSTRAINT "uq_{SONGS_TABLENAME}_{FIELD_SONG_ID}" UNIQUE ("{FIELD_SONG_ID}")
);
"""
CREATE_FINGERPRINTS_TABLE = f"""
CREATE TABLE IF NOT EXISTS "{FINGERPRINTS_TABLENAME}" (
"{FIELD_HASH}" BYTEA NOT NULL
, "{FIELD_SONG_ID}" INT NOT NULL
, "{FIELD_OFFSET}" INT NOT NULL
, "date_created" TIMESTAMP NOT NULL DEFAULT now()
, "date_modified" TIMESTAMP NOT NULL DEFAULT now()
, CONSTRAINT "uq_{FINGERPRINTS_TABLENAME}" UNIQUE ("{FIELD_SONG_ID}", "{FIELD_OFFSET}", "{FIELD_HASH}")
, CONSTRAINT "fk_{FINGERPRINTS_TABLENAME}_{FIELD_SONG_ID}" FOREIGN KEY ("{FIELD_SONG_ID}")
REFERENCES "{SONGS_TABLENAME}"("{FIELD_SONG_ID}") ON DELETE CASCADE
);
CREATE INDEX IF NOT EXISTS "ix_{FINGERPRINTS_TABLENAME}_{FIELD_HASH}" ON "{FINGERPRINTS_TABLENAME}"
USING hash ("{FIELD_HASH}");
"""
CREATE_FINGERPRINTS_TABLE_INDEX = f"""
CREATE INDEX "ix_{FINGERPRINTS_TABLENAME}_{FIELD_HASH}" ON "{FINGERPRINTS_TABLENAME}"
USING hash ("{FIELD_HASH}");
"""
# INSERTS (IGNORES DUPLICATES)
INSERT_FINGERPRINT = f"""
INSERT INTO "{FINGERPRINTS_TABLENAME}" (
"{FIELD_SONG_ID}"
, "{FIELD_HASH}"
, "{FIELD_OFFSET}")
VALUES (%s, decode(%s, 'hex'), %s) ON CONFLICT DO NOTHING;
"""
INSERT_SONG = f"""
INSERT INTO "{SONGS_TABLENAME}" ("{FIELD_SONGNAME}", "{FIELD_FILE_SHA1}","{FIELD_TOTAL_HASHES}")
VALUES (%s, decode(%s, 'hex'), %s)
RETURNING "{FIELD_SONG_ID}";
"""
# SELECTS
SELECT = f"""
SELECT "{FIELD_SONG_ID}", "{FIELD_OFFSET}"
FROM "{FINGERPRINTS_TABLENAME}"
WHERE "{FIELD_HASH}" = decode(%s, 'hex');
"""
SELECT_MULTIPLE = f"""
SELECT upper(encode("{FIELD_HASH}", 'hex')), "{FIELD_SONG_ID}", "{FIELD_OFFSET}"
FROM "{FINGERPRINTS_TABLENAME}"
WHERE "{FIELD_HASH}" IN (%s);
"""
SELECT_ALL = f'SELECT "{FIELD_SONG_ID}", "{FIELD_OFFSET}" FROM "{FINGERPRINTS_TABLENAME}";'
SELECT_SONG = f"""
SELECT
"{FIELD_SONGNAME}"
, upper(encode("{FIELD_FILE_SHA1}", 'hex')) AS "{FIELD_FILE_SHA1}"
, "{FIELD_TOTAL_HASHES}"
FROM "{SONGS_TABLENAME}"
WHERE "{FIELD_SONG_ID}" = %s;
"""
SELECT_NUM_FINGERPRINTS = f'SELECT COUNT(*) AS n FROM "{FINGERPRINTS_TABLENAME}";'
SELECT_UNIQUE_SONG_IDS = f"""
SELECT COUNT("{FIELD_SONG_ID}") AS n
FROM "{SONGS_TABLENAME}"
WHERE "{FIELD_FINGERPRINTED}" = 1;
"""
SELECT_SONGS = f"""
SELECT
"{FIELD_SONG_ID}"
, "{FIELD_SONGNAME}"
, upper(encode("{FIELD_FILE_SHA1}", 'hex')) AS "{FIELD_FILE_SHA1}"
, "{FIELD_TOTAL_HASHES}"
, "date_created"
FROM "{SONGS_TABLENAME}"
WHERE "{FIELD_FINGERPRINTED}" = 1;
"""
# DROPS
DROP_FINGERPRINTS = F'DROP TABLE IF EXISTS "{FINGERPRINTS_TABLENAME}";'
DROP_SONGS = F'DROP TABLE IF EXISTS "{SONGS_TABLENAME}";'
# UPDATE
UPDATE_SONG_FINGERPRINTED = f"""
UPDATE "{SONGS_TABLENAME}" SET
"{FIELD_FINGERPRINTED}" = 1
, "date_modified" = now()
WHERE "{FIELD_SONG_ID}" = %s;
"""
# DELETES
DELETE_UNFINGERPRINTED = f"""
DELETE FROM "{SONGS_TABLENAME}" WHERE "{FIELD_FINGERPRINTED}" = 0;
"""
DELETE_SONGS = f"""
DELETE FROM "{SONGS_TABLENAME}" WHERE "{FIELD_SONG_ID}" IN (%s);
"""
# IN
IN_MATCH = f"decode(%s, 'hex')"
def __init__(self, **options):
super().__init__()
self.cursor = cursor_factory(**options)
self._options = options
def after_fork(self) -> None:
# Clear the cursor cache, we don't want any stale connections from
# the previous process.
Cursor.clear_cache()
def insert_song(self, song_name: str, file_hash: str, total_hashes: int) -> int:
"""
Inserts a song name into the database, returns the new
identifier of the song.
:param song_name: The name of the song.
:param file_hash: Hash from the fingerprinted file.
:param total_hashes: amount of hashes to be inserted on fingerprint table.
:return: the inserted id.
"""
with self.cursor() as cur:
cur.execute(self.INSERT_SONG, (song_name, file_hash, total_hashes))
return cur.fetchone()[0]
def __getstate__(self):
return self._options,
def __setstate__(self, state):
self._options, = state
self.cursor = cursor_factory(**self._options)
def cursor_factory(**factory_options):
def cursor(**options):
options.update(factory_options)
return Cursor(**options)
return cursor
class Cursor(object):
"""
Establishes a connection to the database and returns an open cursor.
# Use as context manager
with Cursor() as cur:
cur.execute(query)
...
"""
def __init__(self, dictionary=False, **options):
super().__init__()
self._cache = queue.Queue(maxsize=5)
try:
conn = self._cache.get_nowait()
# Ping the connection before using it from the cache.
conn.ping(True)
except queue.Empty:
conn = psycopg2.connect(**options)
self.conn = conn
self.dictionary = dictionary
@classmethod
def clear_cache(cls):
cls._cache = queue.Queue(maxsize=5)
def __enter__(self):
if self.dictionary:
self.cursor = self.conn.cursor(cursor_factory=DictCursor)
else:
self.cursor = self.conn.cursor()
return self.cursor
def __exit__(self, extype, exvalue, traceback):
# if we had a PostgreSQL related error we try to rollback the cursor.
if extype is psycopg2.DatabaseError:
self.cursor.rollback()
self.cursor.close()
self.conn.commit()
# Put it back on the queue
try:
self._cache.put_nowait(self.conn)
except queue.Full:
self.conn.close()

373
dejavu/database_sql.py
View file

@ -1,373 +0,0 @@
from __future__ import absolute_import
from itertools import izip_longest
import Queue
import MySQLdb as mysql
from MySQLdb.cursors import DictCursor
from dejavu.database import Database
class SQLDatabase(Database):
"""
Queries:
1) Find duplicates (shouldn't be any, though):
select `hash`, `song_id`, `offset`, count(*) cnt
from fingerprints
group by `hash`, `song_id`, `offset`
having cnt > 1
order by cnt asc;
2) Get number of hashes by song:
select song_id, song_name, count(song_id) as num
from fingerprints
natural join songs
group by song_id
order by count(song_id) desc;
3) get hashes with highest number of collisions
select
hash,
count(distinct song_id) as n
from fingerprints
group by `hash`
order by n DESC;
=> 26 different songs with same fingerprint (392 times):
select songs.song_name, fingerprints.offset
from fingerprints natural join songs
where fingerprints.hash = "08d3c833b71c60a7b620322ac0c0aba7bf5a3e73";
"""
type = "mysql"
# tables
FINGERPRINTS_TABLENAME = "fingerprints"
SONGS_TABLENAME = "songs"
# fields
FIELD_FINGERPRINTED = "fingerprinted"
# creates
CREATE_FINGERPRINTS_TABLE = """
CREATE TABLE IF NOT EXISTS `%s` (
`%s` binary(10) not null,
`%s` mediumint unsigned not null,
`%s` int unsigned not null,
INDEX (%s),
UNIQUE KEY `unique_constraint` (%s, %s, %s),
FOREIGN KEY (%s) REFERENCES %s(%s) ON DELETE CASCADE
) ENGINE=INNODB;""" % (
FINGERPRINTS_TABLENAME, Database.FIELD_HASH,
Database.FIELD_SONG_ID, Database.FIELD_OFFSET, Database.FIELD_HASH,
Database.FIELD_SONG_ID, Database.FIELD_OFFSET, Database.FIELD_HASH,
Database.FIELD_SONG_ID, SONGS_TABLENAME, Database.FIELD_SONG_ID
)
CREATE_SONGS_TABLE = """
CREATE TABLE IF NOT EXISTS `%s` (
`%s` mediumint unsigned not null auto_increment,
`%s` varchar(250) not null,
`%s` tinyint default 0,
`%s` binary(20) not null,
PRIMARY KEY (`%s`),
UNIQUE KEY `%s` (`%s`)
) ENGINE=INNODB;""" % (
SONGS_TABLENAME, Database.FIELD_SONG_ID, Database.FIELD_SONGNAME, FIELD_FINGERPRINTED,
Database.FIELD_FILE_SHA1,
Database.FIELD_SONG_ID, Database.FIELD_SONG_ID, Database.FIELD_SONG_ID,
)
# inserts (ignores duplicates)
INSERT_FINGERPRINT = """
INSERT IGNORE INTO %s (%s, %s, %s) values
(UNHEX(%%s), %%s, %%s);
""" % (FINGERPRINTS_TABLENAME, Database.FIELD_HASH, Database.FIELD_SONG_ID, Database.FIELD_OFFSET)
INSERT_SONG = "INSERT INTO %s (%s, %s) values (%%s, UNHEX(%%s));" % (
SONGS_TABLENAME, Database.FIELD_SONGNAME, Database.FIELD_FILE_SHA1)
# selects
SELECT = """
SELECT %s, %s FROM %s WHERE %s = UNHEX(%%s);
""" % (Database.FIELD_SONG_ID, Database.FIELD_OFFSET, FINGERPRINTS_TABLENAME, Database.FIELD_HASH)
SELECT_MULTIPLE = """
SELECT HEX(%s), %s, %s FROM %s WHERE %s IN (%%s);
""" % (Database.FIELD_HASH, Database.FIELD_SONG_ID, Database.FIELD_OFFSET,
FINGERPRINTS_TABLENAME, Database.FIELD_HASH)
SELECT_ALL = """
SELECT %s, %s FROM %s;
""" % (Database.FIELD_SONG_ID, Database.FIELD_OFFSET, FINGERPRINTS_TABLENAME)
SELECT_SONG = """
SELECT %s, HEX(%s) as %s FROM %s WHERE %s = %%s;
""" % (Database.FIELD_SONGNAME, Database.FIELD_FILE_SHA1, Database.FIELD_FILE_SHA1, SONGS_TABLENAME, Database.FIELD_SONG_ID)
SELECT_NUM_FINGERPRINTS = """
SELECT COUNT(*) as n FROM %s
""" % (FINGERPRINTS_TABLENAME)
SELECT_UNIQUE_SONG_IDS = """
SELECT COUNT(DISTINCT %s) as n FROM %s WHERE %s = 1;
""" % (Database.FIELD_SONG_ID, SONGS_TABLENAME, FIELD_FINGERPRINTED)
SELECT_SONGS = """
SELECT %s, %s, HEX(%s) as %s FROM %s WHERE %s = 1;
""" % (Database.FIELD_SONG_ID, Database.FIELD_SONGNAME, Database.FIELD_FILE_SHA1, Database.FIELD_FILE_SHA1,
SONGS_TABLENAME, FIELD_FINGERPRINTED)
# drops
DROP_FINGERPRINTS = "DROP TABLE IF EXISTS %s;" % FINGERPRINTS_TABLENAME
DROP_SONGS = "DROP TABLE IF EXISTS %s;" % SONGS_TABLENAME
# update
UPDATE_SONG_FINGERPRINTED = """
UPDATE %s SET %s = 1 WHERE %s = %%s
""" % (SONGS_TABLENAME, FIELD_FINGERPRINTED, Database.FIELD_SONG_ID)
# delete
DELETE_UNFINGERPRINTED = """
DELETE FROM %s WHERE %s = 0;
""" % (SONGS_TABLENAME, FIELD_FINGERPRINTED)
def __init__(self, **options):
super(SQLDatabase, self).__init__()
self.cursor = cursor_factory(**options)
self._options = options
def after_fork(self):
# Clear the cursor cache, we don't want any stale connections from
# the previous process.
Cursor.clear_cache()
def setup(self):
"""
Creates any non-existing tables required for dejavu to function.
This also removes all songs that have been added but have no
fingerprints associated with them.
"""
with self.cursor(charset="utf8") as cur:
cur.execute(self.CREATE_SONGS_TABLE)
cur.execute(self.CREATE_FINGERPRINTS_TABLE)
cur.execute(self.DELETE_UNFINGERPRINTED)
def empty(self):
"""
Drops tables created by dejavu and then creates them again
by calling `SQLDatabase.setup`.
.. warning:
This will result in a loss of data
"""
with self.cursor(charset="utf8") as cur:
cur.execute(self.DROP_FINGERPRINTS)
cur.execute(self.DROP_SONGS)
self.setup()
def delete_unfingerprinted_songs(self):
"""
Removes all songs that have no fingerprints associated with them.
"""
with self.cursor(charset="utf8") as cur:
cur.execute(self.DELETE_UNFINGERPRINTED)
def get_num_songs(self):
"""
Returns number of songs the database has fingerprinted.
"""
with self.cursor(charset="utf8") as cur:
cur.execute(self.SELECT_UNIQUE_SONG_IDS)
for count, in cur:
return count
return 0
def get_num_fingerprints(self):
"""
Returns number of fingerprints the database has fingerprinted.
"""
with self.cursor(charset="utf8") as cur:
cur.execute(self.SELECT_NUM_FINGERPRINTS)
for count, in cur:
return count
return 0
def set_song_fingerprinted(self, sid):
"""
Set the fingerprinted flag to TRUE (1) once a song has been completely
fingerprinted in the database.
"""
with self.cursor(charset="utf8") as cur:
cur.execute(self.UPDATE_SONG_FINGERPRINTED, (sid,))
def get_songs(self):
"""
Return songs that have the fingerprinted flag set TRUE (1).
"""
with self.cursor(cursor_type=DictCursor, charset="utf8") as cur:
cur.execute(self.SELECT_SONGS)
for row in cur:
yield row
def get_song_by_id(self, sid):
"""
Returns song by its ID.
"""
with self.cursor(cursor_type=DictCursor, charset="utf8") as cur:
cur.execute(self.SELECT_SONG, (sid,))
return cur.fetchone()
def insert(self, hash, sid, offset):
"""
Insert a (sha1, song_id, offset) row into database.
"""
with self.cursor(charset="utf8") as cur:
cur.execute(self.INSERT_FINGERPRINT, (hash, sid, offset))
def insert_song(self, songname, file_hash):
"""
Inserts song in the database and returns the ID of the inserted record.
"""
with self.cursor(charset="utf8") as cur:
cur.execute(self.INSERT_SONG, (songname, file_hash))
return cur.lastrowid
def query(self, hash):
"""
Return all tuples associated with hash.
If hash is None, returns all entries in the
database (be careful with that one!).
"""
# select all if no key
query = self.SELECT_ALL if hash is None else self.SELECT
with self.cursor(charset="utf8") as cur:
cur.execute(query)
for sid, offset in cur:
yield (sid, offset)
def get_iterable_kv_pairs(self):
"""
Returns all tuples in database.
"""
return self.query(None)
def insert_hashes(self, sid, hashes):
"""
Insert series of hash => song_id, offset
values into the database.
"""
values = []
for hash, offset in hashes:
values.append((hash, sid, offset))
with self.cursor(charset="utf8") as cur:
for split_values in grouper(values, 1000):
cur.executemany(self.INSERT_FINGERPRINT, split_values)
def return_matches(self, hashes):
"""
Return the (song_id, offset_diff) tuples associated with
a list of (sha1, sample_offset) values.
"""
# Create a dictionary of hash => offset pairs for later lookups
mapper = {}
for hash, offset in hashes:
mapper[hash.upper()] = offset
# Get an iteratable of all the hashes we need
values = mapper.keys()
with self.cursor(charset="utf8") as cur:
for split_values in grouper(values, 1000):
# Create our IN part of the query
query = self.SELECT_MULTIPLE
query = query % ', '.join(['UNHEX(%s)'] * len(split_values))
cur.execute(query, split_values)
for hash, sid, offset in cur:
# (sid, db_offset - song_sampled_offset)
yield (sid, offset - mapper[hash])
def __getstate__(self):
return (self._options,)
def __setstate__(self, state):
self._options, = state
self.cursor = cursor_factory(**self._options)
def grouper(iterable, n, fillvalue=None):
args = [iter(iterable)] * n
return (filter(None, values) for values
in izip_longest(fillvalue=fillvalue, *args))
def cursor_factory(**factory_options):
def cursor(**options):
options.update(factory_options)
return Cursor(**options)
return cursor
class Cursor(object):
"""
Establishes a connection to the database and returns an open cursor.
```python
# Use as context manager
with Cursor() as cur:
cur.execute(query)
```
"""
def __init__(self, cursor_type=mysql.cursors.Cursor, **options):
super(Cursor, self).__init__()
self._cache = Queue.Queue(maxsize=5)
try:
conn = self._cache.get_nowait()
except Queue.Empty:
conn = mysql.connect(**options)
else:
# Ping the connection before using it from the cache.
conn.ping(True)
self.conn = conn
self.conn.autocommit(False)
self.cursor_type = cursor_type
@classmethod
def clear_cache(cls):
cls._cache = Queue.Queue(maxsize=5)
def __enter__(self):
self.cursor = self.conn.cursor(self.cursor_type)
return self.cursor
def __exit__(self, extype, exvalue, traceback):
# if we had a MySQL related error we try to rollback the cursor.
if extype is mysql.MySQLError:
self.cursor.rollback()
self.cursor.close()
self.conn.commit()
# Put it back on the queue
try:
self._cache.put_nowait(self.conn)
except Queue.Full:
self.conn.close()

157
dejavu/fingerprint.py
View file

@ -1,157 +0,0 @@
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import (generate_binary_structure,
iterate_structure, binary_erosion)
import hashlib
from operator import itemgetter
IDX_FREQ_I = 0
IDX_TIME_J = 1
######################################################################
# Sampling rate, related to the Nyquist conditions, which affects
# the range frequencies we can detect.
DEFAULT_FS = 44100
######################################################################
# Size of the FFT window, affects frequency granularity
DEFAULT_WINDOW_SIZE = 4096
######################################################################
# Ratio by which each sequential window overlaps the last and the
# next window. Higher overlap will allow a higher granularity of offset
# matching, but potentially more fingerprints.
DEFAULT_OVERLAP_RATIO = 0.5
######################################################################
# Degree to which a fingerprint can be paired with its neighbors --
# higher will cause more fingerprints, but potentially better accuracy.
DEFAULT_FAN_VALUE = 15
######################################################################
# Minimum amplitude in spectrogram in order to be considered a peak.
# This can be raised to reduce number of fingerprints, but can negatively
# affect accuracy.
DEFAULT_AMP_MIN = 10
######################################################################
# Number of cells around an amplitude peak in the spectrogram in order
# for Dejavu to consider it a spectral peak. Higher values mean less
# fingerprints and faster matching, but can potentially affect accuracy.
PEAK_NEIGHBORHOOD_SIZE = 20
######################################################################
# Thresholds on how close or far fingerprints can be in time in order
# to be paired as a fingerprint. If your max is too low, higher values of
# DEFAULT_FAN_VALUE may not perform as expected.
MIN_HASH_TIME_DELTA = 0
MAX_HASH_TIME_DELTA = 200
######################################################################
# If True, will sort peaks temporally for fingerprinting;
# not sorting will cut down number of fingerprints, but potentially
# affect performance.
PEAK_SORT = True
######################################################################
# Number of bits to grab from the front of the SHA1 hash in the
# fingerprint calculation. The more you grab, the more memory storage,
# with potentially lesser collisions of matches.
FINGERPRINT_REDUCTION = 20
def fingerprint(channel_samples, Fs=DEFAULT_FS,
wsize=DEFAULT_WINDOW_SIZE,
wratio=DEFAULT_OVERLAP_RATIO,
fan_value=DEFAULT_FAN_VALUE,
amp_min=DEFAULT_AMP_MIN):
"""
FFT the channel, log transform output, find local maxima, then return
locally sensitive hashes.
"""
# FFT the signal and extract frequency components
arr2D = mlab.specgram(
channel_samples,
NFFT=wsize,
Fs=Fs,
window=mlab.window_hanning,
noverlap=int(wsize * wratio))[0]
# apply log transform since specgram() returns linear array
arr2D = 10 * np.log10(arr2D)
arr2D[arr2D == -np.inf] = 0 # replace infs with zeros
# find local maxima
local_maxima = get_2D_peaks(arr2D, plot=False, amp_min=amp_min)
# return hashes
return generate_hashes(local_maxima, fan_value=fan_value)
def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.iterate_structure.html#scipy.ndimage.iterate_structure
struct = generate_binary_structure(2, 1)
neighborhood = iterate_structure(struct, PEAK_NEIGHBORHOOD_SIZE)
# find local maxima using our filter shape
local_max = maximum_filter(arr2D, footprint=neighborhood) == arr2D
background = (arr2D == 0)
eroded_background = binary_erosion(background, structure=neighborhood,
border_value=1)
# Boolean mask of arr2D with True at peaks (Fixed deprecated boolean operator by changing '-' to '^')
detected_peaks = local_max ^ eroded_background
# extract peaks
amps = arr2D[detected_peaks]
j, i = np.where(detected_peaks)
# filter peaks
amps = amps.flatten()
peaks = zip(i, j, amps)
peaks_filtered = filter(lambda x: x[2]>amp_min, peaks) # freq, time, amp
# get indices for frequency and time
frequency_idx = []
time_idx = []
for x in peaks_filtered:
frequency_idx.append(x[1])
time_idx.append(x[0])
if plot:
# scatter of the peaks
fig, ax = plt.subplots()
ax.imshow(arr2D)
ax.scatter(time_idx, frequency_idx)
ax.set_xlabel('Time')
ax.set_ylabel('Frequency')
ax.set_title("Spectrogram")
plt.gca().invert_yaxis()
plt.show()
return zip(frequency_idx, time_idx)
def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
"""
Hash list structure:
sha1_hash[0:20] time_offset
[(e05b341a9b77a51fd26, 32), ... ]
"""
if PEAK_SORT:
peaks.sort(key=itemgetter(1))
for i in range(len(peaks)):
for j in range(1, fan_value):
if (i + j) < len(peaks):
freq1 = peaks[i][IDX_FREQ_I]
freq2 = peaks[i + j][IDX_FREQ_I]
t1 = peaks[i][IDX_TIME_J]
t2 = peaks[i + j][IDX_TIME_J]
t_delta = t2 - t1
if t_delta >= MIN_HASH_TIME_DELTA and t_delta <= MAX_HASH_TIME_DELTA:
h = hashlib.sha1(
"%s|%s|%s" % (str(freq1), str(freq2), str(t_delta)))
yield (h.hexdigest()[0:FINGERPRINT_REDUCTION], t1)

0
dejavu/logic/__init__.py Normal file
View file

65
dejavu/decoder.py → dejavu/logic/decoder.py
View file

@ -1,40 +1,57 @@
import os
import fnmatch
import os
from hashlib import sha1
from typing import List, Tuple
import numpy as np
from pydub import AudioSegment
from pydub.utils import audioop
import wavio
from hashlib import sha1
def unique_hash(filepath, blocksize=2**20):
from dejavu.third_party import wavio
def unique_hash(file_path: str, block_size: int = 2**20) -> str:
""" Small function to generate a hash to uniquely generate
a file. Inspired by MD5 version here:
http://stackoverflow.com/a/1131255/712997
Works with large files.
Works with large files.
:param file_path: path to file.
:param block_size: read block size.
:return: a hash in an hexagesimal string form.
"""
s = sha1()
with open(filepath , "rb") as f:
with open(file_path, "rb") as f:
while True:
buf = f.read(blocksize)
buf = f.read(block_size)
if not buf:
break
s.update(buf)
return s.hexdigest().upper()
def find_files(path, extensions):
def find_files(path: str, extensions: List[str]) -> List[Tuple[str, str]]:
"""
Get all files that meet the specified extensions.
:param path: path to a directory with audio files.
:param extensions: file extensions to look for.
:return: a list of tuples with file name and its extension.
"""
# Allow both with ".mp3" and without "mp3" to be used for extensions
extensions = [e.replace(".", "") for e in extensions]
results = []
for dirpath, dirnames, files in os.walk(path):
for extension in extensions:
for f in fnmatch.filter(files, "*.%s" % extension):
for f in fnmatch.filter(files, f"*.{extension}"):
p = os.path.join(dirpath, f)
yield (p, extension)
results.append((p, extension))
return results
def read(filename, limit=None):
def read(file_name: str, limit: int = None) -> Tuple[List[List[int]], int, str]:
"""
Reads any file supported by pydub (ffmpeg) and returns the data contained
within. If file reading fails due to input being a 24-bit wav file,
@ -44,24 +61,26 @@ def read(filename, limit=None):
of the file by specifying the `limit` parameter. This is the amount of
seconds from the start of the file.
returns: (channels, samplerate)
:param file_name: file to be read.
:param limit: number of seconds to limit.
:return: tuple list of (channels, sample_rate, content_file_hash).
"""
# pydub does not support 24-bit wav files, use wavio when this occurs
try:
audiofile = AudioSegment.from_file(filename)
audiofile = AudioSegment.from_file(file_name)
if limit:
audiofile = audiofile[:limit * 1000]
data = np.fromstring(audiofile._data, np.int16)
data = np.fromstring(audiofile.raw_data, np.int16)
channels = []
for chn in xrange(audiofile.channels):
for chn in range(audiofile.channels):
channels.append(data[chn::audiofile.channels])
fs = audiofile.frame_rate
audiofile.frame_rate
except audioop.error:
fs, _, audiofile = wavio.readwav(filename)
_, _, audiofile = wavio.readwav(file_name)
if limit:
audiofile = audiofile[:limit * 1000]
@ -73,12 +92,14 @@ def read(filename, limit=None):
for chn in audiofile:
channels.append(chn)
return channels, audiofile.frame_rate, unique_hash(filename)
return channels, audiofile.frame_rate, unique_hash(file_name)
def path_to_songname(path):
def get_audio_name_from_path(file_path: str) -> str:
"""
Extracts song name from a filepath. Used to identify which songs
have already been fingerprinted on disk.
Extracts song name from a file path.
:param file_path: path to an audio file.
:return: file name
"""
return os.path.splitext(os.path.basename(path))[0]
return os.path.splitext(os.path.basename(file_path))[0]

156
dejavu/logic/fingerprint.py Executable file
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@ -0,0 +1,156 @@
import hashlib
from operator import itemgetter
from typing import List, Tuple
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import numpy as np
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import (binary_erosion,
generate_binary_structure,
iterate_structure)
from dejavu.config.settings import (CONNECTIVITY_MASK, DEFAULT_AMP_MIN,
DEFAULT_FAN_VALUE, DEFAULT_FS,
DEFAULT_OVERLAP_RATIO, DEFAULT_WINDOW_SIZE,
FINGERPRINT_REDUCTION, MAX_HASH_TIME_DELTA,
MIN_HASH_TIME_DELTA,
PEAK_NEIGHBORHOOD_SIZE, PEAK_SORT)
def fingerprint(channel_samples: List[int],
Fs: int = DEFAULT_FS,
wsize: int = DEFAULT_WINDOW_SIZE,
wratio: float = DEFAULT_OVERLAP_RATIO,
fan_value: int = DEFAULT_FAN_VALUE,
amp_min: int = DEFAULT_AMP_MIN) -> List[Tuple[str, int]]:
"""
FFT the channel, log transform output, find local maxima, then return locally sensitive hashes.
:param channel_samples: channel samples to fingerprint.
:param Fs: audio sampling rate.
:param wsize: FFT windows size.
:param wratio: ratio by which each sequential window overlaps the last and the next window.
:param fan_value: degree to which a fingerprint can be paired with its neighbors.
:param amp_min: minimum amplitude in spectrogram in order to be considered a peak.
:return: a list of hashes with their corresponding offsets.
"""
# FFT the signal and extract frequency components
arr2D = mlab.specgram(
channel_samples,
NFFT=wsize,
Fs=Fs,
window=mlab.window_hanning,
noverlap=int(wsize * wratio))[0]
# Apply log transform since specgram function returns linear array. 0s are excluded to avoid np warning.
arr2D = 10 * np.log10(arr2D, out=np.zeros_like(arr2D), where=(arr2D != 0))
local_maxima = get_2D_peaks(arr2D, plot=False, amp_min=amp_min)
# return hashes
return generate_hashes(local_maxima, fan_value=fan_value)
def get_2D_peaks(arr2D: np.array, plot: bool = False, amp_min: int = DEFAULT_AMP_MIN)\
-> List[Tuple[List[int], List[int]]]:
"""
Extract maximum peaks from the spectogram matrix (arr2D).
:param arr2D: matrix representing the spectogram.
:param plot: for plotting the results.
:param amp_min: minimum amplitude in spectrogram in order to be considered a peak.
:return: a list composed by a list of frequencies and times.
"""
# Original code from the repo is using a morphology mask that does not consider diagonal elements
# as neighbors (basically a diamond figure) and then applies a dilation over it, so what I'm proposing
# is to change from the current diamond figure to a just a normal square one:
# F T F T T T
# T T T ==> T T T
# F T F T T T
# In my local tests time performance of the square mask was ~3 times faster
# respect to the diamond one, without hurting accuracy of the predictions.
# I've made now the mask shape configurable in order to allow both ways of find maximum peaks.
# That being said, we generate the mask by using the following function
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.generate_binary_structure.html
struct = generate_binary_structure(2, CONNECTIVITY_MASK)
# And then we apply dilation using the following function
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.iterate_structure.html
# Take into account that if PEAK_NEIGHBORHOOD_SIZE is 2 you can avoid the use of the scipy functions and just
# change it by the following code:
# neighborhood = np.ones((PEAK_NEIGHBORHOOD_SIZE * 2 + 1, PEAK_NEIGHBORHOOD_SIZE * 2 + 1), dtype=bool)
neighborhood = iterate_structure(struct, PEAK_NEIGHBORHOOD_SIZE)
# find local maxima using our filter mask
local_max = maximum_filter(arr2D, footprint=neighborhood) == arr2D
# Applying erosion, the dejavu documentation does not talk about this step.
background = (arr2D == 0)
eroded_background = binary_erosion(background, structure=neighborhood, border_value=1)
# Boolean mask of arr2D with True at peaks (applying XOR on both matrices).
detected_peaks = local_max != eroded_background
# extract peaks
amps = arr2D[detected_peaks]
freqs, times = np.where(detected_peaks)
# filter peaks
amps = amps.flatten()
# get indices for frequency and time
filter_idxs = np.where(amps > amp_min)
freqs_filter = freqs[filter_idxs]
times_filter = times[filter_idxs]
if plot:
# scatter of the peaks
fig, ax = plt.subplots()
ax.imshow(arr2D)
ax.scatter(times_filter, freqs_filter)
ax.set_xlabel('Time')
ax.set_ylabel('Frequency')
ax.set_title("Spectrogram")
plt.gca().invert_yaxis()
plt.show()
return list(zip(freqs_filter, times_filter))
def generate_hashes(peaks: List[Tuple[int, int]], fan_value: int = DEFAULT_FAN_VALUE) -> List[Tuple[str, int]]:
"""
Hash list structure:
sha1_hash[0:FINGERPRINT_REDUCTION] time_offset
[(e05b341a9b77a51fd26, 32), ... ]
:param peaks: list of peak frequencies and times.
:param fan_value: degree to which a fingerprint can be paired with its neighbors.
:return: a list of hashes with their corresponding offsets.
"""
# frequencies are in the first position of the tuples
idx_freq = 0
# times are in the second position of the tuples
idx_time = 1
if PEAK_SORT:
peaks.sort(key=itemgetter(1))
hashes = []
for i in range(len(peaks)):
for j in range(1, fan_value):
if (i + j) < len(peaks):
freq1 = peaks[i][idx_freq]
freq2 = peaks[i + j][idx_freq]
t1 = peaks[i][idx_time]
t2 = peaks[i + j][idx_time]
t_delta = t2 - t1
if MIN_HASH_TIME_DELTA <= t_delta <= MAX_HASH_TIME_DELTA:
h = hashlib.sha1(f"{str(freq1)}|{str(freq2)}|{str(t_delta)}".encode('utf-8'))
hashes.append((h.hexdigest()[0:FINGERPRINT_REDUCTION], t1))
return hashes

0
dejavu/logic/recognizer/__init__.py Normal file
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32
dejavu/logic/recognizer/file_recognizer.py Normal file
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@ -0,0 +1,32 @@
from time import time
from typing import Dict
import dejavu.logic.decoder as decoder
from dejavu.base_classes.base_recognizer import BaseRecognizer
from dejavu.config.settings import (ALIGN_TIME, FINGERPRINT_TIME, QUERY_TIME,
RESULTS, TOTAL_TIME)
class FileRecognizer(BaseRecognizer):
def __init__(self, dejavu):
super().__init__(dejavu)
def recognize_file(self, filename: str) -> Dict[str, any]:
channels, self.Fs, _ = decoder.read(filename, self.dejavu.limit)
t = time()
matches, fingerprint_time, query_time, align_time = self._recognize(*channels)
t = time() - t
results = {
TOTAL_TIME: t,
FINGERPRINT_TIME: fingerprint_time,
QUERY_TIME: query_time,
ALIGN_TIME: align_time,
RESULTS: matches
}
return results
def recognize(self, filename: str) -> Dict[str, any]:
return self.recognize_file(filename)

50
dejavu/recognize.py → dejavu/logic/recognizer/microphone_recognizer.py
View file

@ -1,55 +1,17 @@
# encoding: utf-8
import dejavu.fingerprint as fingerprint
import dejavu.decoder as decoder
import numpy as np
import pyaudio
import time
class BaseRecognizer(object):
def __init__(self, dejavu):
self.dejavu = dejavu
self.Fs = fingerprint.DEFAULT_FS
def _recognize(self, *data):
matches = []
for d in data:
matches.extend(self.dejavu.find_matches(d, Fs=self.Fs))
return self.dejavu.align_matches(matches)
def recognize(self):
pass # base class does nothing
class FileRecognizer(BaseRecognizer):
def __init__(self, dejavu):
super(FileRecognizer, self).__init__(dejavu)
def recognize_file(self, filename):
frames, self.Fs, file_hash = decoder.read(filename, self.dejavu.limit)
t = time.time()
match = self._recognize(*frames)
t = time.time() - t
if match:
match['match_time'] = t
return match
def recognize(self, filename):
return self.recognize_file(filename)
from dejavu.base_classes.base_recognizer import BaseRecognizer
class MicrophoneRecognizer(BaseRecognizer):
default_chunksize = 8192
default_format = pyaudio.paInt16
default_channels = 2
default_samplerate = 44100
default_chunksize = 8192
default_format = pyaudio.paInt16
default_channels = 2
default_samplerate = 44100
def __init__(self, dejavu):
super(MicrophoneRecognizer, self).__init__(dejavu)
super().__init__(dejavu)
self.audio = pyaudio.PyAudio()
self.stream = None
self.data = []

0
dejavu/tests/__init__.py Normal file
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424
dejavu/testing.py → dejavu/tests/dejavu_test.py
View file

@ -1,46 +1,221 @@
from __future__ import division
from pydub import AudioSegment
from dejavu.decoder import path_to_songname
from dejavu import Dejavu
from dejavu.fingerprint import *
import traceback
import fnmatch
import os, re, ast
import subprocess
import random
import json
import logging
import random
import re
import subprocess
import traceback
from os import listdir, makedirs, walk
from os.path import basename, exists, isfile, join, splitext
import matplotlib.pyplot as plt
import numpy as np
from pydub import AudioSegment
from dejavu.config.settings import (DEFAULT_FS, DEFAULT_OVERLAP_RATIO,
DEFAULT_WINDOW_SIZE, HASHES_MATCHED,
OFFSET, RESULTS, SONG_NAME, TOTAL_TIME)
from dejavu.logic.decoder import get_audio_name_from_path
class DejavuTest:
def __init__(self, folder, seconds):
super().__init__()
self.test_folder = folder
self.test_seconds = seconds
self.test_songs = []
print("test_seconds", self.test_seconds)
self.test_files = [
f for f in listdir(self.test_folder)
if isfile(join(self.test_folder, f))
and any([x for x in re.findall("[0-9]sec", f) if x in self.test_seconds])
]
print("test_files", self.test_files)
self.n_columns = len(self.test_seconds)
self.n_lines = int(len(self.test_files) / self.n_columns)
print("columns:", self.n_columns)
print("length of test files:", len(self.test_files))
print("lines:", self.n_lines)
# variable match results (yes, no, invalid)
self.result_match = [[0 for x in range(self.n_columns)] for x in range(self.n_lines)]
print("result_match matrix:", self.result_match)
# variable match precision (if matched in the corrected time)
self.result_matching_times = [[0 for x in range(self.n_columns)] for x in range(self.n_lines)]
# variable matching time (query time)
self.result_query_duration = [[0 for x in range(self.n_columns)] for x in range(self.n_lines)]
# variable confidence
self.result_match_confidence = [[0 for x in range(self.n_columns)] for x in range(self.n_lines)]
self.begin()
def get_column_id(self, secs):
for i, sec in enumerate(self.test_seconds):
if secs == sec:
return i
def get_line_id(self, song):
for i, s in enumerate(self.test_songs):
if song == s:
return i
self.test_songs.append(song)
return len(self.test_songs) - 1
def create_plots(self, name, results, results_folder):
for sec in range(0, len(self.test_seconds)):
ind = np.arange(self.n_lines)
width = 0.25 # the width of the bars
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim([-1 * width, 2 * width])
means_dvj = [x[0] for x in results[sec]]
rects1 = ax.bar(ind, means_dvj, width, color='r')
# add some
ax.set_ylabel(name)
ax.set_title(f"{self.test_seconds[sec]} {name} Results")
ax.set_xticks(ind + width)
labels = [0 for x in range(0, self.n_lines)]
for x in range(0, self.n_lines):
labels[x] = f"song {x+1}"
ax.set_xticklabels(labels)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
if name == 'Confidence':
autolabel(rects1, ax)
else:
autolabeldoubles(rects1, ax)
plt.grid()
fig_name = join(results_folder, f"{name}_{self.test_seconds[sec]}.png")
fig.savefig(fig_name)
def begin(self):
for f in self.test_files:
log_msg('--------------------------------------------------')
log_msg(f'file: {f}')
# get column
col = self.get_column_id([x for x in re.findall("[0-9]sec", f) if x in self.test_seconds][0])
# format: XXXX_offset_length.mp3, we also take into account underscores within XXXX
splits = get_audio_name_from_path(f).split("_")
song = "_".join(splits[0:len(get_audio_name_from_path(f).split("_")) - 2])
line = self.get_line_id(song)
result = subprocess.check_output([
"python",
"dejavu.py",
'-r',
'file',
join(self.test_folder, f)])
if result.strip() == "None":
log_msg('No match')
self.result_match[line][col] = 'no'
self.result_matching_times[line][col] = 0
self.result_query_duration[line][col] = 0
self.result_match_confidence[line][col] = 0
else:
result = result.strip()
# we parse the output song back to a json
result = json.loads(result.decode('utf-8').replace("'", '"').replace(': b"', ':"'))
# which song did we predict? We consider only the first match.
match = result[RESULTS][0]
song_result = match[SONG_NAME]
log_msg(f'song: {song}')
log_msg(f'song_result: {song_result}')
if song_result != song:
log_msg('invalid match')
self.result_match[line][col] = 'invalid'
self.result_matching_times[line][col] = 0
self.result_query_duration[line][col] = 0
self.result_match_confidence[line][col] = 0
else:
log_msg('correct match')
print(self.result_match)
self.result_match[line][col] = 'yes'
self.result_query_duration[line][col] = round(result[TOTAL_TIME], 3)
self.result_match_confidence[line][col] = match[HASHES_MATCHED]
# using replace in f for getting rid of underscores in name
song_start_time = re.findall("_[^_]+", f.replace(song, ""))
song_start_time = song_start_time[0].lstrip("_ ")
result_start_time = round((match[OFFSET] * DEFAULT_WINDOW_SIZE *
DEFAULT_OVERLAP_RATIO) / DEFAULT_FS, 0)
self.result_matching_times[line][col] = int(result_start_time) - int(song_start_time)
if abs(self.result_matching_times[line][col]) == 1:
self.result_matching_times[line][col] = 0
log_msg(f'query duration: {round(result[TOTAL_TIME], 3)}')
log_msg(f'confidence: {match[HASHES_MATCHED]}')
log_msg(f'song start_time: {song_start_time}')
log_msg(f'result start time: {result_start_time}')
if self.result_matching_times[line][col] == 0:
log_msg('accurate match')
else:
log_msg('inaccurate match')
log_msg('--------------------------------------------------\n')
def set_seed(seed=None):
"""
`seed` as None means that the sampling will be random.
`seed` as None means that the sampling will be random.
Setting your own seed means that you can produce the
same experiment over and over.
Setting your own seed means that you can produce the
same experiment over and over.
"""
if seed != None:
if seed is not None:
random.seed(seed)
def get_files_recursive(src, fmt):
"""
`src` is the source directory.
`src` is the source directory.
`fmt` is the extension, ie ".mp3" or "mp3", etc.
"""
for root, dirnames, filenames in os.walk(src):
files = []
for root, dirnames, filenames in walk(src):
for filename in fnmatch.filter(filenames, '*' + fmt):
yield os.path.join(root, filename)
files.append(join(root, filename))
return files
def get_length_audio(audiopath, extension):
"""
Returns length of audio in seconds.
Returns None if format isn't supported or in case of error.
Returns length of audio in seconds.
Returns None if format isn't supported or in case of error.
"""
try:
audio = AudioSegment.from_file(audiopath, extension.replace(".", ""))
except:
print "Error in get_length_audio(): %s" % traceback.format_exc()
except Exception:
print(f"Error in get_length_audio(): {traceback.format_exc()}")
return None
return int(len(audio) / 1000.0)
def get_starttime(length, nseconds, padding):
"""
`length` is total audio length in seconds
@ -52,225 +227,60 @@ def get_starttime(length, nseconds, padding):
return 0
return random.randint(padding, maximum)
def generate_test_files(src, dest, nseconds, fmts=[".mp3", ".wav"], padding=10):
"""
Generates a test file for each file recursively in `src` directory
of given format using `nseconds` sampled from the audio file.
of given format using `nseconds` sampled from the audio file.
Results are written to `dest` directory.
`padding` is the number of off-limit seconds and the beginning and
end of a track that won't be sampled in testing. Often you want to
avoid silence, etc.
end of a track that won't be sampled in testing. Often you want to
avoid silence, etc.
"""
# create directories if necessary
for directory in [src, dest]:
try:
os.stat(directory)
except:
os.mkdir(directory)
if not exists(dest):
makedirs(dest)
# find files recursively of a given file format
for fmt in fmts:
testsources = get_files_recursive(src, fmt)
testsources = get_files_recursive(src, fmt)
for audiosource in testsources:
print "audiosource:", audiosource
filename, extension = os.path.splitext(os.path.basename(audiosource))
length = get_length_audio(audiosource, extension)
print("audiosource:", audiosource)
filename, extension = splitext(basename(audiosource))
length = get_length_audio(audiosource, extension)
starttime = get_starttime(length, nseconds, padding)
test_file_name = "%s_%s_%ssec.%s" % (
os.path.join(dest, filename), starttime,
nseconds, extension.replace(".", ""))
test_file_name = f"{join(dest, filename)}_{starttime}_{nseconds}sec.{extension.replace('.', '')}"
subprocess.check_output([
"ffmpeg", "-y",
"-ss", "%d" % starttime,
'-t' , "%d" % nseconds,
"-ss", f"{starttime}",
'-t', f"{nseconds}",
"-i", audiosource,
test_file_name])
def log_msg(msg, log=True, silent=False):
if log:
logging.debug(msg)
if not silent:
print msg
print(msg)
def autolabel(rects, ax):
# attach some text labels
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height,
'%d' % int(height), ha='center', va='bottom')
ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height, f'{int(height)}', ha='center', va='bottom')
def autolabeldoubles(rects, ax):
# attach some text labels
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height,
'%s' % round(float(height), 3), ha='center', va='bottom')
class DejavuTest(object):
def __init__(self, folder, seconds):
super(DejavuTest, self).__init__()
self.test_folder = folder
self.test_seconds = seconds
self.test_songs = []
print "test_seconds", self.test_seconds
self.test_files = [
f for f in os.listdir(self.test_folder)
if os.path.isfile(os.path.join(self.test_folder, f))
and re.findall("[0-9]*sec", f)[0] in self.test_seconds]
print "test_files", self.test_files
self.n_columns = len(self.test_seconds)
self.n_lines = int(len(self.test_files) / self.n_columns)
print "columns:", self.n_columns
print "length of test files:", len(self.test_files)
print "lines:", self.n_lines
# variable match results (yes, no, invalid)
self.result_match = [[0 for x in xrange(self.n_columns)] for x in xrange(self.n_lines)]
print "result_match matrix:", self.result_match
# variable match precision (if matched in the corrected time)
self.result_matching_times = [[0 for x in xrange(self.n_columns)] for x in xrange(self.n_lines)]
# variable mahing time (query time)
self.result_query_duration = [[0 for x in xrange(self.n_columns)] for x in xrange(self.n_lines)]
# variable confidence
self.result_match_confidence = [[0 for x in xrange(self.n_columns)] for x in xrange(self.n_lines)]
self.begin()
def get_column_id (self, secs):
for i, sec in enumerate(self.test_seconds):
if secs == sec:
return i
def get_line_id (self, song):
for i, s in enumerate(self.test_songs):
if song == s:
return i
self.test_songs.append(song)
return len(self.test_songs) - 1
def create_plots(self, name, results, results_folder):
for sec in range(0, len(self.test_seconds)):
ind = np.arange(self.n_lines) #
width = 0.25 # the width of the bars
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim([-1 * width, 2 * width])
means_dvj = [x[0] for x in results[sec]]
rects1 = ax.bar(ind, means_dvj, width, color='r')
# add some
ax.set_ylabel(name)
ax.set_title("%s %s Results" % (self.test_seconds[sec], name))
ax.set_xticks(ind + width)
labels = [0 for x in range(0, self.n_lines)]
for x in range(0, self.n_lines):
labels[x] = "song %s" % (x+1)
ax.set_xticklabels(labels)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
#ax.legend( (rects1[0]), ('Dejavu'), loc='center left', bbox_to_anchor=(1, 0.5))
if name == 'Confidence':
autolabel(rects1, ax)
else:
autolabeldoubles(rects1, ax)
plt.grid()
fig_name = os.path.join(results_folder, "%s_%s.png" % (name, self.test_seconds[sec]))
fig.savefig(fig_name)
def begin(self):
for f in self.test_files:
log_msg('--------------------------------------------------')
log_msg('file: %s' % f)
# get column
col = self.get_column_id(re.findall("[0-9]*sec", f)[0])
# format: XXXX_offset_length.mp3
song = path_to_songname(f).split("_")[0]
line = self.get_line_id(song)
result = subprocess.check_output([
"python",
"dejavu.py",
'-r',
'file',
self.test_folder + "/" + f])
if result.strip() == "None":
log_msg('No match')
self.result_match[line][col] = 'no'
self.result_matching_times[line][col] = 0
self.result_query_duration[line][col] = 0
self.result_match_confidence[line][col] = 0
else:
result = result.strip()
result = result.replace(" \'", ' "')
result = result.replace("{\'", '{"')
result = result.replace("\':", '":')
result = result.replace("\',", '",')
# which song did we predict?
result = ast.literal_eval(result)
song_result = result["song_name"]
log_msg('song: %s' % song)
log_msg('song_result: %s' % song_result)
if song_result != song:
log_msg('invalid match')
self.result_match[line][col] = 'invalid'
self.result_matching_times[line][col] = 0
self.result_query_duration[line][col] = 0
self.result_match_confidence[line][col] = 0
else:
log_msg('correct match')
print self.result_match
self.result_match[line][col] = 'yes'
self.result_query_duration[line][col] = round(result[Dejavu.MATCH_TIME],3)
self.result_match_confidence[line][col] = result[Dejavu.CONFIDENCE]
song_start_time = re.findall("\_[^\_]+",f)
song_start_time = song_start_time[0].lstrip("_ ")
result_start_time = round((result[Dejavu.OFFSET] * DEFAULT_WINDOW_SIZE *
DEFAULT_OVERLAP_RATIO) / (DEFAULT_FS), 0)
self.result_matching_times[line][col] = int(result_start_time) - int(song_start_time)
if (abs(self.result_matching_times[line][col]) == 1):
self.result_matching_times[line][col] = 0
log_msg('query duration: %s' % round(result[Dejavu.MATCH_TIME],3))
log_msg('confidence: %s' % result[Dejavu.CONFIDENCE])
log_msg('song start_time: %s' % song_start_time)
log_msg('result start time: %s' % result_start_time)
if (self.result_matching_times[line][col] == 0):
log_msg('accurate match')
else:
log_msg('inaccurate match')
log_msg('--------------------------------------------------\n')
ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height, f'{round(float(height), 3)}',
ha='center', va='bottom')

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# wavio.py
# Author: Warren Weckesser
# License: BSD 2-Clause (http://opensource.org/licenses/BSD-2-Clause)
# Synopsis: A Python module for reading and writing 24 bit WAV files.
# Github: github.com/WarrenWeckesser/wavio
"""
The wavio module defines the functions:
read(file)
Read a WAV file and return a `wavio.Wav` object, with attributes
`data`, `rate` and `sampwidth`.
write(filename, data, rate, scale=None, sampwidth=None)
Write a numpy array to a WAV file.
-----
Author: Warren Weckesser
License: BSD 2-Clause:
Copyright (c) 2015, Warren Weckesser
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
"""
import wave as _wave
import numpy as _np
__version__ = "0.0.5.dev1"
def _wav2array(nchannels, sampwidth, data):
"""data must be the string containing the bytes from the wav file."""
num_samples, remainder = divmod(len(data), sampwidth * nchannels)
if remainder > 0:
raise ValueError('The length of data is not a multiple of '
'sampwidth * num_channels.')
if sampwidth > 4:
raise ValueError("sampwidth must not be greater than 4.")
if sampwidth == 3:
a = _np.empty((num_samples, nchannels, 4), dtype=_np.uint8)
raw_bytes = _np.frombuffer(data, dtype=_np.uint8)
a[:, :, :sampwidth] = raw_bytes.reshape(-1, nchannels, sampwidth)
a[:, :, sampwidth:] = (a[:, :, sampwidth - 1:sampwidth] >> 7) * 255
result = a.view('<i4').reshape(a.shape[:-1])
else:
# 8 bit samples are stored as unsigned ints; others as signed ints.
dt_char = 'u' if sampwidth == 1 else 'i'
a = _np.frombuffer(data, dtype=f'<{dt_char}{sampwidth}')
result = a.reshape(-1, nchannels)
return result
def _array2wav(a, sampwidth):
"""
Convert the input array `a` to a string of WAV data.
a.dtype must be one of uint8, int16 or int32. Allowed sampwidth
values are:
dtype sampwidth
uint8 1
int16 2
int32 3 or 4
When sampwidth is 3, the *low* bytes of `a` are assumed to contain
the values to include in the string.
"""
if sampwidth == 3:
# `a` must have dtype int32
if a.ndim == 1:
# Convert to a 2D array with a single column.
a = a.reshape(-1, 1)
# By shifting first 0 bits, then 8, then 16, the resulting output
# is 24 bit little-endian.
a8 = (a.reshape(a.shape + (1,)) >> _np.array([0, 8, 16])) & 255
wavdata = a8.astype(_np.uint8).tostring()
else:
# Make sure the array is little-endian, and then convert using
# tostring()
a = a.astype('<' + a.dtype.str[1:], copy=False)
wavdata = a.tostring()
return wavdata
class Wav(object):
"""
Object returned by `wavio.read`. Attributes are:
data : numpy array
The array of data read from the WAV file.
rate : float
The sample rate of the WAV file.
sampwidth : int
The sample width (i.e. number of bytes per sample) of the WAV file.
For example, `sampwidth == 3` is a 24 bit WAV file.
"""
def __init__(self, data, rate, sampwidth):
self.data = data
self.rate = rate
self.sampwidth = sampwidth
def __repr__(self):
s = (f"Wav(data.shape={self.data.shape}, data.dtype={self.data.dtype}, "
f"rate={self.rate}, sampwidth={self.sampwidth})")
return s
def read(file):
"""
Read a WAV file.
Parameters
----------
file : string or file object
Either the name of a file or an open file pointer.
Returns
-------
wav : wavio.Wav() instance
The return value is an instance of the class `wavio.Wav`,
with the following attributes:
data : numpy array
The array containing the data. The shape of the array
is (num_samples, num_channels). num_channels is the
number of audio channels (1 for mono, 2 for stereo).
rate : float
The sampling frequency (i.e. frame rate)
sampwidth : float
The sample width, in bytes. E.g. for a 24 bit WAV file,
sampwidth is 3.
Notes
-----
This function uses the `wave` module of the Python standard libary
to read the WAV file, so it has the same limitations as that library.
In particular, the function does not read compressed WAV files, and
it does not read files with floating point data.
The array returned by `wavio.read` is always two-dimensional. If the
WAV data is mono, the array will have shape (num_samples, 1).
`wavio.read()` does not scale or normalize the data. The data in the
array `wav.data` is the data that was in the file. When the file
contains 24 bit samples, the resulting numpy array is 32 bit integers,
with values that have been sign-extended.
"""
wav = _wave.open(file)
rate = wav.getframerate()
nchannels = wav.getnchannels()
sampwidth = wav.getsampwidth()
nframes = wav.getnframes()
data = wav.readframes(nframes)
wav.close()
array = _wav2array(nchannels, sampwidth, data)
w = Wav(data=array, rate=rate, sampwidth=sampwidth)
return w
_sampwidth_dtypes = {1: _np.uint8,
2: _np.int16,
3: _np.int32,
4: _np.int32}
_sampwidth_ranges = {1: (0, 256),
2: (-2**15, 2**15),
3: (-2**23, 2**23),
4: (-2**31, 2**31)}
def _scale_to_sampwidth(data, sampwidth, vmin, vmax):
# Scale and translate the values to fit the range of the data type
# associated with the given sampwidth.
data = data.clip(vmin, vmax)
dt = _sampwidth_dtypes[sampwidth]
if vmax == vmin:
data = _np.zeros(data.shape, dtype=dt)
else:
outmin, outmax = _sampwidth_ranges[sampwidth]
if outmin != vmin or outmax != vmax:
vmin = float(vmin)
vmax = float(vmax)
data = (float(outmax - outmin) * (data - vmin) /
(vmax - vmin)).astype(_np.int64) + outmin
data[data == outmax] = outmax - 1
data = data.astype(dt)
return data
def write(file, data, rate, scale=None, sampwidth=None):
"""
Write the numpy array `data` to a WAV file.
The Python standard library "wave" is used to write the data
to the file, so this function has the same limitations as that
module. In particular, the Python library does not support
floating point data. When given a floating point array, this
function converts the values to integers. See below for the
conversion rules.
Parameters
----------
file : string, or file object open for writing in binary mode
Either the name of a file or an open file pointer.
data : numpy array, 1- or 2-dimensional, integer or floating point
If it is 2-d, the rows are the frames (i.e. samples) and the
columns are the channels.
rate : float
The sampling frequency (i.e. frame rate) of the data.
sampwidth : int, optional
The sample width, in bytes, of the output file.
If `sampwidth` is not given, it is inferred (if possible) from
the data type of `data`, as follows::
data.dtype sampwidth
---------- ---------
uint8, int8 1
uint16, int16 2
uint32, int32 4
For any other data types, or to write a 24 bit file, `sampwidth`
must be given.
scale : tuple or str, optional
By default, the data written to the file is scaled up or down to
occupy the full range of the output data type. So, for example,
the unsigned 8 bit data [0, 1, 2, 15] would be written to the file
as [0, 17, 30, 255]. More generally, the default behavior is
(roughly)::
vmin = data.min()
vmax = data.max()
outmin = <minimum integer of the output dtype>
outmax = <maximum integer of the output dtype>
outdata = (outmax - outmin)*(data - vmin)/(vmax - vmin) + outmin
The `scale` argument allows the scaling of the output data to be
changed. `scale` can be a tuple of the form `(vmin, vmax)`, in which
case the given values override the use of `data.min()` and
`data.max()` for `vmin` and `vmax` shown above. (If either value
is `None`, the value shown above is used.) Data outside the
range (vmin, vmax) is clipped. If `vmin == vmax`, the output is
all zeros.
If `scale` is the string "none", then `vmin` and `vmax` are set to
`outmin` and `outmax`, respectively. This means the data is written
to the file with no scaling. (Note: `scale="none" is not the same
as `scale=None`. The latter means "use the default behavior",
which is to scale by the data minimum and maximum.)
If `scale` is the string "dtype-limits", then `vmin` and `vmax`
are set to the minimum and maximum integers of `data.dtype`.
The string "dtype-limits" is not allowed when the `data` is a
floating point array.
If using `scale` results in values that exceed the limits of the
output sample width, the data is clipped. For example, the
following code::
>> x = np.array([-100, 0, 100, 200, 300, 325])
>> wavio.write('foo.wav', x, 8000, scale='none', sampwidth=1)
will write the values [0, 0, 100, 200, 255, 255] to the file.
Example
-------
Create a 3 second 440 Hz sine wave, and save it in a 24-bit WAV file.
>> import numpy as np
>> import wavio
>> rate = 22050 # samples per second
>> T = 3 # sample duration (seconds)
>> f = 440.0 # sound frequency (Hz)
>> t = np.linspace(0, T, T*rate, endpoint=False)
>> x = np.sin(2*np.pi * f * t)
>> wavio.write("sine24.wav", x, rate, sampwidth=3)
Create a file that contains the 16 bit integer values -10000 and 10000
repeated 100 times. Don't automatically scale the values. Use a sample
rate 8000.
>> x = np.empty(200, dtype=np.int16)
>> x[::2] = -10000
>> x[1::2] = 10000
>> wavio.write("foo.wav", x, 8000, scale='none')
Check that the file contains what we expect.
>> w = wavio.read("foo.wav")
>> np.all(w.data[:, 0] == x)
True
In the following, the values -10000 and 10000 (from within the 16 bit
range [-2**15, 2**15-1]) are mapped to the corresponding values 88 and
168 (in the range [0, 2**8-1]).
>> wavio.write("foo.wav", x, 8000, sampwidth=1, scale='dtype-limits')
>> w = wavio.read("foo.wav")
>> w.data[:4, 0]
array([ 88, 168, 88, 168], dtype=uint8)
"""
if sampwidth is None:
if not _np.issubdtype(data.dtype, _np.integer) or data.itemsize > 4:
raise ValueError('when data.dtype is not an 8-, 16-, or 32-bit integer type, sampwidth must be specified.')
sampwidth = data.itemsize
else:
if sampwidth not in [1, 2, 3, 4]:
raise ValueError('sampwidth must be 1, 2, 3 or 4.')
outdtype = _sampwidth_dtypes[sampwidth]
outmin, outmax = _sampwidth_ranges[sampwidth]
if scale == "none":
data = data.clip(outmin, outmax-1).astype(outdtype)
elif scale == "dtype-limits":
if not _np.issubdtype(data.dtype, _np.integer):
raise ValueError("scale cannot be 'dtype-limits' with non-integer data.")
# Easy transforms that just changed the signedness of the data.
if sampwidth == 1 and data.dtype == _np.int8:
data = (data.astype(_np.int16) + 128).astype(_np.uint8)
elif sampwidth == 2 and data.dtype == _np.uint16:
data = (data.astype(_np.int32) - 32768).astype(_np.int16)
elif sampwidth == 4 and data.dtype == _np.uint32:
data = (data.astype(_np.int64) - 2**31).astype(_np.int32)
elif data.itemsize != sampwidth:
# Integer input, but rescaling is needed to adjust the
# input range to the output sample width.
ii = _np.iinfo(data.dtype)
vmin = ii.min
vmax = ii.max
data = _scale_to_sampwidth(data, sampwidth, vmin, vmax)
else:
if scale is None:
vmin = data.min()
vmax = data.max()
else:
# scale must be a tuple of the form (vmin, vmax)
vmin, vmax = scale
if vmin is None:
vmin = data.min()
if vmax is None:
vmax = data.max()
data = _scale_to_sampwidth(data, sampwidth, vmin, vmax)
# At this point, `data` has been converted to have one of the following:
# sampwidth dtype
# --------- -----
# 1 uint8
# 2 int16
# 3 int32
# 4 int32
# The values in `data` are in the form in which they will be saved;
# no more scaling will take place.
if data.ndim == 1:
data = data.reshape(-1, 1)
wavdata = _array2wav(data, sampwidth)
w = _wave.open(file, 'wb')
w.setnchannels(data.shape[1])
w.setsampwidth(sampwidth)
w.setframerate(rate)
w.writeframes(wavdata)
w.close()

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dejavu/wavio.py
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# wavio.py
# Author: Warren Weckesser
# License: BSD 3-Clause (http://opensource.org/licenses/BSD-3-Clause)
# Synopsis: A Python module for reading and writing 24 bit WAV files.
# Github: github.com/WarrenWeckesser/wavio
import wave as _wave
import numpy as _np
def _wav2array(nchannels, sampwidth, data):
"""data must be the string containing the bytes from the wav file."""
num_samples, remainder = divmod(len(data), sampwidth * nchannels)
if remainder > 0:
raise ValueError('The length of data is not a multiple of '
'sampwidth * num_channels.')
if sampwidth > 4:
raise ValueError("sampwidth must not be greater than 4.")
if sampwidth == 3:
a = _np.empty((num_samples, nchannels, 4), dtype=_np.uint8)
raw_bytes = _np.fromstring(data, dtype=_np.uint8)
a[:, :, :sampwidth] = raw_bytes.reshape(-1, nchannels, sampwidth)
a[:, :, sampwidth:] = (a[:, :, sampwidth - 1:sampwidth] >> 7) * 255
result = a.view('<i4').reshape(a.shape[:-1])
else:
# 8 bit samples are stored as unsigned ints; others as signed ints.
dt_char = 'u' if sampwidth == 1 else 'i'
a = _np.fromstring(data, dtype='<%s%d' % (dt_char, sampwidth))
result = a.reshape(-1, nchannels)
return result
def readwav(file):
"""
Read a WAV file.
Parameters
----------
file : string or file object
Either the name of a file or an open file pointer.
Return Values
-------------
rate : float
The sampling frequency (i.e. frame rate)
sampwidth : float
The sample width, in bytes. E.g. for a 24 bit WAV file,
sampwidth is 3.
data : numpy array
The array containing the data. The shape of the array is
(num_samples, num_channels). num_channels is the number of
audio channels (1 for mono, 2 for stereo).
Notes
-----
This function uses the `wave` module of the Python standard libary
to read the WAV file, so it has the same limitations as that library.
In particular, the function does not read compressed WAV files.
"""
wav = _wave.open(file)
rate = wav.getframerate()
nchannels = wav.getnchannels()
sampwidth = wav.getsampwidth()
nframes = wav.getnframes()
data = wav.readframes(nframes)
wav.close()
array = _wav2array(nchannels, sampwidth, data)
return rate, sampwidth, array
def writewav24(filename, rate, data):
"""
Create a 24 bit wav file.
Parameters
----------
filename : string
Name of the file to create.
rate : float
The sampling frequency (i.e. frame rate) of the data.
data : array-like collection of integer or floating point values
data must be "array-like", either 1- or 2-dimensional. If it
is 2-d, the rows are the frames (i.e. samples) and the columns
are the channels.
Notes
-----
The data is assumed to be signed, and the values are assumed to be
within the range of a 24 bit integer. Floating point values are
converted to integers. The data is not rescaled or normalized before
writing it to the file.
Example
-------
Create a 3 second 440 Hz sine wave.
>>> rate = 22050 # samples per second
>>> T = 3 # sample duration (seconds)
>>> f = 440.0 # sound frequency (Hz)
>>> t = np.linspace(0, T, T*rate, endpoint=False)
>>> x = (2**23 - 1) * np.sin(2 * np.pi * f * t)
>>> writewav24("sine24.wav", rate, x)
"""
a32 = _np.asarray(data, dtype=_np.int32)
if a32.ndim == 1:
# Convert to a 2D array with a single column.
a32.shape = a32.shape + (1,)
# By shifting first 0 bits, then 8, then 16, the resulting output
# is 24 bit little-endian.
a8 = (a32.reshape(a32.shape + (1,)) >> _np.array([0, 8, 16])) & 255
wavdata = a8.astype(_np.uint8).tostring()
w = _wave.open(filename, 'wb')
w.setnchannels(a32.shape[1])
w.setsampwidth(3)
w.setframerate(rate)
w.writeframes(wavdata)
w.close()

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example.py
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@ -1,35 +0,0 @@
import warnings
import json
warnings.filterwarnings("ignore")
from dejavu import Dejavu
from dejavu.recognize import FileRecognizer, MicrophoneRecognizer
# load config from a JSON file (or anything outputting a python dictionary)
with open("dejavu.cnf.SAMPLE") as f:
config = json.load(f)
if __name__ == '__main__':
# create a Dejavu instance
djv = Dejavu(config)
# Fingerprint all the mp3's in the directory we give it
djv.fingerprint_directory("mp3", [".mp3"])
# Recognize audio from a file
song = djv.recognize(FileRecognizer, "mp3/Sean-Fournier--Falling-For-You.mp3")
print "From file we recognized: %s\n" % song
# Or recognize audio from your microphone for `secs` seconds
secs = 5
song = djv.recognize(MicrophoneRecognizer, seconds=secs)
if song is None:
print "Nothing recognized -- did you play the song out loud so your mic could hear it? :)"
else:
print "From mic with %d seconds we recognized: %s\n" % (secs, song)
# Or use a recognizer without the shortcut, in anyway you would like
recognizer = FileRecognizer(djv)
song = recognizer.recognize_file("mp3/Josh-Woodward--I-Want-To-Destroy-Something-Beautiful.mp3")
print "No shortcut, we recognized: %s\n" % song

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import json
from dejavu import Dejavu
from dejavu.logic.recognizer.file_recognizer import FileRecognizer
from dejavu.logic.recognizer.microphone_recognizer import MicrophoneRecognizer
# load config from a JSON file (or anything outputting a python dictionary)
with open("dejavu.cnf.SAMPLE") as f:
config = json.load(f)
if __name__ == '__main__':
# create a Dejavu instance
djv = Dejavu(config)
# Fingerprint all the mp3's in the directory we give it
djv.fingerprint_directory("test", [".wav"])
# Recognize audio from a file
results = djv.recognize(FileRecognizer, "mp3/Josh-Woodward--I-Want-To-Destroy-Something-Beautiful.mp3")
print(f"From file we recognized: {results}\n")
# Or recognize audio from your microphone for `secs` seconds
secs = 5
results = djv.recognize(MicrophoneRecognizer, seconds=secs)
if results is None:
print("Nothing recognized -- did you play the song out loud so your mic could hear it? :)")
else:
print(f"From mic with {secs} seconds we recognized: {results}\n")
# Or use a recognizer without the shortcut, in anyway you would like
recognizer = FileRecognizer(djv)
results = recognizer.recognize_file("mp3/Josh-Woodward--I-Want-To-Destroy-Something-Beautiful.mp3")
print(f"No shortcut, we recognized: {results}\n")

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requirements.txt
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# requirements file
### BEGIN ###
pydub>=0.9.4
PyAudio>=0.2.7
numpy>=1.8.2
scipy>=0.12.1
matplotlib>=1.3.1
### END ###
pydub==0.23.1
PyAudio==0.2.11
numpy==1.17.2
scipy==1.3.1
matplotlib==3.1.1
mysql-connector-python==8.0.17
psycopg2==2.8.3

280
run_tests.py
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from dejavu.testing import *
from dejavu import Dejavu
from optparse import OptionParser
import matplotlib.pyplot as plt
import argparse
import logging
import time
import shutil
from os import makedirs
from os.path import exists, join
from shutil import rmtree
usage = "usage: %prog [options] TESTING_AUDIOFOLDER"
parser = OptionParser(usage=usage, version="%prog 1.1")
parser.add_option("--secs",
action="store",
dest="secs",
default=5,
type=int,
help='Number of seconds starting from zero to test')
parser.add_option("--results",
action="store",
dest="results_folder",
default="./dejavu_test_results",
help='Sets the path where the results are saved')
parser.add_option("--temp",
action="store",
dest="temp_folder",
default="./dejavu_temp_testing_files",
help='Sets the path where the temp files are saved')
parser.add_option("--log",
action="store_true",
dest="log",
default=True,
help='Enables logging')
parser.add_option("--silent",
action="store_false",
dest="silent",
default=False,
help='Disables printing')
parser.add_option("--log-file",
dest="log_file",
default="results-compare.log",
help='Set the path and filename of the log file')
parser.add_option("--padding",
action="store",
dest="padding",
default=10,
type=int,
help='Number of seconds to pad choice of place to test from')
parser.add_option("--seed",
action="store",
dest="seed",
default=None,
type=int,
help='Random seed')
options, args = parser.parse_args()
test_folder = args[0]
import matplotlib.pyplot as plt
import numpy as np
# set random seed if set by user
set_seed(options.seed)
from dejavu.tests.dejavu_test import (DejavuTest, autolabeldoubles,
generate_test_files, log_msg, set_seed)
# ensure results folder exists
try:
os.stat(options.results_folder)
except:
os.mkdir(options.results_folder)
# set logging
if options.log:
logging.basicConfig(filename=options.log_file, level=logging.DEBUG)
def main(seconds: int, results_folder: str, temp_folder: str, log: bool, silent: bool,
log_file: str, padding: int, seed: int, src: str):
# set test seconds
test_seconds = ['%dsec' % i for i in range(1, options.secs + 1, 1)]
# set random seed if set by user
set_seed(seed)
# generate testing files
for i in range(1, options.secs + 1, 1):
generate_test_files(test_folder, options.temp_folder,
i, padding=options.padding)
# ensure results folder exists
if not exists(results_folder):
makedirs(results_folder)
# scan files
log_msg("Running Dejavu fingerprinter on files in %s..." % test_folder,
log=options.log, silent=options.silent)
# set logging
if log:
logging.basicConfig(filename=log_file, level=logging.DEBUG)
tm = time.time()
djv = DejavuTest(options.temp_folder, test_seconds)
log_msg("finished obtaining results from dejavu in %s" % (time.time() - tm),
log=options.log, silent=options.silent)
# set test seconds
test_seconds = [f'{i}sec' for i in range(1, seconds + 1, 1)]
tests = 1 # djv
n_secs = len(test_seconds)
# generate testing files
for i in range(1, seconds + 1, 1):
generate_test_files(src, temp_folder, i, padding=padding)
# set result variables -> 4d variables
all_match_counter = [[[0 for x in xrange(tests)] for x in xrange(3)] for x in xrange(n_secs)]
all_matching_times_counter = [[[0 for x in xrange(tests)] for x in xrange(2)] for x in xrange(n_secs)]
all_query_duration = [[[0 for x in xrange(tests)] for x in xrange(djv.n_lines)] for x in xrange(n_secs)]
all_match_confidence = [[[0 for x in xrange(tests)] for x in xrange(djv.n_lines)] for x in xrange(n_secs)]
# scan files
log_msg(f"Running Dejavu fingerprinter on files in {src}...", log=log, silent=silent)
# group results by seconds
for line in range(0, djv.n_lines):
for col in range(0, djv.n_columns):
# for dejavu
all_query_duration[col][line][0] = djv.result_query_duration[line][col]
all_match_confidence[col][line][0] = djv.result_match_confidence[line][col]
tm = time.time()
djv = DejavuTest(temp_folder, test_seconds)
log_msg(f"finished obtaining results from dejavu in {(time.time() - tm)}", log=log, silent=silent)
djv_match_result = djv.result_match[line][col]
tests = 1 # djv
n_secs = len(test_seconds)
if djv_match_result == 'yes':
all_match_counter[col][0][0] += 1
elif djv_match_result == 'no':
all_match_counter[col][1][0] += 1
else:
all_match_counter[col][2][0] += 1
# set result variables -> 4d variables
all_match_counter = [[[0 for x in range(tests)] for x in range(3)] for x in range(n_secs)]
all_matching_times_counter = [[[0 for x in range(tests)] for x in range(2)] for x in range(n_secs)]
all_query_duration = [[[0 for x in range(tests)] for x in range(djv.n_lines)] for x in range(n_secs)]
all_match_confidence = [[[0 for x in range(tests)] for x in range(djv.n_lines)] for x in range(n_secs)]
djv_match_acc = djv.result_matching_times[line][col]
# group results by seconds
for line in range(0, djv.n_lines):
for col in range(0, djv.n_columns):
# for dejavu
all_query_duration[col][line][0] = djv.result_query_duration[line][col]
all_match_confidence[col][line][0] = djv.result_match_confidence[line][col]
if djv_match_acc == 0 and djv_match_result == 'yes':
all_matching_times_counter[col][0][0] += 1
elif djv_match_acc != 0:
all_matching_times_counter[col][1][0] += 1
djv_match_result = djv.result_match[line][col]
# create plots
djv.create_plots('Confidence', all_match_confidence, options.results_folder)
djv.create_plots('Query duration', all_query_duration, options.results_folder)
if djv_match_result == 'yes':
all_match_counter[col][0][0] += 1
elif djv_match_result == 'no':
all_match_counter[col][1][0] += 1
else:
all_match_counter[col][2][0] += 1
for sec in range(0, n_secs):
ind = np.arange(3) #
width = 0.25 # the width of the bars
djv_match_acc = djv.result_matching_times[line][col]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim([-1 * width, 2.75])
if djv_match_acc == 0 and djv_match_result == 'yes':
all_matching_times_counter[col][0][0] += 1
elif djv_match_acc != 0:
all_matching_times_counter[col][1][0] += 1
means_dvj = [round(x[0] * 100 / djv.n_lines, 1) for x in all_match_counter[sec]]
rects1 = ax.bar(ind, means_dvj, width, color='r')
# create plots
djv.create_plots('Confidence', all_match_confidence, results_folder)
djv.create_plots('Query duration', all_query_duration, results_folder)
# add some
ax.set_ylabel('Matching Percentage')
ax.set_title('%s Matching Percentage' % test_seconds[sec])
ax.set_xticks(ind + width)
for sec in range(0, n_secs):
ind = np.arange(3)
width = 0.25 # the width of the bars
labels = ['yes','no','invalid']
ax.set_xticklabels( labels )
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim([-1 * width, 2.75])
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
#ax.legend((rects1[0]), ('Dejavu'), loc='center left', bbox_to_anchor=(1, 0.5))
autolabeldoubles(rects1,ax)
plt.grid()
means_dvj = [round(x[0] * 100 / djv.n_lines, 1) for x in all_match_counter[sec]]
rects1 = ax.bar(ind, means_dvj, width, color='r')
fig_name = os.path.join(options.results_folder, "matching_perc_%s.png" % test_seconds[sec])
fig.savefig(fig_name)
# add some
ax.set_ylabel('Matching Percentage')
ax.set_title(f'{test_seconds[sec]} Matching Percentage')
ax.set_xticks(ind + width)
for sec in range(0, n_secs):
ind = np.arange(2) #
width = 0.25 # the width of the bars
labels = ['yes', 'no', 'invalid']
ax.set_xticklabels(labels)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim([-1*width, 1.75])
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
autolabeldoubles(rects1, ax)
plt.grid()
div = all_match_counter[sec][0][0]
if div == 0 :
div = 1000000
fig_name = join(results_folder, f"matching_perc_{test_seconds[sec]}.png")
fig.savefig(fig_name)
means_dvj = [round(x[0] * 100 / div, 1) for x in all_matching_times_counter[sec]]
rects1 = ax.bar(ind, means_dvj, width, color='r')
for sec in range(0, n_secs):
ind = np.arange(2)
width = 0.25 # the width of the bars
# add some
ax.set_ylabel('Matching Accuracy')
ax.set_title('%s Matching Times Accuracy' % test_seconds[sec])
ax.set_xticks(ind + width)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim([-1 * width, 1.75])
labels = ['yes','no']
ax.set_xticklabels( labels )
div = all_match_counter[sec][0][0]
if div == 0:
div = 1000000
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
means_dvj = [round(x[0] * 100 / div, 1) for x in all_matching_times_counter[sec]]
rects1 = ax.bar(ind, means_dvj, width, color='r')
#ax.legend( (rects1[0]), ('Dejavu'), loc='center left', bbox_to_anchor=(1, 0.5))
autolabeldoubles(rects1,ax)
# add some
ax.set_ylabel('Matching Accuracy')
ax.set_title(f'{test_seconds[sec]} Matching Times Accuracy')
ax.set_xticks(ind + width)
plt.grid()
labels = ['yes', 'no']
ax.set_xticklabels(labels)
fig_name = os.path.join(options.results_folder, "matching_acc_%s.png" % test_seconds[sec])
fig.savefig(fig_name)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
autolabeldoubles(rects1, ax)
# remove temporary folder
shutil.rmtree(options.temp_folder)
plt.grid()
fig_name = join(results_folder, f"matching_acc_{test_seconds[sec]}.png")
fig.savefig(fig_name)
# remove temporary folder
rmtree(temp_folder)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=f'Runs a few tests for dejavu to evaluate '
f'its configuration performance. '
f'Usage: %(prog).py [options] TESTING_AUDIOFOLDER'
)
parser.add_argument("-sec", "--seconds", action="store", default=5, type=int,
help='Number of seconds starting from zero to test.')
parser.add_argument("-res", "--results-folder", action="store", default="./dejavu_test_results",
help='Sets the path where the results are saved.')
parser.add_argument("-temp", "--temp-folder", action="store", default="./dejavu_temp_testing_files",
help='Sets the path where the temp files are saved.')
parser.add_argument("-l", "--log", action="store_true", default=False, help='Enables logging.')
parser.add_argument("-sl", "--silent", action="store_false", default=False, help='Disables printing.')
parser.add_argument("-lf", "--log-file", default="results-compare.log",
help='Set the path and filename of the log file.')
parser.add_argument("-pad", "--padding", action="store", default=10, type=int,
help='Number of seconds to pad choice of place to test from.')
parser.add_argument("-sd", "--seed", action="store", default=None, type=int, help='Random seed.')
parser.add_argument("src", type=str, help='Source folder for audios to use as tests.')
args = parser.parse_args()
main(args.seconds, args.results_folder, args.temp_folder, args.log, args.silent, args.log_file, args.padding,
args.seed, args.src)

3
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@ -0,0 +1,3 @@
[flake8]
max-line-length = 120

18
setup.py
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@ -1,5 +1,4 @@
from setuptools import setup, find_packages
# import os, sys
from setuptools import find_packages, setup
def parse_requirements(requirements):
@ -7,26 +6,27 @@ def parse_requirements(requirements):
with open(requirements) as f:
lines = [l for l in f]
# remove spaces
stripped = map((lambda x: x.strip()), lines)
stripped = list(map((lambda x: x.strip()), lines))
# remove comments
nocomments = filter((lambda x: not x.startswith('#')), stripped)
nocomments = list(filter((lambda x: not x.startswith('#')), stripped))
# remove empty lines
reqs = filter((lambda x: x), nocomments)
reqs = list(filter((lambda x: x), nocomments))
return reqs
PACKAGE_NAME = "PyDejavu"
PACKAGE_VERSION = "0.1.3"
SUMMARY = 'Dejavu: Audio Fingerprinting in Python'
DESCRIPTION = """
Audio fingerprinting and recognition algorithm implemented in Python
See the explanation here:
See the explanation here:
`http://willdrevo.com/fingerprinting-and-audio-recognition-with-python/`__
Dejavu can memorize recorded audio by listening to it once and fingerprinting
it. Then by playing a song and recording microphone input or on disk file,
Dejavu attempts to match the audio against the fingerprints held in the
Dejavu can memorize recorded audio by listening to it once and fingerprinting
it. Then by playing a song and recording microphone input or on disk file,
Dejavu attempts to match the audio against the fingerprints held in the
database, returning the song or recording being played.
__ http://willdrevo.com/fingerprinting-and-audio-recognition-with-python/

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