Several fixes and changes:

- isort fixes
- flake8 fixes
- fixed bug of getting duplicated hashes because of sending channels in parallel.
- fixed bug of assigning matches to one offset when the same hash is present in several offsets of a song.
- added type hints and docstring for almost everything.
- added code to list fingerprinted songs in the database.
- added code to delete songs from the database.
- split time in several times: fingerprint time, query time and align time.
- turned the list generator into lists (necessary for split times)
- changed dejavu response.
- added two types of confidences, one that is based on the hashes matched vs the hashes in the db, and another one that is hashes matched vs the hashes from the input song.
- refactored the logic to return more than one result.

dejavu.py

@@ -1,12 +1,12 @@
 import argparse
 import json
-from os.path import isdir
 import sys
 from argparse import RawTextHelpFormatter
+from os.path import isdir
 
 from dejavu import Dejavu
-from dejavu.logic.recognizer.microphone_recognizer import MicrophoneRecognizer
 from dejavu.logic.recognizer.file_recognizer import FileRecognizer
+from dejavu.logic.recognizer.microphone_recognizer import MicrophoneRecognizer
 
 DEFAULT_CONFIG_FILE = "dejavu.cnf.SAMPLE"
 
@@ -41,7 +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')

dejavu/__init__.py

@@ -2,13 +2,19 @@ import multiprocessing
 import os
 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 (CONFIDENCE, DEFAULT_FS,
+from dejavu.config.settings import (DEFAULT_FS, DEFAULT_OVERLAP_RATIO,
-                                    DEFAULT_OVERLAP_RATIO, DEFAULT_WINDOW_SIZE,
+                                    DEFAULT_WINDOW_SIZE, FIELD_FILE_SHA1,
-                                    FIELD_FILE_SHA1, OFFSET, OFFSET_SECS,
+                                    FIELD_TOTAL_HASHES,
-                                    SONG_ID, SONG_NAME, TOPN)
+                                    FINGERPRINTED_CONFIDENCE,
+                                    FINGERPRINTED_HASHES, HASHES_MATCHED,
+                                    INPUT_CONFIDENCE, INPUT_HASHES, OFFSET,
+                                    OFFSET_SECS, SONG_ID, SONG_NAME, TOPN)
 from dejavu.logic.fingerprint import fingerprint
 
 
@@ -27,9 +33,13 @@ class Dejavu:
         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
@@ -37,7 +47,27 @@ class Dejavu:
             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_ids(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_ids(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()
@@ -61,7 +91,7 @@ class Dejavu:
         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:
@@ -76,25 +106,31 @@ class Dejavu:
                 # 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):
+    def fingerprint_file(self, file_path: str, song_name: str = None) -> None:
-        songname = decoder.path_to_songname(filepath)
+        """
-        song_hash = decoder.unique_hash(filepath)
+        Given a path to a file the method generates hashes for it and stores them in the database
-        song_name = song_name or songname
+        for later being 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(f"{song_name} already fingerprinted, continuing...")
         else:
-            song_name, hashes, file_hash = _fingerprint_worker(
+            song_name, hashes, file_hash = Dejavu._fingerprint_worker(
-                filepath,
+                file_path,
                 self.limit,
                 song_name=song_name
             )
@@ -102,117 +138,115 @@ class Dejavu:
 
             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=DEFAULT_FS):
+    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).
+        :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.
+        """
+        t = time()
         hashes = fingerprint(samples, Fs=Fs)
-        return self.db.return_matches(hashes)
+        fingerprint_time = time() - t
+        return hashes, fingerprint_time
 
-    def align_matches(self, matches, topn=TOPN):
+    def find_matches(self, hashes: List[Tuple[str, int]]) -> Tuple[List[Tuple[int, int]], Dict[str, int], float]:
+        """
+        Finds the corresponding matches on the fingerprinted audios for the given hashes.
+        :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.
+
+        """
+        t = time()
+        matches, dedup_hashes = self.db.return_matches(hashes)
+        query_time = time() - t
+
+        return matches, dedup_hashes, query_time
+
+    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
-            Returns a list of dictionaries (based on topn) with match information.
+        :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.
         """
-        # align by diffs
+        # count offset occurrences per song and keep only the maximum ones.
-        diff_counter = {}
+        sorted_matches = sorted(matches, key=lambda m: (m[0], m[1]))
-        largest_count = 0
+        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
+        )
 
-        # TODO: review logic to get topn results.
-        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
-
-            if diff_counter[diff][sid] > largest_count:
-                largest_count = diff_counter[diff][sid]
-
-        # create dic where key are songs ids
-        songs_num_matches = {}
-        for dc in diff_counter:
-            for sid in diff_counter[dc]:
-                match_val = diff_counter[dc][sid]
-                if (sid not in songs_num_matches) or (match_val > songs_num_matches[sid]['value']):
-                    songs_num_matches[sid] = {
-                        'sid': sid,
-                        'value': match_val,
-                        'largest': dc
-                    }
-
-        # use dicc of songs to create an ordered (descending) list using the match value property assigned to each song
-        songs_num_matches_list = []
-        for s in songs_num_matches:
-            songs_num_matches_list.append({
-                'sid': s,
-                'object': songs_num_matches[s]
-            })
-
-        songs_num_matches_list_ordered = sorted(songs_num_matches_list, key=lambda x: x['object']['value'],
-                                                reverse=True)
-
-        # iterate the ordered list and fill results
         songs_result = []
-        for s in songs_num_matches_list_ordered:
+        for song_id, offset, _ in songs_matches[0:topn]:  # consider topn elements in the result
-
-            # get expected variable by the original code
-            song_id = s['object']['sid']
-            largest = s['object']['largest']
-            largest_count = s['object']['value']
-
-            # extract identification
             song = self.db.get_song_by_id(song_id)
-            if song:
-                # TODO: Clarify what `get_song_by_id` should return.
-                songname = song.get(SONG_NAME, None)
 
-                # return match info
+            song_name = song.get(SONG_NAME, None)
-                nseconds = round(float(largest) / DEFAULT_FS *
+            song_hashes = song.get(FIELD_TOTAL_HASHES, None)
-                                 DEFAULT_WINDOW_SIZE *
+            nseconds = round(float(offset) / DEFAULT_FS * DEFAULT_WINDOW_SIZE * DEFAULT_OVERLAP_RATIO, 5)
-                                 DEFAULT_OVERLAP_RATIO, 5)
+            hashes_matched = dedup_hashes[song_id]
+
             song = {
                 SONG_ID: song_id,
-                    SONG_NAME: songname.encode("utf8"),
+                SONG_NAME: song_name.encode("utf8"),
-                    CONFIDENCE: largest_count,
+                INPUT_HASHES: queried_hashes,
-                    OFFSET: int(largest),
+                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)
 
-                # only consider up to topn elements in the result
-                if len(songs_result) > topn:
-                    break
         return songs_result
 
-    def recognize(self, recognizer, *options, **kwoptions):
+    def recognize(self, recognizer, *options, **kwoptions) -> Dict[str, any]:
         r = recognizer(self)
         return r.recognize(*options, **kwoptions)
 
-
+    @staticmethod
-def _fingerprint_worker(filename, limit=None, song_name=None):
+    def _fingerprint_worker(arguments):
         # Pool.imap sends arguments as tuples so we have to unpack
         # them ourself.
         try:
-        filename, limit = filename
+            file_name, limit = arguments
         except ValueError:
             pass
 
-    songname, extension = os.path.splitext(os.path.basename(filename))
+        song_name, extension = os.path.splitext(os.path.basename(file_name))
-    song_name = song_name or songname
+
-    channels, fs, file_hash = decoder.read(filename, limit)
+        fingerprints, file_hash = Dejavu.get_file_fingerprints(file_name, limit, print_output=True)
-    result = set()
+
+        return song_name, fingerprints, 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}")
 
-    for channeln, channel in enumerate(channels):
-        # TODO: Remove prints or change them into optional logging.
-        print(f"Fingerprinting channel {channeln + 1}/{channel_amount} for {filename}")
             hashes = fingerprint(channel, Fs=fs)
-        print(f"Finished channel {channeln + 1}/{channel_amount} for {filename}")
-        result |= set(hashes)
 
-    return song_name, result, file_hash
+            if print_output:
+                print(f"Finished channel {channeln}/{channel_amount} for {file_name}")
+
+            fingerprints |= set(hashes)
+
+        return fingerprints, file_hash

dejavu/base_classes/base_database.py

@@ -1,6 +1,6 @@
 import abc
 import importlib
-from typing import Dict
+from typing import Dict, List, Tuple
 
 from dejavu.config.settings import DATABASES
 
@@ -13,13 +13,13 @@ class BaseDatabase(object, metaclass=abc.ABCMeta):
     def __init__(self):
         super().__init__()
 
-    def before_fork(self):
+    def before_fork(self) -> None:
         """
         Called before the database instance is given to the new process
         """
         pass
 
-    def after_fork(self):
+    def after_fork(self) -> None:
         """
         Called after the database instance has been given to the new process
 
@@ -27,21 +27,21 @@ class BaseDatabase(object, metaclass=abc.ABCMeta):
         """
         pass
 
-    def setup(self):
+    def setup(self) -> None:
         """
         Called on creation or shortly afterwards.
         """
         pass
 
     @abc.abstractmethod
-    def empty(self):
+    def empty(self) -> None:
         """
         Called when the database should be cleared of all data.
         """
         pass
 
     @abc.abstractmethod
-    def delete_unfingerprinted_songs(self):
+    def delete_unfingerprinted_songs(self) -> None:
         """
         Called to remove any song entries that do not have any fingerprints
         associated with them.
@@ -49,110 +49,141 @@ class BaseDatabase(object, metaclass=abc.ABCMeta):
         pass
 
     @abc.abstractmethod
-    def get_num_songs(self):
+    def get_num_songs(self) -> int:
         """
-        Returns the amount of songs in the database.
+        Returns the song's count stored.
+
+        :return: the amount of songs in the database.
         """
         pass
 
     @abc.abstractmethod
-    def get_num_fingerprints(self):
+    def get_num_fingerprints(self) -> int:
         """
-        Returns the number of fingerprints in the database.
+        Returns the fingerprints' count stored.
+
+        :return: the number of fingerprints in the database.
         """
         pass
 
     @abc.abstractmethod
-    def set_song_fingerprinted(self, sid):
+    def set_song_fingerprinted(self, song_id: int):
         """
         Sets a specific song as having all fingerprints in the database.
 
-        sid: Song identifier
+        :param song_id: song identifier.
         """
         pass
 
     @abc.abstractmethod
-    def get_songs(self) -> Dict[str, str]:
+    def get_songs(self) -> List[Dict[str, str]]:
         """
-        Returns all fully fingerprinted songs in the database. Result must be a Dictionary.
+        Returns all fully fingerprinted songs in the database
+
+        :return: a dictionary with the songs info.
         """
         pass
 
     @abc.abstractmethod
-    def get_song_by_id(self, sid) -> Dict[str, str]:
+    def get_song_by_id(self, song_id: int) -> Dict[str, str]:
         """
-        Return a song by its identifier. Result must be a Dictionary.
+        Brings the song info from the database.
-        sid: Song identifier
+
+        :param song_id: song identifier.
+        :return: a song by its identifier. Result must be a Dictionary.
         """
         pass
 
     @abc.abstractmethod
-    def insert(self, hash, sid, offset):
+    def insert(self, fingerprint: str, song_id: int, offset: int):
         """
         Inserts a single fingerprint into the database.
 
-          hash: Part of a sha1 hash, in hexadecimal format
+        :param fingerprint: Part of a sha1 hash, in hexadecimal format
-           sid: Song identifier this fingerprint is off
+        :param song_id: Song identifier this fingerprint is off
-        offset: The offset this hash is from
+        :param offset: The offset this fingerprint is from.
         """
         pass
 
     @abc.abstractmethod
-    def insert_song(self, song_name):
+    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.
 
-        song_name: The name 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, hash):
+    def query(self, fingerprint: str = None) -> List[Tuple]:
         """
         Returns all matching fingerprint entries associated with
-        the given hash as parameter.
+        the given hash as parameter, if None is passed it returns all entries.
 
-        hash: Part of a sha1 hash, in hexadecimal format
+        :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):
+    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, sid, hashes, batch=1000):
+    def insert_hashes(self, song_id: int, hashes: List[Tuple[str, int]], batch_size: int = 1000) -> None:
         """
         Insert a multitude of fingerprints.
 
-           sid: Song identifier the fingerprints belong to
+        :param song_id: Song identifier the fingerprints belong to
-        hashes: A sequence of tuples in the format (hash, offset)
+        :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):
+    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.
 
-        hashes: A sequence of tuples in the format (hash, offset)
+        :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
 
-        Returns a sequence of (sid, offset_difference) tuples.
+    @abc.abstractmethod
-
+    def delete_songs_by_ids(self, song_ids: List[int], batch_size: int = 1000) -> None:
-                      sid: Song identifier
+        """
-        offset_difference: (offset - database_offset)
+        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="mysql"):
+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)

dejavu/base_classes/base_recognizer.py

@@ -1,4 +1,8 @@
 import abc
+from time import time
+from typing import Dict, List, Tuple
+
+import numpy as np
 
 from dejavu.config.settings import DEFAULT_FS
 
@@ -8,12 +12,22 @@ class BaseRecognizer(object, metaclass=abc.ABCMeta):
         self.dejavu = dejavu
         self.Fs = DEFAULT_FS
 
-    def _recognize(self, *data):
+    def _recognize(self, *data) -> Tuple[List[Dict[str, any]], int, int, int]:
-        matches = []
+        fingerprint_times = []
-        for d in data:
+        hashes = set()  # to remove possible duplicated fingerprints we built a set.
-            matches.extend(self.dejavu.find_matches(d, Fs=self.Fs))
+        for channel in data:
-        return self.dejavu.align_matches(matches)
+            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):
+    def recognize(self) -> Dict[str, any]:
         pass  # base class does nothing

dejavu/base_classes/common_database.py

@@ -1,4 +1,5 @@
 import abc
+from typing import Dict, List, Tuple
 
 from dejavu.base_classes.base_database import BaseDatabase
 
@@ -11,13 +12,13 @@ class CommonDatabase(BaseDatabase, metaclass=abc.ABCMeta):
     def __init__(self):
         super().__init__()
 
-    def before_fork(self):
+    def before_fork(self) -> None:
         """
         Called before the database instance is given to the new process
         """
         pass
 
-    def after_fork(self):
+    def after_fork(self) -> None:
         """
         Called after the database instance has been given to the new process
 
@@ -25,7 +26,7 @@ class CommonDatabase(BaseDatabase, metaclass=abc.ABCMeta):
         """
         pass
 
-    def setup(self):
+    def setup(self) -> None:
         """
         Called on creation or shortly afterwards.
         """
@@ -34,7 +35,7 @@ class CommonDatabase(BaseDatabase, metaclass=abc.ABCMeta):
             cur.execute(self.CREATE_FINGERPRINTS_TABLE)
             cur.execute(self.DELETE_UNFINGERPRINTED)
 
-    def empty(self):
+    def empty(self) -> None:
         """
         Called when the database should be cleared of all data.
         """
@@ -44,7 +45,7 @@ class CommonDatabase(BaseDatabase, metaclass=abc.ABCMeta):
 
         self.setup()
 
-    def delete_unfingerprinted_songs(self):
+    def delete_unfingerprinted_songs(self) -> None:
         """
         Called to remove any song entries that do not have any fingerprints
         associated with them.
@@ -52,9 +53,11 @@ class CommonDatabase(BaseDatabase, metaclass=abc.ABCMeta):
         with self.cursor() as cur:
             cur.execute(self.DELETE_UNFINGERPRINTED)
 
-    def get_num_songs(self):
+    def get_num_songs(self) -> int:
         """
-        Returns the amount of songs in the database.
+        Returns the song's count stored.
+
+        :return: the amount of songs in the database.
         """
         with self.cursor() as cur:
             cur.execute(self.SELECT_UNIQUE_SONG_IDS)
@@ -62,9 +65,11 @@ class CommonDatabase(BaseDatabase, metaclass=abc.ABCMeta):
 
         return count
 
-    def get_num_fingerprints(self):
+    def get_num_fingerprints(self) -> int:
         """
-        Returns the number of fingerprints in the database.
+        Returns the fingerprints' count stored.
+
+        :return: the number of fingerprints in the database.
         """
         with self.cursor() as cur:
             cur.execute(self.SELECT_NUM_FINGERPRINTS)
@@ -72,122 +77,155 @@ class CommonDatabase(BaseDatabase, metaclass=abc.ABCMeta):
 
         return count
 
-    def set_song_fingerprinted(self, sid):
+    def set_song_fingerprinted(self, song_id):
         """
         Sets a specific song as having all fingerprints in the database.
 
-        sid: Song identifier
+        :param song_id: song identifier.
         """
         with self.cursor() as cur:
-            cur.execute(self.UPDATE_SONG_FINGERPRINTED, (sid,))
+            cur.execute(self.UPDATE_SONG_FINGERPRINTED, (song_id,))
 
-    def get_songs(self):
+    def get_songs(self) -> List[Dict[str, str]]:
         """
-        Returns all fully fingerprinted songs in the database. Result must be a Dictionary.
+        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)
-            for row in cur:
+            return list(cur)
-                yield row
 
-    def get_song_by_id(self, sid):
+    def get_song_by_id(self, song_id: int) -> Dict[str, str]:
         """
-        Return a song by its identifier. Result must be a Dictionary.
+        Brings the song info from the database.
-        sid: Song identifier
+
+        :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, (sid,))
+            cur.execute(self.SELECT_SONG, (song_id,))
             return cur.fetchone()
 
-    def insert(self, fingerprint, sid, offset):
+    def insert(self, fingerprint: str, song_id: int, offset: int):
         """
         Inserts a single fingerprint into the database.
 
-          fingerprint: Part of a sha1 hash, in hexadecimal format
+        :param fingerprint: Part of a sha1 hash, in hexadecimal format
-           sid: Song identifier this fingerprint is off
+        :param song_id: Song identifier this fingerprint is off
-        offset: The offset this fingerprint is from
+        :param offset: The offset this fingerprint is from.
         """
         with self.cursor() as cur:
-            cur.execute(self.INSERT_FINGERPRINT, (fingerprint, sid, offset))
+            cur.execute(self.INSERT_FINGERPRINT, (fingerprint, song_id, offset))
 
     @abc.abstractmethod
-    def insert_song(self, song_name):
+    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.
 
-        song_name: The name 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):
+    def query(self, fingerprint: str = None) -> List[Tuple]:
         """
         Returns all matching fingerprint entries associated with
-        the given fingerprint as parameter.
+        the given hash as parameter, if None is passed it returns all entries.
 
-        fingerprint: Part of a sha1 hash, in hexadecimal format
+        :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:
-            with self.cursor() as cur:
                 cur.execute(self.SELECT, (fingerprint,))
-                for sid, offset in cur:
-                    yield (sid, offset)
             else:  # select all if no key
-            with self.cursor() as cur:
                 cur.execute(self.SELECT_ALL)
-                for sid, offset in cur:
+            return list(cur)
-                    yield (sid, offset)
 
-    def get_iterable_kv_pairs(self):
+    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, sid, hashes, batch=1000):
+    def insert_hashes(self, song_id: int, hashes: List[Tuple[str, int]], batch_size: int = 1000) -> None:
         """
         Insert a multitude of fingerprints.
 
-           sid: Song identifier the fingerprints belong to
+        :param song_id: Song identifier the fingerprints belong to
-        hashes: A sequence of tuples in the format (hash, offset)
+        :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 = [(sid, hsh, int(offset)) for hsh, offset in hashes]
+        values = [(song_id, hsh, int(offset)) for hsh, offset in hashes]
 
         with self.cursor() as cur:
-            for index in range(0, len(hashes), batch):
+            for index in range(0, len(hashes), batch_size):
-                cur.executemany(self.INSERT_FINGERPRINT, values[index: index + batch])
+                cur.executemany(self.INSERT_FINGERPRINT, values[index: index + batch_size])
 
-    def return_matches(self, hashes, batch=1000):
+    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.
 
-        hashes: A sequence of tuples in the format (hash, offset)
+        :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.
-        Returns a sequence of (sid, offset_difference) tuples.
+        :return: a list of (sid, offset_difference) tuples and a
-
+        dictionary with the amount of hashes matched (not considering
-                      sid: Song identifier
+        duplicated hashes) in each song.
-        offset_difference: (offset - database_offset)
+            - 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:
-            mapper[hsh.upper()] = offset
+            if hsh.upper() in mapper.keys():
+                mapper[hsh.upper()].append(offset)
+            else:
+                mapper[hsh.upper()] = [offset]
 
-        # Get an iterable of all the hashes we need
         values = list(mapper.keys())
 
-        with self.cursor() as cur:
+        # in order to count each hash only once per db offset we use the dic below
-            for index in range(0, len(values), batch):
+        dedup_hashes = {}
-                # Create our IN part of the query
-                query = self.SELECT_MULTIPLE
-                query = query % ', '.join([self.IN_MATCH] * len(values[index: index + batch]))
 
-                cur.execute(query, values[index: index + batch])
+        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:
-                    # (sid, db_offset - song_sampled_offset)
+                    if sid not in dedup_hashes.keys():
-                    yield (sid, offset - mapper[hsh])
+                        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_ids(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])

dejavu/config/settings.py

@@ -1,8 +1,26 @@
 # Dejavu
+
+# DEJAVU JSON RESPONSE
 SONG_ID = "song_id"
 SONG_NAME = 'song_name'
-CONFIDENCE = 'confidence'
+RESULTS = 'results'
-MATCH_TIME = 'match_time'
+
+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'
 
@@ -20,6 +38,7 @@ 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"
@@ -43,7 +62,7 @@ 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
+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
@@ -53,7 +72,7 @@ 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
+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

dejavu/database_handler/mysql_database.py

@@ -6,8 +6,8 @@ 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, FINGERPRINTS_TABLENAME,
+                                    FIELD_SONGNAME, FIELD_TOTAL_HASHES,
-                                    SONGS_TABLENAME)
+                                    FINGERPRINTS_TABLENAME, SONGS_TABLENAME)
 
 
 class MySQLDatabase(CommonDatabase):
@@ -20,6 +20,7 @@ class MySQLDatabase(CommonDatabase):
         ,   `{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}`)
@@ -52,8 +53,8 @@ class MySQLDatabase(CommonDatabase):
     """
 
     INSERT_SONG = f"""
-        INSERT INTO `{SONGS_TABLENAME}` (`{FIELD_SONGNAME}`,`{FIELD_FILE_SHA1}`)
+        INSERT INTO `{SONGS_TABLENAME}` (`{FIELD_SONGNAME}`,`{FIELD_FILE_SHA1}`,`{FIELD_TOTAL_HASHES}`)
-        VALUES (%s, UNHEX(%s));
+        VALUES (%s, UNHEX(%s), %s);
     """
 
     # SELECTS
@@ -72,7 +73,7 @@ class MySQLDatabase(CommonDatabase):
     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}`
+        SELECT `{FIELD_SONGNAME}`, HEX(`{FIELD_FILE_SHA1}`) AS `{FIELD_FILE_SHA1}`, `{FIELD_TOTAL_HASHES}`
         FROM `{SONGS_TABLENAME}`
         WHERE `{FIELD_SONG_ID}` = %s;
     """
@@ -90,6 +91,8 @@ class MySQLDatabase(CommonDatabase):
             `{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;
     """
@@ -98,16 +101,20 @@ class MySQLDatabase(CommonDatabase):
     DROP_FINGERPRINTS = f"DROP TABLE IF EXISTS `{FINGERPRINTS_TABLENAME}`;"
     DROP_SONGS = f"DROP TABLE IF EXISTS `{SONGS_TABLENAME}`;"
 
-    # update
+    # UPDATE
     UPDATE_SONG_FINGERPRINTED = f"""
         UPDATE `{SONGS_TABLENAME}` SET `{FIELD_FINGERPRINTED}` = 1 WHERE `{FIELD_SONG_ID}` = %s;
     """
 
-    # DELETE
+    # 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)"
 
@@ -116,17 +123,23 @@ class MySQLDatabase(CommonDatabase):
         self.cursor = cursor_factory(**options)
         self._options = options
 
-    def after_fork(self):
+    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, file_hash):
+    def insert_song(self, song_name: str, file_hash: str, total_hashes: int) -> int:
         """
-        Inserts song in the database and returns the ID of the inserted record.
+        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))
+            cur.execute(self.INSERT_SONG, (song_name, file_hash, total_hashes))
             return cur.lastrowid
 
     def __getstate__(self):

dejavu/database_handler/postgres_database.py

@@ -6,8 +6,8 @@ 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, FINGERPRINTS_TABLENAME,
+                                    FIELD_SONGNAME, FIELD_TOTAL_HASHES,
-                                    SONGS_TABLENAME)
+                                    FINGERPRINTS_TABLENAME, SONGS_TABLENAME)
 
 
 class PostgreSQLDatabase(CommonDatabase):
@@ -20,6 +20,7 @@ class PostgreSQLDatabase(CommonDatabase):
         ,   "{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}")
@@ -58,8 +59,8 @@ class PostgreSQLDatabase(CommonDatabase):
     """
 
     INSERT_SONG = f"""
-        INSERT INTO "{SONGS_TABLENAME}" ("{FIELD_SONGNAME}", "{FIELD_FILE_SHA1}")
+        INSERT INTO "{SONGS_TABLENAME}" ("{FIELD_SONGNAME}", "{FIELD_FILE_SHA1}","{FIELD_TOTAL_HASHES}")
-        VALUES (%s, decode(%s, 'hex'))
+        VALUES (%s, decode(%s, 'hex'), %s)
         RETURNING "{FIELD_SONG_ID}";
     """
 
@@ -79,7 +80,10 @@ class PostgreSQLDatabase(CommonDatabase):
     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}"
+        SELECT
+            "{FIELD_SONGNAME}"
+        ,   upper(encode("{FIELD_FILE_SHA1}", 'hex')) AS "{FIELD_FILE_SHA1}"
+        ,   "{FIELD_TOTAL_HASHES}"
         FROM "{SONGS_TABLENAME}"
         WHERE "{FIELD_SONG_ID}" = %s;
     """
@@ -97,6 +101,8 @@ class PostgreSQLDatabase(CommonDatabase):
             "{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;
     """
@@ -113,11 +119,15 @@ class PostgreSQLDatabase(CommonDatabase):
         WHERE "{FIELD_SONG_ID}" = %s;
     """
 
-    # DELETE
+    # 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')"
 
@@ -126,17 +136,23 @@ class PostgreSQLDatabase(CommonDatabase):
         self.cursor = cursor_factory(**options)
         self._options = options
 
-    def after_fork(self):
+    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, file_hash):
+    def insert_song(self, song_name: str, file_hash: str, total_hashes: int) -> int:
         """
-        Inserts song in the database and returns the ID of the inserted record.
+        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))
+            cur.execute(self.INSERT_SONG, (song_name, file_hash, total_hashes))
             return cur.fetchone()[0]
 
     def __getstate__(self):

dejavu/logic/decoder.py

@@ -1,6 +1,7 @@
 import fnmatch
 import os
 from hashlib import sha1
+from typing import List, Tuple
 
 import numpy as np
 from pydub import AudioSegment
@@ -9,35 +10,47 @@ from pydub.utils import audioop
 from dejavu.third_party import wavio
 
 
-def unique_hash(filepath, blocksize=2**20):
+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.
+
+    :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, 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,
@@ -47,11 +60,13 @@ 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]
@@ -64,7 +79,7 @@ def read(filename, limit=None):
 
         audiofile.frame_rate
     except audioop.error:
-        _, _, audiofile = wavio.readwav(filename)
+        _, _, audiofile = wavio.readwav(file_name)
 
         if limit:
             audiofile = audiofile[:limit * 1000]
@@ -76,12 +91,12 @@ 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
+    Extracts song name from a file path.
-    have already been fingerprinted on disk.
+    :param file_path: path to an audio file.
     """
-    return os.path.splitext(os.path.basename(path))[0]
+    return os.path.splitext(os.path.basename(file_path))[0]

dejavu/logic/fingerprint.py

@@ -93,6 +93,7 @@ def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
     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):
@@ -105,4 +106,7 @@ def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
 
                 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'))
-                    yield (h.hexdigest()[0:FINGERPRINT_REDUCTION], t1)
+
+                    hashes.append((h.hexdigest()[0:FINGERPRINT_REDUCTION], t1))
+
+    return hashes

dejavu/logic/recognizer/file_recognizer.py

@@ -1,24 +1,32 @@
-import time
+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):
+    def recognize_file(self, filename: str) -> Dict[str, any]:
-        frames, self.Fs, file_hash = decoder.read(filename, self.dejavu.limit)
+        channels, self.Fs, _ = decoder.read(filename, self.dejavu.limit)
 
-        t = time.time()
+        t = time()
-        matches = self._recognize(*frames)
+        matches, fingerprint_time, query_time, align_time = self._recognize(*channels)
-        t = time.time() - t
+        t = time() - t
 
-        for match in matches:
+        results = {
-            match['match_time'] = t
+            TOTAL_TIME: t,
+            FINGERPRINT_TIME: fingerprint_time,
+            QUERY_TIME: query_time,
+            ALIGN_TIME: align_time,
+            RESULTS: matches
+        }
 
-        return matches
+        return results
 
-    def recognize(self, filename):
+    def recognize(self, filename: str) -> Dict[str, any]:
         return self.recognize_file(filename)

dejavu/tests/dejavu_test.py

@@ -12,10 +12,10 @@ import matplotlib.pyplot as plt
 import numpy as np
 from pydub import AudioSegment
 
-from dejavu.config.settings import (CONFIDENCE, DEFAULT_FS,
+from dejavu.config.settings import (DEFAULT_FS, DEFAULT_OVERLAP_RATIO,
-                                    DEFAULT_OVERLAP_RATIO, DEFAULT_WINDOW_SIZE,
+                                    DEFAULT_WINDOW_SIZE, HASHES_MATCHED,
-                                    MATCH_TIME, OFFSET, SONG_NAME)
+                                    OFFSET, RESULTS, SONG_NAME, TOTAL_TIME)
-from dejavu.logic.decoder import path_to_songname
+from dejavu.logic.decoder import get_audio_name_from_path
 
 
 class DejavuTest:
@@ -115,8 +115,8 @@ class DejavuTest:
             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 = path_to_songname(f).split("_")
+            splits = get_audio_name_from_path(f).split("_")
-            song = "_".join(splits[0:len(path_to_songname(f).split("_")) - 2])
+            song = "_".join(splits[0:len(get_audio_name_from_path(f).split("_")) - 2])
             line = self.get_line_id(song)
             result = subprocess.check_output([
                 "python",
@@ -138,8 +138,8 @@ class DejavuTest:
                 result = json.loads(result.decode('utf-8').replace("'", '"').replace(': b"', ':"'))
 
                 # which song did we predict? We consider only the first match.
-                result = result[0]
+                match = result[RESULTS][0]
-                song_result = result[SONG_NAME]
+                song_result = match[SONG_NAME]
                 log_msg(f'song: {song}')
                 log_msg(f'song_result: {song_result}')
 
@@ -153,22 +153,22 @@ class DejavuTest:
                     log_msg('correct match')
                     print(self.result_match)
                     self.result_match[line][col] = 'yes'
-                    self.result_query_duration[line][col] = round(result[MATCH_TIME], 3)
+                    self.result_query_duration[line][col] = round(result[TOTAL_TIME], 3)
-                    self.result_match_confidence[line][col] = result[CONFIDENCE]
+                    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((result[OFFSET] * DEFAULT_WINDOW_SIZE *
+                    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[MATCH_TIME], 3)}')
+                    log_msg(f'query duration: {round(result[TOTAL_TIME], 3)}')
-                    log_msg(f'confidence: {result[CONFIDENCE]}')
+                    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}')
 

example_script.py

@@ -1,8 +1,8 @@
 import json
 
 from dejavu import Dejavu
-from dejavu.logic.recognizer.microphone_recognizer import MicrophoneRecognizer
 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:
@@ -17,18 +17,26 @@ if __name__ == '__main__':
     djv.fingerprint_directory("test", [".wav"])
 
     # Recognize audio from a file
-    song = djv.recognize(FileRecognizer, "mp3/Josh-Woodward--I-Want-To-Destroy-Something-Beautiful.mp3")
+    results = djv.recognize(FileRecognizer, "mp3/Josh-Woodward--I-Want-To-Destroy-Something-Beautiful.mp3")
-    print(f"From file we recognized: {song}\n")
+    print(f"From file we recognized: {results}\n")
 
     # Or recognize audio from your microphone for `secs` seconds
     secs = 5
-    song = djv.recognize(MicrophoneRecognizer, seconds=secs)
+    results = djv.recognize(MicrophoneRecognizer, seconds=secs)
-    if song is None:
+    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: {song}\n")
+        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)
-    song = recognizer.recognize_file("mp3/Josh-Woodward--I-Want-To-Destroy-Something-Beautiful.mp3")
+    results = recognizer.recognize_file("mp3/Josh-Woodward--I-Want-To-Destroy-Something-Beautiful.mp3")
-    print(f"No shortcut, we recognized: {song}\n")
+    print(f"No shortcut, we recognized: {results}\n")
+
+    # To list all fingerprinted songs in the db you can use the following:
+    # fingerprinted_songs = djv.get_fingerprinted_songs()
+    # print(fingerprinted_songs)
+
+    # And to delete a song or a set of songs you can use the following:
+    # song_ids_to_delete = [1]
+    # djv.delete_songs_by_ids(song_ids_to_delete)

requirements.txt

@@ -5,4 +5,3 @@ scipy==1.3.1
 matplotlib==3.1.1
 mysql-connector-python==8.0.17
 psycopg2==2.8.3
-

setup.py

@@ -1,4 +1,4 @@
-from setuptools import setup, find_packages
+from setuptools import find_packages, setup
 
 
 def parse_requirements(requirements):