code: def align_matches(self, matches: list)->Optional[dict]:
          
          # align by diffs
          diff_counter: dict = {}
          largest = 0
          largest_count = 0
          song_id = -1
          for sid, diff in matches:
  
              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 = diff
                  largest_count = diff_counter[diff][sid]
                  song_id = sid
  
          # extract idenfication
          song = database.get_song_by_id(song_id)
          if song:
              songname = song.song_name
          else:
              return None
  
          # return match info
          nseconds = round(
              float(largest) / fingerprint.DEFAULT_FS *
              fingerprint.DEFAULT_WINDOW_SIZE * fingerprint.DEFAULT_OVERLAP_RATIO,
              5
          )
          song = {
              'song_id': song_id,
              'song_name': songname,
              MetaMusic.CONFIDENCE: largest_count,
              MetaMusic.OFFSET: int(largest),
              'offset_seconds': nseconds,
              'file_sha1': binascii.hexlify(song.file_sha1).decode('utf-8'),
          }
          return song
docs: Finds hash matches that align in time with other matches and finds
              consensus about which hashes are "true" signal from the audio.
              Returns a dictionary with match information.