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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def align_matches(self, matches: list)->Optional[dict]: """ 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. """

        # 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