148 lines
4.9 KiB
Python
148 lines
4.9 KiB
Python
#!/usr/bin/env python3.12
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from difflib import SequenceMatcher
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from typing import List, Optional, Tuple
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import logging
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import regex
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class TrackMatcher:
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"""Track Matcher"""
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def __init__(self, threshold: float = 0.85):
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"""
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Initialize the TrackMatcher with a similarity threshold.
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Args:
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threshold (float): Minimum similarity score to consider a match valid
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(between 0 and 1, default 0.85)
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"""
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self.threshold = threshold
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def find_best_match(self, input_track: str, candidate_tracks: List[tuple[int|str, str]]) -> Optional[Tuple[str, float]]:
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"""
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Find the best matching track from the candidate list.
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Args:
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input_track (str): Input track in "ARTIST - SONG" format
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candidate_tracks (List[tuple[int|str, str]]): List of candidate tracks
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Returns:
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Optional[Tuple[int, str, float]]: Tuple of (best matching track, similarity score)
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or None if no good match found
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"""
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if not input_track or not candidate_tracks:
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return None
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# Normalize input track
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input_track = self._normalize_string(input_track)
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best_match = None
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best_score = 0
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for candidate in candidate_tracks:
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normalized_candidate = self._normalize_string(candidate[1])
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# Calculate various similarity scores
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exact_score = 1.0 if input_track == normalized_candidate else 0.0
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sequence_score = SequenceMatcher(None, input_track, normalized_candidate).ratio()
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token_score = self._calculate_token_similarity(input_track, normalized_candidate)
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# Take the maximum of the different scoring methods
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final_score = max(exact_score, sequence_score, token_score)
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if final_score > best_score:
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best_score = final_score
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best_match = candidate
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# Return the match only if it meets the threshold
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return (best_match, round(best_score * 100)) if best_score >= self.threshold else None
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def _normalize_string(self, text: str) -> str:
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"""
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Normalize string for comparison by removing special characters,
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extra spaces, and converting to lowercase.
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Args:
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text (str): The text to normalize
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Returns:
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str: Normalized text
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"""
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# Remove special characters and convert to lowercase
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text = regex.sub(r'[^\w\s-]', '', text).lower()
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# Normalize spaces
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text = ' '.join(text.split())
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return text
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def _calculate_token_similarity(self, str1: str, str2: str) -> float:
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"""
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Calculate similarity based on matching tokens (words).
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Args:
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str1 (str): string 1 to compare
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str2 (str): string 2 to compare
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Returns:
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float: The token similarity score
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"""
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tokens1 = set(str1.split())
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tokens2 = set(str2.split())
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if not tokens1 or not tokens2:
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return 0.0
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intersection = tokens1.intersection(tokens2)
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union = tokens1.union(tokens2)
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return len(intersection) / len(union)
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class DataUtils:
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"""
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Data Utils
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"""
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def __init__(self):
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self.lrc_regex = regex.compile(r'\[([0-9]{2}:[0-9]{2})\.[0-9]{1,3}\](\s(.*)){0,}')
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def scrub_lyrics(self, lyrics: str) -> str:
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"""
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Lyric Scrub Regex Chain
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Args:
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lyrics (str): The lyrics to scrub
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Returns:
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str: Regex scrubbed lyrics
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"""
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lyrics = regex.sub(r'(\[.*?\])(\s){0,}(\:){0,1}', '', lyrics)
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lyrics = regex.sub(r'(\d?)(Embed\b)', '', lyrics, flags=regex.IGNORECASE)
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lyrics = regex.sub(r'\n{2}', '\n', lyrics) # Gaps between verses
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lyrics = regex.sub(r'[0-9]\b$', '', lyrics)
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return lyrics
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def create_lrc_object(self, lrc_str: str) -> list[dict]:
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"""
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Create LRC Object
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Args:
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lrc_str (str): The raw LRCLib syncedLyrics
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Returns:
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list[dict]: LRC Object comprised of timestamps/lyrics
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"""
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lrc_out: list = []
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for line in lrc_str.split("\n"):
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_timetag = None
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_words = None
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if not line.strip():
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continue
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reg_helper = regex.findall(self.lrc_regex, line.strip())
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if not reg_helper:
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continue
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reg_helper = reg_helper[0]
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logging.debug("Reg helper: %s for line: %s; len: %s",
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reg_helper, line, len(reg_helper))
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_timetag = reg_helper[0]
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if not reg_helper[1].strip():
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_words = "♪"
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else:
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_words = reg_helper[1]
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lrc_out.append({
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"timeTag": _timetag,
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"words": _words,
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})
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return lrc_out |