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752 lines
35 KiB
752 lines
35 KiB
from typing import List, Optional, Dict, Any, Tuple
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import re
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from dataclasses import dataclass
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from difflib import SequenceMatcher
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from unidecode import unidecode
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from utils.logging_config import get_logger
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from core.spotify_client import Track as SpotifyTrack
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from core.plex_client import PlexTrackInfo
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from core.soulseek_client import TrackResult
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logger = get_logger("matching_engine")
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@dataclass
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class MatchResult:
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spotify_track: SpotifyTrack
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plex_track: Optional[PlexTrackInfo]
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confidence: float
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match_type: str
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@property
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def is_match(self) -> bool:
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return self.plex_track is not None and self.confidence >= 0.8
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class MusicMatchingEngine:
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def __init__(self):
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# Conservative title patterns - only remove clear noise, preserve meaningful differences like remixes
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self.title_patterns = [
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# Only remove explicit/clean markers - preserve remixes, versions, and content after hyphens
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r'\s*\(explicit\)',
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r'\s*\(clean\)',
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# Remove featuring artists from the title itself
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r'\sfeat\.?.*',
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r'\sft\.?.*',
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r'\sfeaturing.*'
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]
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self.artist_patterns = [
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# Only remove featured artists, not parts of main artist names
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r'\s*feat\..*',
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r'\s*ft\..*',
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r'\s*featuring.*',
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# REMOVED: r'\s*&.*' - This breaks "Daryl Hall & John Oates", "Blood & Water"
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# REMOVED: r'\s*and.*' - This breaks artist names with "and"
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# REMOVED: r',.*' - This can break legitimate artist names with commas
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]
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def normalize_string(self, text: str) -> str:
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"""
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Normalizes string by handling common stylizations, converting to ASCII,
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lowercasing, and replacing separators with spaces.
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"""
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if not text:
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return ""
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# Handle Korn/KoЯn variations - both uppercase Я (U+042F) and lowercase я (U+044F)
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char_map = {
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'Я': 'R', # Cyrillic 'Ya' to 'R'
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'я': 'r', # Lowercase Cyrillic 'ya' to 'r'
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}
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# Apply the character replacements before other normalization steps
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for original, replacement in char_map.items():
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text = text.replace(original, replacement)
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text = unidecode(text)
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text = text.lower()
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# Expand specific abbreviations for better matching
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abbreviation_map = {
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r'\bpt\.': 'part', # "pt." → "part"
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r'\bvol\.': 'volume', # "vol." → "volume"
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r'\bfeat\.': 'featured' # "feat." → "featured"
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# Removed "ft." → "featured" (ambiguous: could be "feet" in measurements)
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}
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for pattern, replacement in abbreviation_map.items():
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text = re.sub(pattern, replacement, text)
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# --- IMPROVEMENT V4 ---
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# The user correctly pointed out that replacing '$' with 's' was incorrect
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# as it breaks searching for stylized names like A$AP Rocky.
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# The new approach is to PRESERVE the '$' symbol during normalization.
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# Replace common separators with spaces to preserve word boundaries.
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text = re.sub(r'[._/]', ' ', text)
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# Keep alphanumeric characters, spaces, hyphens, AND the '$' sign.
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text = re.sub(r'[^a-z0-9\s$-]', '', text)
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# Consolidate multiple spaces into one
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text = re.sub(r'\s+', ' ', text).strip()
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return text
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def get_core_string(self, text: str) -> str:
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"""Returns a 'core' version of a string with only letters and numbers for a strict comparison."""
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if not text:
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return ""
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# Use normalize_string first to get abbreviation expansion, then strip to core
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normalized = self.normalize_string(text)
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return re.sub(r'[^a-z0-9]', '', normalized)
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def clean_title(self, title: str) -> str:
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"""Cleans title by removing common extra info using regex for fuzzy matching."""
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cleaned = title
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for pattern in self.title_patterns:
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cleaned = re.sub(pattern, '', cleaned, flags=re.IGNORECASE).strip()
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return self.normalize_string(cleaned)
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def clean_artist(self, artist: str) -> str:
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"""Cleans artist name by removing featured artists and other noise."""
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cleaned = artist
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for pattern in self.artist_patterns:
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cleaned = re.sub(pattern, '', cleaned, flags=re.IGNORECASE).strip()
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return self.normalize_string(cleaned)
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def clean_album_name(self, album_name: str) -> str:
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"""Clean album name by removing version info, deluxe editions, etc."""
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if not album_name:
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return ""
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cleaned = album_name
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# Common album suffixes to remove
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album_patterns = [
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# Add pattern to remove trailing info after a hyphen, common for remasters/editions.
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r'\s-\s.*',
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r'\s*\(deluxe\s*edition?\)',
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r'\s*\(expanded\s*edition?\)',
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r'\s*\(platinum\s*edition?\)', # Fix for "Fearless (Platinum Edition)"
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r'\s*\(remastered?\)',
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r'\s*\(remaster\)',
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r'\s*\(anniversary\s*edition?\)',
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r'\s*\(special\s*edition?\)',
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r'\s*\(bonus\s*track\s*version\)',
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r'\s*\(.*version\)', # Covers "Taylor's Version", "Radio Version", etc.
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r'\s*\[deluxe\]',
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r'\s*\[remastered?\]',
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r'\s*\[.*version\]',
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r'\s*-\s*deluxe',
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r'\s*-\s*platinum\s*edition?', # Handle "Album - Platinum Edition"
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r'\s*-\s*remastered?',
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r'\s+platinum\s*edition?$', # Handle "Album Platinum Edition" at end
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r'\s*\d{4}\s*remaster', # Year remaster
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r'\s*\(\d{4}\s*remaster\)'
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]
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for pattern in album_patterns:
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cleaned = re.sub(pattern, '', cleaned, flags=re.IGNORECASE).strip()
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return self.normalize_string(cleaned)
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def similarity_score(self, str1: str, str2: str) -> float:
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"""Calculates similarity score between two strings with enhanced version handling."""
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if not str1 or not str2:
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return 0.0
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# Standard similarity
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standard_ratio = SequenceMatcher(None, str1, str2).ratio()
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# Enhanced logic: Check if one string is a version of the other
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# This handles cases like "Back & forth" vs "Back & forth original mix"
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shorter, longer = (str1, str2) if len(str1) <= len(str2) else (str2, str1)
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# If the shorter string is at the start of the longer string
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if longer.startswith(shorter):
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# Extract the extra content
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extra_content = longer[len(shorter):].strip()
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# Check if the extra content looks like version info
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version_keywords = [
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'original mix', 'radio mix', 'club mix', 'extended mix',
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'slowed', 'reverb', 'sped up', 'acoustic', 'remix', 'remaster',
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'live', 'demo', 'instrumental', 'clean', 'explicit',
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'radio edit', 'extended', 'version'
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]
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# Normalize extra content for comparison
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extra_normalized = extra_content.lower().strip(' -()[]')
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# If the extra content matches version keywords, boost the similarity
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for keyword in version_keywords:
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if keyword in extra_normalized:
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# High similarity but not perfect (to distinguish from exact matches)
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return max(standard_ratio, 0.85)
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return standard_ratio
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def duration_similarity(self, duration1: int, duration2: int) -> float:
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"""Calculates similarity score based on track duration (in ms)."""
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if duration1 == 0 or duration2 == 0:
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return 0.5 # Neutral score if a duration is missing
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# Allow a 5-second tolerance (5000 ms)
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if abs(duration1 - duration2) <= 5000:
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return 1.0
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diff_ratio = abs(duration1 - duration2) / max(duration1, duration2)
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return max(0, 1.0 - diff_ratio * 5)
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def calculate_match_confidence(self, spotify_track: SpotifyTrack, plex_track: PlexTrackInfo) -> Tuple[float, str]:
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"""Calculates a confidence score using a prioritized model, starting with a strict 'core' title check."""
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# --- Artist Scoring (calculated once) ---
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spotify_artists_cleaned = [self.clean_artist(a) for a in spotify_track.artists if a]
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plex_artist_normalized = self.normalize_string(plex_track.artist)
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plex_artist_cleaned = self.clean_artist(plex_track.artist)
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best_artist_score = 0.0
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for spotify_artist in spotify_artists_cleaned:
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if spotify_artist and spotify_artist in plex_artist_normalized:
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best_artist_score = 1.0
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break
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score = self.similarity_score(spotify_artist, plex_artist_cleaned)
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if score > best_artist_score:
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best_artist_score = score
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artist_score = best_artist_score
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# --- Priority 1: Core Title Match (for exact matches like "Girls", "APT.", "LIL DEMON") ---
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spotify_core_title = self.get_core_string(spotify_track.name)
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plex_core_title = self.get_core_string(plex_track.title)
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if spotify_core_title and spotify_core_title == plex_core_title:
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# SAFETY CHECK: Only give high confidence if artist also matches reasonably well
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# This prevents "Artist A - Girls" from matching "Artist Z - Girls" with high confidence
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if artist_score >= 0.75: # Require decent artist match
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# If the core titles are identical and artists match, we are highly confident
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confidence = 0.90 + (artist_score * 0.09) # Max score of 0.99
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return confidence, "core_title_match"
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# If artist score is too low, fall through to standard weighted calculation
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# --- Priority 2: Fuzzy Title Match (for variations, typos, etc.) ---
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spotify_title_cleaned = self.clean_title(spotify_track.name)
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plex_title_cleaned = self.clean_title(plex_track.title)
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title_score = self.similarity_score(spotify_title_cleaned, plex_title_cleaned)
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duration_score = self.duration_similarity(spotify_track.duration_ms, plex_track.duration if plex_track.duration else 0)
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# Use a standard weighted calculation if the core titles didn't match
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confidence = (title_score * 0.60) + (artist_score * 0.30) + (duration_score * 0.10)
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match_type = "standard_match"
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return confidence, match_type
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def find_best_match(self, spotify_track: SpotifyTrack, plex_tracks: List[PlexTrackInfo]) -> MatchResult:
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"""Finds the best Plex track match from a list of candidates."""
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best_match = None
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best_confidence = 0.0
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best_match_type = "no_match"
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if not plex_tracks:
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return MatchResult(spotify_track, None, 0.0, "no_candidates")
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for plex_track in plex_tracks:
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confidence, match_type = self.calculate_match_confidence(spotify_track, plex_track)
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if confidence > best_confidence:
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best_confidence = confidence
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best_match = plex_track
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best_match_type = match_type
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return MatchResult(
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spotify_track=spotify_track,
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plex_track=best_match,
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confidence=best_confidence,
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match_type=best_match_type
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)
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def detect_album_in_title(self, track_title: str, album_name: str = None) -> Tuple[str, bool]:
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"""
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Detect if album name appears in track title and return cleaned version.
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Returns (cleaned_title, album_detected) tuple.
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"""
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if not track_title:
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return "", False
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original_title = track_title
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title_lower = track_title.lower()
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# Common patterns where album name appears in track titles
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album_patterns = [
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r'\s*-\s*(.+)$', # "Track - Album" (most common)
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r'\s*\|\s*(.+)$', # "Track | Album"
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r'\s*\(\s*(.+)\s*\)$' # "Track (Album)"
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]
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# If we have album name, check if it appears in the title
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if album_name:
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album_clean = album_name.lower().strip()
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for pattern in album_patterns:
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match = re.search(pattern, track_title)
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if match:
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potential_album = match.group(1).lower().strip()
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# Check if the extracted part matches the album name with better fuzzy matching
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similarity_threshold = 0.8
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# Calculate similarity between potential album and actual album
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if potential_album == album_clean:
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similarity = 1.0 # Exact match
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elif potential_album in album_clean or album_clean in potential_album:
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# Substring match - calculate how much overlap
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shorter = min(len(potential_album), len(album_clean))
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longer = max(len(potential_album), len(album_clean))
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similarity = shorter / longer if longer > 0 else 0.0
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else:
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# Use string similarity for fuzzy matching
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similarity = self.similarity_score(potential_album, album_clean)
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if similarity >= similarity_threshold:
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# Remove the album part from the title
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cleaned_title = re.sub(pattern, '', track_title).strip()
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# SAFETY CHECK: Don't return empty or too-short titles
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if not cleaned_title or len(cleaned_title.strip()) < 2:
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logger.warning(f"Album removal would create empty title: '{original_title}' → '{cleaned_title}' - keeping original")
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return track_title, False
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# SAFETY CHECK: Don't remove if it would leave only articles or very short words
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words = cleaned_title.split()
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meaningful_words = [w for w in words if len(w) > 2 and w.lower() not in ['the', 'and', 'or', 'of', 'a', 'an']]
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if not meaningful_words:
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logger.warning(f"Album removal would leave only short words: '{original_title}' → '{cleaned_title}' - keeping original")
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return track_title, False
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logger.debug(f"Detected album in title: '{original_title}' → '{cleaned_title}' (removed: '{match.group(1)}', similarity: {similarity:.2f})")
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return cleaned_title, True
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# Fallback: detect common album-like suffixes even without album context
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# Look for patterns that might be album names (usually after dash)
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dash_pattern = r'\s*-\s*([A-Za-z][A-Za-z0-9\s&\-\']{3,30})$'
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match = re.search(dash_pattern, track_title)
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if match:
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potential_album_part = match.group(1).strip()
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# Heuristics: likely an album name if it:
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# - Doesn't contain common track descriptors
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# - Is reasonable length (4-30 chars)
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# - Doesn't look like a feature/remix indicator
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exclude_patterns = [
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r'\b(remix|mix|edit|version|live|acoustic|instrumental|demo|feat|ft|featuring)\b'
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]
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is_likely_album = True
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for exclude_pattern in exclude_patterns:
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if re.search(exclude_pattern, potential_album_part.lower()):
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is_likely_album = False
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break
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if is_likely_album and 4 <= len(potential_album_part) <= 30:
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cleaned_title = re.sub(dash_pattern, '', track_title).strip()
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print(f"🎵 Heuristic album detection: '{original_title}' → '{cleaned_title}' (removed: '{potential_album_part}')")
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return cleaned_title, True
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return track_title, False
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def generate_download_queries(self, spotify_track: SpotifyTrack) -> List[str]:
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"""
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Generate multiple search query variations for better matching.
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Returns queries in order of preference (cleaned titles first, then original).
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"""
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queries = []
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if not spotify_track.artists:
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# No artist info - just use track name variations
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queries.append(self.clean_title(spotify_track.name))
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return queries
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artist = self.clean_artist(spotify_track.artists[0])
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original_title = spotify_track.name
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# Get album name if available - try multiple attribute names
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album_name = None
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for attr in ['album', 'album_name', 'album_title']:
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album_name = getattr(spotify_track, attr, None)
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if album_name:
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break
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# PRIORITY 1: Try removing potential album from title FIRST
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cleaned_title, album_detected = self.detect_album_in_title(original_title, album_name)
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if album_detected and cleaned_title != original_title:
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cleaned_track = self.clean_title(cleaned_title)
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if cleaned_track:
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queries.append(f"{artist} {cleaned_track}".strip())
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logger.debug(f"PRIORITY 1: Album-cleaned query: '{artist} {cleaned_track}'")
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# PRIORITY 2: Try simplified versions, but preserve important version info
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# Only remove content that's likely to be album names or noise, not version info
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# Pattern 1: Intelligently handle content after " - "
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# Only remove if it looks like album names, preserve version info like "slowed", "remix", etc.
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dash_pattern = r'^([^-]+?)\s*-\s*(.+)$'
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match = re.search(dash_pattern, original_title.strip())
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if match:
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title_part = match.group(1).strip()
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dash_content = match.group(2).strip().lower()
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# Define version keywords that should be preserved
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preserve_keywords = [
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'slowed', 'reverb', 'sped up', 'speed up', 'spedup', 'slowdown',
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'remix', 'mix', 'edit', 'version', 'remaster', 'acoustic',
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'live', 'demo', 'instrumental', 'radio', 'extended', 'club',
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'original', 'clean', 'explicit', 'mashup', 'bootleg'
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]
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# Check if the dash content contains version keywords
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should_preserve = any(keyword in dash_content for keyword in preserve_keywords)
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if not should_preserve and title_part and len(title_part) >= 3:
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# This looks like album content, safe to remove
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dash_clean = self.clean_title(title_part)
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if dash_clean and dash_clean not in [self.clean_title(q.split(' ', 1)[1]) for q in queries if ' ' in q]:
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queries.append(f"{artist} {dash_clean}".strip())
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logger.debug(f"PRIORITY 2: Dash-cleaned query (removed album): '{artist} {dash_clean}'")
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elif should_preserve:
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logger.debug(f"PRESERVED: Keeping dash content '{dash_content}' as it appears to be version info")
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# Pattern 2: Only remove parentheses that contain noise (feat, explicit, etc), not version info
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# Check if parentheses contain version-related keywords before removing
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paren_pattern = r'^(.+?)\s*\(([^)]+)\)(.*)$'
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paren_match = re.search(paren_pattern, original_title)
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if paren_match:
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before_paren = paren_match.group(1).strip()
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paren_content = paren_match.group(2).strip().lower()
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after_paren = paren_match.group(3).strip()
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# Define what we consider "noise" vs "important version info"
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noise_keywords = ['feat', 'ft', 'featuring', 'explicit', 'clean']
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# Expanded version keywords to match the dash preserve keywords
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version_keywords = [
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'slowed', 'reverb', 'sped up', 'speed up', 'spedup', 'slowdown',
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'remix', 'mix', 'edit', 'version', 'remaster', 'acoustic',
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'live', 'demo', 'instrumental', 'radio', 'extended', 'club',
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'original', 'mashup', 'bootleg'
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]
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# Only remove parentheses if they contain noise, not version info
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is_noise = any(keyword in paren_content for keyword in noise_keywords)
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|
is_version = any(keyword in paren_content for keyword in version_keywords)
|
|
|
|
if is_noise and not is_version and before_paren:
|
|
simple_title = (before_paren + ' ' + after_paren).strip()
|
|
if simple_title and len(simple_title) >= 3:
|
|
simple_clean = self.clean_title(simple_title)
|
|
if simple_clean and simple_clean not in [self.clean_title(q.split(' ', 1)[1]) for q in queries if ' ' in q]:
|
|
queries.append(f"{artist} {simple_clean}".strip())
|
|
logger.debug(f"PRIORITY 2: Noise-removed query: '{artist} {simple_clean}'")
|
|
elif is_version:
|
|
logger.debug(f"PRESERVED: Keeping parentheses content '({paren_content})' as it appears to be version info")
|
|
|
|
# PRIORITY 3: Original query (ONLY if no album was detected or if it's different)
|
|
original_track_clean = self.clean_title(original_title)
|
|
if not album_detected or not queries: # Only add original if no album detected or no other queries
|
|
if original_track_clean not in [q.split(' ', 1)[1] for q in queries if ' ' in q]:
|
|
queries.append(f"{artist} {original_track_clean}".strip())
|
|
logger.debug(f"PRIORITY 3: Original query: '{artist} {original_track_clean}'")
|
|
|
|
# Remove duplicates while preserving order
|
|
unique_queries = []
|
|
seen = set()
|
|
for query in queries:
|
|
if query.lower() not in seen:
|
|
unique_queries.append(query)
|
|
seen.add(query.lower())
|
|
|
|
return unique_queries
|
|
|
|
def generate_download_query(self, spotify_track: SpotifyTrack) -> str:
|
|
"""
|
|
Generate optimized search query for downloading tracks.
|
|
Returns the most specific query (backward compatibility).
|
|
"""
|
|
queries = self.generate_download_queries(spotify_track)
|
|
return queries[0] if queries else ""
|
|
|
|
|
|
def calculate_slskd_match_confidence(self, spotify_track: SpotifyTrack, slskd_track: TrackResult) -> float:
|
|
"""
|
|
Calculates a confidence score for a Soulseek track against a Spotify track.
|
|
This is the core of the new matching logic.
|
|
"""
|
|
# Normalize the Spotify track info once for efficiency
|
|
spotify_title_norm = self.normalize_string(spotify_track.name)
|
|
spotify_artists_norm = [self.normalize_string(a) for a in spotify_track.artists]
|
|
|
|
# The slskd filename is our primary source of truth, so normalize it
|
|
slskd_filename_norm = self.normalize_string(slskd_track.filename)
|
|
|
|
# 1. Title Score: How well does the Spotify title appear in the filename?
|
|
# We use the cleaned, core title for a strict check. This avoids matching remixes.
|
|
spotify_cleaned_title = self.clean_title(spotify_track.name)
|
|
title_score = 0.0
|
|
if spotify_cleaned_title in slskd_filename_norm:
|
|
title_score = 0.9 # High score for direct inclusion
|
|
# Bonus for being a standalone word/phrase, penalizing partial matches like 'in' in 'finland'
|
|
if re.search(r'\b' + re.escape(spotify_cleaned_title) + r'\b', slskd_filename_norm):
|
|
title_score = 1.0
|
|
|
|
# 2. Artist Score: How well do the Spotify artists appear in the filename?
|
|
artist_score = 0.0
|
|
for artist in spotify_artists_norm:
|
|
if artist in slskd_filename_norm:
|
|
artist_score = 1.0 # Perfect match if any artist is found
|
|
break
|
|
|
|
# 3. Duration Score: How similar are the track lengths?
|
|
# We give this a lower weight as slskd duration data can be unreliable.
|
|
duration_score = self.duration_similarity(spotify_track.duration_ms, slskd_track.duration if slskd_track.duration else 0)
|
|
|
|
# 4. Quality Bonus: Add a small bonus for higher quality formats
|
|
quality_bonus = 0.0
|
|
if slskd_track.quality:
|
|
if slskd_track.quality.lower() == 'flac':
|
|
quality_bonus = 0.07 # Reduced from 0.1 to prevent low-confidence FLAC beating high-confidence MP3
|
|
elif slskd_track.quality.lower() == 'mp3' and (slskd_track.bitrate or 0) >= 320:
|
|
quality_bonus = 0.05
|
|
|
|
# --- Final Weighted Score ---
|
|
# Title and Artist are the most important factors for an accurate match.
|
|
final_confidence = (title_score * 0.60) + (artist_score * 0.35) + (duration_score * 0.05)
|
|
|
|
# Add the quality bonus to the final score
|
|
final_confidence += quality_bonus
|
|
|
|
# Ensure the final score doesn't exceed 1.0
|
|
return min(final_confidence, 1.0)
|
|
|
|
|
|
def find_best_slskd_matches(self, spotify_track: SpotifyTrack, slskd_results: List[TrackResult]) -> List[TrackResult]:
|
|
"""
|
|
Scores and sorts a list of Soulseek results against a Spotify track.
|
|
Returns the list of candidates sorted from best to worst match.
|
|
"""
|
|
if not slskd_results:
|
|
return []
|
|
|
|
scored_results = []
|
|
for slskd_track in slskd_results:
|
|
confidence = self.calculate_slskd_match_confidence(spotify_track, slskd_track)
|
|
# We temporarily store the confidence score on the object itself for sorting
|
|
slskd_track.confidence = confidence
|
|
scored_results.append(slskd_track)
|
|
|
|
# Sort by confidence score (descending), and then by size as a tie-breaker
|
|
sorted_results = sorted(scored_results, key=lambda r: (r.confidence, r.size), reverse=True)
|
|
|
|
# Filter out very low-confidence results to avoid bad matches.
|
|
# A threshold of 0.6 means the title and artist had to have some reasonable similarity.
|
|
confident_results = [r for r in sorted_results if r.confidence > 0.6]
|
|
|
|
return confident_results
|
|
|
|
def detect_version_type(self, filename: str) -> Tuple[str, float]:
|
|
"""
|
|
Detect version type from filename and return (version_type, penalty).
|
|
Penalties are applied to prefer original versions over variants.
|
|
"""
|
|
if not filename:
|
|
return 'original', 0.0
|
|
|
|
filename_lower = filename.lower()
|
|
|
|
# Define version patterns and their penalties (higher penalty = lower priority)
|
|
version_patterns = {
|
|
'remix': {
|
|
'patterns': [r'\bremix\b', r'\brmx\b', r'\brework\b', r'\bedit\b(?!ion)'],
|
|
'penalty': 0.15 # -15% penalty for remixes
|
|
},
|
|
'live': {
|
|
'patterns': [r'\blive\b', r'\bconcert\b', r'\btour\b', r'\bperformance\b'],
|
|
'penalty': 0.20 # -20% penalty for live versions
|
|
},
|
|
'acoustic': {
|
|
'patterns': [r'\bacoustic\b', r'\bunplugged\b', r'\bstripped\b'],
|
|
'penalty': 0.12 # -12% penalty for acoustic
|
|
},
|
|
'instrumental': {
|
|
'patterns': [r'\binstrumental\b', r'\bkaraoke\b', r'\bminus one\b'],
|
|
'penalty': 0.25 # -25% penalty for instrumentals (most different from original)
|
|
},
|
|
'radio': {
|
|
'patterns': [r'\bradio\s*edit\b', r'\bradio\s*version\b', r'\bclean\s*edit\b'],
|
|
'penalty': 0.08 # -8% penalty for radio edits (minor difference)
|
|
},
|
|
'extended': {
|
|
'patterns': [r'\bextended\b', r'\bfull\s*version\b', r'\blong\s*version\b'],
|
|
'penalty': 0.05 # -5% penalty for extended (close to original)
|
|
},
|
|
'demo': {
|
|
'patterns': [r'\bdemo\b', r'\broughcut\b', r'\bunreleased\b'],
|
|
'penalty': 0.18 # -18% penalty for demos
|
|
},
|
|
'explicit': {
|
|
'patterns': [r'\bexplicit\b', r'\buncensored\b'],
|
|
'penalty': 0.02 # -2% minor penalty (might be preferred by some)
|
|
}
|
|
}
|
|
|
|
# Check each version type
|
|
for version_type, config in version_patterns.items():
|
|
for pattern in config['patterns']:
|
|
if re.search(pattern, filename_lower):
|
|
return version_type, config['penalty']
|
|
|
|
# No version indicators found - assume original
|
|
return 'original', 0.0
|
|
|
|
def calculate_slskd_match_confidence_enhanced(self, spotify_track: SpotifyTrack, slskd_track: TrackResult) -> Tuple[float, str]:
|
|
"""
|
|
Enhanced version of calculate_slskd_match_confidence with version-aware scoring.
|
|
Returns (confidence, version_type) tuple.
|
|
"""
|
|
# Get base confidence using existing logic
|
|
base_confidence = self.calculate_slskd_match_confidence(spotify_track, slskd_track)
|
|
|
|
# Detect version type and get penalty
|
|
version_type, penalty = self.detect_version_type(slskd_track.filename)
|
|
|
|
# Apply version penalty
|
|
if version_type != 'original':
|
|
adjusted_confidence = max(0.0, base_confidence - penalty)
|
|
# Store version info on the track object for UI display
|
|
slskd_track.version_type = version_type
|
|
slskd_track.version_penalty = penalty
|
|
else:
|
|
adjusted_confidence = base_confidence
|
|
slskd_track.version_type = 'original'
|
|
slskd_track.version_penalty = 0.0
|
|
|
|
return adjusted_confidence, version_type
|
|
|
|
def find_best_slskd_matches_enhanced(self, spotify_track: SpotifyTrack, slskd_results: List[TrackResult]) -> List[TrackResult]:
|
|
"""
|
|
Enhanced version of find_best_slskd_matches with version-aware scoring.
|
|
Returns candidates sorted by adjusted confidence (preferring originals).
|
|
"""
|
|
if not slskd_results:
|
|
return []
|
|
|
|
scored_results = []
|
|
for slskd_track in slskd_results:
|
|
# Use enhanced confidence calculation
|
|
confidence, version_type = self.calculate_slskd_match_confidence_enhanced(spotify_track, slskd_track)
|
|
|
|
# Store the adjusted confidence and version info
|
|
slskd_track.confidence = confidence
|
|
slskd_track.version_type = getattr(slskd_track, 'version_type', 'original')
|
|
scored_results.append(slskd_track)
|
|
|
|
# Sort by confidence score (descending), then by version preference, then by size
|
|
def sort_key(r):
|
|
# Primary: confidence score
|
|
# Secondary: prefer originals (original=0, others=penalty value for tie-breaking)
|
|
version_priority = 0.0 if r.version_type == 'original' else getattr(r, 'version_penalty', 0.1)
|
|
# Tertiary: file size
|
|
return (r.confidence, -version_priority, r.size)
|
|
|
|
sorted_results = sorted(scored_results, key=sort_key, reverse=True)
|
|
|
|
# Filter out very low-confidence results
|
|
# Lower the threshold to 0.45 to account for version penalties and album-in-title scenarios
|
|
confident_results = [r for r in sorted_results if r.confidence > 0.45]
|
|
|
|
# Debug logging for troubleshooting
|
|
if scored_results and not confident_results:
|
|
print(f"⚠️ DEBUG: Found {len(scored_results)} scored results but none met confidence threshold 0.45")
|
|
for i, result in enumerate(sorted_results[:3]): # Show top 3
|
|
print(f" {i+1}. {result.confidence:.3f} - {getattr(result, 'version_type', 'unknown')} - {result.filename[:60]}...")
|
|
elif confident_results:
|
|
print(f"✅ DEBUG: {len(confident_results)} results passed confidence threshold 0.45")
|
|
for i, result in enumerate(confident_results[:3]): # Show top 3
|
|
print(f" {i+1}. {result.confidence:.3f} - {getattr(result, 'version_type', 'unknown')} - {result.filename[:60]}...")
|
|
|
|
return confident_results
|
|
|
|
def calculate_album_confidence(self, spotify_album, plex_album_info: Dict[str, Any]) -> float:
|
|
"""Calculate confidence score for album matching"""
|
|
if not spotify_album or not plex_album_info:
|
|
return 0.0
|
|
|
|
score = 0.0
|
|
|
|
# 1. Album name similarity (40% weight)
|
|
spotify_album_clean = self.clean_album_name(spotify_album.name)
|
|
plex_album_clean = self.clean_album_name(plex_album_info['title'])
|
|
|
|
name_similarity = self.similarity_score(spotify_album_clean, plex_album_clean)
|
|
score += name_similarity * 0.4
|
|
|
|
# 2. Artist similarity (40% weight)
|
|
if spotify_album.artists and plex_album_info.get('artist'):
|
|
spotify_artist_clean = self.clean_artist(spotify_album.artists[0])
|
|
plex_artist_clean = self.clean_artist(plex_album_info['artist'])
|
|
|
|
artist_similarity = self.similarity_score(spotify_artist_clean, plex_artist_clean)
|
|
score += artist_similarity * 0.4
|
|
|
|
# 3. Track count similarity (10% weight)
|
|
spotify_track_count = getattr(spotify_album, 'total_tracks', 0)
|
|
plex_track_count = plex_album_info.get('track_count', 0)
|
|
|
|
if spotify_track_count > 0 and plex_track_count > 0:
|
|
# Calculate track count similarity (perfect match = 1.0, close matches get partial credit)
|
|
track_diff = abs(spotify_track_count - plex_track_count)
|
|
if track_diff == 0:
|
|
track_similarity = 1.0
|
|
elif track_diff <= 2: # Allow for slight differences (bonus tracks, etc.)
|
|
track_similarity = 0.8
|
|
elif track_diff <= 5:
|
|
track_similarity = 0.5
|
|
else:
|
|
track_similarity = 0.2
|
|
|
|
score += track_similarity * 0.1
|
|
|
|
# 4. Year similarity bonus (10% weight)
|
|
spotify_year = spotify_album.release_date[:4] if spotify_album.release_date else None
|
|
plex_year = str(plex_album_info.get('year', '')) if plex_album_info.get('year') else None
|
|
|
|
if spotify_year and plex_year:
|
|
if spotify_year == plex_year:
|
|
score += 0.1 # Perfect year match
|
|
elif abs(int(spotify_year) - int(plex_year)) <= 1:
|
|
score += 0.05 # Close year match (remaster, etc.)
|
|
|
|
return min(score, 1.0) # Cap at 1.0
|
|
|
|
def find_best_album_match(self, spotify_album, plex_albums: List[Dict[str, Any]]) -> Tuple[Optional[Dict[str, Any]], float]:
|
|
"""Find the best matching album from Plex candidates"""
|
|
if not plex_albums:
|
|
return None, 0.0
|
|
|
|
best_match = None
|
|
best_confidence = 0.0
|
|
|
|
for plex_album in plex_albums:
|
|
confidence = self.calculate_album_confidence(spotify_album, plex_album)
|
|
|
|
if confidence > best_confidence:
|
|
best_confidence = confidence
|
|
best_match = plex_album
|
|
|
|
# Only return matches above confidence threshold
|
|
if best_confidence >= 0.8: # High threshold for album matching
|
|
return best_match, best_confidence
|
|
else:
|
|
return None, best_confidence
|