d08a2e91a2
Enrich downloaded audio files with external identifiers and improved genre metadata in a single post-processing write. During metadata enhancement, the app now looks up the MusicBrainz recording and artist MBIDs, retrieves the ISRC and MusicBrainz genres from a follow-up detail lookup, merges them with Spotify's artist-level genres (deduplicated, capped at 5), and embeds everything alongside the Spotify/iTunes track, artist, and album IDs. All MusicBrainz API calls are serialized through the existing global rate limiter, making concurrent download workers safe without needing to pause the background worker. Includes a database migration adding Spotify/iTunes ID columns to the library tables.
433 lines
17 KiB
Python
433 lines
17 KiB
Python
from typing import Optional, Dict, Any
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import json
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from datetime import datetime, timedelta
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from difflib import SequenceMatcher
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from utils.logging_config import get_logger
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from core.musicbrainz_client import MusicBrainzClient
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from database.music_database import MusicDatabase
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logger = get_logger("musicbrainz_service")
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class MusicBrainzService:
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"""Service layer for MusicBrainz integration with caching and matching logic"""
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def __init__(self, database: MusicDatabase, app_name: str = "SoulSync", app_version: str = "1.0", contact_email: str = ""):
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self.db = database
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self.mb_client = MusicBrainzClient(app_name, app_version, contact_email)
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self.retry_days = 30 # Retry 'not_found' items after 30 days
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def _calculate_similarity(self, str1: str, str2: str) -> float:
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"""Calculate string similarity score (0.0 to 1.0)"""
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if not str1 or not str2:
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return 0.0
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# Normalize for comparison
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s1 = str1.lower().strip()
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s2 = str2.lower().strip()
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if s1 == s2:
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return 1.0
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return SequenceMatcher(None, s1, s2).ratio()
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def _check_cache(self, entity_type: str, entity_name: str, artist_name: Optional[str] = None) -> Optional[Dict[str, Any]]:
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"""Check if we have a cached MusicBrainz result"""
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conn = None
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try:
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conn = self.db._get_connection()
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cursor = conn.cursor()
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# Fix: Match exact artist_name (not OR artist_name IS NULL)
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# This prevents getting wrong cached results
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if artist_name is not None:
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cursor.execute("""
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SELECT musicbrainz_id, metadata_json, match_confidence, last_updated
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FROM musicbrainz_cache
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WHERE entity_type = ? AND entity_name = ? AND artist_name = ?
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ORDER BY last_updated DESC
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LIMIT 1
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""", (entity_type, entity_name, artist_name))
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else:
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cursor.execute("""
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SELECT musicbrainz_id, metadata_json, match_confidence, last_updated
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FROM musicbrainz_cache
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WHERE entity_type = ? AND entity_name = ? AND artist_name IS NULL
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ORDER BY last_updated DESC
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LIMIT 1
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""", (entity_type, entity_name))
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row = cursor.fetchone()
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if row:
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# Don't use cache if it's older than 90 days
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last_updated = datetime.fromisoformat(row[3]) if row[3] else None
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if last_updated and (datetime.now() - last_updated).days > 90:
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logger.debug(f"Cache entry for {entity_type} '{entity_name}' is stale (> 90 days)")
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return None
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# Parse JSON with error handling
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try:
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metadata = json.loads(row[1]) if row[1] else None
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except json.JSONDecodeError:
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logger.warning(f"Invalid JSON in cache for {entity_type} '{entity_name}', ignoring")
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metadata = None
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return {
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'musicbrainz_id': row[0],
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'metadata': metadata,
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'confidence': row[2]
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}
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return None
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except Exception as e:
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logger.error(f"Error checking cache: {e}")
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return None
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finally:
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if conn:
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conn.close()
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def _save_to_cache(self, entity_type: str, entity_name: str, artist_name: Optional[str],
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musicbrainz_id: Optional[str], metadata: Optional[Dict], confidence: int):
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"""Save MusicBrainz result to cache"""
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conn = None
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try:
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conn = self.db._get_connection()
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cursor = conn.cursor()
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metadata_json = json.dumps(metadata) if metadata else None
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cursor.execute("""
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INSERT OR REPLACE INTO musicbrainz_cache
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(entity_type, entity_name, artist_name, musicbrainz_id, metadata_json, match_confidence, last_updated)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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""", (entity_type, entity_name, artist_name, musicbrainz_id, metadata_json, confidence, datetime.now()))
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conn.commit()
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logger.debug(f"Cached {entity_type} '{entity_name}' (MBID: {musicbrainz_id}, confidence: {confidence})")
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except Exception as e:
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logger.error(f"Error saving to cache: {e}")
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if conn:
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conn.rollback()
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finally:
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if conn:
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conn.close()
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def match_artist(self, artist_name: str) -> Optional[Dict[str, Any]]:
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"""
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Match an artist by name to MusicBrainz
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Returns:
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Dict with 'mbid', 'name', 'confidence' or None if no good match
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"""
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# Check cache first
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cached = self._check_cache('artist', artist_name)
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if cached:
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logger.debug(f"Cache hit for artist '{artist_name}'")
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return {
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'mbid': cached['musicbrainz_id'],
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'name': artist_name,
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'confidence': cached['confidence'],
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'cached': True
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}
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# Search MusicBrainz
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try:
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results = self.mb_client.search_artist(artist_name, limit=5)
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if not results:
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logger.info(f"No MusicBrainz results for artist '{artist_name}'")
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self._save_to_cache('artist', artist_name, None, None, None, 0)
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return None
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# Find best match
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best_match = None
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best_confidence = 0
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for result in results:
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mb_name = result.get('name', '')
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mb_score = result.get('score', 0) # MusicBrainz search score
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# Calculate our own similarity
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similarity = self._calculate_similarity(artist_name, mb_name)
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# Combine MusicBrainz score with our similarity (weighted)
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# Cap at 100 to prevent edge cases where MB score > 100
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confidence = min(100, int((similarity * 60) + (mb_score / 100 * 40)))
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if confidence > best_confidence:
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best_confidence = confidence
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best_match = result
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# Only return matches with confidence >= 70%
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if best_match and best_confidence >= 70:
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mbid = best_match.get('id')
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mb_name = best_match.get('name')
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# Save to cache
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self._save_to_cache('artist', artist_name, None, mbid, best_match, best_confidence)
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logger.info(f"Matched artist '{artist_name}' → '{mb_name}' (MBID: {mbid}, confidence: {best_confidence})")
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return {
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'mbid': mbid,
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'name': mb_name,
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'confidence': best_confidence,
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'cached': False
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}
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else:
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logger.info(f"Low confidence match for artist '{artist_name}' (best: {best_confidence})")
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self._save_to_cache('artist', artist_name, None, None, None, best_confidence)
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return None
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except Exception as e:
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logger.error(f"Error matching artist '{artist_name}': {e}")
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return None
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def match_release(self, album_name: str, artist_name: Optional[str] = None) -> Optional[Dict[str, Any]]:
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"""
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Match a release (album) by name to MusicBrainz
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Returns:
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Dict with 'mbid', 'title', 'confidence' or None if no good match
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"""
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# Check cache first
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cached = self._check_cache('release', album_name, artist_name)
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if cached:
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logger.debug(f"Cache hit for release '{album_name}'")
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return {
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'mbid': cached['musicbrainz_id'],
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'title': album_name,
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'confidence': cached['confidence'],
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'cached': True
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}
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# Search MusicBrainz
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try:
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results = self.mb_client.search_release(album_name, artist_name, limit=5)
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if not results:
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logger.info(f"No MusicBrainz results for release '{album_name}'")
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self._save_to_cache('release', album_name, artist_name, None, None, 0)
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return None
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# Find best match
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best_match = None
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best_confidence = 0
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for result in results:
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mb_title = result.get('title', '')
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mb_score = result.get('score', 0)
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# Calculate title similarity
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title_similarity = self._calculate_similarity(album_name, mb_title)
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# If we have artist info, check artist match too
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artist_bonus = 0
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if artist_name and 'artist-credit' in result:
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artist_credits = result['artist-credit']
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for credit in artist_credits:
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if isinstance(credit, dict) and 'artist' in credit:
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mb_artist = credit['artist'].get('name', '')
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artist_similarity = self._calculate_similarity(artist_name, mb_artist)
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if artist_similarity > 0.7:
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artist_bonus = 20
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break
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# Combine scores - cap at 100
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confidence = min(100, int((title_similarity * 50) + (mb_score / 100 * 30) + artist_bonus))
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if confidence > best_confidence:
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best_confidence = confidence
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best_match = result
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# Only return matches with confidence >= 70%
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if best_match and best_confidence >= 70:
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mbid = best_match.get('id')
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mb_title = best_match.get('title')
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# Save to cache
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self._save_to_cache('release', album_name, artist_name, mbid, best_match, best_confidence)
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logger.info(f"Matched release '{album_name}' → '{mb_title}' (MBID: {mbid}, confidence: {best_confidence})")
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return {
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'mbid': mbid,
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'title': mb_title,
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'confidence': best_confidence,
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'cached': False
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}
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else:
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logger.info(f"Low confidence match for release '{album_name}' (best: {best_confidence})")
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self._save_to_cache('release', album_name, artist_name, None, None, best_confidence)
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return None
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except Exception as e:
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logger.error(f"Error matching release '{album_name}': {e}")
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return None
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def match_recording(self, track_name: str, artist_name: Optional[str] = None) -> Optional[Dict[str, Any]]:
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"""
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Match a recording (track) by name to MusicBrainz
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Returns:
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Dict with 'mbid', 'title', 'confidence' or None if no good match
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"""
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# Check cache first
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cached = self._check_cache('recording', track_name, artist_name)
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if cached:
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logger.debug(f"Cache hit for recording '{track_name}'")
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return {
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'mbid': cached['musicbrainz_id'],
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'title': track_name,
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'confidence': cached['confidence'],
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'cached': True
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}
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# Search MusicBrainz
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try:
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results = self.mb_client.search_recording(track_name, artist_name, limit=5)
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if not results:
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logger.info(f"No MusicBrainz results for recording '{track_name}'")
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self._save_to_cache('recording', track_name, artist_name, None, None, 0)
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return None
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# Find best match
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best_match = None
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best_confidence = 0
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for result in results:
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mb_title = result.get('title', '')
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mb_score = result.get('score', 0)
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# Calculate title similarity
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title_similarity = self._calculate_similarity(track_name, mb_title)
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# If we have artist info, check artist match too
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artist_bonus = 0
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if artist_name and 'artist-credit' in result:
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artist_credits = result['artist-credit']
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for credit in artist_credits:
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if isinstance(credit, dict) and 'artist' in credit:
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mb_artist = credit['artist'].get('name', '')
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artist_similarity = self._calculate_similarity(artist_name, mb_artist)
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if artist_similarity > 0.7:
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artist_bonus = 20
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break
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# Combine scores - cap at 100
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confidence = min(100, int((title_similarity * 50) + (mb_score / 100 * 30) + artist_bonus))
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if confidence > best_confidence:
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best_confidence = confidence
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best_match = result
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# Only return matches with confidence >= 70%
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if best_match and best_confidence >= 70:
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mbid = best_match.get('id')
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mb_title = best_match.get('title')
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# Save to cache
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self._save_to_cache('recording', track_name, artist_name, mbid, best_match, best_confidence)
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logger.info(f"Matched recording '{track_name}' → '{mb_title}' (MBID: {mbid}, confidence: {best_confidence})")
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return {
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'mbid': mbid,
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'title': mb_title,
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'confidence': best_confidence,
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'cached': False
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}
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else:
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logger.info(f"Low confidence match for recording '{track_name}' (best: {best_confidence})")
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self._save_to_cache('recording', track_name, artist_name, None, None, best_confidence)
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return None
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except Exception as e:
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logger.error(f"Error matching recording '{track_name}': {e}")
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return None
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def update_artist_mbid(self, artist_id: int, mbid: Optional[str], status: str):
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"""Update artist with MusicBrainz ID"""
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conn = None
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try:
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conn = self.db._get_connection()
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cursor = conn.cursor()
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cursor.execute("""
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UPDATE artists
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SET musicbrainz_id = ?,
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musicbrainz_last_attempted = ?,
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musicbrainz_match_status = ?
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WHERE id = ?
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""", (mbid, datetime.now(), status, artist_id))
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conn.commit()
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logger.debug(f"Updated artist {artist_id} with MBID: {mbid}, status: {status}")
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except Exception as e:
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logger.error(f"Error updating artist {artist_id}: {e}")
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if conn:
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conn.rollback()
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finally:
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if conn:
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conn.close()
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def update_album_mbid(self, album_id: int, mbid: Optional[str], status: str):
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"""Update album with MusicBrainz release ID"""
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conn = None
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try:
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conn = self.db._get_connection()
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cursor = conn.cursor()
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cursor.execute("""
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UPDATE albums
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SET musicbrainz_release_id = ?,
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musicbrainz_last_attempted = ?,
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musicbrainz_match_status = ?
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WHERE id = ?
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""", (mbid, datetime.now(), status, album_id))
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conn.commit()
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logger.debug(f"Updated album {album_id} with MBID: {mbid}, status: {status}")
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except Exception as e:
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logger.error(f"Error updating album {album_id}: {e}")
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if conn:
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conn.rollback()
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finally:
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if conn:
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conn.close()
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def update_track_mbid(self, track_id: int, mbid: Optional[str], status: str):
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"""Update track with MusicBrainz recording ID"""
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conn = None
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try:
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conn = self.db._get_connection()
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cursor = conn.cursor()
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cursor.execute("""
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UPDATE tracks
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SET musicbrainz_recording_id = ?,
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musicbrainz_last_attempted = ?,
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musicbrainz_match_status = ?
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WHERE id = ?
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""", (mbid, datetime.now(), status, track_id))
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conn.commit()
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logger.debug(f"Updated track {track_id} with MBID: {mbid}, status: {status}")
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except Exception as e:
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logger.error(f"Error updating track {track_id}: {e}")
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if conn:
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conn.rollback()
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finally:
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if conn:
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conn.close()
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