adding shazam, and LRClib support

core/config.py

@@ -38,7 +38,23 @@ class TranscribeConfig:
     batch_size: int = 8
     gigaam_provider: str = "auto"
     local_files_only: bool = False
-    vad_filter: bool = True
+
+
+@dataclass
+class ShazamConfig:
+    enabled: bool = False
+
+
+@dataclass
+class LyricsConfig:
+    enabled: bool = False
+    user_agent: str = "audio-transcriber/1.0"
+    timeout: float = 10.0
+    use_tags_first: bool = True
+    use_shazam_result: bool = True
+    try_after_asr: bool = True
+    fuzzy_threshold: float = 0.6          # difflib ratio, 0..1
+    transcribe_if_identified_no_lyrics: bool = True
 
 
 @dataclass
@@ -48,11 +64,13 @@ class PipelineConfig:
     recursive: bool = False
     keep_intermediate: bool = True
     overwrite: bool = True
-    mode: str = "sequential"           # "sequential" | "staged"
+    mode: str = "sequential"
 
     convert: ConvertConfig = field(default_factory=ConvertConfig)
     separate: SeparateConfig = field(default_factory=SeparateConfig)
     transcribe: TranscribeConfig = field(default_factory=TranscribeConfig)
+    shazam: ShazamConfig = field(default_factory=ShazamConfig)
+    lyrics: LyricsConfig = field(default_factory=LyricsConfig)
 
     def to_dict(self) -> dict:
         return asdict(self)

core/lyrics.py

@@ -0,0 +1,115 @@
+"""Lyrics lookup via LRCLIB (https://lrclib.net) — free, no API key.
+
+Rate limit: LRCLIB allows ~90 req/min with a proper User-Agent,
+~45 without. We throttle to 0.4s between requests (150/min theoretical,
+well within limits) and always send a UA.
+"""
+from __future__ import annotations
+
+import threading
+import time
+from dataclasses import dataclass
+from typing import Optional
+
+import requests
+
+
+DEFAULT_USER_AGENT = "audio-transcriber/1.0"
+_API_BASE = "https://lrclib.net/api"
+
+_throttle_lock = threading.Lock()
+_last_request_at = 0.0
+_MIN_INTERVAL = 0.4  # seconds
+
+
+@dataclass
+class LyricsMatch:
+    title: str
+    artist: str
+    lyrics: str
+    duration: Optional[float] = None
+
+
+def _throttle() -> None:
+    global _last_request_at
+    with _throttle_lock:
+        now = time.monotonic()
+        wait = _last_request_at + _MIN_INTERVAL - now
+        if wait > 0:
+            time.sleep(wait)
+        _last_request_at = time.monotonic()
+
+
+def _get(url: str, params: dict, user_agent: str, timeout: float = 10.0) -> Optional[dict]:
+    _throttle()
+    try:
+        resp = requests.get(
+            url, params=params,
+            headers={"User-Agent": user_agent},
+            timeout=timeout,
+        )
+        if resp.status_code == 404:
+            return None
+        resp.raise_for_status()
+        return resp.json()
+    except requests.RequestException:
+        return None
+
+
+def _make_match(item: dict) -> Optional[LyricsMatch]:
+    lyrics = item.get("plainLyrics") or item.get("syncedLyrics")
+    if not lyrics:
+        return None
+    return LyricsMatch(
+        title=item.get("trackName", ""),
+        artist=item.get("artistName", ""),
+        lyrics=lyrics.strip(),
+        duration=item.get("duration"),
+    )
+
+
+def search_by_identity(
+    title: Optional[str],
+    artist: Optional[str],
+    album: Optional[str] = None,
+    duration: Optional[float] = None,
+    user_agent: str = DEFAULT_USER_AGENT,
+    timeout: float = 10.0,
+) -> Optional[LyricsMatch]:
+    """Search LRCLIB by track metadata. Works even with messy tags."""
+    if not title and not artist:
+        return None
+
+    query = " ".join(filter(None, [artist or "", title or ""])).strip()
+    if not query:
+        return None
+
+    data = _get(f"{_API_BASE}/search", {"q": query}, user_agent, timeout)
+    if not isinstance(data, list) or not data:
+        return None
+
+    for item in data[:5]:
+        match = _make_match(item)
+        if match:
+            return match
+    return None
+
+
+def search_by_text(
+    query: str,
+    limit: int = 5,
+    user_agent: str = DEFAULT_USER_AGENT,
+    timeout: float = 10.0,
+) -> list[LyricsMatch]:
+    """Search LRCLIB by plain text (e.g. first words of recognized speech)."""
+    if not query or not query.strip():
+        return []
+    data = _get(f"{_API_BASE}/search", {"q": query.strip()}, user_agent, timeout)
+    if not isinstance(data, list):
+        return []
+    matches: list[LyricsMatch] = []
+    for item in data[:limit]:
+        match = _make_match(item)
+        if match:
+            matches.append(match)
+    return matches

core/metadata.py

@@ -0,0 +1,58 @@
+"""Read audio file tags via mutagen. Always safe — never raises."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+
+@dataclass
+class TrackMetadata:
+    title: Optional[str] = None
+    artist: Optional[str] = None
+    album: Optional[str] = None
+    duration: Optional[float] = None
+    has_identity: bool = False
+
+
+def read_metadata(path: Path) -> TrackMetadata:
+    """Extract title/artist/album/duration from tags. Never raises."""
+    try:
+        from mutagen import File
+    except ImportError:
+        return TrackMetadata()
+
+    try:
+        audio = File(str(path), easy=True)
+    except Exception:
+        return TrackMetadata()
+
+    if audio is None:
+        return TrackMetadata()
+
+    def _first(key: str) -> Optional[str]:
+        v = audio.get(key)
+        if isinstance(v, list):
+            return v[0] if v else None
+        if isinstance(v, str):
+            return v
+        return None
+
+    title = _first("title")
+    artist = _first("artist")
+    album = _first("album")
+
+    duration: Optional[float] = None
+    try:
+        if audio.info is not None:
+            duration = float(audio.info.length)
+    except Exception:
+        pass
+
+    return TrackMetadata(
+        title=title,
+        artist=artist,
+        album=album,
+        duration=duration,
+        has_identity=bool(title and artist),
+    )

core/pipeline.py

@@ -1,24 +1,33 @@
-"""Orchestrate convert -> separate -> transcribe.
+"""Orchestrate identify -> convert -> separate -> transcribe -> lyrics-match.
+
+Pipeline order per track (short-circuits as soon as text is found):
+  1. metadata tags -> LRCLIB search (if `use_tags_first`)
+  2. Shazam fingerprint (if enabled)
+  3. Shazam result -> LRCLIB search (if `use_shazam_result`)
+  4. full pipeline: convert -> separate -> transcribe
+  5. post-ASR: LRCLIB search by recognized text (fuzzy match)
 
 Two modes:
-  - sequential: for each file, run all enabled stages before moving to the next.
-  - staged:     run stage 1 over all files, then stage 2 over all files, then stage 3.
-
-Intermediate artifacts (convert.wav, vocals.wav) are written to a temp dir and
-NEVER persisted to the output directory — unless `keep_intermediate=True`.
-Only the artifact of the LAST enabled stage is moved into `<output>/<stem>/`.
+  - sequential: run all of the above per file
+  - staged:     identification pass first, then convert/separate/transcribe
+                passes only for unresolved files, then post-ASR lyrics pass.
 """
 from __future__ import annotations
 
+import difflib
+import json
 import shutil
 import tempfile
 import threading
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Callable, Optional
 
 from .config import PipelineConfig
 from .convert import convert_to_wav
+from .lyrics import LyricsMatch, search_by_identity, search_by_text
+from .metadata import TrackMetadata, read_metadata
+from .recognize import ShazamResult, recognize
 from .separate import separate_vocals
 from .transcribe import transcribe
 
@@ -28,7 +37,7 @@ AUDIO_EXTENSIONS = {
 }
 
 LogFn = Callable[[str], None]
-ProgressFn = Callable[[int, int, str], None]   # done, total, stage
+ProgressFn = Callable[[int, int, str], None]
 
 
 @dataclass
@@ -37,6 +46,8 @@ class FileResult:
     text: Optional[str]
     output_dir: Optional[Path]
     error: Optional[str] = None
+    source: Optional[str] = None
+    metadata: Optional[dict] = field(default=None)
 
 
 class Cancelled(Exception):
@@ -45,68 +56,218 @@ class Cancelled(Exception):
 
 # ---------------------------------------------------------------- helpers
 
-_ARTIFACT_NAMES = {
-    "convert": "convert.wav",
-    "separate": "vocals.wav",
-    "transcribe": "text.txt",
-}
-
-
 def collect_audio_files(input_dir: Path, recursive: bool) -> list[Path]:
     it = input_dir.rglob("*") if recursive else input_dir.iterdir()
     return sorted(p for p in it if p.is_file() and p.suffix.lower() in AUDIO_EXTENSIONS)
 
 
-def _final_stage(config: PipelineConfig) -> Optional[str]:
-    """The stage whose artifact is the final result (single element)."""
-    if config.transcribe.enabled:
-        return "transcribe"
-    if config.separate.enabled:
-        return "separate"
-    if config.convert.enabled:
-        return "convert"
-    return None
-
-
 def _check_cancel(stop_event: Optional[threading.Event]) -> None:
     if stop_event is not None and stop_event.is_set():
         raise Cancelled()
 
 
-def _persist_final(work_dir: Path, persist_dir: Path, final_stage: str, log: LogFn) -> None:
-    """Move the final-stage artifact from work_dir into persist_dir."""
-    name = _ARTIFACT_NAMES[final_stage]
-    src = work_dir / name
-    if not src.exists():
-        raise FileNotFoundError(f"Final artifact missing: {src}")
+def _text_head(text: str, n_words: int = 8) -> str:
+    return " ".join(text.split()[:n_words])
+
+
+def _build_metadata_dict(
+    metadata: TrackMetadata,
+    shazam: Optional[ShazamResult],
+    source: Optional[str],
+) -> Optional[dict]:
+    d: dict = {}
+    if metadata.has_identity:
+        d["tags"] = {
+            "title": metadata.title,
+            "artist": metadata.artist,
+            "album": metadata.album,
+            "duration": metadata.duration,
+        }
+    if shazam:
+        d["shazam"] = {
+            "title": shazam.title,
+            "artist": shazam.artist,
+            "album": shazam.album,
+            "year": shazam.year,
+        }
+    if source:
+        d["source"] = source
+    return d if d else None
+
+
+def _write_metadata_json(path: Path, data: Optional[dict]) -> None:
+    if not data:
+        return
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(
+        json.dumps(data, indent=2, ensure_ascii=False),
+        encoding="utf-8",
+    )
+
+
+@dataclass
+class IdentifyResult:
+    metadata: TrackMetadata
+    shazam: Optional[ShazamResult] = None
+    text: Optional[str] = None
+    source: Optional[str] = None
+    needs_asr: bool = True
+
+
+def _identify_file(
+    input_path: Path,
+    work_dir: Path,
+    config: PipelineConfig,
+    log: LogFn,
+) -> IdentifyResult:
+    """Run cheap identification steps. May populate .text and .source,
+    and set .needs_asr=False if the track is fully resolved."""
+    result = IdentifyResult(metadata=read_metadata(input_path))
+
+    # --- tags -> LRCLIB ---
+    if (
+        config.lyrics.enabled
+        and config.lyrics.use_tags_first
+        and result.metadata.has_identity
+    ):
+        log("    LRCLIB по тегам...")
+        match = search_by_identity(
+            title=result.metadata.title,
+            artist=result.metadata.artist,
+            album=result.metadata.album,
+            duration=result.metadata.duration,
+            user_agent=config.lyrics.user_agent,
+            timeout=config.lyrics.timeout,
+        )
+        if match:
+            result.text = match.lyrics
+            result.source = "tag+lrclib"
+            result.needs_asr = False
+            log(f"    ✓ {match.artist} — {match.title}")
+            return result
+
+    # --- Shazam ---
+    if config.shazam.enabled:
+        log("    Shazam...")
+        shazam_wav = work_dir / "shazam_16k.wav"
+        try:
+            convert_to_wav(
+                input_path, shazam_wav,
+                sample_rate=16000, channels=1,
+                ffmpeg_path=config.convert.ffmpeg_path,
+            )
+            result.shazam = recognize(shazam_wav)
+        except Exception as e:  # noqa: BLE001
+            log(f"    ⚠ Shazam error: {e}")
+            result.shazam = None
+
+        if result.shazam:
+            log(f"    ✓ {result.shazam.artist} — {result.shazam.title}")
+
+            if config.lyrics.enabled and config.lyrics.use_shazam_result:
+                match = search_by_identity(
+                    title=result.shazam.title,
+                    artist=result.shazam.artist,
+                    album=result.shazam.album,
+                    duration=result.metadata.duration,
+                    user_agent=config.lyrics.user_agent,
+                    timeout=config.lyrics.timeout,
+                )
+                if match:
+                    result.text = match.lyrics
+                    result.source = "shazam+lrclib"
+                    result.needs_asr = False
+                    log("    ✓ текст найден через LRCLIB")
+                    return result
+
+            # Identified, but no lyrics
+            result.source = "shazam"
+            if not config.lyrics.transcribe_if_identified_no_lyrics:
+                result.needs_asr = False
+            log("    ⚠ текста нет")
+        else:
+            log("    ⚠ не распознан")
+
+    return result
+
+
+def _try_post_asr_match(
+    text: str,
+    config: PipelineConfig,
+    log: LogFn,
+) -> Optional[str]:
+    """Try to find the actual lyrics via LRCLIB using recognized text. Returns
+    the lyrics text on a confident match, else None."""
+    if not text or len(text.split()) < 3:
+        return None
+
+    head = _text_head(text, 8)
+    log(f"    LRCLIB по тексту: {head!r}...")
+
+    try:
+        candidates = search_by_text(
+            head, limit=5,
+            user_agent=config.lyrics.user_agent,
+            timeout=config.lyrics.timeout,
+        )
+    except Exception as e:  # noqa: BLE001
+        log(f"    ⚠ LRCLIB error: {e}")
+        return None
+
+    if not candidates:
+        log("    ⚠ нет кандидатов")
+        return None
+
+    best: Optional[LyricsMatch] = None
+    best_ratio = 0.0
+    for c in candidates:
+        ratio = difflib.SequenceMatcher(None, text.lower(), c.lyrics.lower()).ratio()
+        if ratio > best_ratio:
+            best_ratio = ratio
+            best = c
+
+    if best is not None and best_ratio >= config.lyrics.fuzzy_threshold:
+        log(f"    ✓ совпадение {best_ratio:.2f}: {best.artist} — {best.title}")
+        return best.lyrics
+
+    log(f"    ⚠ лучшее совпадение {best_ratio:.2f} < {config.lyrics.fuzzy_threshold}")
+    return None
+
+
+def _persist_artifacts(
+    work_dir: Path,
+    persist_dir: Path,
+    config: PipelineConfig,
+    has_text: bool,
+    has_metadata: bool,
+    log: LogFn,
+) -> None:
+    """Move wanted artifacts from work_dir to persist_dir."""
     persist_dir.mkdir(parents=True, exist_ok=True)
+
+    names: list[str] = []
+    if has_metadata and (work_dir / "metadata.json").exists():
+        names.append("metadata.json")
+    if has_text and (work_dir / "text.txt").exists():
+        names.append("text.txt")
+
+    if config.keep_intermediate:
+        for n in ("convert.wav", "vocals.wav"):
+            if (work_dir / n).exists():
+                names.append(n)
+    elif not has_text:
+        if config.separate.enabled and (work_dir / "vocals.wav").exists():
+            names.append("vocals.wav")
+        elif config.convert.enabled and (work_dir / "convert.wav").exists():
+            names.append("convert.wav")
+
+    for name in names:
+        src = work_dir / name
         dst = persist_dir / name
         if dst.exists():
             dst.unlink()
         shutil.move(str(src), str(dst))
-    log(f"    [persist] {final_stage} -> {persist_dir.name}/{name}")
-
-
-def _work_dir_for(
-    config: PipelineConfig,
-    stem: str,
-    output_root: Path,
-    tmp_root: Optional[Path],
-) -> tuple[Path, Path]:
-    """Return (work_dir, persist_dir).
-
-    - keep_intermediate=True  -> work_dir == persist_dir == output_root/stem
-    - keep_intermediate=False -> work_dir = tmp_root/stem, persist_dir = output_root/stem
-    """
-    persist_dir = output_root / stem
-    if config.keep_intermediate:
-        persist_dir.mkdir(parents=True, exist_ok=True)
-        return persist_dir, persist_dir
-    if tmp_root is None:
-        raise ValueError("tmp_root required when keep_intermediate=False")
-    work_dir = tmp_root / stem
-    work_dir.mkdir(parents=True, exist_ok=True)
-    return work_dir, persist_dir
+        log(f"    [persist] {name}")
 
 
 # ---------------------------------------------------------------- entry
@@ -130,10 +291,14 @@ def run_pipeline(
         return []
 
     mode = (config.mode or "sequential").lower()
-    log(f"Найдено треков: {len(files)}   |   режим: {mode}   |   "
-        f"keep_intermediate={config.keep_intermediate}")
-    if not config.keep_intermediate:
-        log("  Промежуточные файлы будут во временной папке и удалены после прогона.")
+    log(
+        f"Найдено треков: {len(files)}   |   режим: {mode}   |   "
+        f"keep_intermediate={config.keep_intermediate}"
+    )
+    if config.shazam.enabled:
+        log("  Shazam: включён")
+    if config.lyrics.enabled:
+        log(f"  LRCLIB: включён (порог fuzzy={config.lyrics.fuzzy_threshold:.2f})")
 
     if mode == "staged":
         return _run_staged(config, files, log, on_progress, stop_event)
@@ -150,7 +315,6 @@ def _run_sequential(
     stop_event: Optional[threading.Event],
 ) -> list[FileResult]:
     total = len(files)
-    final_stage = _final_stage(config)
     results: list[FileResult] = []
     output_root = Path(config.output_dir)
 
@@ -163,18 +327,43 @@ def _run_sequential(
         log(f"\n▶ [{idx}/{total}] {f.name}")
 
         tmp_root = Path(tempfile.mkdtemp(prefix=f"at_{f.stem}_"))
-        work_dir, persist_dir = _work_dir_for(config, f.stem, output_root, tmp_root)
+        track_out = output_root / f.stem
+        track_out.mkdir(parents=True, exist_ok=True)
 
-        audio_path: Path = f
         text: Optional[str] = None
+        source: Optional[str] = None
         error: Optional[str] = None
-        processed_ok = False
+        metadata_dict: Optional[dict] = None
 
         try:
+            # --- Phase 1: identification ---
+            ident = _identify_file(f, tmp_root, config, log)
+
+            if not ident.needs_asr:
+                text = ident.text
+                source = ident.source
+                metadata_dict = _build_metadata_dict(ident.metadata, ident.shazam, source)
+                if text:
+                    (tmp_root / "text.txt").write_text(text, encoding="utf-8")
+                    log(f"  ✓ text.txt ({len(text)} символов, {source})")
+                if metadata_dict:
+                    _write_metadata_json(tmp_root / "metadata.json", metadata_dict)
+                    log("  ✓ metadata.json")
+                _persist_artifacts(
+                    tmp_root, track_out, config,
+                    has_text=text is not None,
+                    has_metadata=bool(metadata_dict),
+                    log=log,
+                )
+                results.append(FileResult(f, text, track_out, None, source, metadata_dict))
+                continue
+
+            # --- Phase 2: full pipeline ---
+            audio_path: Path = f
             if config.convert.enabled:
                 _check_cancel(stop_event)
                 log("  [1] Конвертация в WAV...")
-                target = work_dir / "convert.wav"
+                target = tmp_root / "convert.wav"
                 if config.overwrite or not target.exists():
                     convert_to_wav(
                         f, target,
@@ -188,7 +377,7 @@ def _run_sequential(
             if config.separate.enabled:
                 _check_cancel(stop_event)
                 log("  [2] Отделение вокала...")
-                target = work_dir / "vocals.wav"
+                target = tmp_root / "vocals.wav"
                 with tempfile.TemporaryDirectory() as tmp:
                     raw = separate_vocals(
                         audio_path, Path(tmp),
@@ -214,12 +403,28 @@ def _run_sequential(
                     batch_size=config.transcribe.batch_size,
                     gigaam_provider=config.transcribe.gigaam_provider,
                     local_files_only=config.transcribe.local_files_only,
-                    vad_filter=config.transcribe.vad_filter,
                 )
-                (work_dir / "text.txt").write_text(text, encoding="utf-8")
-                log(f"  ✓ text.txt ({len(text)} символов)")
+                source = config.transcribe.engine
+                log(f"  ✓ распознано {len(text)} символов")
 
-            processed_ok = True
+                if config.lyrics.enabled and config.lyrics.try_after_asr:
+                    matched = _try_post_asr_match(text, config, log)
+                    if matched:
+                        text = matched
+                        source = f"{source}+lrclib"
+
+                (tmp_root / "text.txt").write_text(text, encoding="utf-8")
+
+            metadata_dict = _build_metadata_dict(ident.metadata, ident.shazam, source)
+            if metadata_dict:
+                _write_metadata_json(tmp_root / "metadata.json", metadata_dict)
+
+            _persist_artifacts(
+                tmp_root, track_out, config,
+                has_text=text is not None,
+                has_metadata=bool(metadata_dict),
+                log=log,
+            )
 
         except Cancelled:
             log("⏹ Отменено")
@@ -228,17 +433,14 @@ def _run_sequential(
             error = f"{type(e).__name__}: {e}"
             log(f"  ✗ Ошибка: {error}")
         finally:
-            if processed_ok and not config.keep_intermediate and final_stage is not None:
-                try:
-                    _persist_final(work_dir, persist_dir, final_stage, log)
-                except Exception as e:  # noqa: BLE001
-                    error = f"persist failed: {e}"
-                    log(f"  ✗ {error}")
             shutil.rmtree(tmp_root, ignore_errors=True)
             if on_progress:
                 on_progress(idx, total, "file")
-            results.append(FileResult(f, text, persist_dir if not error else None, error))
+            results.append(
+                FileResult(f, text, track_out if not error else None, error, source, metadata_dict)
+            )
 
+    _log_summary(results, log)
     return results
 
 
@@ -253,42 +455,76 @@ def _run_staged(
 ) -> list[FileResult]:
     output_root = Path(config.output_dir)
     total = len(files)
-    final_stage = _final_stage(config)
 
-    # One tmp_root for the whole batch: stage 2 needs convert.wav of every file,
-    # stage 3 needs vocals.wav of every file. All of it is removed at the end.
-    tmp_root_ctx = tempfile.mkdtemp(prefix="at_staged_")
-    tmp_root = Path(tmp_root_ctx)
-
-    state: dict[Path, dict] = {}
-    for f in files:
-        work_dir, persist_dir = _work_dir_for(config, f.stem, output_root, tmp_root)
-        state[f] = {
+    state: dict[Path, dict] = {
+        f: {
             "audio_path": f,
-            "work_dir": work_dir,
-            "persist_dir": persist_dir,
+            "ident": None,
             "text": None,
+            "source": None,
+            "metadata_dict": None,
             "error": None,
+            "resolved": False,
+        }
+        for f in files
     }
 
     def active() -> list[Path]:
-        return [f for f in files if state[f]["error"] is None]
+        return [f for f in files if state[f]["error"] is None and not state[f]["resolved"]]
 
     def cancelled() -> bool:
         return stop_event is not None and stop_event.is_set()
 
+    # --- Stage 0: identification ---
+    if config.shazam.enabled or config.lyrics.enabled:
+        log(f"\n══ Этап 0 · Идентификация ({total} файлов) ══")
+        ident_tmp = Path(tempfile.mkdtemp(prefix="at_ident_"))
         try:
-        # ---- Stage 1: convert ----
-        if config.convert.enabled:
-            log(f"\n══ Этап 1/3 · Конвертация ({total} файлов) ══")
             for i, f in enumerate(files, 1):
                 if cancelled():
                     log("⏹ Отменено")
                     break
                 if on_progress:
-                    on_progress(i, total, "convert")
+                    on_progress(i, total, "identify")
                 try:
-                    target = state[f]["work_dir"] / "convert.wav"
+                    ident = _identify_file(f, ident_tmp / f.stem, config, log)
+                    state[f]["ident"] = ident
+
+                    if not ident.needs_asr:
+                        state[f]["resolved"] = True
+                        state[f]["text"] = ident.text
+                        state[f]["source"] = ident.source
+                        state[f]["metadata_dict"] = _build_metadata_dict(
+                            ident.metadata, ident.shazam, ident.source,
+                        )
+                        # Persist resolved tracks right away
+                        track_out = output_root / f.stem
+                        track_out.mkdir(parents=True, exist_ok=True)
+                        if ident.text:
+                            (track_out / "text.txt").write_text(ident.text, encoding="utf-8")
+                        if state[f]["metadata_dict"]:
+                            _write_metadata_json(track_out / "metadata.json", state[f]["metadata_dict"])
+                        log(f"  ✓ {f.name} [{ident.source}]")
+                    else:
+                        log(f"  → {f.name} (нужен полный пайплайн)")
+                except Exception as e:  # noqa: BLE001
+                    state[f]["error"] = f"{type(e).__name__}: {e}"
+                    log(f"  ✗ {f.name}: {e}")
+        finally:
+            shutil.rmtree(ident_tmp, ignore_errors=True)
+
+    # --- Stage 1: convert ---
+    if config.convert.enabled and not cancelled():
+        todo = active()
+        log(f"\n══ Этап 1 · Конвертация ({len(todo)} файлов) ══")
+        for i, f in enumerate(todo, 1):
+            if cancelled():
+                log("⏹ Отменено")
+                break
+            if on_progress:
+                on_progress(i, len(todo), "convert")
+            try:
+                target = Path(tempfile.mkdtemp(prefix=f"at_{f.stem}_")) / "convert.wav"
                 if config.overwrite or not target.exists():
                     convert_to_wav(
                         f, target,
@@ -302,10 +538,10 @@ def _run_staged(
                 state[f]["error"] = f"{type(e).__name__}: {e}"
                 log(f"  ✗ {f.name}: {e}")
 
-        # ---- Stage 2: separate ----
+    # --- Stage 2: separate ---
     if config.separate.enabled and not cancelled():
         todo = active()
-            log(f"\n══ Этап 2/3 · Отделение вокала ({len(todo)} файлов) ══")
+        log(f"\n══ Этап 2 · Отделение вокала ({len(todo)} файлов) ══")
         for i, f in enumerate(todo, 1):
             if cancelled():
                 log("⏹ Отменено")
@@ -313,10 +549,11 @@ def _run_staged(
             if on_progress:
                 on_progress(i, len(todo), "separate")
             try:
-                    target = state[f]["work_dir"] / "vocals.wav"
+                src = state[f]["audio_path"]
+                target = src.parent / "vocals.wav"
                 with tempfile.TemporaryDirectory() as tmp:
                     raw = separate_vocals(
-                            state[f]["audio_path"], Path(tmp),
+                        src, Path(tmp),
                         model=config.separate.model,
                         isolate_stem=config.separate.isolate_stem,
                         unblend_path=config.separate.unblend_path,
@@ -329,10 +566,10 @@ def _run_staged(
                 state[f]["error"] = f"{type(e).__name__}: {e}"
                 log(f"  ✗ {f.name}: {e}")
 
-        # ---- Stage 3: transcribe ----
+    # --- Stage 3: transcribe ---
     if config.transcribe.enabled and not cancelled():
         todo = active()
-            log(f"\n══ Этап 3/3 · Транскрибация ({len(todo)} файлов) ══")
+        log(f"\n══ Этап 3 · Транскрибация ({len(todo)} файлов) ══")
         for i, f in enumerate(todo, 1):
             if cancelled():
                 log("⏹ Отменено")
@@ -350,37 +587,85 @@ def _run_staged(
                     batch_size=config.transcribe.batch_size,
                     gigaam_provider=config.transcribe.gigaam_provider,
                     local_files_only=config.transcribe.local_files_only,
-                        vad_filter=config.transcribe.vad_filter,
                 )
-                    (state[f]["work_dir"] / "text.txt").write_text(text, encoding="utf-8")
                 state[f]["text"] = text
+                state[f]["source"] = config.transcribe.engine
                 log(f"  ✓ {f.name} ({len(text)} символов)")
             except Exception as e:  # noqa: BLE001
                 state[f]["error"] = f"{type(e).__name__}: {e}"
                 log(f"  ✗ {f.name}: {e}")
 
-        # ---- Persist final artifacts ----
-        if not config.keep_intermediate and final_stage is not None:
-            log(f"\n══ Перенос финальных файлов ({final_stage}) ══")
+    # --- Stage 4: post-ASR lyrics match + persist ---
+    if not cancelled():
+        log("\n══ Финализация ══")
         for f in files:
             s = state[f]
             if s["error"] is not None:
                 continue
-                try:
-                    _persist_final(s["work_dir"], s["persist_dir"], final_stage, log)
-                except Exception as e:  # noqa: BLE001
-                    s["error"] = f"persist failed: {e}"
-                    log(f"  ✗ {f.name}: {e}")
+            if s["resolved"]:
+                continue  # already persisted in identification pass
 
-    finally:
-        shutil.rmtree(tmp_root, ignore_errors=True)
+            text = s["text"]
+            source = s["source"]
 
-    return [
+            if text and config.lyrics.enabled and config.lyrics.try_after_asr:
+                matched = _try_post_asr_match(text, config, log)
+                if matched:
+                    text = matched
+                    source = f"{source}+lrclib"
+                    s["text"] = text
+                    s["source"] = source
+
+            ident = s["ident"]
+            metadata_dict = _build_metadata_dict(
+                ident.metadata, ident.shazam, source,
+            ) if ident else None
+            s["metadata_dict"] = metadata_dict
+
+            track_out = output_root / f.stem
+            track_out.mkdir(parents=True, exist_ok=True)
+
+            audio_work = s["audio_path"]
+            work_dir = audio_work.parent if isinstance(audio_work, Path) else track_out
+            if text:
+                (work_dir / "text.txt").write_text(text, encoding="utf-8")
+            if metadata_dict:
+                _write_metadata_json(work_dir / "metadata.json", metadata_dict)
+
+            _persist_artifacts(
+                work_dir, track_out, config,
+                has_text=text is not None,
+                has_metadata=bool(metadata_dict),
+                log=log,
+            )
+            log(f"  ✓ {f.name}")
+
+    # cleanup temp dirs
+    for f in files:
+        audio_path = state[f]["audio_path"]
+        if isinstance(audio_path, Path) and audio_path.parent.name.startswith(f"at_{f.stem}"):
+            shutil.rmtree(audio_path.parent, ignore_errors=True)
+
+    results = [
         FileResult(
             f,
             state[f]["text"],
-            state[f]["persist_dir"] if state[f]["error"] is None else None,
+            output_root / f.stem if state[f]["error"] is None else None,
             state[f]["error"],
+            state[f]["source"],
+            state[f]["metadata_dict"],
         )
         for f in files
     ]
+    _log_summary(results, log)
+    return results
+
+
+# ---------------------------------------------------------------- summary
+
+def _log_summary(results: list[FileResult], log: LogFn) -> None:
+    from collections import Counter
+    counts = Counter(r.source or ("error" if r.error else "no_result") for r in results)
+    log("\n══ Итог ══")
+    for src, n in sorted(counts.items(), key=lambda kv: -kv[1]):
+        log(f"  {src}: {n}")

core/recognize.py

@@ -0,0 +1,52 @@
+"""Audio fingerprinting via Shazam (unofficial, no API key needed).
+
+Wraps shazamlite. If Shazam can't identify the track — return None.
+Never raises on 'not found', only on missing dependency.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+
+@dataclass
+class ShazamResult:
+    title: str
+    artist: str
+    album: Optional[str] = None
+    year: Optional[str] = None
+
+
+def recognize(audio_wav: Path) -> Optional[ShazamResult]:
+    """Run Shazam fingerprinting on a WAV file. Returns None if not found."""
+    try:
+        from shazamlite import Shazam
+    except ImportError as e:
+        raise RuntimeError(
+            "shazamlite не установлен. Установите: pip install shazamlite numpy soundfile"
+        ) from e
+
+    shazam = Shazam()
+    try:
+        track = shazam.recognize(str(audio_wav))
+    except Exception:
+        return None
+
+    if track is None:
+        return None
+
+    title = getattr(track, "title", None) or getattr(track, "track", None)
+    artist = getattr(track, "artist", None) or getattr(track, "subtitle", None)
+    album = getattr(track, "album", None)
+    year = getattr(track, "year", None) or getattr(track, "released", None)
+
+    if not title or not artist:
+        return None
+
+    return ShazamResult(
+        title=str(title),
+        artist=str(artist),
+        album=str(album) if album else None,
+        year=str(year) if year else None,
+    )

gui/main_window.py

@@ -8,13 +8,16 @@ from typing import Optional
 from PySide6.QtCore import Qt, QUrl
 from PySide6.QtGui import QColor, QDesktopServices
 from PySide6.QtWidgets import (
-    QCheckBox, QComboBox, QFileDialog, QFormLayout, QGroupBox,
+    QCheckBox, QComboBox, QDoubleSpinBox, QFileDialog, QFormLayout, QGroupBox,
     QHBoxLayout, QLabel, QLineEdit, QMainWindow, QMessageBox,
     QPlainTextEdit, QProgressBar, QPushButton, QSpinBox, QTabWidget,
     QVBoxLayout, QWidget,
 )
 
-from core.config import ConvertConfig, PipelineConfig, SeparateConfig, TranscribeConfig
+from core.config import (
+    ConvertConfig, LyricsConfig, PipelineConfig, SeparateConfig,
+    ShazamConfig, TranscribeConfig,
+)
 from gui.worker import PipelineWorker
 
 
@@ -24,6 +27,7 @@ ENGINES = ["whisperx", "gigaam"]
 
 STAGE_LABELS = {
     "file": "Файлы",
+    "identify": "Идентификация",
     "convert": "Конвертация",
     "separate": "Отделение",
     "transcribe": "Транскрибация",
@@ -36,7 +40,6 @@ MODE_LABELS = {
 
 
 def _detect_device_info() -> tuple[str, str]:
-    """Return (display_text, color_hex) describing the compute device."""
     try:
         import torch
     except ImportError:
@@ -57,7 +60,7 @@ class MainWindow(QMainWindow):
     def __init__(self) -> None:
         super().__init__()
         self.setWindowTitle("Song Transcriber")
-        self.resize(1100, 850)
+        self.resize(1140, 880)
         self._worker: Optional[PipelineWorker] = None
         self._build_ui()
         self._apply_defaults()
@@ -74,19 +77,16 @@ class MainWindow(QMainWindow):
         root.setContentsMargins(18, 16, 18, 16)
         root.setSpacing(12)
 
-        # --- header row: title + device badge ---
         header = QHBoxLayout()
         title = QLabel("🎵 Song Transcriber")
         title.setObjectName("appTitle")
         header.addWidget(title)
         header.addStretch(1)
-
         self.device_label = QLabel("")
         self.device_label.setObjectName("deviceInfo")
         header.addWidget(self.device_label)
         root.addLayout(header)
 
-        # --- top settings ---
         top_box = QGroupBox("Общие настройки")
         top_layout = QFormLayout(top_box)
         top_layout.setSpacing(10)
@@ -114,19 +114,21 @@ class MainWindow(QMainWindow):
 
         root.addWidget(top_box)
 
-        # --- stages ---
         self.tabs = QTabWidget()
+        self.tabs.addTab(self._build_shazam_tab(), "Shazam")
+        self.tabs.addTab(self._build_lrclib_tab(), "LRCLIB")
         self.tabs.addTab(self._build_convert_tab(), "Конвертация")
         self.tabs.addTab(self._build_separate_tab(), "Отделение вокала")
         self.tabs.addTab(self._build_transcribe_tab(), "Транскрибация")
         root.addWidget(self.tabs)
 
+        self.shazam_enabled_cb.toggled.connect(self._sync_tab_indicators)
+        self.lyrics_enabled_cb.toggled.connect(self._sync_tab_indicators)
         self.convert_enabled_cb.toggled.connect(self._sync_tab_indicators)
         self.separate_enabled_cb.toggled.connect(self._sync_tab_indicators)
         self.transcribe_enabled_cb.toggled.connect(self._sync_tab_indicators)
         self.engine_cb.currentTextChanged.connect(self._sync_engine_fields)
 
-        # --- controls row ---
         row = QHBoxLayout()
         row.setSpacing(10)
         self.start_btn = QPushButton("▶  Запустить")
@@ -136,7 +138,6 @@ class MainWindow(QMainWindow):
         self.stop_btn.setObjectName("danger")
         self.stop_btn.setEnabled(False)
         self.stop_btn.clicked.connect(self._on_stop)
-
         self.open_out_btn = QPushButton("📂  Открыть результат")
         self.open_out_btn.clicked.connect(self._open_output_dir)
         self.save_preset_btn = QPushButton("💾  Сохранить пресет")
@@ -152,12 +153,10 @@ class MainWindow(QMainWindow):
         row.addWidget(self.load_preset_btn)
         root.addLayout(row)
 
-        # --- progress ---
         self.progress = QProgressBar()
         self.progress.setFormat("Ожидание")
         root.addWidget(self.progress)
 
-        # --- log ---
         self.log_view = QPlainTextEdit()
         self.log_view.setObjectName("log")
         self.log_view.setReadOnly(True)
@@ -176,6 +175,64 @@ class MainWindow(QMainWindow):
         h.addWidget(btn)
         return w
 
+    def _build_shazam_tab(self) -> QWidget:
+        w = QWidget()
+        form = QFormLayout(w)
+        form.setContentsMargins(16, 16, 16, 16)
+        form.setSpacing(10)
+
+        self.shazam_enabled_cb = QCheckBox("Включён")
+        form.addRow("", self.shazam_enabled_cb)
+
+        info = QLabel(
+            "Идентификация трека через Shazam (unofficial API).\n"
+            "При совпадении сохраняются метаданные и (если возможно) текст из LRCLIB.\n"
+            "Требует ffmpeg (для конвертации в 16 кГц моно)."
+        )
+        info.setWordWrap(True)
+        info.setStyleSheet("color: #a6adc8;")
+        form.addRow(info)
+
+        return w
+
+    def _build_lrclib_tab(self) -> QWidget:
+        w = QWidget()
+        form = QFormLayout(w)
+        form.setContentsMargins(16, 16, 16, 16)
+        form.setSpacing(10)
+
+        self.lyrics_enabled_cb = QCheckBox("Включён")
+        form.addRow("", self.lyrics_enabled_cb)
+
+        self.lyrics_user_agent = QLineEdit()
+        form.addRow("User-Agent:", self.lyrics_user_agent)
+
+        self.lyrics_use_tags_cb = QCheckBox("Искать по тегам (artist + title)")
+        form.addRow("", self.lyrics_use_tags_cb)
+
+        self.lyrics_use_shazam_cb = QCheckBox("Искать по результату Shazam")
+        form.addRow("", self.lyrics_use_shazam_cb)
+
+        self.lyrics_after_asr_cb = QCheckBox("Проверять после ASR (fuzzy-match)")
+        form.addRow("", self.lyrics_after_asr_cb)
+
+        self.lyrics_transcribe_unresolved_cb = QCheckBox(
+            "Запускать ASR, если Shazam опознал, но текста нет"
+        )
+        form.addRow("", self.lyrics_transcribe_unresolved_cb)
+
+        self.lyrics_threshold = QDoubleSpinBox()
+        self.lyrics_threshold.setRange(0.0, 1.0)
+        self.lyrics_threshold.setSingleStep(0.05)
+        self.lyrics_threshold.setDecimals(2)
+        self.lyrics_threshold.setToolTip(
+            "Насколько должен совпасть распознанный ASR-текст с текстом из LRCLIB,\n"
+            "чтобы считать это тем же треком. 1.0 = точное совпадение, 0.0 = любое."
+        )
+        form.addRow("Порог fuzzy:", self.lyrics_threshold)
+
+        return w
+
     def _build_convert_tab(self) -> QWidget:
         w = QWidget()
         form = QFormLayout(w)
@@ -241,8 +298,8 @@ class MainWindow(QMainWindow):
         self.lang_cb.setEditable(True)
         self.lang_cb.addItems(LANGUAGES)
         self.lang_cb.setToolTip(
-            "'auto' — WhisperX определяет язык на каждом файле отдельно.\n"
-            "Это заметно медленнее. Если знаешь язык — выбери его явно."
+            "'auto' — WhisperX определяет язык на каждом файле.\n"
+            "Если знаешь язык — выбери его явно, будет быстрее."
         )
         form.addRow("Язык:", self.lang_cb)
 
@@ -258,28 +315,19 @@ class MainWindow(QMainWindow):
         self.batch_spin.setRange(1, 512)
         self.batch_spin.setToolTip(
             "Размер батча сегментов ВНУТРИ одного файла (не файлов!).\n"
-            "Больше ≠ всегда быстрее: упирается в VRAM.\n"
-            "8–16 для 8 ГБ VRAM, 32–64 для 24 ГБ.\n"
-            "При 'CUDA out of memory' — уменьшайте."
+            "8–16 для 8 ГБ VRAM, 32–64 для 24 ГБ."
         )
         form.addRow("Batch size:", self.batch_spin)
 
         self.gigaam_provider_cb = QComboBox()
         self.gigaam_provider_cb.addItems(["auto", "cuda", "cpu"])
         form.addRow("GigaAM provider:", self.gigaam_provider_cb)
+
         self.local_files_only_cb = QCheckBox("Не проверять HuggingFace (offline)")
         self.local_files_only_cb.setToolTip(
-            "Если модель уже скачана — включите, чтобы не ждать сетевую проверку.\n"
-            "При первом запуске (модели нет) оставьте выключенным."
+            "Если модель уже скачана — включите, чтобы не ждать сетевую проверку."
         )
         form.addRow("", self.local_files_only_cb)
-
-        self.vad_filter_cb = QCheckBox("Использовать VAD (pyannote)")
-        self.vad_filter_cb.setChecked(True)
-        self.vad_filter_cb.setToolTip(
-            "Отключите, если загрузка модели виснет на 'Performing voice activity detection'."
-        )
-        form.addRow("", self.vad_filter_cb)
         return w
 
     # ---------------- visual sync ----------------
@@ -294,9 +342,11 @@ class MainWindow(QMainWindow):
         active = QColor("#89b4fa")
         inactive = QColor("#585b70")
         bar = self.tabs.tabBar()
-        bar.setTabTextColor(0, active if self.convert_enabled_cb.isChecked() else inactive)
-        bar.setTabTextColor(1, active if self.separate_enabled_cb.isChecked() else inactive)
-        bar.setTabTextColor(2, active if self.transcribe_enabled_cb.isChecked() else inactive)
+        bar.setTabTextColor(0, active if self.shazam_enabled_cb.isChecked() else inactive)
+        bar.setTabTextColor(1, active if self.lyrics_enabled_cb.isChecked() else inactive)
+        bar.setTabTextColor(2, active if self.convert_enabled_cb.isChecked() else inactive)
+        bar.setTabTextColor(3, active if self.separate_enabled_cb.isChecked() else inactive)
+        bar.setTabTextColor(4, active if self.transcribe_enabled_cb.isChecked() else inactive)
 
     def _sync_engine_fields(self) -> None:
         is_whisperx = self.engine_cb.currentText() == "whisperx"
@@ -320,6 +370,18 @@ class MainWindow(QMainWindow):
         if i >= 0:
             self.mode_cb.setCurrentIndex(i)
 
+        self.shazam_enabled_cb.setChecked(cfg.shazam.enabled)
+
+        self.lyrics_enabled_cb.setChecked(cfg.lyrics.enabled)
+        self.lyrics_user_agent.setText(cfg.lyrics.user_agent)
+        self.lyrics_use_tags_cb.setChecked(cfg.lyrics.use_tags_first)
+        self.lyrics_use_shazam_cb.setChecked(cfg.lyrics.use_shazam_result)
+        self.lyrics_after_asr_cb.setChecked(cfg.lyrics.try_after_asr)
+        self.lyrics_transcribe_unresolved_cb.setChecked(
+            cfg.lyrics.transcribe_if_identified_no_lyrics
+        )
+        self.lyrics_threshold.setValue(cfg.lyrics.fuzzy_threshold)
+
         self.convert_enabled_cb.setChecked(cfg.convert.enabled)
         i = self.convert_sr.findData(cfg.convert.sample_rate)
         if i >= 0:
@@ -344,7 +406,6 @@ class MainWindow(QMainWindow):
         self.batch_spin.setValue(cfg.transcribe.batch_size)
         self.gigaam_provider_cb.setCurrentText(cfg.transcribe.gigaam_provider)
         self.local_files_only_cb.setChecked(cfg.transcribe.local_files_only)
-        self.vad_filter_cb.setChecked(cfg.transcribe.vad_filter)
 
     def _collect_config(self) -> PipelineConfig:
         cfg = PipelineConfig()
@@ -355,6 +416,18 @@ class MainWindow(QMainWindow):
         cfg.overwrite = self.overwrite_cb.isChecked()
         cfg.mode = self.mode_cb.currentData()
 
+        cfg.shazam = ShazamConfig(enabled=self.shazam_enabled_cb.isChecked())
+
+        cfg.lyrics = LyricsConfig(
+            enabled=self.lyrics_enabled_cb.isChecked(),
+            user_agent=self.lyrics_user_agent.text().strip() or "audio-transcriber/1.0",
+            use_tags_first=self.lyrics_use_tags_cb.isChecked(),
+            use_shazam_result=self.lyrics_use_shazam_cb.isChecked(),
+            try_after_asr=self.lyrics_after_asr_cb.isChecked(),
+            transcribe_if_identified_no_lyrics=self.lyrics_transcribe_unresolved_cb.isChecked(),
+            fuzzy_threshold=float(self.lyrics_threshold.value()),
+        )
+
         cfg.convert = ConvertConfig(
             enabled=self.convert_enabled_cb.isChecked(),
             sample_rate=int(self.convert_sr.currentData()),
@@ -380,9 +453,8 @@ class MainWindow(QMainWindow):
             compute_type=None if compute == "auto" else compute,
             batch_size=self.batch_spin.value(),
             gigaam_provider=self.gigaam_provider_cb.currentText(),
+            local_files_only=self.local_files_only_cb.isChecked(),
         )
-        cfg.transcribe.local_files_only = self.local_files_only_cb.isChecked()
-        cfg.transcribe.vad_filter = self.vad_filter_cb.isChecked()
         return cfg
 
     # ---------------- handlers ----------------
@@ -425,10 +497,15 @@ class MainWindow(QMainWindow):
         if not cfg.input_dir or not Path(cfg.input_dir).is_dir():
             QMessageBox.warning(self, "Нет входной папки", "Выберите существующую входную папку.")
             return
-        if not (cfg.convert.enabled or cfg.separate.enabled or cfg.transcribe.enabled):
+        has_work = any([
+            cfg.shazam.enabled, cfg.lyrics.enabled,
+            cfg.convert.enabled, cfg.separate.enabled, cfg.transcribe.enabled,
+        ])
+        if not has_work:
             QMessageBox.warning(self, "Нет этапов", "Включите хотя бы один этап.")
             return
-        if cfg.convert.enabled and not shutil.which(cfg.convert.ffmpeg_path):
+        needs_ffmpeg = cfg.convert.enabled or cfg.shazam.enabled
+        if needs_ffmpeg and not shutil.which(cfg.convert.ffmpeg_path):
             QMessageBox.warning(self, "ffmpeg не найден",
                                 f"Команда '{cfg.convert.ffmpeg_path}' не найдена в PATH.")
             return
@@ -440,8 +517,7 @@ class MainWindow(QMainWindow):
         self.log_view.clear()
         self._log_device_info()
         if cfg.transcribe.enabled and cfg.transcribe.language is None:
-            self._append_log("⚠ Язык = auto: WhisperX определит язык на каждом файле. "
-                             "Если язык известен — задайте его явно, будет быстрее.")
+            self._append_log("⚠ Язык = auto: WhisperX определит язык на каждом файле.")
 
         self.progress.setValue(0)
         self.progress.setFormat("Запуск…")

requirements.txt

@@ -5,6 +5,11 @@ PySide6>=6.6
 whisperx>=3.1
 onnx-asr>=0.4
 
+# Recognition & lyrics
+shazamlite>=0.1
+mutagen>=1.47
+requests>=2.31
+
 # WhisperX pulls torch; for CUDA you must install the matching torch build, e.g.:
 #   pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
 # For cuDNN preload helper used in main.py: