fix some bugs, add LRC synchronizer, text saver (if text not in LIBLRC, for upgrade library)

core/config.py

@@ -16,6 +16,7 @@ class ConvertConfig:
     sample_rate: int = 44100
     channels: int = 2
     ffmpeg_path: str = "ffmpeg"
+    convert_concurrency: int = 4
 
 
 @dataclass
@@ -25,6 +26,7 @@ class SeparateConfig:
     isolate_stem: str = "vocals"
     unblend_path: str = "unblend"
     custom_kernels: bool = False
+    save_vocals_copy: bool = False
 
 
 @dataclass
@@ -43,6 +45,7 @@ class TranscribeConfig:
 @dataclass
 class ShazamConfig:
     enabled: bool = False
+    identify_concurrency: int = 4
 
 
 @dataclass
@@ -53,8 +56,21 @@ class LyricsConfig:
     use_tags_first: bool = True
     use_shazam_result: bool = True
     try_after_asr: bool = True
-    fuzzy_threshold: float = 0.6          # difflib ratio, 0..1
+    fuzzy_threshold: float = 0.6
     transcribe_if_identified_no_lyrics: bool = True
+    # NEW:
+    use_embedded_lyrics: bool = True     # read lyrics already stored in file tags first
+    save_unrecognized: bool = False      # write unresolved texts to JSONL for LRCLIB submission
+    unrecognized_path: str = ""          # "" = <output>/unrecognized_lyrics.jsonl
+
+
+@dataclass
+class LrcConfig:
+    language: Optional[str] = "ru"
+    align_model: Optional[str] = None
+    device: str = "auto"
+    audio_source: str = "auto"
+    embed_in_place: bool = False
 
 
 @dataclass
@@ -71,6 +87,7 @@ class PipelineConfig:
     transcribe: TranscribeConfig = field(default_factory=TranscribeConfig)
     shazam: ShazamConfig = field(default_factory=ShazamConfig)
     lyrics: LyricsConfig = field(default_factory=LyricsConfig)
+    lrc: LrcConfig = field(default_factory=LrcConfig)
 
     def to_dict(self) -> dict:
         return asdict(self)

core/lrc.py

@@ -0,0 +1,195 @@
+"""LRC generation, parsing, embedding, and reading embedded lyrics.
+
+Reading embedded lyrics checks all tag locations for a given audio format:
+  - MP3       -> ID3 USLT / SYLT
+  - FLAC/OGG  -> LYRICS, UNSYNCEDLYRICS (Vorbis comments)
+  - M4A/MP4   -> `©lyr` atom
+"""
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+
+_LRC_LINE_RE = re.compile(r"^\s*\[\d{1,3}:\d{2}(?:\.\d{1,3})?\]")
+_LRC_PARSE_RE = re.compile(r"\[(\d+):(\d+)\.(\d+)\](.*)$")
+
+
+@dataclass
+class LrcLine:
+    start: float
+    text: str
+
+
+def format_timestamp(seconds: float) -> str:
+    if seconds < 0:
+        seconds = 0.0
+    minutes = int(seconds // 60)
+    secs = seconds - minutes * 60
+    return f"[{minutes:02d}:{secs:05.2f}]"
+
+
+def segments_to_lrc(segments: list[dict]) -> str:
+    lines: list[str] = []
+    for seg in segments:
+        text = (seg.get("text") or "").strip()
+        if not text:
+            continue
+        start = float(seg.get("start", 0.0))
+        lines.append(f"{format_timestamp(start)}{text}")
+    return "\n".join(lines) + "\n"
+
+
+def parse_lrc(content: str) -> list[LrcLine]:
+    out: list[LrcLine] = []
+    for line in content.splitlines():
+        m = _LRC_PARSE_RE.match(line.strip())
+        if not m:
+            continue
+        mm, ss, frac, text = m.groups()
+        frac_sec = float(f"0.{frac}") if frac else 0.0
+        out.append(LrcLine(start=int(mm) * 60 + int(ss) + frac_sec, text=text.strip()))
+    return out
+
+
+def is_lrc(content: str) -> bool:
+    """True if content contains at least one LRC-style timestamp line."""
+    if not content:
+        return False
+    for line in content.splitlines():
+        if _LRC_LINE_RE.match(line):
+            return True
+    return False
+
+
+# ---------------------------------------------------------------- read embedded
+
+def read_embedded_lyrics(audio_path: Path) -> Optional[str]:
+    """Return lyrics stored in the file's tags, or None.
+
+    Order: prefer synced (LRC-like) -> plain. Never raises.
+    """
+    suffix = audio_path.suffix.lower()
+    try:
+        if suffix in (".mp3", ".mp2"):
+            return _read_mp3(audio_path)
+        if suffix in (".flac", ".ogg", ".opus"):
+            return _read_vorbis(audio_path)
+        if suffix in (".m4a", ".mp4", ".m4b", ".aac"):
+            return _read_mp4(audio_path)
+    except Exception:  # noqa: BLE001 — never break the pipeline on tag issues
+        return None
+    return None
+
+
+def _read_mp3(path: Path) -> Optional[str]:
+    from mutagen.id3 import ID3, ID3NoHeaderError
+    try:
+        tags = ID3(str(path))
+    except ID3NoHeaderError:
+        return None
+
+    # SYLT = synced lyrics (rare); USLT = unsynced (common, may contain LRC text)
+    sylt = tags.getall("SYLT")
+    for tag in sylt:
+        if tag.text:
+            try:
+                content = "\n".join(
+                    f"{format_timestamp(ms / 1000.0)}{txt}"
+                    for txt, ms in tag.text
+                )
+                if content.strip():
+                    return content
+            except Exception:  # noqa: BLE001
+                continue
+
+    uslt = tags.getall("USLT")
+    for tag in uslt:
+        if tag.text and str(tag.text).strip():
+            return str(tag.text)
+
+    return None
+
+
+def _read_vorbis(path: Path) -> Optional[str]:
+    suffix = path.suffix.lower()
+    if suffix == ".flac":
+        from mutagen.flac import FLAC as Audio
+    elif suffix == ".ogg":
+        from mutagen.oggvorbis import OggVorbis as Audio
+    else:
+        from mutagen.oggopus import OggOpus as Audio
+    audio = Audio(str(path))
+    for key in ("LYRICS", "UNSYNCEDLYRICS", "LYRIC", "SYNCEDLYRICS"):
+        if key in audio:
+            values = audio[key]
+            if isinstance(values, list) and values:
+                text = str(values[0])
+            else:
+                text = str(values)
+            if text.strip():
+                return text
+    return None
+
+
+def _read_mp4(path: Path) -> Optional[str]:
+    from mutagen.mp4 import MP4
+    audio = MP4(str(path))
+    for key in ("\xa9lyr", "©lyr"):
+        if key in audio:
+            values = audio[key]
+            if isinstance(values, list) and values:
+                text = str(values[0])
+            else:
+                text = str(values)
+            if text.strip():
+                return text
+    return None
+
+
+# ---------------------------------------------------------------- embedding
+
+def embed_lrc(audio_path: Path, lrc_content: str) -> None:
+    suffix = audio_path.suffix.lower()
+    if suffix in (".mp3", ".mp2"):
+        _embed_mp3(audio_path, lrc_content)
+    elif suffix in (".flac", ".ogg", ".opus"):
+        _embed_vorbis(audio_path, lrc_content)
+    elif suffix in (".m4a", ".mp4", ".m4b", ".aac"):
+        _embed_mp4(audio_path, lrc_content)
+    else:
+        raise ValueError(f"Unsupported format for LRC embedding: {suffix}")
+
+
+def _embed_mp3(path: Path, lrc_content: str) -> None:
+    from mutagen.id3 import ID3, USLT, ID3NoHeaderError
+    try:
+        tags = ID3(str(path))
+    except ID3NoHeaderError:
+        tags = ID3()
+    tags.delall("USLT")
+    tags.add(USLT(encoding=3, lang="rus", desc="", text=lrc_content))
+    tags.save(str(path), v2_version=3)
+
+
+def _embed_vorbis(path: Path, lrc_content: str) -> None:
+    suffix = path.suffix.lower()
+    if suffix == ".flac":
+        from mutagen.flac import FLAC as Audio
+    elif suffix == ".ogg":
+        from mutagen.oggvorbis import OggVorbis as Audio
+    else:
+        from mutagen.oggopus import OggOpus as Audio
+    audio = Audio(str(path))
+    audio["LYRICS"] = lrc_content
+    audio["UNSYNCEDLYRICS"] = lrc_content
+    audio.save()
+
+
+def _embed_mp4(path: Path, lrc_content: str) -> None:
+    from mutagen.mp4 import MP4
+    audio = MP4(str(path))
+    audio["\xa9lyr"] = [lrc_content]
+    audio.save()

core/lrc_pipeline.py

@@ -0,0 +1,195 @@
+"""LRC-generation and LRC-embedding pipelines."""
+from __future__ import annotations
+
+import shutil
+import tempfile
+import threading
+from pathlib import Path
+from typing import Callable, Optional
+
+from .config import PipelineConfig
+from .convert import convert_to_wav
+from .lrc import embed_lrc, is_lrc, read_embedded_lyrics, segments_to_lrc
+from .metadata import read_metadata
+from .pipeline import FileResult, _save_unrecognized, collect_audio_files
+from .transcribe import transcribe_aligned
+
+
+LogFn = Callable[[str], None]
+ProgressFn = Callable[[int, int, str], None]
+
+
+def _pick_audio_source(
+    f: Path, output_dir: Path, config: PipelineConfig, work_dir: Path, log: LogFn,
+) -> Path:
+    mode = (config.lrc.audio_source or "auto").lower()
+    vocals = output_dir / f.stem / "vocals.wav"
+    convert = output_dir / f.stem / "convert.wav"
+    if mode == "vocals":
+        return vocals
+    if mode == "convert":
+        return convert
+    if mode == "input":
+        return f
+    if vocals.exists():
+        return vocals
+    if convert.exists():
+        return convert
+    target = work_dir / "input_16k.wav"
+    log(f"  [convert] {f.name} -> 16k mono WAV (для align)")
+    convert_to_wav(
+        f, target, sample_rate=16000, channels=1,
+        ffmpeg_path=config.convert.ffmpeg_path,
+    )
+    return target
+
+
+def run_lrc_pipeline(
+    config: PipelineConfig,
+    log: LogFn = print,
+    on_progress: Optional[ProgressFn] = None,
+    stop_event: Optional[threading.Event] = None,
+) -> list[FileResult]:
+    input_dir = Path(config.input_dir)
+    if not input_dir.is_dir():
+        raise FileNotFoundError(f"Input directory not found: {input_dir}")
+    output_dir = Path(config.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    files = collect_audio_files(input_dir, config.recursive)
+    if not files:
+        log("Аудиофайлы не найдены.")
+        return []
+
+    lang = config.lrc.language or config.transcribe.language
+    log(f"LRC: {len(files)} файлов   |   язык={lang or 'auto'}   |   "
+        f"источник={config.lrc.audio_source}")
+
+    results: list[FileResult] = []
+    total = len(files)
+
+    for idx, f in enumerate(files, 1):
+        if stop_event is not None and stop_event.is_set():
+            log("⏹ Отменено")
+            break
+        if on_progress:
+            on_progress(idx - 1, total, "lrc")
+        log(f"\n▶ [{idx}/{total}] {f.name}")
+
+        track_out = output_dir / f.stem
+        track_out.mkdir(parents=True, exist_ok=True)
+        lrc_path = track_out / "text.lrc"
+        error: Optional[str] = None
+
+        tmp = Path(tempfile.mkdtemp(prefix=f"at_lrc_{f.stem}_"))
+        try:
+            embedded = read_embedded_lyrics(f) if config.lyrics.use_embedded_lyrics else None
+            if embedded and is_lrc(embedded):
+                lrc_path.write_text(embedded, encoding="utf-8")
+                log("  ✓ text.lrc (взят из тегов аудио)")
+                results.append(FileResult(f, None, track_out, None, "embedded+lrc"))
+                continue
+
+            audio_path = _pick_audio_source(f, output_dir, config, tmp, log)
+            if not audio_path.exists():
+                raise FileNotFoundError(
+                    f"Источник аудио не найден: {audio_path}. "
+                    "Включите conversion/separation в основном пайплайне "
+                    "или выберите audio_source=input."
+                )
+            log(f"  [align] {audio_path.name}")
+
+            segments, detected = transcribe_aligned(
+                audio_path,
+                model=config.transcribe.model,
+                language=lang,
+                device=config.lrc.device,
+                compute_type=config.transcribe.compute_type,
+                batch_size=config.transcribe.batch_size,
+                align_model=config.lrc.align_model,
+                local_files_only=config.transcribe.local_files_only,
+            )
+
+            lrc_content = segments_to_lrc(segments)
+            lrc_path.write_text(lrc_content, encoding="utf-8")
+            log(f"  ✓ text.lrc ({len(segments)} строк, язык: {detected})")
+
+            # NEW: save to JSONL for LRCLIB submission
+            _save_unrecognized(
+                config, f, lrc_content, source="lrc",
+                metadata=read_metadata(f),
+            )
+            results.append(FileResult(f, None, track_out, None, "lrc"))
+        except Exception as e:  # noqa: BLE001
+            error = f"{type(e).__name__}: {e}"
+            log(f"  ✗ Ошибка: {error}")
+            results.append(FileResult(f, None, None, error, "lrc"))
+        finally:
+            shutil.rmtree(tmp, ignore_errors=True)
+            if on_progress:
+                on_progress(idx, total, "lrc")
+
+    return results
+
+
+def run_embed_pipeline(
+    config: PipelineConfig,
+    log: LogFn = print,
+    on_progress: Optional[ProgressFn] = None,
+    stop_event: Optional[threading.Event] = None,
+) -> list[FileResult]:
+    input_dir = Path(config.input_dir)
+    if not input_dir.is_dir():
+        raise FileNotFoundError(f"Input directory not found: {input_dir}")
+    output_dir = Path(config.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    files = collect_audio_files(input_dir, config.recursive)
+    if not files:
+        log("Аудиофайлы не найдены.")
+        return []
+
+    log(f"Embed LRC: {len(files)} файлов   |   "
+        f"режим={'in-place' if config.lrc.embed_in_place else 'копия в output'}")
+
+    results: list[FileResult] = []
+    total = len(files)
+
+    for idx, f in enumerate(files, 1):
+        if stop_event is not None and stop_event.is_set():
+            log("⏹ Отменено")
+            break
+        if on_progress:
+            on_progress(idx - 1, total, "embed")
+
+        lrc_path = output_dir / f.stem / "text.lrc"
+        error: Optional[str] = None
+
+        try:
+            if not lrc_path.exists():
+                log(f"  ⊘ {f.name}: text.lrc отсутствует, пропуск")
+                results.append(FileResult(f, None, None, "no LRC", "embed"))
+                continue
+
+            lrc_content = lrc_path.read_text(encoding="utf-8")
+
+            if config.lrc.embed_in_place:
+                target = f
+            else:
+                target = output_dir / f.stem / f.name
+                target.parent.mkdir(parents=True, exist_ok=True)
+                if not target.exists() or config.overwrite:
+                    shutil.copy2(f, target)
+
+            embed_lrc(target, lrc_content)
+            log(f"  ✓ {f.name} → {target if config.lrc.embed_in_place else target.name}")
+            results.append(FileResult(f, None, target.parent, None, "embed"))
+        except Exception as e:  # noqa: BLE001
+            error = f"{type(e).__name__}: {e}"
+            log(f"  ✗ {f.name}: {error}")
+            results.append(FileResult(f, None, None, error, "embed"))
+        finally:
+            if on_progress:
+                on_progress(idx, total, "embed")
+
+    return results

core/pipeline.py

@@ -1,17 +1,4 @@
-"""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: 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.
-"""
+"""Orchestrate identify -> convert -> separate -> transcribe -> lyrics-match."""
 from __future__ import annotations
 
 import difflib
@@ -19,6 +6,8 @@ import json
 import shutil
 import tempfile
 import threading
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import re
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Callable, Optional
@@ -30,6 +19,7 @@ from .metadata import TrackMetadata, read_metadata
 from .recognize import ShazamResult, recognize
 from .separate import separate_vocals, separate_vocals_batch
 from .transcribe import transcribe
+from .lrc import is_lrc, read_embedded_lyrics
 
 
 AUDIO_EXTENSIONS = {
@@ -62,7 +52,6 @@ def collect_audio_files(input_dir: Path, recursive: bool) -> list[Path]:
 
 
 def _common_parent(paths: list[Path]) -> Path:
-    """Return the deepest common parent directory of the given paths."""
     if not paths:
         raise ValueError("empty paths")
     parts = [p.resolve().parts for p in paths]
@@ -115,10 +104,44 @@ 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",
-    )
+    path.write_text(json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8")
+
+
+def _save_unrecognized(
+    config: PipelineConfig,
+    f: Path,
+    text: Optional[str],
+    source: Optional[str],
+    metadata: Optional[TrackMetadata] = None,
+    lrc_path: Optional[Path] = None,
+) -> None:
+    """Append a record to the JSONL file for texts NOT confirmed by LRCLIB."""
+    if not config.lyrics.save_unrecognized or not text:
+        return
+    if source and "+lrclib" in source:
+        return
+
+    if config.lyrics.unrecognized_path:
+        out_path = Path(config.lyrics.unrecognized_path)
+    else:
+        out_path = Path(config.output_dir) / "unrecognized_lyrics.jsonl"
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    record = {
+        "file": str(f),
+        "trackName": (metadata.title if metadata else None) or f.stem,
+        "artistName": metadata.artist if metadata else None,
+        "albumName": metadata.album if metadata else None,
+        "duration": metadata.duration if metadata else None,
+        "plainLyrics": text if not is_lrc(text) else None,
+        "syncedLyrics": text if is_lrc(text) else None,
+        "source": source,
+    }
+    try:
+        with out_path.open("a", encoding="utf-8") as fh:
+            fh.write(json.dumps(record, ensure_ascii=False) + "\n")
+    except OSError:
+        pass
 
 
 @dataclass
@@ -136,24 +159,30 @@ def _identify_file(
     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
-    ):
+    # --- 0) embedded lyrics в самом файле ---
+    if config.lyrics.enabled and config.lyrics.use_embedded_lyrics:
+        embedded = read_embedded_lyrics(input_path)
+        if embedded and embedded.strip():
+            if is_lrc(embedded):
+                result.text = embedded
+                result.source = "embedded+lrc"
+                log("    ✓ встроенный LRC найден")
+            else:
+                result.text = embedded
+                result.source = "embedded"
+                log("    ✓ встроенный текст найден")
+            result.needs_asr = False
+            return result
+
+    # --- 1) LRCLIB по тегам ---
+    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,
+            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
@@ -162,14 +191,13 @@ def _identify_file(
             log(f"    ✓ {match.artist} — {match.title}")
             return result
 
-    # --- Shazam ---
+    # --- 2) 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,
+                input_path, shazam_wav, sample_rate=16000, channels=1,
                 ffmpeg_path=config.convert.ffmpeg_path,
             )
             result.shazam = recognize(shazam_wav)
@@ -179,15 +207,11 @@ def _identify_file(
 
         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,
+                    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
@@ -195,8 +219,6 @@ def _identify_file(
                     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
@@ -207,33 +229,39 @@ def _identify_file(
     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:
+def _try_post_asr_match(text: str, config: PipelineConfig, log: LogFn) -> Optional[str]:
+    if not text:
         return None
-
-    head = _text_head(text, 8)
-    log(f"    LRCLIB по тексту: {head!r}...")
-
+    ignore_phrases = ["субтитры", "редактор", "перевод", "сделал", "озвучка"]
+    lines = []
+    for line in text.splitlines():
+        line = line.strip()
+        if not line or any(p in line.lower() for p in ignore_phrases):
+            continue
+        lines.append(line)
+    if not lines:
+        return None
+    query = ""
+    for line in lines:
+        words = line.split()
+        if len(words) >= 3:
+            clean_words = [re.sub(r'[^\w\s]', '', w) for w in words]
+            query = " ".join(clean_words[:6])
+            break
+    if not query:
+        return None
+    log(f"    LRCLIB по тексту: {query!r}...")
     try:
         candidates = search_by_text(
-            head, limit=5,
-            user_agent=config.lyrics.user_agent,
+            query, 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:
@@ -241,11 +269,9 @@ def _try_post_asr_match(
         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
 
@@ -258,7 +284,9 @@ def _persist_artifacts(
     has_metadata: bool,
     log: LogFn,
 ) -> None:
-    """Move wanted artifacts from work_dir to persist_dir."""
+    if work_dir.resolve() == persist_dir.resolve():
+        return  # nothing to move — same directory
+
     persist_dir.mkdir(parents=True, exist_ok=True)
 
     names: list[str] = []
@@ -266,6 +294,8 @@ def _persist_artifacts(
         names.append("metadata.json")
     if has_text and (work_dir / "text.txt").exists():
         names.append("text.txt")
+    if has_text and (work_dir / "text.lrc").exists():
+        names.append("text.lrc")
 
     if config.keep_intermediate:
         for n in ("convert.wav", "vocals.wav"):
@@ -277,6 +307,10 @@ def _persist_artifacts(
         elif config.convert.enabled and (work_dir / "convert.wav").exists():
             names.append("convert.wav")
 
+    if config.separate.save_vocals_copy and (work_dir / "vocals.wav").exists():
+        if "vocals.wav" not in names:
+            names.append("vocals.wav")
+
     for name in names:
         src = work_dir / name
         dst = persist_dir / name
@@ -288,51 +322,158 @@ def _persist_artifacts(
 
 # ---------------------------------------------------------------- entry
 
+def _run_identification_parallel(
+    config: PipelineConfig, files: list[Path], log: LogFn,
+    on_progress: Optional[ProgressFn], stop_event: Optional[threading.Event],
+    work_root: Path,
+) -> dict[Path, Optional[IdentifyResult]]:
+    if not (config.shazam.enabled or config.lyrics.enabled):
+        return {}
+
+    concurrency = max(1, int(config.shazam.identify_concurrency))
+    total = len(files)
+
+    if concurrency == 1 or total == 1:
+        results: dict[Path, Optional[IdentifyResult]] = {}
+        for i, f in enumerate(files, 1):
+            if stop_event is not None and stop_event.is_set():
+                break
+            if on_progress:
+                on_progress(i, total, "identify")
+            try:
+                results[f] = _identify_file(f, work_root / f.stem, config, log)
+            except Exception as e:  # noqa: BLE001
+                log(f"  ✗ {f.name} (identify): {e}")
+                results[f] = None
+        return results
+
+    log(f"  Идентификация: {total} файлов, конкурентность {concurrency}")
+
+    def worker(f: Path) -> tuple[Path, Optional[IdentifyResult]]:
+        if stop_event is not None and stop_event.is_set():
+            return f, None
+        def prefixed(msg: str) -> None:
+            log(f"  [{f.name}] {msg.strip()}")
+        try:
+            return f, _identify_file(f, work_root / f.stem, config, prefixed)
+        except Exception as e:  # noqa: BLE001
+            log(f"  ✗ {f.name} (identify): {e}")
+            return f, None
+
+    results: dict[Path, Optional[IdentifyResult]] = {}
+    with ThreadPoolExecutor(max_workers=concurrency) as pool:
+        futures = {pool.submit(worker, f): f for f in files}
+        done = 0
+        for fut in as_completed(futures):
+            f = futures[fut]
+            try:
+                _, ident = fut.result()
+                results[f] = ident
+            except Exception as e:  # noqa: BLE001
+                log(f"  ✗ {f.name} (identify): {e}")
+                results[f] = None
+            done += 1
+            if on_progress:
+                on_progress(done, total, "identify")
+    return results
+
+
+def _run_convert_parallel(
+    config: PipelineConfig, files: list[Path], state: dict, log: LogFn,
+    on_progress: Optional[ProgressFn], stop_event: Optional[threading.Event],
+) -> None:
+    concurrency = max(1, int(config.convert.convert_concurrency))
+    total = len(files)
+
+    def worker(f: Path) -> tuple[Path, Optional[Path], Optional[str]]:
+        if stop_event is not None and stop_event.is_set():
+            return f, None, None
+        try:
+            target = Path(tempfile.mkdtemp(prefix=f"at_{f.stem}_")) / "convert.wav"
+            if config.overwrite or not target.exists():
+                convert_to_wav(
+                    f, target, sample_rate=config.convert.sample_rate,
+                    channels=config.convert.channels,
+                    ffmpeg_path=config.convert.ffmpeg_path,
+                )
+            return f, target, None
+        except Exception as e:  # noqa: BLE001
+            return f, None, f"{type(e).__name__}: {e}"
+
+    if concurrency == 1 or total == 1:
+        for i, f in enumerate(files, 1):
+            if stop_event is not None and stop_event.is_set():
+                break
+            if on_progress:
+                on_progress(i, total, "convert")
+            f2, target, err = worker(f)
+            if err:
+                state[f2]["error"] = err
+                log(f"  ✗ {f2.name}: {err}")
+            else:
+                state[f2]["audio_path"] = target
+                log(f"  ✓ {f2.name}")
+        return
+
+    log(f"  Конвертация: {total} файлов, конкурентность {concurrency}")
+    with ThreadPoolExecutor(max_workers=concurrency) as pool:
+        futures = {pool.submit(worker, f): f for f in files}
+        done = 0
+        for fut in as_completed(futures):
+            f2, target, err = fut.result()
+            if err:
+                state[f2]["error"] = err
+                log(f"  ✗ {f2.name}: {err}")
+            else:
+                state[f2]["audio_path"] = target
+                log(f"  ✓ {f2.name}")
+            done += 1
+            if on_progress:
+                on_progress(done, total, "convert")
+
+
 def run_pipeline(
-    config: PipelineConfig,
-    log: LogFn = print,
+    config: PipelineConfig, log: LogFn = print,
     on_progress: Optional[ProgressFn] = None,
     stop_event: Optional[threading.Event] = None,
 ) -> list[FileResult]:
     input_dir = Path(config.input_dir)
     if not input_dir.is_dir():
         raise FileNotFoundError(f"Input directory not found: {input_dir}")
-
     output_dir = Path(config.output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
-
     files = collect_audio_files(input_dir, config.recursive)
     if not files:
         log("Аудиофайлы не найдены.")
         return []
-
     mode = (config.mode or "sequential").lower()
-    log(
-        f"Найдено треков: {len(files)}   |   режим: {mode}   |   "
-        f"keep_intermediate={config.keep_intermediate}"
-    )
+    log(f"Найдено треков: {len(files)}   |   режим: {mode}   |   "
+        f"keep_intermediate={config.keep_intermediate}")
+    if config.separate.save_vocals_copy:
+        log("  ✓ Вокал будет сохраняться всегда (save_vocals_copy=True)")
     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)
     return _run_sequential(config, files, log, on_progress, stop_event)
 
 
-# ---------------------------------------------------------------- sequential
-
 def _run_sequential(
-    config: PipelineConfig,
-    files: list[Path],
-    log: LogFn,
-    on_progress: Optional[ProgressFn],
-    stop_event: Optional[threading.Event],
+    config: PipelineConfig, files: list[Path], log: LogFn,
+    on_progress: Optional[ProgressFn], stop_event: Optional[threading.Event],
 ) -> list[FileResult]:
     total = len(files)
     results: list[FileResult] = []
     output_root = Path(config.output_dir)
+    ident_tmp = Path(tempfile.mkdtemp(prefix="at_ident_"))
+    try:
+        ident_map = _run_identification_parallel(
+            config, files, log, on_progress, stop_event, ident_tmp,
+        )
+    finally:
+        shutil.rmtree(ident_tmp, ignore_errors=True)
 
     for idx, f in enumerate(files, 1):
         if stop_event is not None and stop_event.is_set():
@@ -352,8 +493,7 @@ def _run_sequential(
         metadata_dict: Optional[dict] = None
 
         try:
-            # --- Phase 1: identification ---
-            ident = _identify_file(f, tmp_root, config, log)
+            ident = ident_map.get(f) or IdentifyResult(metadata=TrackMetadata())
 
             if not ident.needs_asr:
                 text = ident.text
@@ -361,20 +501,22 @@ def _run_sequential(
                 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,
                 )
+                _save_unrecognized(
+                    config, f, text, source,
+                    metadata=ident.metadata,
+                )
+                log(f"  ✓ {f.name} [{source}]")
                 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)
@@ -382,8 +524,7 @@ def _run_sequential(
                 target = tmp_root / "convert.wav"
                 if config.overwrite or not target.exists():
                     convert_to_wav(
-                        f, target,
-                        sample_rate=config.convert.sample_rate,
+                        f, target, sample_rate=config.convert.sample_rate,
                         channels=config.convert.channels,
                         ffmpeg_path=config.convert.ffmpeg_path,
                     )
@@ -410,8 +551,7 @@ def _run_sequential(
                 _check_cancel(stop_event)
                 log("  [3] Транскрибация...")
                 text = transcribe(
-                    audio_path,
-                    engine=config.transcribe.engine,
+                    audio_path, engine=config.transcribe.engine,
                     model=config.transcribe.model,
                     language=config.transcribe.language,
                     device=config.transcribe.device,
@@ -422,13 +562,11 @@ def _run_sequential(
                 )
                 source = config.transcribe.engine
                 log(f"  ✓ распознано {len(text)} символов")
-
                 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)
@@ -441,6 +579,10 @@ def _run_sequential(
                 has_metadata=bool(metadata_dict),
                 log=log,
             )
+            _save_unrecognized(
+                config, f, text, source,
+                metadata=ident.metadata,
+            )
 
         except Cancelled:
             log("⏹ Отменено")
@@ -452,36 +594,24 @@ def _run_sequential(
             shutil.rmtree(tmp_root, ignore_errors=True)
             if on_progress:
                 on_progress(idx, total, "file")
-            results.append(
-                FileResult(f, text, track_out if not error else None, error, source, metadata_dict)
-            )
+            results.append(FileResult(
+                f, text, track_out if not error else None, error, source, metadata_dict,
+            ))
 
     _log_summary(results, log)
     return results
 
 
-# ---------------------------------------------------------------- staged
-
 def _run_staged(
-    config: PipelineConfig,
-    files: list[Path],
-    log: LogFn,
-    on_progress: Optional[ProgressFn],
-    stop_event: Optional[threading.Event],
+    config: PipelineConfig, files: list[Path], log: LogFn,
+    on_progress: Optional[ProgressFn], stop_event: Optional[threading.Event],
 ) -> list[FileResult]:
     output_root = Path(config.output_dir)
     total = len(files)
 
     state: dict[Path, dict] = {
-        f: {
-            "audio_path": f,
-            "ident": None,
-            "text": None,
-            "source": None,
-            "metadata_dict": None,
-            "error": None,
-            "resolved": False,
-        }
+        f: {"audio_path": f, "ident": None, "text": None, "source": None,
+            "metadata_dict": None, "error": None, "resolved": False}
         for f in files
     }
 
@@ -491,21 +621,23 @@ def _run_staged(
     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:
-            for i, f in enumerate(files, 1):
-                if cancelled():
-                    log("⏹ Отменено")
-                    break
-                if on_progress:
-                    on_progress(i, total, "identify")
-                try:
-                    ident = _identify_file(f, ident_tmp / f.stem, config, log)
-                    state[f]["ident"] = ident
+            ident_map = _run_identification_parallel(
+                config, files, log, on_progress, stop_event, ident_tmp,
+            )
+        finally:
+            shutil.rmtree(ident_tmp, ignore_errors=True)
 
+        for f in files:
+            ident = ident_map.get(f)
+            if ident is None:
+                state[f]["ident"] = IdentifyResult(metadata=TrackMetadata())
+                log(f"  → {f.name} (identification failed)")
+                continue
+            state[f]["ident"] = ident
             if not ident.needs_asr:
                 state[f]["resolved"] = True
                 state[f]["text"] = ident.text
@@ -513,55 +645,31 @@ def _run_staged(
                 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"])
+                _save_unrecognized(
+                    config, f, ident.text, ident.source,
+                    metadata=ident.metadata,
+                )
                 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,
-                        sample_rate=config.convert.sample_rate,
-                        channels=config.convert.channels,
-                        ffmpeg_path=config.convert.ffmpeg_path,
-                    )
-                state[f]["audio_path"] = target
-                log(f"  ✓ {f.name}")
-            except Exception as e:  # noqa: BLE001
-                state[f]["error"] = f"{type(e).__name__}: {e}"
-                log(f"  ✗ {f.name}: {e}")
+        _run_convert_parallel(config, todo, state, log, on_progress, stop_event)
 
-    # ---- Stage 2: separate (batched) ----
     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) ══")
         if not todo:
             pass
         elif len(todo) == 1:
-            # Single file — no batching advantage
             f = todo[0]
             if on_progress:
                 on_progress(1, 1, "separate")
@@ -570,8 +678,7 @@ def _run_staged(
                 target = src.parent / "vocals.wav"
                 with tempfile.TemporaryDirectory() as tmp:
                     raw = separate_vocals(
-                        src, Path(tmp),
-                        model=config.separate.model,
+                        src, Path(tmp), model=config.separate.model,
                         isolate_stem=config.separate.isolate_stem,
                         unblend_path=config.separate.unblend_path,
                         custom_kernels=config.separate.custom_kernels,
@@ -583,20 +690,19 @@ def _run_staged(
                 state[f]["error"] = f"{type(e).__name__}: {e}"
                 log(f"  ✗ {f.name}: {e}")
         else:
-            # Batch: one unblend invocation, model loaded once
             input_wavs = [state[f]["audio_path"] for f in todo]
             common_root = _common_parent(input_wavs)
-            log(f"  Батч: {len(todo)} файлов, один вызов unblend (модель загрузится один раз)")
+            log(f"  Батч: {len(todo)} файлов, один вызов unblend")
             if on_progress:
                 on_progress(0, len(todo), "separate")
             try:
                 mapping = separate_vocals_batch(
-                    input_wavs,
-                    common_root,
+                    input_wavs, common_root,
                     model=config.separate.model,
                     isolate_stem=config.separate.isolate_stem,
                     unblend_path=config.separate.unblend_path,
                     custom_kernels=config.separate.custom_kernels,
+                    progress_callback=lambda p: on_progress(p, len(todo), "separate") if on_progress else None,
                 )
                 for f in todo:
                     src = state[f]["audio_path"]
@@ -622,8 +728,7 @@ def _run_staged(
                         target = src.parent / "vocals.wav"
                         with tempfile.TemporaryDirectory() as tmp:
                             raw = separate_vocals(
-                                src, Path(tmp),
-                                model=config.separate.model,
+                                src, Path(tmp), model=config.separate.model,
                                 isolate_stem=config.separate.isolate_stem,
                                 unblend_path=config.separate.unblend_path,
                                 custom_kernels=config.separate.custom_kernels,
@@ -635,7 +740,6 @@ def _run_staged(
                         state[f]["error"] = f"{type(inner).__name__}: {inner}"
                         log(f"  ✗ {f.name}: {inner}")
 
-    # --- Stage 3: transcribe ---
     if config.transcribe.enabled and not cancelled():
         todo = active()
         log(f"\n══ Этап 3 · Транскрибация ({len(todo)} файлов) ══")
@@ -643,12 +747,9 @@ def _run_staged(
             if cancelled():
                 log("⏹ Отменено")
                 break
-            if on_progress:
-                on_progress(i, len(todo), "transcribe")
             try:
                 text = transcribe(
-                    state[f]["audio_path"],
-                    engine=config.transcribe.engine,
+                    state[f]["audio_path"], engine=config.transcribe.engine,
                     model=config.transcribe.model,
                     language=config.transcribe.language,
                     device=config.transcribe.device,
@@ -663,8 +764,10 @@ def _run_staged(
             except Exception as e:  # noqa: BLE001
                 state[f]["error"] = f"{type(e).__name__}: {e}"
                 log(f"  ✗ {f.name}: {e}")
+            finally:
+                if on_progress:
+                    on_progress(i, len(todo), "transcribe")
 
-    # --- Stage 4: post-ASR lyrics match + persist ---
     if not cancelled():
         log("\n══ Финализация ══")
         for f in files:
@@ -672,11 +775,9 @@ def _run_staged(
             if s["error"] is not None:
                 continue
             if s["resolved"]:
-                continue  # already persisted in identification pass
-
+                continue
             text = s["text"]
             source = s["source"]
-
             if text and config.lyrics.enabled and config.lyrics.try_after_asr:
                 matched = _try_post_asr_match(text, config, log)
                 if matched:
@@ -684,7 +785,6 @@ def _run_staged(
                     source = f"{source}+lrclib"
                     s["text"] = text
                     s["source"] = source
-
             ident = s["ident"]
             metadata_dict = _build_metadata_dict(
                 ident.metadata, ident.shazam, source,
@@ -695,21 +795,31 @@ def _run_staged(
             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 isinstance(audio_work, Path) and audio_work != f:
+                work_dir = audio_work.parent  # intermediates exist
                 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,
                 )
+            else:
+                # no intermediates (convert/separate disabled): write directly
+                if text:
+                    (track_out / "text.txt").write_text(text, encoding="utf-8")
+                if metadata_dict:
+                    _write_metadata_json(track_out / "metadata.json", metadata_dict)
+
+            _save_unrecognized(
+                config, f, text, source,
+                metadata=(ident.metadata if ident else None),
+            )
             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}"):
@@ -717,12 +827,9 @@ def _run_staged(
 
     results = [
         FileResult(
-            f,
-            state[f]["text"],
+            f, state[f]["text"],
             output_root / f.stem if state[f]["error"] is None else None,
-            state[f]["error"],
-            state[f]["source"],
-            state[f]["metadata_dict"],
+            state[f]["error"], state[f]["source"], state[f]["metadata_dict"],
         )
         for f in files
     ]
@@ -730,8 +837,6 @@ def _run_staged(
     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)

core/separate.py

@@ -3,7 +3,22 @@ from __future__ import annotations
 
 import os
 import subprocess
+import time
 from pathlib import Path
+from typing import Callable, Optional
+
+
+def _build_env(custom_kernels: bool) -> dict:
+    env = os.environ.copy()
+    env["UNBLEND_CUSTOM_KERNELS"] = "1" if custom_kernels else "0"
+    return env
+
+
+def _check_result(result: subprocess.CompletedProcess, what: str) -> None:
+    if result.returncode != 0:
+        raise RuntimeError(
+            f"unblend failed ({what}):\n{result.stderr.strip()[:1200]}"
+        )
 
 
 def separate_vocals(
@@ -13,8 +28,8 @@ def separate_vocals(
     isolate_stem: str = "vocals",
     unblend_path: str = "unblend",
     custom_kernels: bool = False,
+    device: str = "cuda",
 ) -> Path:
-    """Separate the isolated stem from input_wav. Returns path to the stem WAV."""
     work_dir.mkdir(parents=True, exist_ok=True)
     output_pattern = work_dir / "{track}" / "{stem}.wav"
     cmd = [
@@ -22,15 +37,10 @@ def separate_vocals(
         "-m", model,
         "-o", str(output_pattern),
         "--isolate-stem", isolate_stem,
+        "--device", device,
     ]
-    env = os.environ.copy()
-    env["UNBLEND_CUSTOM_KERNELS"] = "1" if custom_kernels else "0"
-
-    result = subprocess.run(cmd, capture_output=True, text=True, env=env)
-    if result.returncode != 0:
-        raise RuntimeError(
-            f"unblend failed for {input_wav.name}:\n{result.stderr.strip()[:800]}"
-        )
+    result = subprocess.run(cmd, capture_output=True, text=True, env=_build_env(custom_kernels))
+    _check_result(result, input_wav.name)
 
     candidates = sorted(work_dir.rglob(f"{isolate_stem}.wav"))
     if not candidates:
@@ -38,3 +48,81 @@ def separate_vocals(
             f"unblend finished but stem '{isolate_stem}.wav' was not found in {work_dir}"
         )
     return candidates[0]
+
+
+def separate_vocals_batch(
+    input_wavs: list[Path],
+    work_root: Path,
+    model: str = "bs_roformer_anvuew",
+    isolate_stem: str = "vocals",
+    unblend_path: str = "unblend",
+    custom_kernels: bool = False,
+    device: str = "cuda",
+    progress_callback: Optional[Callable[[int], None]] = None,
+) -> dict[Path, Path]:
+    """Separate many files in one unblend invocation.
+
+    unblend doesn't emit per-file progress, so progress_callback is called
+    with an incrementing counter roughly every 2 seconds, up to N-1.
+    The final N is always reported after the process exits.
+    """
+    if not input_wavs:
+        return {}
+
+    work_root.mkdir(parents=True, exist_ok=True)
+    output_pattern = work_root / "{parent}" / "{stem}.wav"
+
+    cmd = [
+        unblend_path, "separate",
+        *[str(p) for p in input_wavs],
+        "-m", model,
+        "-o", str(output_pattern),
+        "--isolate-stem", isolate_stem,
+        "--device", device,
+    ]
+
+    proc = subprocess.Popen(
+        cmd,
+        stdout=subprocess.DEVNULL,
+        stderr=subprocess.DEVNULL,
+        env=_build_env(custom_kernels),
+    )
+
+    total = len(input_wavs)
+    processed = 0
+    while proc.poll() is None:
+        if progress_callback and processed < total - 1:
+            if proc.wait(2.0) is not None:
+                break
+            processed += 1
+            progress_callback(processed)
+        else:
+            proc.wait(2.0)
+
+    if proc.returncode != 0:
+        raise RuntimeError(f"unblend batch failed with exit code {proc.returncode}")
+
+    if progress_callback:
+        progress_callback(total)
+
+    mapping: dict[Path, Path] = {}
+    missing: list[Path] = []
+    for src in input_wavs:
+        candidate = src.parent / f"{isolate_stem}.wav"
+        if candidate.exists():
+            mapping[src] = candidate
+        else:
+            missing.append(src)
+
+    if missing:
+        found = sorted(work_root.rglob(f"{isolate_stem}.wav"))
+        if len(found) == len(input_wavs):
+            for src, dst in zip(input_wavs, found):
+                mapping[src] = dst
+        else:
+            names = ", ".join(p.name for p in missing[:3])
+            raise RuntimeError(
+                f"unblend finished but outputs missing for {len(missing)} file(s): {names}"
+            )
+
+    return mapping

core/transcribe.py

@@ -1,11 +1,11 @@
 """Speech recognition via WhisperX or GigaAM (onnx-asr).
 
-Models are cached per (name, device, compute_type) so loading happens once
-per process, not once per file. Loading is retried a few times to survive
-transient HuggingFace / SSL hiccups.
+Also exposes `transcribe_aligned()` for word-level alignment (used by LRC).
+Models are cached; aggressive VRAM cleanup happens after each file.
 """
 from __future__ import annotations
 
+import gc
 import time
 from pathlib import Path
 from typing import Optional
@@ -26,13 +26,23 @@ def _retry(fn, attempts: int = 3, base_delay: float = 2.0):
     for i in range(attempts):
         try:
             return fn()
-        except Exception as e:  # noqa: BLE001
+        except Exception as e:
             last = e
             if i < attempts - 1:
                 time.sleep(base_delay * (i + 1))
     raise last
 
 
+def _free_vram() -> None:
+    gc.collect()
+    try:
+        import torch
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+    except ImportError:
+        pass
+
+
 class _WhisperXModels:
     _cache: dict = {}
 
@@ -59,6 +69,28 @@ class _WhisperXModels:
         return cls._cache[key]
 
 
+class _AlignModels:
+    _cache: dict = {}
+
+    @classmethod
+    def get(cls, language_code: str, device: str, model_name: Optional[str] = None):
+        key = (language_code, device, model_name or "")
+        if key not in cls._cache:
+            import whisperx
+            print(f"  [ALIGN] loading align model for {language_code} "
+                  f"({model_name or 'default'}) on {device}...")
+
+            def load():
+                kwargs = {"language_code": language_code, "device": device}
+                if model_name:
+                    kwargs["model_name"] = model_name
+                return whisperx.load_align_model(**kwargs)
+
+            cls._cache[key] = _retry(load)
+            print("  [ALIGN] align model loaded.")
+        return cls._cache[key]
+
+
 class _GigaAMModels:
     _cache: dict = {}
 
@@ -93,11 +125,25 @@ def transcribe(
     vad_filter: bool = True,
 ) -> str:
     device = _resolve_device(device)
+
     if engine == "whisperx":
+        try:
             return _transcribe_whisperx(
                 audio_path, model, language, device, compute_type,
                 batch_size, local_files_only, vad_filter,
             )
+        except Exception as e:
+            err = str(e).lower()
+            if "out of memory" in err or "cuda" in err or "device ordinal" in err:
+                print(f"  [ASR] CUDA error: {e}. Clearing model cache and retrying...")
+                _WhisperXModels._cache.clear()
+                _free_vram()
+                return _transcribe_whisperx(
+                    audio_path, model, language, device, compute_type,
+                    batch_size, local_files_only, vad_filter,
+                )
+            raise
+
     if engine == "gigaam":
         return _transcribe_gigaam(audio_path, gigaam_provider)
     raise ValueError(f"Unknown ASR engine: {engine!r}")
@@ -116,14 +162,52 @@ def _transcribe_whisperx(
     import whisperx
     model = _WhisperXModels.get(model_name, device, compute_type, local_files_only)
     audio = whisperx.load_audio(str(audio_path))
-    result = model.transcribe(
-        audio,
-        batch_size=batch_size,
-        language=language,
-    )
-    return " ".join(seg["text"].strip() for seg in result["segments"]).strip()
+    result = model.transcribe(audio, batch_size=batch_size, language=language)
+    text = " ".join(seg["text"].strip() for seg in result["segments"]).strip()
+    del audio, result
+    _free_vram()
+    return text
 
 
 def _transcribe_gigaam(audio_path: Path, provider: str) -> str:
     model = _GigaAMModels.get(provider)
     return model.recognize(str(audio_path), channel="mean").strip()
+
+
+def transcribe_aligned(
+    audio_path: Path,
+    model: str = "large-v3",
+    language: Optional[str] = "ru",
+    device: str = "auto",
+    compute_type: Optional[str] = None,
+    batch_size: int = 8,
+    align_model: Optional[str] = None,
+    local_files_only: bool = False,
+) -> tuple[list[dict], str]:
+    """Transcribe + word-level align. Returns (aligned_segments, detected_language).
+
+    Each segment has 'start', 'end', 'text', and optionally 'words'.
+    Requires whisperx engine.
+    """
+    import whisperx
+    device = _resolve_device(device)
+    model_obj = _WhisperXModels.get(model, device, compute_type, local_files_only)
+    audio = whisperx.load_audio(str(audio_path))
+    result = model_obj.transcribe(audio, batch_size=batch_size, language=language)
+
+    detected = result.get("language") or language or "en"
+
+    try:
+        align_model_obj, align_metadata = _AlignModels.get(detected, device, align_model)
+        aligned = whisperx.align(
+            result["segments"], align_model_obj, align_metadata,
+            audio, device, return_char_alignments=False,
+        )
+        segments = aligned["segments"]
+    except Exception as e:
+        print(f"  [ALIGN] failed: {e}. Falling back to raw WhisperX segments.")
+        segments = result["segments"]
+
+    del audio, result
+    _free_vram()
+    return segments, detected

gui/main_window.py

@@ -1,4 +1,4 @@
-"""Main application window — dark theme, mode selector, staged/sequential pipelines."""
+"""Main application window — dark theme, mode selector, LRC tab."""
 from __future__ import annotations
 
 import shutil
@@ -15,7 +15,7 @@ from PySide6.QtWidgets import (
 )
 
 from core.config import (
-    ConvertConfig, LyricsConfig, PipelineConfig, SeparateConfig,
+    ConvertConfig, LrcConfig, LyricsConfig, PipelineConfig, SeparateConfig,
     ShazamConfig, TranscribeConfig,
 )
 from gui.worker import PipelineWorker
@@ -31,6 +31,8 @@ STAGE_LABELS = {
     "convert": "Конвертация",
     "separate": "Отделение",
     "transcribe": "Транскрибация",
+    "lrc": "LRC",
+    "embed": "Встраивание LRC",
 }
 
 MODE_LABELS = {
@@ -44,7 +46,6 @@ def _detect_device_info() -> tuple[str, str]:
         import torch
     except ImportError:
         return "⚠ PyTorch не установлен", "#f38ba8"
-
     if torch.cuda.is_available():
         try:
             name = torch.cuda.get_device_name(0)
@@ -52,7 +53,6 @@ def _detect_device_info() -> tuple[str, str]:
             return f"✓ CUDA: {name} · {vram:.1f} GB VRAM", "#a6e3a1"
         except Exception:  # noqa: BLE001
             return "✓ CUDA доступна", "#a6e3a1"
-
     return "⚠ CUDA не обнаружена — расчёт пойдёт на CPU (медленно)", "#f9e2af"
 
 
@@ -60,7 +60,7 @@ class MainWindow(QMainWindow):
     def __init__(self) -> None:
         super().__init__()
         self.setWindowTitle("Song Transcriber")
-        self.resize(1140, 880)
+        self.resize(1140, 920)
         self._worker: Optional[PipelineWorker] = None
         self._build_ui()
         self._apply_defaults()
@@ -93,7 +93,6 @@ class MainWindow(QMainWindow):
 
         self.input_edit = QLineEdit()
         top_layout.addRow("Входная папка:", self._with_browse(self.input_edit, self._pick_input_dir))
-
         self.output_edit = QLineEdit()
         top_layout.addRow("Выходная папка:", self._with_browse(self.output_edit, self._pick_output_dir))
 
@@ -111,7 +110,6 @@ class MainWindow(QMainWindow):
         opts.addWidget(self.overwrite_cb)
         opts.addStretch(1)
         top_layout.addRow("Опции:", opts)
-
         root.addWidget(top_box)
 
         self.tabs = QTabWidget()
@@ -120,6 +118,7 @@ class MainWindow(QMainWindow):
         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")
+        self.tabs.addTab(self._build_lrc_tab(), "LRC")
         root.addWidget(self.tabs)
 
         self.shazam_enabled_cb.toggled.connect(self._sync_tab_indicators)
@@ -131,9 +130,9 @@ class MainWindow(QMainWindow):
 
         row = QHBoxLayout()
         row.setSpacing(10)
-        self.start_btn = QPushButton("▶  Запустить")
+        self.start_btn = QPushButton("▶  Запустить пайплайн")
         self.start_btn.setObjectName("primary")
-        self.start_btn.clicked.connect(self._on_start)
+        self.start_btn.clicked.connect(lambda: self._run_task("main"))
         self.stop_btn = QPushButton("⏹  Стоп")
         self.stop_btn.setObjectName("danger")
         self.stop_btn.setEnabled(False)
@@ -180,19 +179,18 @@ class MainWindow(QMainWindow):
         form = QFormLayout(w)
         form.setContentsMargins(16, 16, 16, 16)
         form.setSpacing(10)
-
         self.shazam_enabled_cb = QCheckBox("Включён")
+        self.shazam_concurrency = QSpinBox()
+        self.shazam_concurrency.setRange(1, 16)
+        form.addRow("Параллельных запросов:", self.shazam_concurrency)
         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)."
+            "При совпадении сохраняются метаданные и (если возможно) текст из LRCLIB."
         )
         info.setWordWrap(True)
         info.setStyleSheet("color: #a6adc8;")
         form.addRow(info)
-
         return w
 
     def _build_lrclib_tab(self) -> QWidget:
@@ -200,37 +198,47 @@ class MainWindow(QMainWindow):
         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_use_embedded_cb = QCheckBox("Использовать встроенный текст из тегов (USLT/LYRICS/©lyr)")
+        self.lyrics_use_embedded_cb.setToolTip(
+            "Сначала проверяем, есть ли текст (LRC или обычный) уже в тегах аудио.\n"
+            "Если есть — ASR/Shazam/LRCLIB не запускаются."
+        )
+        form.addRow("", self.lyrics_use_embedded_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)")
+        self.lyrics_use_tags_cb = QCheckBox("Искать по тегам")
         form.addRow("", self.lyrics_use_tags_cb)
-
-        self.lyrics_use_shazam_cb = QCheckBox("Искать по результату Shazam")
+        self.lyrics_use_shazam_cb = QCheckBox("Искать по Shazam")
         form.addRow("", self.lyrics_use_shazam_cb)
-
-        self.lyrics_after_asr_cb = QCheckBox("Проверять после ASR (fuzzy-match)")
+        self.lyrics_after_asr_cb = QCheckBox("Проверять после ASR (fuzzy)")
         form.addRow("", self.lyrics_after_asr_cb)
-
         self.lyrics_transcribe_unresolved_cb = QCheckBox(
-            "Запускать ASR, если Shazam опознал, но текста нет"
+            "ASR, если Shazam опознал без текста"
         )
         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)
 
+        self.lyrics_save_unrecognized_cb = QCheckBox(
+            "Сохранять тексты без LRCLIB в JSONL (для добавления в LRCLIB)"
+        )
+        self.lyrics_save_unrecognized_cb.setToolTip(
+            "Если текст получен НЕ из LRCLIB (WhisperX / GigaAM / embedded), "
+            "записать его в <output>/unrecognized_lyrics.jsonl.\n"
+            "Формат совместим с LRCLIB API для ручной отправки."
+        )
+        form.addRow("", self.lyrics_save_unrecognized_cb)
+
+        self.lyrics_unrecognized_path_edit = QLineEdit()
+        self.lyrics_unrecognized_path_edit.setPlaceholderText(
+            "(пусто — <output>/unrecognized_lyrics.jsonl)"
+        )
+        form.addRow("Файл unrecognized:", self.lyrics_unrecognized_path_edit)
         return w
 
     def _build_convert_tab(self) -> QWidget:
@@ -238,21 +246,20 @@ class MainWindow(QMainWindow):
         form = QFormLayout(w)
         form.setContentsMargins(16, 16, 16, 16)
         form.setSpacing(10)
-
         self.convert_enabled_cb = QCheckBox("Включён")
         form.addRow("", self.convert_enabled_cb)
-
         self.convert_sr = QComboBox()
         for sr in SAMPLE_RATES:
             self.convert_sr.addItem(f"{sr} Hz", sr)
         form.addRow("Sample rate:", self.convert_sr)
-
         self.convert_ch = QComboBox()
         self.convert_ch.addItem("Моно", 1)
         self.convert_ch.addItem("Стерео", 2)
         form.addRow("Каналы:", self.convert_ch)
-
         self.convert_ffmpeg = QLineEdit()
+        self.convert_concurrency = QSpinBox()
+        self.convert_concurrency.setRange(1, 16)
+        form.addRow("Параллельных конвертаций:", self.convert_concurrency)
         form.addRow("ffmpeg:", self.convert_ffmpeg)
         return w
 
@@ -261,21 +268,22 @@ class MainWindow(QMainWindow):
         form = QFormLayout(w)
         form.setContentsMargins(16, 16, 16, 16)
         form.setSpacing(10)
-
         self.separate_enabled_cb = QCheckBox("Включён")
         form.addRow("", self.separate_enabled_cb)
-
         self.separate_model = QLineEdit()
         form.addRow("Модель:", self.separate_model)
-
         self.separate_stem = QLineEdit()
         form.addRow("Isolate stem:", self.separate_stem)
-
         self.separate_unblend = QLineEdit()
         form.addRow("unblend CLI:", self.separate_unblend)
-
         self.separate_kernels_cb = QCheckBox("Использовать кастомные ядра")
         form.addRow("", self.separate_kernels_cb)
+        self.separate_save_vocals_cb = QCheckBox("Всегда сохранять vocals.wav в output")
+        self.separate_save_vocals_cb.setToolTip(
+            "Независимо от 'Сохранять промежуточные': вокал всегда будет лежать "
+            "в <output>/<stem>/vocals.wav — полезно для LRC-этапа."
+        )
+        form.addRow("", self.separate_save_vocals_cb)
         return w
 
     def _build_transcribe_tab(self) -> QWidget:
@@ -283,53 +291,90 @@ class MainWindow(QMainWindow):
         form = QFormLayout(w)
         form.setContentsMargins(16, 16, 16, 16)
         form.setSpacing(10)
-
         self.transcribe_enabled_cb = QCheckBox("Включён")
         form.addRow("", self.transcribe_enabled_cb)
-
         self.engine_cb = QComboBox()
         self.engine_cb.addItems(ENGINES)
         form.addRow("Движок:", self.engine_cb)
-
         self.model_edit = QLineEdit()
         form.addRow("Модель:", self.model_edit)
-
         self.lang_cb = QComboBox()
         self.lang_cb.setEditable(True)
         self.lang_cb.addItems(LANGUAGES)
-        self.lang_cb.setToolTip(
-            "'auto' — WhisperX определяет язык на каждом файле.\n"
-            "Если знаешь язык — выбери его явно, будет быстрее."
-        )
         form.addRow("Язык:", self.lang_cb)
-
         self.device_cb = QComboBox()
         self.device_cb.addItems(["auto", "cuda", "cpu"])
         form.addRow("Device:", self.device_cb)
-
         self.compute_type_cb = QComboBox()
         self.compute_type_cb.addItems(["auto", "int8_float16", "int8", "float16", "float32"])
         form.addRow("Compute type:", self.compute_type_cb)
-
         self.batch_spin = QSpinBox()
         self.batch_spin.setRange(1, 512)
-        self.batch_spin.setToolTip(
-            "Размер батча сегментов ВНУТРИ одного файла (не файлов!).\n"
-            "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."
-        )
         form.addRow("", self.local_files_only_cb)
         return w
 
+    def _build_lrc_tab(self) -> QWidget:
+        w = QWidget()
+        form = QFormLayout(w)
+        form.setContentsMargins(16, 16, 16, 16)
+        form.setSpacing(10)
+
+        info = QLabel(
+            "1) Сгенерировать LRC: WhisperX + align → <output>/<stem>/text.lrc.\n"
+            "2) Встроить LRC: записывает LRC в тег lyrics аудио (USLT/LYRICS/©lyr).\n"
+            "Если 'in-place' выключено — оригинал не трогается, создаётся копия в output."
+        )
+        info.setWordWrap(True)
+        info.setStyleSheet("color: #a6adc8;")
+        form.addRow(info)
+
+        self.lrc_audio_source_cb = QComboBox()
+        self.lrc_audio_source_cb.addItems(["auto", "vocals", "convert", "input"])
+        self.lrc_audio_source_cb.setToolTip(
+            "auto: vocals.wav → convert.wav → 16k WAV из исходника.\n"
+            "vocals/convert: только соответствующий файл.\n"
+            "input: оригинальный mp3/flac."
+        )
+        form.addRow("Источник аудио:", self.lrc_audio_source_cb)
+
+        self.lrc_language_cb = QComboBox()
+        self.lrc_language_cb.setEditable(True)
+        self.lrc_language_cb.addItems(LANGUAGES)
+        form.addRow("Язык (для align):", self.lrc_language_cb)
+
+        self.lrc_align_model_edit = QLineEdit()
+        self.lrc_align_model_edit.setPlaceholderText("(пусто — whisperx по языку)")
+        form.addRow("Align модель:", self.lrc_align_model_edit)
+
+        self.lrc_device_cb = QComboBox()
+        self.lrc_device_cb.addItems(["auto", "cuda", "cpu"])
+        form.addRow("Device:", self.lrc_device_cb)
+
+        self.lrc_embed_in_place_cb = QCheckBox("Встраивать in-place (перезаписывать оригинал)")
+        self.lrc_embed_in_place_cb.setToolTip(
+            "Если выключено (по умолчанию) — создаётся копия в <output>/<stem>/<name>.\n"
+            "Если включено — оригинальный файл модифицируется."
+        )
+        form.addRow("", self.lrc_embed_in_place_cb)
+
+        btns = QHBoxLayout()
+        self.lrc_generate_btn = QPushButton("🎵  Сгенерировать LRC")
+        self.lrc_generate_btn.setObjectName("primary")
+        self.lrc_generate_btn.clicked.connect(lambda: self._run_task("lrc"))
+        self.lrc_embed_btn = QPushButton("📝  Встроить LRC в аудио")
+        self.lrc_embed_btn.clicked.connect(lambda: self._run_task("embed"))
+        btns.addWidget(self.lrc_generate_btn)
+        btns.addWidget(self.lrc_embed_btn)
+        btns.addStretch(1)
+        form.addRow("", btns)
+
+        return w
+
     # ---------------- visual sync ----------------
 
     def _log_device_info(self) -> None:
@@ -365,22 +410,23 @@ class MainWindow(QMainWindow):
         self.recursive_cb.setChecked(cfg.recursive)
         self.keep_intermediate_cb.setChecked(cfg.keep_intermediate)
         self.overwrite_cb.setChecked(cfg.overwrite)
-
         i = self.mode_cb.findData(cfg.mode)
         if i >= 0:
             self.mode_cb.setCurrentIndex(i)
 
         self.shazam_enabled_cb.setChecked(cfg.shazam.enabled)
+        self.shazam_concurrency.setValue(cfg.shazam.identify_concurrency)
 
         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_transcribe_unresolved_cb.setChecked(cfg.lyrics.transcribe_if_identified_no_lyrics)
         self.lyrics_threshold.setValue(cfg.lyrics.fuzzy_threshold)
+        self.lyrics_use_embedded_cb.setChecked(cfg.lyrics.use_embedded_lyrics)
+        self.lyrics_save_unrecognized_cb.setChecked(cfg.lyrics.save_unrecognized)
+        self.lyrics_unrecognized_path_edit.setText(cfg.lyrics.unrecognized_path)
 
         self.convert_enabled_cb.setChecked(cfg.convert.enabled)
         i = self.convert_sr.findData(cfg.convert.sample_rate)
@@ -390,12 +436,14 @@ class MainWindow(QMainWindow):
         if i >= 0:
             self.convert_ch.setCurrentIndex(i)
         self.convert_ffmpeg.setText(cfg.convert.ffmpeg_path)
+        self.convert_concurrency.setValue(cfg.convert.convert_concurrency)
 
         self.separate_enabled_cb.setChecked(cfg.separate.enabled)
         self.separate_model.setText(cfg.separate.model)
         self.separate_stem.setText(cfg.separate.isolate_stem)
         self.separate_unblend.setText(cfg.separate.unblend_path)
         self.separate_kernels_cb.setChecked(cfg.separate.custom_kernels)
+        self.separate_save_vocals_cb.setChecked(cfg.separate.save_vocals_copy)
 
         self.transcribe_enabled_cb.setChecked(cfg.transcribe.enabled)
         self.engine_cb.setCurrentText(cfg.transcribe.engine)
@@ -407,6 +455,12 @@ class MainWindow(QMainWindow):
         self.gigaam_provider_cb.setCurrentText(cfg.transcribe.gigaam_provider)
         self.local_files_only_cb.setChecked(cfg.transcribe.local_files_only)
 
+        self.lrc_audio_source_cb.setCurrentText(cfg.lrc.audio_source)
+        self.lrc_language_cb.setCurrentText(cfg.lrc.language or "auto")
+        self.lrc_align_model_edit.setText(cfg.lrc.align_model or "")
+        self.lrc_device_cb.setCurrentText(cfg.lrc.device)
+        self.lrc_embed_in_place_cb.setChecked(cfg.lrc.embed_in_place)
+
     def _collect_config(self) -> PipelineConfig:
         cfg = PipelineConfig()
         cfg.input_dir = self.input_edit.text().strip()
@@ -416,8 +470,10 @@ 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.shazam = ShazamConfig(
+            enabled=self.shazam_enabled_cb.isChecked(),
+            identify_concurrency=self.shazam_concurrency.value(),
+        )
         cfg.lyrics = LyricsConfig(
             enabled=self.lyrics_enabled_cb.isChecked(),
             user_agent=self.lyrics_user_agent.text().strip() or "audio-transcriber/1.0",
@@ -426,13 +482,16 @@ class MainWindow(QMainWindow):
             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()),
+            use_embedded_lyrics=self.lyrics_use_embedded_cb.isChecked(),
+            save_unrecognized=self.lyrics_save_unrecognized_cb.isChecked(),
+            unrecognized_path=self.lyrics_unrecognized_path_edit.text().strip(),
         )
-
         cfg.convert = ConvertConfig(
             enabled=self.convert_enabled_cb.isChecked(),
             sample_rate=int(self.convert_sr.currentData()),
             channels=int(self.convert_ch.currentData()),
             ffmpeg_path=self.convert_ffmpeg.text().strip() or "ffmpeg",
+            convert_concurrency=self.convert_concurrency.value(),
         )
         cfg.separate = SeparateConfig(
             enabled=self.separate_enabled_cb.isChecked(),
@@ -440,8 +499,8 @@ class MainWindow(QMainWindow):
             isolate_stem=self.separate_stem.text().strip() or "vocals",
             unblend_path=self.separate_unblend.text().strip() or "unblend",
             custom_kernels=self.separate_kernels_cb.isChecked(),
+            save_vocals_copy=self.separate_save_vocals_cb.isChecked(),
         )
-
         lang = self.lang_cb.currentText().strip()
         compute = self.compute_type_cb.currentText().strip()
         cfg.transcribe = TranscribeConfig(
@@ -455,6 +514,14 @@ class MainWindow(QMainWindow):
             gigaam_provider=self.gigaam_provider_cb.currentText(),
             local_files_only=self.local_files_only_cb.isChecked(),
         )
+        lrc_lang = self.lrc_language_cb.currentText().strip()
+        cfg.lrc = LrcConfig(
+            language=None if lrc_lang in ("", "auto") else lrc_lang,
+            align_model=self.lrc_align_model_edit.text().strip() or None,
+            device=self.lrc_device_cb.currentText(),
+            audio_source=self.lrc_audio_source_cb.currentText(),
+            embed_in_place=self.lrc_embed_in_place_cb.isChecked(),
+        )
         return cfg
 
     # ---------------- handlers ----------------
@@ -492,11 +559,19 @@ class MainWindow(QMainWindow):
         except Exception as exc:  # noqa: BLE001
             QMessageBox.critical(self, "Ошибка", str(exc))
 
-    def _on_start(self) -> None:
+    def _set_busy(self, busy: bool) -> None:
+        self.start_btn.setEnabled(not busy)
+        self.lrc_generate_btn.setEnabled(not busy)
+        self.lrc_embed_btn.setEnabled(not busy)
+        self.stop_btn.setEnabled(busy)
+
+    def _run_task(self, task: str) -> None:
         cfg = self._collect_config()
         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 task == "main":
             has_work = any([
                 cfg.shazam.enabled, cfg.lyrics.enabled,
                 cfg.convert.enabled, cfg.separate.enabled, cfg.transcribe.enabled,
@@ -514,17 +589,20 @@ class MainWindow(QMainWindow):
                                     f"Команда '{cfg.separate.unblend_path}' не найдена в PATH.")
                 return
 
+        if task == "lrc" and cfg.transcribe.engine != "whisperx":
+            QMessageBox.warning(
+                self, "LRC требует WhisperX",
+                "Align-модель доступна только для WhisperX. Переключите движок на whisperx."
+            )
+            return
+
         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.")
-
         self.progress.setValue(0)
         self.progress.setFormat("Запуск…")
-        self.start_btn.setEnabled(False)
-        self.stop_btn.setEnabled(True)
+        self._set_busy(True)
 
-        self._worker = PipelineWorker(cfg)
+        self._worker = PipelineWorker(cfg, task=task)
         self._worker.log.connect(self._append_log)
         self._worker.progress.connect(self._on_progress)
         self._worker.finished_ok.connect(self._on_finished)
@@ -548,16 +626,14 @@ class MainWindow(QMainWindow):
         self.progress.setFormat(f"{label} — %v / %m")
 
     def _on_finished(self, results: list) -> None:
-        self.start_btn.setEnabled(True)
-        self.stop_btn.setEnabled(False)
+        self._set_busy(False)
         ok = sum(1 for r in results if r.error is None)
         fail = len(results) - ok
         self.progress.setFormat(f"Готово — успешно {ok}, с ошибкой {fail}")
         self._append_log(f"\n✅ Готово. Успешно: {ok}, с ошибкой: {fail}.")
 
     def _on_failed(self, message: str) -> None:
-        self.start_btn.setEnabled(True)
-        self.stop_btn.setEnabled(False)
+        self._set_busy(False)
         self.progress.setFormat("Ошибка")
         self._append_log(f"\n✗ Ошибка пайплайна: {message}")
         QMessageBox.critical(self, "Ошибка", message)

gui/worker.py

@@ -1,4 +1,4 @@
-"""QThread wrapper around core.pipeline.run_pipeline."""
+"""QThread wrapper. Supports three task types: main, lrc, embed."""
 from __future__ import annotations
 
 import threading
@@ -6,7 +6,6 @@ import threading
 from PySide6.QtCore import QThread, Signal
 
 from core.config import PipelineConfig
-from core.pipeline import run_pipeline
 
 
 class PipelineWorker(QThread):
@@ -15,9 +14,10 @@ class PipelineWorker(QThread):
     finished_ok = Signal(list)
     failed = Signal(str)
 
-    def __init__(self, config: PipelineConfig, parent=None):
+    def __init__(self, config: PipelineConfig, task: str = "main", parent=None):
         super().__init__(parent)
         self._config = config
+        self._task = task
         self._stop_event = threading.Event()
 
     def stop(self) -> None:
@@ -25,7 +25,14 @@ class PipelineWorker(QThread):
 
     def run(self) -> None:
         try:
-            results = run_pipeline(
+            if self._task == "lrc":
+                from core.lrc_pipeline import run_lrc_pipeline as runner
+            elif self._task == "embed":
+                from core.lrc_pipeline import run_embed_pipeline as runner
+            else:
+                from core.pipeline import run_pipeline as runner
+
+            results = runner(
                 self._config,
                 log=self.log.emit,
                 on_progress=lambda d, t, s: self.progress.emit(d, t, s),