align LRC from ready text.txt via stable-ts and auto-detect language

- add align_text_to_audio(): forced alignment without ASR, fast
- add _StableTSModels cache; detect language via stable-ts when language=None
- LRC stage: use existing text.txt, copy it if already LRC, fall back to ASR only
- filter junk lyrics from tags and ASR hallucinations (promo phrases, tics)
- mark empty ASR results with metadata.empty_text and skip them on resume
- warn in GUI when LRC is run with transcription disabled
- add mark_no_speech.py one-shot script for existing tracks

core/lrc.py

@@ -70,6 +70,35 @@ def is_lrc(content: str) -> bool:
 
 # ---------------------------------------------------------------- read embedded
 
+_JUNK_LYRIC_MARKERS = (
+    "created with",
+    "russian-area.de",
+    "http://", "https://",
+    "www.",
+    "mp3tag",
+    "tag&rename",
+    "foobar2000",
+    "itunes",
+)
+
+
+def _is_junk_lyrics(text: str) -> bool:
+    """Reject tag values that are really URL/signature junk, not lyrics."""
+    if not text or len(text.strip()) < 3:
+        return True
+    low = text.lower()
+    if any(m in low for m in _JUNK_LYRIC_MARKERS):
+        return True
+    # A "lyrics" blob that is just an email or URL — reject.
+    import re
+    stripped = re.sub(r"\s+", "", low)
+    if re.fullmatch(r"[a-z0-9.\-_/]+", stripped) and (
+        "." in stripped or "/" in stripped
+    ):
+        return True
+    return False
+
+
 def read_embedded_lyrics(audio_path: Path) -> Optional[str]:
     """Return lyrics stored in the file's tags, or None.
 
@@ -78,14 +107,18 @@ def read_embedded_lyrics(audio_path: Path) -> Optional[str]:
     suffix = audio_path.suffix.lower()
     try:
         if suffix in (".mp3", ".mp2"):
-            return _read_mp3(audio_path)
+            text = _read_mp3(audio_path)
-        if suffix in (".flac", ".ogg", ".opus"):
+        elif suffix in (".flac", ".ogg", ".opus"):
-            return _read_vorbis(audio_path)
+            text = _read_vorbis(audio_path)
-        if suffix in (".m4a", ".mp4", ".m4b", ".aac"):
+        elif suffix in (".m4a", ".mp4", ".m4b", ".aac"):
-            return _read_mp4(audio_path)
+            text = _read_mp4(audio_path)
-    except Exception:  # noqa: BLE001 — never break the pipeline on tag issues
+        else:
+            return None
+    except Exception:  # noqa: BLE001
         return None
-    return None
+    if text and _is_junk_lyrics(text):
+        return None
+    return text
 
 
 def _read_mp3(path: Path) -> Optional[str]:

core/lrc_pipeline.py

@@ -15,7 +15,9 @@ from .pipeline import (
     FileResult, _artifact_belongs_to, _output_dir_for, _save_unrecognized,
     collect_audio_files,
 )
-from .transcribe import CudaOOMError, transcribe_aligned
+from .transcribe import (
+    CudaOOMError, align_text_to_audio, transcribe, transcribe_aligned,
+)
 
 
 LogFn = Callable[[str], None]
@@ -86,7 +88,17 @@ def run_lrc_pipeline(
         log("Аудиофайлы не найдены.")
         return []
 
-    lang = config.lrc.language or config.transcribe.language
+    # sanity: если ASR и конвертация/отделение выключены — alignment имеет смысл
+    # только для треков, у которых УЖЕ есть text.txt + vocals/convert.wav.
+    # Иначе почти всё упадёт. Предупредить заранее.
+    if not config.transcribe.enabled and not config.convert.enabled and not config.separate.enabled:
+        log("  ⚠ ASR, конвертация и отделение выключены. "
+            "LRC будет построен только для треков, где уже есть text.txt + vocals.wav "
+            "(или convert.wav). Остальные упадут с ошибкой.")
+
+    # `None` means "auto" — the language will be auto-detected during ASR
+    # (transcribe_aligned) or by the alignment model (align_text_to_audio).
+    lang = config.lrc.language
     log(f"LRC: {len(files)} файлов   |   язык={lang or 'auto'}   |   "
         f"источник={config.lrc.audio_source}")
 
@@ -108,6 +120,23 @@ def run_lrc_pipeline(
         lrc_path = track_out / "text.lrc"
         error: Optional[str] = None
 
+        if config.lrc.skip_existing:
+            ready = _ready_lrc(track_out, f)
+            if ready is not None:
+                log("  ⊘ text.lrc уже готов — пропуск")
+                results.append(FileResult(f, None, track_out, None, "existing+lrc"))
+                if on_progress:
+                    on_progress("lrc", idx, total)
+                if on_overall_progress:
+                    on_overall_progress(idx, total)
+                continue
+
+        track_out = _output_dir_for(f, input_dir, output_dir)
+        track_out.mkdir(parents=True, exist_ok=True)
+        lrc_path = track_out / "text.lrc"
+        text_file = track_out / "text.txt"
+        error: Optional[str] = None
+
         if config.lrc.skip_existing:
             ready = _ready_lrc(track_out, f)
             if ready is not None:
@@ -121,6 +150,7 @@ def run_lrc_pipeline(
 
         tmp = Path(tempfile.mkdtemp(prefix=f"at_lrc_{f.stem}_"))
         try:
+            # 0) embedded LRC from tags — бесплатно
             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")
@@ -128,40 +158,87 @@ def run_lrc_pipeline(
                 results.append(FileResult(f, None, track_out, None, "embedded+lrc"))
                 continue
 
+            # 1) готовый текст из text.txt
+            text: Optional[str] = None
+            if text_file.is_file():
+                try:
+                    text = text_file.read_text(encoding="utf-8").strip()
+                except OSError as e:
+                    raise RuntimeError(f"не читается text.txt: {e}") from e
+            if not text:
+                text = None
+
+            # 1a) text.txt уже LRC → писать его как есть, alignment не нужен
+            if text is not None and is_lrc(text):
+                lrc_path.write_text(text, encoding="utf-8")
+                log("  ✓ text.lrc (text.txt уже содержит LRC-таймкоды)")
+                results.append(FileResult(f, None, track_out, None, "text_already_lrc"))
+                continue
+
+            # 2) аудио для alignment
             audio_path = _pick_audio_source(
                 f, input_dir, output_dir, config, tmp, log,
             )
             if not audio_path.exists():
+                # Если convert/separate выключены и vocals/convert.wav отсутствуют —
+                # понятная ошибка вместо «файл не найден»
+                if not config.convert.enabled and not config.separate.enabled:
+                    raise FileNotFoundError(
+                        f"для alignment нет аудио: {audio_path}. "
+                        "Включите конвертацию/отделение в основном пайплайне, "
+                        "либо запустите их один раз, либо положите vocals.wav/convert.wav."
+                    )
                 raise FileNotFoundError(
-                    f"Источник аудио не найден: {audio_path}. "
+                    f"источник аудио не найден: {audio_path}. "
-                    "Включите conversion/separation в основном пайплайне "
+                    f"Источник={config.lrc.audio_source}, "
-                    "или выберите audio_source=input."
+                    f"convert.enabled={config.convert.enabled}, "
+                    f"separate.enabled={config.separate.enabled}"
                 )
             log(f"  [align] {audio_path.name}")
 
-            segments, detected = transcribe_aligned(
+            # 3) готовый текст → forced alignment (быстро, без ASR)
-                audio_path,
+            if text is not None:
-                model=config.transcribe.model,
+                log(f"  [align-only] text.txt найден ({len(text)} символов)")
-                language=lang,
+                segments, detected = align_text_to_audio(
-                device=config.lrc.device,
+                    audio_path,
-                compute_type=config.transcribe.compute_type,
+                    text,
-                batch_size=config.transcribe.batch_size,
+                    language=lang,
-                align_model=config.lrc.align_model,
+                    model=config.transcribe.model,
-                local_files_only=config.transcribe.local_files_only,
+                    device=config.lrc.device,
-            )
+                    compute_type=config.transcribe.compute_type,
+                    local_files_only=config.transcribe.local_files_only,
+                )
+            else:
+                # 3a) нет text.txt — ASR разрешён только если transcribe.enabled
+                if not config.transcribe.enabled:
+                    raise FileNotFoundError(
+                        "text.txt отсутствует, а транскрибация выключена. "
+                        "Включите транскрибацию в основном пайплайне или "
+                        "положите text.txt в папку трека вручную."
+                    )
+                log("  [asr+align] text.txt нет — выполняю ASR заново")
+                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)
             if not lrc_content.strip():
-                log(f"  ⚠ {f.name}: текст не извлечён (пустой результат ASR)")
+                log(f"  ⚠ {f.name}: пустой LRC (нет сегментов)")
                 results.append(FileResult(
                     f, None, None,
-                    "EmptyResult: ASR returned no text", "lrc+empty",
+                    "EmptyResult: alignment returned no text", "lrc+empty",
                 ))
                 continue
             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),

core/pipeline.py

@@ -228,6 +228,16 @@ def _is_track_finished(
     """True when ``skip_existing`` may skip this output folder entirely."""
     if not belongs:
         return False
+    # If metadata says this track had an empty ASR result, treat it as
+    # "finished" so skip_existing does not keep re‑running it.
+    meta = track_out / "metadata.json"
+    if meta.is_file():
+        try:
+            data = json.loads(meta.read_text(encoding="utf-8"))
+            if isinstance(data, dict) and data.get("empty_text"):
+                return True
+        except (OSError, ValueError):
+            pass
     if _final_artifact_present(track_out, config):
         return True
     # Identification-only runs still finish with text.txt (no other stage
@@ -879,6 +889,8 @@ def _run_sequential(
             metadata_dict = _build_metadata_dict(
                 ident.metadata, ident.shazam, source, input_path=f,
             )
+            if metadata_dict.get("empty_text") is None:
+                metadata_dict["empty_text"] = False
             if belongs:
                 metadata_dict = _merge_metadata_file(
                     track_out / "metadata.json", metadata_dict,
@@ -1175,6 +1187,9 @@ def _run_staged_batch(
                         track_out.mkdir(parents=True, exist_ok=True)
                         if ident.text:
                             (track_out / "text.txt").write_text(ident.text, encoding="utf-8")
+                            md = state[f].setdefault("metadata_dict", {})
+                            if md.get("empty_text") is None:
+                                md["empty_text"] = False
                         if state[f]["metadata_dict"]:
                             _write_metadata_json(track_out / "metadata.json", state[f]["metadata_dict"])
                         _save_unrecognized(
@@ -1434,6 +1449,9 @@ def _run_staged_batch(
             else:
                 if text:
                     (track_out / "text.txt").write_text(text, encoding="utf-8")
+                    md = metadata_dict.setdefault("empty_text", False)
+                else:
+                    md = metadata_dict.setdefault("empty_text", True)
                 if metadata_dict:
                     _write_metadata_json(track_out / "metadata.json", metadata_dict)
 

core/transcribe.py

@@ -35,10 +35,100 @@ def _is_dead_cuda(exc: BaseException) -> bool:
     return any(m in err for m in _DEAD_CUDA_MARKERS)
 
 
+    _WhisperXModels._cache.clear()
+    _AlignModels._cache.clear()
+    _GigaAMModels._cache.clear()
+    _StableTSModels._cache.clear()
+    _REUSE_LOGGED.clear()
+
+
+# Phrases that are strong signals of ASR hallucination (end credits)
+_HALLUCINATION_PHRASES = (
+    "продолжение следует",
+    "спасибо за внимание",
+    "спасибо за просмотр",
+    "спасибо за подписку",
+    "подписывайтесь на канал",
+    "ставьте лайки",
+    "не забудьте подписаться",
+    "субтитры создавал",
+    "субтитры сделал",
+    "субтитры подготовил",
+    "редактор субтитров",
+    "корректор",
+    "thank you for watching",
+    "thanks for watching",
+    "please subscribe",
+    "subscribe to my channel",
+    "like and subscribe",
+)
+
+
+def _is_hallucination(text: str) -> bool:
+    """Return True if the text looks like an ASR hallucination.
+
+    Checks:
+      - known call-to-action / promo phrases;
+      - repeated-word tic ("Музыка Музыка").
+    """
+    if not text:
+        return False
+    t = " ".join(text.lower().split()).strip(" .!?…,;:\"'«»")
+    for phrase in _HALLUCINATION_PHRASES:
+        if phrase in t:
+            return True
+    words = t.split()
+    if len(words) == 2 and words[0] == words[1]:
+        return True
+    return False
+
+
+def _detect_language(model_obj, audio_or_path, fallback: str = "ru") -> str:
+    """Detect the spoken language from audio using the ASR model itself.
+
+    Returns a lowercase ISO code or `fallback` on failure.
+    """
+    try:
+        lang, _prob = model_obj.detect_language(audio_or_path)
+        if lang:
+            return lang.lower()
+    except Exception:
+        pass
+    return fallback
+
+
+class _StableTSModels:
+    _cache: dict = {}
+
+    @classmethod
+    def get(cls, model_name: str, device: str, compute_type: Optional[str],
+            local_files_only: bool = False) -> Any:
+        if compute_type is None:
+            compute_type = "int8_float16" if device == "cuda" else "int8"
+        key = (model_name, device, compute_type, local_files_only)
+        with _MODEL_LOCK:
+            if key not in cls._cache:
+                import stable_whisper
+
+                print(f"  [ALIGN] loading stable-ts {model_name} on {device} "
+                      f"({compute_type}, local_files_only={local_files_only})...")
+
+                def load() -> 'faster_whisper.WhisperModel':
+                    return stable_whisper.load_faster_whisper(
+                        model_name, device=device, compute_type=compute_type,
+                        local_files_only=local_files_only,
+                    )
+
+                cls._cache[key] = _retry(load)
+                print("  [ALIGN] stable-ts model loaded once — reuse.")
+            return cls._cache[key]
+
+
 def _clear_model_caches() -> None:
     _WhisperXModels._cache.clear()
     _AlignModels._cache.clear()
     _GigaAMModels._cache.clear()
+    _StableTSModels._cache.clear()
     _REUSE_LOGGED.clear()
 
 
@@ -285,7 +375,21 @@ def _transcribe_whisperx(
     result = _model_transcribe_with_retry(
         model, audio, language, batch_size, name=audio_path.name,
     )
-    text = " ".join(seg["text"].strip() for seg in result["segments"]).strip()
+
+    # Filter out hallucinated / spammy segments.
+    segments = result.get("segments") or []
+    kept = []
+    for seg in segments:
+        t = (seg.get("text") or "").strip()
+        if not t or _is_hallucination(t):
+            continue
+        if seg.get("avg_logprob", 0.0) < -1.2:
+            continue
+        kept.append(seg)
+    text = " ".join(s["text"].strip() for s in kept).strip()
+    if _is_hallucination(text):
+        text = ""
+
     del audio, result
     _free_vram()
     return text
@@ -333,6 +437,47 @@ def transcribe_aligned(
         print(f"  [ALIGN] failed: {e}. Falling back to raw WhisperX segments.")
         segments = result["segments"]
 
+    # Filter out hallucinated segments.
+    segments = [s for s in segments if not _is_hallucination(s.get("text", ""))]
+
     del audio, result
     _free_vram()
     return segments, detected
+
+
+def align_text_to_audio(
+    audio_path: Path,
+    text: str,
+    language: Optional[str] = "ru",
+    model: str = "large-v3",
+    device: str = "auto",
+    compute_type: Optional[str] = None,
+    local_files_only: bool = False,
+) -> tuple[list[dict], str]:
+    """Forced alignment of EXISTING text to audio. No ASR.
+
+    When `language` is None, the language is auto-detected from the audio
+    using the alignment model itself (which is a faster-whisper model and
+    exposes `detect_language`).
+    """
+    import stable_whisper as _sw
+    device = _resolve_device(device)
+    model_obj = _StableTSModels.get(model, device, compute_type, local_files_only)
+
+    if language:
+        lang = language
+    else:
+        lang = _detect_language(model_obj, str(audio_path), fallback="ru")
+
+    try:
+        result = model_obj.align(str(audio_path), text, language=lang)
+    except Exception as e:  # noqa: BLE001
+        raise RuntimeError(f"align failed on {audio_path.name}: {e}") from e
+
+    segments = [
+        {"start": float(seg.start), "end": float(seg.end),
+         "text": (seg.text or "").strip()}
+        for seg in result.segments
+        if (seg.text or "").strip()
+    ]
+    return segments, lang

gui/main_window.py

@@ -728,6 +728,12 @@ class MainWindow(QMainWindow):
                 "Этап генерации LRC выключен. Включите его на вкладке LRC."
             )
             return
+        if task == "lrc" and not cfg.transcribe.enabled:
+            QMessageBox.information(
+                self, "Транскрибация выключена",
+                "Транскрибация отключена, ASR запускаться не будет."
+                "LRC будет построен только по готовым text.txt."
+            )
         if task == "embed" and not cfg.lrc.embed_enabled:
             QMessageBox.warning(
                 self, "Встраивание LRC отключено",

mark_no_speech.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python3
+"""Mark tracks with metadata.empty_text=true if their text is missing or hallucinated."""
+import json, sys
+from pathlib import Path
+
+HALLUCINATION = (
+    "продолжение следует", "субтитры сделан", "субтитры создава",
+    "created with", "russian-area.de", "музыка музыка",
+)
+
+
+def main(root: Path) -> int:
+    count = 0
+    for meta in root.rglob("metadata.json"):
+        track = meta.parent
+        txt = track / "text.txt"
+        if txt.is_file():
+            body = txt.read_text(encoding="utf-8", errors="ignore").strip().lower()
+            # If the text is real (not hallucination) and not empty, skip marking.
+            if body and not any(h in body for h in HALLUCINATION):
+                continue
+            # Remove the text.txt so the next run will re-process it.
+            txt.unlink()
+        # Set empty_text=True in metadata.
+        try:
+            data = json.loads(meta.read_text(encoding="utf-8"))
+        except (OSError, ValueError):
+            data = {}
+        data["empty_text"] = True
+        meta.write_text(
+            json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8",
+        )
+        print(f"marked: {track}")
+        count += 1
+    print(f"\nmarked {count} tracks")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main(Path(sys.argv[1] if len(sys.argv) > 1 else ".")))

requirements.txt

@@ -3,6 +3,7 @@ PySide6>=6.6
 
 # ASR
 whisperx>=3.1
+stable-ts>=2.16
 onnx-asr>=0.4
 
 # Recognition & lyrics