update shazam and liblrc services. adding text synchronizations

core/pipeline.py

@@ -19,6 +19,7 @@ import json
 import shutil
 import tempfile
 import threading
+import re
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Callable, Optional
@@ -212,17 +213,40 @@ def _try_post_asr_match(
     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:
+    if not text:
         return None
 
-    head = _text_head(text, 8)
-    log(f"    LRCLIB по тексту: {head!r}...")
+    # Filter out common ASR watermarks/credits
+    ignore_phrases = ["субтитры", "редактор", "перевод", "сделал", "озвучка"]
+    lines = []
+    for line in text.splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        if any(p in line.lower() for p in ignore_phrases):
+            continue
+        lines.append(line)
+
+    if not lines:
+        return None
+
+    # Take the first line with at least 3 words, strip punctuation
+    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,
+            query, limit=5,
             user_agent=config.lyrics.user_agent,
             timeout=config.lyrics.timeout,
         )
@@ -597,6 +621,7 @@ def _run_staged(
                     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"]

core/separate.py

@@ -1,4 +1,11 @@
-"""Vocal separation via unblend (Mel-Band RoFormer)."""
+"""Vocal separation via unblend (Mel-Band RoFormer).
+
+Two entry points:
+
+- separate_vocals()       single file, subprocess per call (sequential mode)
+- separate_vocals_batch() many files in ONE subprocess, model loaded once
+                          (staged mode — big speedup on batches)
+"""
 from __future__ import annotations
 
 import os
@@ -6,6 +13,19 @@ import subprocess
 from pathlib import Path
 
 
+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(
     input_wav: Path,
     work_dir: Path,
@@ -13,8 +33,9 @@ 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."""
+    """Separate one file. Returns path to the isolated stem WAV."""
     work_dir.mkdir(parents=True, exist_ok=True)
     output_pattern = work_dir / "{track}" / "{stem}.wav"
     cmd = [
@@ -22,15 +43,10 @@ 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 +54,62 @@ 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",
+) -> dict[Path, Path]:
+    """Separate many files in one unblend invocation.
+
+    Input WAVs are expected to live in distinct directories
+    (e.g. <work_root>/<stem>/convert.wav). The output pattern uses {parent}
+    so each result lands next to its input as <parent>/vocals.wav.
+
+    Returns: {input_path: vocal_path}
+    """
+    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,
+    ]
+
+    result = subprocess.run(cmd, capture_output=True, text=True, env=_build_env(custom_kernels))
+    _check_result(result, f"{len(input_wavs)} files")
+
+    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:
+        # Fallback: search anywhere under work_root
+        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.
+Includes aggressive VRAM cleanup to prevent OOM on long batches,
+and automatic model reload if the CUDA context gets corrupted.
 """
 from __future__ import annotations
 
+import gc
 import time
 from pathlib import Path
 from typing import Optional
@@ -26,7 +26,7 @@ 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))
@@ -93,11 +93,31 @@ 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()
+                gc.collect()
+                try:
+                    import torch
+                    if torch.cuda.is_available():
+                        torch.cuda.empty_cache()
+                except ImportError:
+                    pass
+                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}")
@@ -121,7 +141,21 @@ def _transcribe_whisperx(
         batch_size=batch_size,
         language=language,
     )
-    return " ".join(seg["text"].strip() for seg in result["segments"]).strip()
+    
+    text = " ".join(seg["text"].strip() for seg in result["segments"]).strip()
+    
+    # Free VRAM aggressively to prevent fragmentation/OOM on long batches
+    del audio
+    del result
+    gc.collect()
+    try:
+        import torch
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+    except ImportError:
+        pass
+        
+    return text
 
 
 def _transcribe_gigaam(audio_path: Path, provider: str) -> str: