feat(web): speaker diarization via pyannote (parity with whisper_stt)

- Add app/diarize.py: local snapshot, A/B labels, disclaimer text
- transcribe_file and async jobs support diarize flag; Form diarize on API
- UI checkbox (default on); requirements: pyannote.audio, huggingface_hub
- README: env vars and model notes

Made-with: Cursor

app/diarize.py

@@ -0,0 +1,172 @@
+"""웹 STT용 화자 분리 — whisper_stt.py와 동일한 pyannote 로컬 스냅샷 + 타임라인 정렬."""
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from typing import Any
+
+from .pyannote_auth import load_pyannote_pipeline
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+DEFAULT_DIARIZE_MODEL_DIR = PROJECT_ROOT / "models" / "pyannote-diarization-3.1"
+
+DIARIZE_DISCLAIMER_KO = (
+    "※ 화자 A, B, C… 는 실제 이름이 아니라, 이 녹음에서 말이 처음 잡힌 순서로 붙인 구분자입니다.\n"
+    "※ 같은 사람이 여러 구간으로 나뉘면 라벨이 바뀌거나 섞일 수 있으니, 중요한 회의는 검수가 필요합니다.\n\n"
+)
+
+
+def diarization_annotation(diarization: Any) -> Any:
+    """pyannote.audio 4.x는 DiarizeOutput; 구간은 .speaker_diarization에 있다."""
+    ann = getattr(diarization, "speaker_diarization", None)
+    return diarization if ann is None else ann
+
+
+def validate_pyannote_snapshot(model_dir: Path | str) -> None:
+    cfg = Path(model_dir) / "config.yaml"
+    if cfg.is_file():
+        return
+    p = Path(model_dir).resolve()
+    raise ValueError(
+        f"pyannote 모델 폴더가 불완전합니다 (config.yaml 없음): {p}. "
+        "hf download pyannote/speaker-diarization-3.1 --local-dir ./models/pyannote-diarization-3.1"
+    )
+
+
+def resolve_local_diarize_dir(override: str | None) -> Path:
+    if override:
+        path = Path(override).expanduser().resolve()
+        if path.is_dir():
+            return path
+        raise ValueError(f"화자 분리 모델 폴더가 없습니다: {path}")
+
+    for cand in (os.environ.get("WHISPER_DIARIZE_MODEL_DIR"), os.environ.get("PYANNOTE_MODEL_DIR")):
+        if cand:
+            path = Path(cand).expanduser().resolve()
+            if path.is_dir():
+                return path
+
+    path = DEFAULT_DIARIZE_MODEL_DIR.resolve()
+    if path.is_dir():
+        return path
+    raise ValueError(
+        f"화자 분리 모델 폴더가 없습니다: {path}. "
+        "프로젝트 루트에서: hf download pyannote/speaker-diarization-3.1 "
+        "--local-dir ./models/pyannote-diarization-3.1 (약관 동의·HF 토큰 필요)"
+    )
+
+
+def speaker_turns(audio_path: str, *, model_dir: str | None = None) -> list[tuple[float, float, str]]:
+    import torch
+
+    resolved = resolve_local_diarize_dir(model_dir)
+    validate_pyannote_snapshot(resolved)
+    pipeline = load_pyannote_pipeline(resolved)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    pipeline.to(device)
+    diarization = pipeline(audio_path)
+    ann = diarization_annotation(diarization)
+    turns: list[tuple[float, float, str]] = []
+    for segment, _, label in ann.itertracks(yield_label=True):
+        turns.append((float(segment.start), float(segment.end), str(label)))
+    turns.sort(key=lambda x: x[0])
+    return turns
+
+
+def _overlap_sec(a0: float, a1: float, b0: float, b1: float) -> float:
+    return max(0.0, min(a1, b1) - max(a0, b0))
+
+
+def _assign_speaker(
+    seg_start: float, seg_end: float, turns: list[tuple[float, float, str]]
+) -> str | None:
+    best: str | None = None
+    best_ov = 0.0
+    for t0, t1, sp in turns:
+        ov = _overlap_sec(seg_start, seg_end, t0, t1)
+        if ov > best_ov:
+            best_ov = ov
+            best = sp
+    if best is None or best_ov < 0.05:
+        return None
+    return best
+
+
+def speaker_label_order(turns: list[tuple[float, float, str]]) -> dict[str, str]:
+    order: list[str] = []
+    for t0, _, sp in sorted(turns, key=lambda x: x[0]):
+        if sp not in order:
+            order.append(sp)
+
+    def letter(i: int) -> str:
+        if i < 26:
+            return chr(ord("A") + i)
+        return f"SP{i + 1}"
+
+    return {sp: letter(i) for i, sp in enumerate(order)}
+
+
+def format_diarized_text(
+    whisper_segments: list[dict[str, Any]],
+    turns: list[tuple[float, float, str]],
+) -> str:
+    labels = speaker_label_order(turns)
+    lines: list[str] = []
+    current_letter: str | None = None
+    current_parts: list[str] = []
+
+    def flush() -> None:
+        nonlocal current_letter, current_parts
+        if current_letter is not None and current_parts:
+            lines.append(f"{current_letter}: {' '.join(current_parts).strip()}")
+        current_letter = None
+        current_parts = []
+
+    for seg in whisper_segments:
+        text = (seg.get("text") or "").strip()
+        if not text:
+            continue
+        start = float(seg["start"])
+        end = float(seg["end"])
+        sp = _assign_speaker(start, end, turns)
+        letter = labels.get(sp, "?") if sp is not None else "?"
+
+        if letter == current_letter:
+            current_parts.append(text)
+        else:
+            flush()
+            current_letter = letter
+            current_parts = [text]
+
+    flush()
+    return "\n".join(lines)
+
+
+def segments_with_speakers(
+    whisper_segments: list[dict[str, Any]],
+    turns: list[tuple[float, float, str]],
+) -> list[dict[str, Any]]:
+    labels = speaker_label_order(turns)
+    out: list[dict[str, Any]] = []
+    for seg in whisper_segments:
+        text = (seg.get("text") or "").strip()
+        if not text:
+            continue
+        sp = _assign_speaker(float(seg["start"]), float(seg["end"]), turns)
+        letter = labels.get(sp, "?") if sp is not None else "?"
+        out.append({**seg, "text": text, "speaker": letter})
+    return out
+
+
+def build_diarized_output(
+    whisper_segments: list[dict[str, Any]],
+    audio_path: str,
+    *,
+    model_dir: str | None = None,
+    with_disclaimer: bool = True,
+) -> tuple[str, list[dict[str, Any]]]:
+    turns = speaker_turns(audio_path, model_dir=model_dir)
+    body = format_diarized_text(whisper_segments, turns)
+    text = (DIARIZE_DISCLAIMER_KO + body) if with_disclaimer else body
+    segs = segments_with_speakers(whisper_segments, turns)
+    return text, segs