diff --git a/README.md b/README.md
index 3a9e24d..2fb335a 100644
--- a/README.md
+++ b/README.md
@@ -86,13 +86,32 @@ pip install -r requirements-whisper-stt.txt
```bash
conda activate stt # 또는 사용 중인 env (예: ncue)
-pip uninstall -y torch torchvision torchaudio
-pip uninstall -y torch torchvision torchaudio # Skipping만 나올 때까지 반복
+pip uninstall -y torch torchvision torchaudio functorch
+pip uninstall -y torch torchvision torchaudio functorch # Skipping만 나올 때까지 반복
pip cache purge
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements-whisper-stt.txt
```
+**같은 오류(`ATen.h` 없음 등)가 `torch` 재설치 시에도 반복되면**
+`pip uninstall`만으로는 깨진 `site-packages/torch` 폴더가 남는 경우가 있습니다. 아래로 **잔여 디렉터리를 직접 삭제**한 뒤 다시 설치하세요. (`python3.11`은 `python -c "import sys; print(sys.version_info[:2])"`로 맞춤.)
+
+```bash
+conda activate ncue # 문제 나는 env
+pip uninstall -y torch torchvision torchaudio functorch 2>/dev/null || true
+rm -rf "$CONDA_PREFIX/lib/python3.11/site-packages/torch" \
+ "$CONDA_PREFIX/lib/python3.11/site-packages/torch-"*.dist-info \
+ "$CONDA_PREFIX/lib/python3.11/site-packages/torchaudio" \
+ "$CONDA_PREFIX/lib/python3.11/site-packages/torchaudio-"*.dist-info \
+ "$CONDA_PREFIX/lib/python3.11/site-packages/torchgen" \
+ "$CONDA_PREFIX/lib/python3.11/site-packages/functorch"
+pip cache purge
+pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
+pip install -r requirements-whisper-stt.txt
+```
+
+그래도 실패하면 **새 conda 환경**(`conda create -n stt-whisper python=3.11 -y`)을 만들고, 위 README의 **conda로 PyTorch 먼저** 절차만 그 env에서 진행하는 것이 가장 확실합니다.
+
애초에 꼬이지 않게 하려면 **PyTorch를 conda로 먼저** 깐 뒤 위 requirements만 pip로 설치하는 것을 권장합니다.
```bash
@@ -102,6 +121,8 @@ pip install -r requirements-whisper-stt.txt
```
- **Hugging Face `hf` CLI**: `pip install huggingface_hub` 후 `hf auth login`, `hf download …` (화자 구분용 pyannote 모델 등).
+ - $ hf auth login
+ - $ hf download pyannote/speaker-diarization-3.1 --local-dir ./models/pyannote-diarization-3.1
- **화자 구분(기본 켜짐)**: `./models/pyannote-diarization-3.1` 에 pyannote 스냅샷이 있어야 합니다. 없으면 스크립트가 `hf download` 안내 후 종료합니다. 모델 받기: [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) 약관 동의 후 `hf auth login`, `hf download … --local-dir ./models/pyannote-diarization-3.1`. 다른 경로는 `--diarize-model-dir` 또는 `WHISPER_DIARIZE_MODEL_DIR` 로 지정.
- **화자 구분 끄기**: `python whisper_stt.py 입력.m4a 출력.txt --no-diarize` (Whisper 통문만 저장)
@@ -131,12 +152,15 @@ uvicorn app.main:app --reload --host 127.0.0.1 --port 8025
브라우저에서 `http://127.0.0.1:8025` 접속.
+업로드 전사가 끝나면 **`app/diarize.py`** 가 `whisper_stt.py`와 같은 방식으로 pyannote 화자 구분을 시도합니다. 저장소 루트의 **`models/pyannote-diarization-3.1`** (`config.yaml` 포함)이 있어야 하며, `requirements.txt`에 `pyannote.audio`가 포함되어 있습니다. 스냅샷이 없거나 오류면 전사만 반환하고, 응답에 `speaker_diarization: false` 와 `diarize_skip_reason` 이 붙을 수 있습니다.
+
---
## 옵션·환경 변수
- **모델**: 기본 `small` (정확도/속도 균형). `APP_WHISPER_MODEL=base|small|medium|large-v3` 등으로 변경 가능.
- **디바이스**: 기본 CPU. Apple Silicon에서 Metal은 `faster-whisper` 단독으로는 제한이 있어 CPU 기본값을 권장.
+- **웹 화자 구분**: `APP_DIARIZE=1`(기본) — `0`/`false`/`off` 이면 pyannote 단계 생략. `APP_PYANNOTE_MODEL_DIR` 로 스냅샷 경로 지정(없으면 프로젝트 `models/pyannote-diarization-3.1`).
- **기타**: `APP_WHISPER_DEVICE`, `APP_WHISPER_COMPUTE_TYPE`, 업로드 크기 등은 `app/main.py` 및 `.env` 예시를 참고.
---
diff --git a/app/diarize.py b/app/diarize.py
new file mode 100644
index 0000000..cafbe61
--- /dev/null
+++ b/app/diarize.py
@@ -0,0 +1,186 @@
+"""
+업로드 STT 결과에 pyannote 화자 구분을 합칩니다 (whisper_stt.py 와 동일한 규칙).
+환경변수 APP_DIARIZE=0 이면 비활성화. 모델: APP_PYANNOTE_MODEL_DIR 또는 프로젝트 models/pyannote-diarization-3.1
+"""
+from __future__ import annotations
+
+import logging
+import os
+from pathlib import Path
+from typing import Any
+
+log = logging.getLogger(__name__)
+
+_APP_DIR = Path(__file__).resolve().parent
+_PROJECT_ROOT = _APP_DIR.parent
+_DEFAULT_SNAPSHOT = _PROJECT_ROOT / "models" / "pyannote-diarization-3.1"
+
+_DISCLAIMER = (
+ "※ 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 _env_disabled() -> bool:
+ v = os.getenv("APP_DIARIZE", "1").strip().lower()
+ return v in ("0", "false", "no", "off")
+
+
+def resolve_snapshot_dir() -> Path | None:
+ raw = os.getenv("APP_PYANNOTE_MODEL_DIR", "").strip()
+ if raw:
+ p = Path(raw).expanduser()
+ if not p.is_absolute():
+ p = (_PROJECT_ROOT / p).resolve()
+ else:
+ p = _DEFAULT_SNAPSHOT.resolve()
+ if (p / "config.yaml").is_file():
+ return p
+ return None
+
+
+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 _merge_segments(
+ whisper_segments: list[dict[str, Any]],
+ turns: list[tuple[float, float, str]],
+) -> tuple[str, list[dict[str, Any]]]:
+ labels = _speaker_label_order(turns)
+ merged_lines: list[str] = []
+ out_segments: list[dict[str, Any]] = []
+
+ current_letter: str | None = None
+ current_parts: list[str] = []
+ current_start: float | None = None
+ current_end: float | None = None
+
+ def flush() -> None:
+ nonlocal current_letter, current_parts, current_start, current_end
+ if current_letter is not None and current_parts and current_start is not None and current_end is not None:
+ line = " ".join(current_parts).strip()
+ merged_lines.append(f"{current_letter}: {line}")
+ out_segments.append(
+ {
+ "start": current_start,
+ "end": current_end,
+ "speaker": current_letter,
+ "text": line,
+ }
+ )
+ current_letter = None
+ current_parts = []
+ current_start = None
+ current_end = None
+
+ 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)
+ current_end = end
+ else:
+ flush()
+ current_letter = letter
+ current_parts = [text]
+ current_start = start
+ current_end = end
+
+ flush()
+ body = "\n".join(merged_lines).strip()
+ return body, out_segments
+
+
+def _run_pyannote(audio_path: str, model_dir: Path) -> list[tuple[float, float, str]]:
+ import torch
+ from pyannote.audio import Pipeline
+
+ pipeline = Pipeline.from_pretrained(str(model_dir))
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ pipeline.to(device)
+ diarization = pipeline(audio_path)
+ turns: list[tuple[float, float, str]] = []
+ for segment, _, label in diarization.itertracks(yield_label=True):
+ turns.append((float(segment.start), float(segment.end), str(label)))
+ turns.sort(key=lambda x: x[0])
+ return turns
+
+
+def apply_speaker_diarization(result: dict[str, Any], audio_path: str) -> dict[str, Any]:
+ """
+ transcribe_file 결과에 speaker 필드·A:/B: 본문을 반영.
+ 실패·비활성 시 원본 유지 및 메타만 추가.
+ """
+ out = dict(result)
+ out.setdefault("speaker_diarization", False)
+ out.pop("diarize_skip_reason", None)
+
+ if _env_disabled():
+ out["diarize_skip_reason"] = "APP_DIARIZE=0"
+ return out
+
+ snap = resolve_snapshot_dir()
+ if snap is None:
+ out["diarize_skip_reason"] = f"pyannote 스냅샷 없음(config.yaml): {_DEFAULT_SNAPSHOT}"
+ log.warning("Speaker diarization skipped: %s", out["diarize_skip_reason"])
+ return out
+
+ try:
+ import pyannote.audio # noqa: F401
+ except ImportError:
+ out["diarize_skip_reason"] = "pyannote.audio 미설치"
+ log.warning("Speaker diarization skipped: pyannote not installed")
+ return out
+
+ segs = list(out.get("segments") or [])
+ if not segs:
+ out["diarize_skip_reason"] = "세그먼트 없음"
+ return out
+
+ try:
+ turns = _run_pyannote(audio_path, snap)
+ body, new_segs = _merge_segments(segs, turns)
+ out["text"] = _DISCLAIMER + body if body else out.get("text", "")
+ out["segments"] = new_segs
+ out["speaker_diarization"] = True
+ out.pop("diarize_skip_reason", None)
+ except Exception as e:
+ out["diarize_skip_reason"] = str(e)
+ log.exception("Speaker diarization failed")
+ return out
diff --git a/app/main.py b/app/main.py
index 9abe7aa..02035bb 100644
--- a/app/main.py
+++ b/app/main.py
@@ -18,6 +18,7 @@ from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from . import db
+from .diarize import apply_speaker_diarization
from .stt import transcribe_file, transcribe_iter
@@ -74,6 +75,8 @@ class _Job:
created_at: float = dataclasses.field(default_factory=time.time)
updated_at: float = dataclasses.field(default_factory=time.time)
cancel_event: threading.Event = dataclasses.field(default_factory=threading.Event, repr=False)
+ speaker_diarization: bool = False
+ diarize_skip_reason: str | None = None
_JOBS: dict[str, _Job] = {}
@@ -111,6 +114,8 @@ def _job_public(job: _Job) -> dict[str, Any]:
"language_probability": job.language_probability,
"duration_sec": job.duration_sec,
"error": job.error,
+ "speaker_diarization": job.speaker_diarization,
+ "diarize_skip_reason": job.diarize_skip_reason,
"created_at": job.created_at,
"updated_at": job.updated_at,
}
@@ -204,6 +209,7 @@ async def api_transcribe(
vad_filter=bool(vad_filter),
beam_size=int(beam_size),
)
+ result = apply_speaker_diarization(result, saved_path)
# 단발성 API도 DB 저장
try:
db.insert_record(
@@ -428,6 +434,20 @@ def _run_job(job_id: str) -> None:
if cancelled or job.cancel_event.is_set():
job.status = "cancelled"
else:
+ merged = apply_speaker_diarization(
+ {
+ "text": job.text,
+ "segments": list(job.segments),
+ "detected_language": job.detected_language,
+ "language_probability": job.language_probability,
+ "duration_sec": job.duration_sec,
+ },
+ tmp_path,
+ )
+ job.text = merged.get("text", job.text)
+ job.segments = merged.get("segments", job.segments)
+ job.speaker_diarization = bool(merged.get("speaker_diarization"))
+ job.diarize_skip_reason = merged.get("diarize_skip_reason")
job.status = "completed"
job.progress = 1.0
job.updated_at = time.time()
diff --git a/app/static/index.html b/app/static/index.html
index 7c725da..8615298 100644
--- a/app/static/index.html
+++ b/app/static/index.html
@@ -314,7 +314,8 @@
- 허용: mp3, m4a, wav, mp4, aac, ogg, flac, webm
- - 첫 실행 시 Whisper 모델 다운로드로 시간이 걸릴 수 있습니다.
+ - 첫 실행 시 Whisper 모델 다운로드로 시간이 걸릴 수 있습니다.
+ - 완료 후 pyannote로 화자 구분을 시도합니다 (models/pyannote-diarization-3.1 필요).
@@ -613,7 +614,10 @@
const lang = body.detected_language ? `${body.detected_language}` : "-";
const prob = typeof body.language_probability === "number" ? body.language_probability.toFixed(3) : "-";
const dur = typeof body.duration_sec === "number" ? `${body.duration_sec.toFixed(1)}s` : "-";
- metaEl.textContent = `감지 언어: ${lang} (p=${prob}), 오디오 길이: ${dur}`;
+ let diarizeMeta = "";
+ if (body.speaker_diarization === true) diarizeMeta = " · 화자 구분: 적용";
+ else if (body.diarize_skip_reason) diarizeMeta = " · 화자 구분: 생략";
+ metaEl.textContent = `감지 언어: ${lang} (p=${prob}), 오디오 길이: ${dur}${diarizeMeta}`;
if (startedAt) {
timingEl.textContent = `${((performance.now() - startedAt) / 1000).toFixed(2)}s`;
diff --git a/requirements-whisper-stt.txt b/requirements-whisper-stt.txt
index dba3a9e..f5470a1 100644
--- a/requirements-whisper-stt.txt
+++ b/requirements-whisper-stt.txt
@@ -13,12 +13,28 @@
#
# 2) 이미 깨진 경우(위 오류가 난 뒤) 복구:
# conda activate stt # 또는 ncue 등 해당 env
-# pip uninstall -y torch torchvision torchaudio
-# pip uninstall -y torch torchvision torchaudio # "WARNING: Skipping" 나올 때까지 반복
+# pip uninstall -y torch torchvision torchaudio functorch
+# pip uninstall -y torch torchvision torchaudio functorch # Skipping만 나올 때까지 반복
# pip cache purge
# pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
# pip install -r requirements-whisper-stt.txt
#
+# 3) 2)를 해도 "ATen.h" / OSError 가 나면 — site-packages 에 깨진 torch 잔여 디렉터리가 남은 경우:
+# conda activate
+# pip uninstall -y torch torchvision torchaudio functorch 2>/dev/null; true
+# rm -rf "$CONDA_PREFIX/lib/python3.11/site-packages/torch" \
+# "$CONDA_PREFIX/lib/python3.11/site-packages/torch-"*.dist-info \
+# "$CONDA_PREFIX/lib/python3.11/site-packages/torchaudio" \
+# "$CONDA_PREFIX/lib/python3.11/site-packages/torchaudio-"*.dist-info \
+# "$CONDA_PREFIX/lib/python3.11/site-packages/torchgen" \
+# "$CONDA_PREFIX/lib/python3.11/site-packages/functorch"
+# (Python 버전이 3.12면 경로의 python3.11 을 python3.12 로 바꿀 것)
+# pip cache purge
+# pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
+# pip install -r requirements-whisper-stt.txt
+#
+# 4) 그래도 안 되면: conda create -n stt-whisper python=3.11 -y 후 1)부터 새 env에만 설치
+#
# GPU(CUDA) 쓰는 경우: https://pytorch.org/get-started/locally/ 에서 맞는 pip/conda 명령 사용
#
# CLI: PATH에 hf — pip install huggingface_hub 후 hf auth login
diff --git a/requirements.txt b/requirements.txt
index 0ae3437..ab9aabf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,5 +4,6 @@ python-multipart
pydantic
faster-whisper
imageio-ffmpeg
+pyannote.audio>=3.1.0
psycopg[binary]
python-dotenv
diff --git a/whisper_stt.py b/whisper_stt.py
index a49480b..e72b38b 100644
--- a/whisper_stt.py
+++ b/whisper_stt.py
@@ -16,6 +16,31 @@ import whisper.audio as whisper_audio
DEFAULT_DIARIZE_MODEL_DIR = "./models/pyannote-diarization-3.1"
+def _validate_pyannote_snapshot(model_dir: str) -> None:
+ """README만 있거나 중간에 끊긴 다운로드면 config.yaml 이 없다."""
+ cfg = os.path.join(model_dir, "config.yaml")
+ if os.path.isfile(cfg):
+ return
+ abs_dir = os.path.abspath(model_dir)
+ print(
+ f"오류: pyannote 모델 폴더가 불완전합니다 (config.yaml 없음): {abs_dir}\n"
+ "Hugging Face에서 README만 받아졌거나, 다운로드가 중간에 끊긴 상태일 수 있습니다.\n\n"
+ "프로젝트 루트에서 로그인 후 전체 스냅샷을 다시 받으세요:\n"
+ " hf auth login\n"
+ " hf download pyannote/speaker-diarization-3.1 \\\n"
+ f" --local-dir {DEFAULT_DIARIZE_MODEL_DIR}\n\n"
+ "기존 폴더를 비우고 받으려면(주의: 폴더 안 파일 전부 삭제):\n"
+ f" rm -rf \"{abs_dir}\"/*\n"
+ " hf download pyannote/speaker-diarization-3.1 \\\n"
+ f" --local-dir {DEFAULT_DIARIZE_MODEL_DIR}\n\n"
+ "https://hf.co/pyannote/speaker-diarization-3.1 에서 모델 약관 동의가 되어 있어야 합니다.\n"
+ "화자 구분 없이 Whisper만 쓰려면:\n"
+ " python whisper_stt.py 입력.m4a 출력.txt --no-diarize\n",
+ file=sys.stderr,
+ )
+ sys.exit(1)
+
+
def _resolve_ffmpeg_exe() -> str:
"""PATH의 ffmpeg 또는 imageio-ffmpeg 번들 바이너리."""
path = shutil.which("ffmpeg")
@@ -177,6 +202,7 @@ def _run_diarization(audio_path: str, *, diarize_model_dir: str | None) -> list[
from pyannote.audio import Pipeline
model_dir = _resolve_local_diarize_dir(diarize_model_dir)
+ _validate_pyannote_snapshot(model_dir)
print(f"[4/4] 화자 분리(pyannote) — 로컬 모델: {model_dir}", flush=True)
print("[4/4] 화자 분리 실행 중... (수 분 걸릴 수 있음)", flush=True)
@@ -186,8 +212,8 @@ def _run_diarization(audio_path: str, *, diarize_model_dir: str | None) -> list[
except Exception as e:
print(
f"오류: pyannote 파이프라인을 불러오지 못했습니다: {e}\n\n"
- "모델 파일이 손상되었거나 하위 가중치가 빠졌을 수 있습니다.\n"
- "다시 받기:\n"
+ "config.yaml 은 있는데도 실패하면 하위 체크포인트(segmentation·embedding 등)가 빠졌을 수 있습니다.\n"
+ "폴더를 비운 뒤 전체 재다운로드:\n"
" hf download pyannote/speaker-diarization-3.1 \\\n"
f" --local-dir {DEFAULT_DIARIZE_MODEL_DIR}\n",
file=sys.stderr,