feat(server): add workbench API, DSH planner worker and RAG pipeline

server/app/rag/media.py

@@ -0,0 +1,100 @@
+"""音视频转写：faster-whisper 本地模型 + ffmpeg 抽音轨。
+输出与 .srt 同格式的「去时间戳、按语义间隙合并」文本，直接进入既有 RAG 分块管线。
+"""
+from __future__ import annotations
+
+import os
+import re
+import subprocess
+import threading
+from pathlib import Path
+from .parse import clean_text
+
+MEDIA_SUFFIXES = {".mp4", ".mov", ".m4a", ".mp3", ".wav", ".flv", ".mkv", ".avi", ".webm", ".aac", ".ogg", ".flac"}
+
+# 模型目录：优先环境变量，其次 modelscope 缓存里的 gpustack/faster-whisper-small
+_MODEL_DIR_CANDIDATES = [
+    os.environ.get("WHISPER_MODEL_DIR", ""),
+    str(Path.home() / ".cache/modelscope/models/gpustack--faster-whisper-small/snapshots/master"),
+]
+_model = None
+_model_lock = threading.Lock()
+
+
+def _get_model():
+    global _model
+    if _model is not None:
+        return _model
+    with _model_lock:
+        if _model is not None:
+            return _model
+        from faster_whisper import WhisperModel
+        for cand in _MODEL_DIR_CANDIDATES:
+            if cand and Path(cand).exists():
+                _model = WhisperModel(cand, device="cpu", compute_type="int8", local_files_only=True)
+                return _model
+        _model = WhisperModel("small", device="cpu", compute_type="int8")
+        return _model
+
+
+def is_media(path: Path) -> bool:
+    return path.suffix.lower() in MEDIA_SUFFIXES
+
+
+def _probe_duration(path: Path) -> float:
+    try:
+        r = subprocess.run(
+            ["ffprobe", "-v", "quiet", "-show_entries", "format=duration",
+             "-of", "csv=p=0", str(path)],
+            capture_output=True, text=True, timeout=30)
+        return float(r.stdout.strip() or 0)
+    except Exception:
+        return 0.0
+
+
+def transcribe_media(path: Path, progress_cb=None) -> list[dict]:
+    """音视频 → 语义段落列表 [{text, meta:{heading}}]。
+    超长音先压缩码率降体积；VAD 过滤静音；按标点+间隙合并成语义段。"""
+    src = Path(path)
+    work = src
+    # 大文件先转 16k mono wav（whisper 输入要求，同时把视频降为纯音轨）
+    if src.suffix.lower() not in (".wav",) or _probe_duration(src) > 0:
+        work = src.with_suffix(".wav.wav")
+        cmd = ["ffmpeg", "-y", "-i", str(src), "-vn", "-ar", "16000", "-ac", "1",
+               "-b:a", "64k", str(work)]
+        r = subprocess.run(cmd, capture_output=True, text=True, timeout=3600)
+        if r.returncode != 0 or not work.exists() or work.stat().st_size == 0:
+            raise ValueError(f"ffmpeg 抽音轨失败: {r.stderr[-200:]}")
+    try:
+        model = _get_model()
+        segments, _info = model.transcribe(str(work), language="zh", vad_filter=True,
+                                           vad_parameters={"min_silence_duration_ms": 700})
+        lines: list[str] = []
+        for seg in segments:
+            t = (seg.text or "").strip()
+            if t:
+                lines.append(t)
+            if progress_cb and len(lines) % 20 == 0:
+                progress_cb(len(lines))
+        if not lines:
+            return []
+        # 合并成语义段：约 15 句或 1200 字一段（与既有 2200 字分块目标匹配）
+        merged, buf, size = [], [], 0
+        for t in lines:
+            buf.append(t)
+            size += len(t)
+            if size >= 1200 or t.endswith(("。", "！", "？", "!", "?")) and size >= 600:
+                merged.append(" ".join(buf))
+                buf, size = [], 0
+        if buf:
+            merged.append(" ".join(buf))
+        # 转写结果也经过同一套结构清洗：去掉字幕式序号/时间戳、不可见字符和句首独立口头填充词，
+        # 保留正文中的日期、金额、型号等数字。
+        return [{"text": clean_text(m), "meta": {"heading": src.stem}}
+                for m in merged if clean_text(m)]
+    finally:
+        if work != src:
+            try:
+                work.unlink(missing_ok=True)
+            except OSError:
+                pass