feat: AI 场景路由、ASR 服务及前后端全链路同步

- 新增 AI 场景路由控制器和管理接口 - 新增 ASR 语音识别服务及前后端集成 - 同步 AI Runtime 客户端到 Web/小程序/Life-Script - 完善 AI 配置测试修复和管理后台路由配置 - 新增数据库迁移脚本 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-23 13:25:21 +08:00
parent d77090aa5e
commit 89fc42819d
72 changed files with 4584 additions and 383 deletions
@@ -0,0 +1,114 @@
+import os
+import sys
+import tempfile
+import time
+import types
+import importlib.machinery
+from pathlib import Path
+from threading import Lock
+
+from fastapi import FastAPI, File, UploadFile
+
+app = FastAPI(title="Emotion Museum ASR")
+
+MODEL_NAME = os.getenv("ASR_MODEL", "/data/programs/emotion-museum/asr-service/models/paraformer-zh-onnx")
+DEVICE = os.getenv("ASR_DEVICE", "cpu")
+WORK_DIR = Path(os.getenv("ASR_WORK_DIR", "/tmp/emotion-museum-asr"))
+WORK_DIR.mkdir(parents=True, exist_ok=True)
+
+_model = None
+_model_lock = Lock()
+
+
+def get_model():
+    global _model
+    with _model_lock:
+        if _model is None:
+            # funasr-onnx imports the optional SenseVoice module from package
+            # __init__, which imports torch even when we only use Paraformer.
+            # This service intentionally runs the ONNX path without PyTorch.
+            if "torch" not in sys.modules:
+                torch_stub = types.ModuleType("torch")
+                torch_stub.__spec__ = importlib.machinery.ModuleSpec("torch", loader=None)
+                torch_stub.Tensor = type("Tensor", (), {})
+                sys.modules["torch"] = torch_stub
+            from funasr_onnx import Paraformer
+
+            _model = Paraformer(
+                MODEL_NAME,
+                batch_size=1,
+                device_id=-1,
+                quantize=True,
+                intra_op_num_threads=2,
+            )
+        return _model
+
+
+def clean_text(text):
+    if isinstance(text, (list, tuple)):
+        text = text[0] if text else ""
+    if not text:
+        return ""
+    markers = ["<|zh|>", "<|en|>", "<|yue|>", "<|ja|>", "<|ko|>", "<|nospeech|>", "<|withitn|>", "<|woitn|>"]
+    for marker in markers:
+        text = text.replace(marker, "")
+    return text.strip()
+
+
+@app.get("/health")
+def health():
+    return {
+        "status": "ok",
+        "engine": "funasr-onnx",
+        "model": MODEL_NAME,
+        "device": DEVICE,
+        "loaded": _model is not None,
+    }
+
+
+@app.post("/transcribe")
+async def transcribe(file: UploadFile = File(...)):
+    started = time.time()
+    suffix = Path(file.filename or "audio.wav").suffix or ".wav"
+    tmp_path = None
+
+    try:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix, dir=WORK_DIR) as tmp:
+            tmp_path = Path(tmp.name)
+            while True:
+                chunk = await file.read(1024 * 1024)
+                if not chunk:
+                    break
+                tmp.write(chunk)
+
+        model = get_model()
+        result = model([str(tmp_path)])
+        first = result[0] if isinstance(result, list) and result else result
+        text = clean_text(first.get("preds", first.get("text", "")) if isinstance(first, dict) else str(first or ""))
+        language = first.get("language") if isinstance(first, dict) else None
+
+        return {
+            "success": bool(text),
+            "text": text,
+            "language": language,
+            "durationMs": int((time.time() - started) * 1000),
+            "engine": "funasr-onnx",
+            "model": MODEL_NAME,
+            "errorMessage": None if text else "empty recognition result",
+        }
+    except Exception as exc:
+        return {
+            "success": False,
+            "text": "",
+            "language": None,
+            "durationMs": int((time.time() - started) * 1000),
+            "engine": "funasr-onnx",
+            "model": MODEL_NAME,
+            "errorMessage": str(exc),
+        }
+    finally:
+        if tmp_path:
+            try:
+                tmp_path.unlink(missing_ok=True)
+            except Exception:
+                pass