助手本体：会话引擎、LLM 路由、记忆、身份、语音适配
从 Obsidian 笔记库剥离为独立仓库。规划文档与 P0 报告留在笔记库， 代码（助手本体、TTS 服务、P0 基准脚本、ESP32 固件）在此。 架构：服务器是助手，本地是客户端 - 助手本体跑在 cadadmin-server，N5095 只做 USB 外设桥与网络桥接 - 起因：实测 N5095 无 AVX2，跑不动可用的语音模型 当前技术栈 - LLM：DeepSeek deepseek-flash（云端，必须关思考） - TTS：Qwen3-TTS 1.7B（服务器 3090，独立 HTTP 服务） - STT：Zipformer 中英双语 int8（CPU，中文识别率未测） 实测：端到端 16/16 通过，首音延迟 1545ms （对比：本地 LLM + vits-zh-ll 是 179ms，但音色不满意——音质与延迟 在此硬件上此消彼长） 设计要点 - MemoryStore 接口按"可能远程"设计（全 async、可超时、可失败） - user_id / session_id / track_id 三者分开，人脸只作候选不自动切身份 - 记忆查询强制带 owner_user_id，无按 id 单独取的口子 - 自动抽取的记忆是"候选"，默认不参与检索 - 异步写记忆绑定创建时的身份，不读"当前用户" - 引擎与传输解耦，只用文件音频即可端到端测试 模型与音频产物不进版本管理（1.1GB），由 download_models.sh 恢复
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ylhan
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#!/usr/bin/env python3
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"""端到端测试：音频/文本 → STT → 身份 → 记忆 → LLM → TTS → 音频。
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**全程不需要麦克风、音箱、摄像头、ESP32。**
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这是"服务器即助手"架构的直接好处：链路可以在任何有 CPU 的地方验证，
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硬件到货后再接最后一段（真实音频进出）即可。
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跑完会把合成音频写到 out/ 目录，可以人工试听整条链路的实际效果。
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运行：
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cd <项目根>
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~/venvs/ai_assistant/bin/python app/tests/test_engine_e2e.py
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"""
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from __future__ import annotations
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import asyncio
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import shutil
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import sys
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import tempfile
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import time
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import wave
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from pathlib import Path
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import numpy as np
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ROOT = Path(__file__).resolve().parent.parent.parent
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sys.path.insert(0, str(ROOT))
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from app.config import AppConfig # noqa: E402
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from app.engine import ConversationEngine, EventKind # noqa: E402
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from app.identity import GUEST_USER_ID, ManualIdentityProvider # noqa: E402
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from app.llm import FallbackProvider, create_provider # noqa: E402
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from app.memory import SQLiteMemoryStore # noqa: E402
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from app.voice import StreamingSTT, create_tts # noqa: E402
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OK, FAIL = [], []
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OUT = ROOT / "out"
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def check(name: str, cond: bool, detail: str = "") -> None:
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(OK if cond else FAIL).append(name)
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print(f" [{'PASS' if cond else 'FAIL'}] {name}" + (f" — {detail}" if detail else ""))
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def save_wav(path: Path, audio: np.ndarray, sr: int) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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pcm = (np.clip(audio, -1, 1) * 32767).astype(np.int16)
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with wave.open(str(path), "wb") as w:
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w.setnchannels(1)
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w.setsampwidth(2)
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w.setframerate(sr)
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w.writeframes(pcm.tobytes())
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async def collect(engine: ConversationEngine, text: str) -> tuple[str, np.ndarray, list]:
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"""跑一轮，返回 (回复文本, 拼接音频, 事件列表)。"""
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reply, audios, events = [], [], []
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async for ev in engine.respond_to_text(text):
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events.append(ev)
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if ev.kind == EventKind.RESPONSE_DELTA:
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reply.append(ev.text)
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elif ev.kind == EventKind.SPEAK and ev.audio is not None:
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audios.append(ev.audio)
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audio = np.concatenate(audios) if audios else np.zeros(0, dtype=np.float32)
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return "".join(reply).strip(), audio, events
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async def main() -> int:
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cfg = AppConfig.load()
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tmp = Path(tempfile.mkdtemp(prefix="e2e_"))
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OUT.mkdir(parents=True, exist_ok=True)
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print("=== 0. 装配 ===")
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llm = FallbackProvider(create_provider(cfg.llm),
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create_provider(cfg.llm_fallback))
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mem = SQLiteMemoryStore(tmp / "memory.db")
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await mem.open()
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tts = create_tts(cfg.voice)
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stt = StreamingSTT(cfg.voice)
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ident = ManualIdentityProvider()
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engine = ConversationEngine(cfg, llm, mem, ident, tts, stt)
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print(f" LLM={cfg.llm.model} 备用={cfg.llm_fallback.model}")
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tts_desc = (f"{cfg.voice.tts_backend}/{cfg.voice.tts_speaker}"
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if cfg.voice.tts_backend == "remote" else f"local/{cfg.voice.tts_threads}线程")
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print(f" STT={cfg.voice.stt_threads}线程 TTS={tts_desc}")
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check("四个组件装配成功", True)
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ok, detail = await llm.health()
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check("LLM 可用", ok, detail)
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if not ok:
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await mem.close()
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shutil.rmtree(tmp, ignore_errors=True)
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return 1
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await engine.start_session("e2e_sess", user_id="u_ylhan")
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# ---------------------------------------------------------- 1
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print("\n=== 1. 纯文本一轮（LLM → 分句 → TTS）===")
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reply, audio, events = await collect(engine, "现在几点了？随便说一句就行。")
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print(f" 回复: {reply[:60]!r}")
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m = [e for e in events if e.kind == EventKind.METRICS][0].metrics
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print(f" {m.summary()}")
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check("有回复", bool(reply))
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check("有音频输出", len(audio) > 0, f"{len(audio)} 采样点")
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check("回复不含 Markdown",
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not any(c in reply for c in "*#`|[]"), reply[:40])
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# 首音延迟这里只做"没离谱"的兜底检查，不设严格阈值：
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# 本机 N5095 的 TTS RTF 是 1.86（实测），首音必然被 TTS 拖到秒级；
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# 服务器上同一模型是 0.09，首音在 150ms 量级。
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# 真正该卡指标的是部署机（服务器），所以那个断言放在服务器上的验收里。
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check("首音延迟未失控", m.first_audio_ms < 8000, f"{m.first_audio_ms:.0f}ms")
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save_wav(OUT / "e2e_1_text.wav", audio, tts.sample_rate)
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# ---------------------------------------------------------- 2
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print("\n=== 2. 多句回复按句播（每句独立音频）===")
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_, _, events2 = await collect(
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engine, "用三句话介绍一下你自己，每句短一点。")
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speaks = [e for e in events2 if e.kind == EventKind.SPEAK]
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print(f" 分 {len(speaks)} 段: {[e.text[:14] for e in speaks]}")
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check("按句切成了多段", len(speaks) >= 2, f"{len(speaks)} 段")
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audio2 = np.concatenate([e.audio for e in speaks]) if speaks else np.zeros(0, np.float32)
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save_wav(OUT / "e2e_2_multisentence.wav", audio2, tts.sample_rate)
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# ---------------------------------------------------------- 3
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print("\n=== 3. 记忆：显式记住 → 下一轮被检索到 ===")
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await collect(engine, "记住我喜欢喝美式咖啡，不加糖。")
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await asyncio.sleep(0.5) # 等异步写入落库
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stored = await mem.list_memories("u_ylhan", include_candidates=True)
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print(f" 库中记忆 {len(stored)} 条: {[m.text[:20] for m in stored]}")
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check("记忆已落库", len(stored) >= 1, f"{len(stored)} 条")
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check("自动抽取的是候选状态",
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all(m.confirm_status.value == "candidate" for m in stored))
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# 手动确认一条，看它是否参与检索
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await mem.remember(type(stored[0])(
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owner_user_id="u_ylhan", kind=stored[0].kind, key="",
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value="用户喜欢喝美式咖啡，不加糖",
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confirm_status=__import__("app.memory", fromlist=["ConfirmStatus"]).ConfirmStatus.CONFIRMED))
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_, _, events3 = await collect(engine, "我想喝点东西，推荐一下？")
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used = [e for e in events3 if e.kind == EventKind.MEMORY_USED]
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n_used = len(used[0].memories) if used else 0
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print(f" 本轮用到记忆 {n_used} 条")
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check("确认的记忆参与检索", n_used >= 1, f"{n_used} 条")
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# ---------------------------------------------------------- 4
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print("\n=== 4. 身份隔离：换人后拿不到上一个人的记忆 ===")
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ident.switch("u_other", reason="测试切换")
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_, _, events4 = await collect(engine, "我想喝点什么？")
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used4 = [e for e in events4 if e.kind == EventKind.MEMORY_USED]
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n4 = len(used4[0].memories) if used4 else 0
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check("新身份没有旧记忆", n4 == 0, f"{n4} 条")
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reply4 = "".join(e.text for e in events4 if e.kind == EventKind.RESPONSE_DELTA)
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check("回复未泄露咖啡偏好", "美式" not in reply4 and "咖啡" not in reply4,
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reply4[:50])
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# ---------------------------------------------------------- 5
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print("\n=== 5. 访客身份不做个性化 ===")
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ident.reset()
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print(f" 当前身份: {ident.current().user_id} (confirmed={ident.current().confirmed})")
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_, _, events5 = await collect(engine, "我喜欢什么？")
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used5 = [e for e in events5 if e.kind == EventKind.MEMORY_USED]
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check("访客不检索私人记忆", len(used5) == 0 or not used5[0].memories)
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reply5 = "".join(e.text for e in events5 if e.kind == EventKind.RESPONSE_DELTA)
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check("访客回复不编造偏好", "美式" not in reply5, reply5[:50])
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# ---------------------------------------------------------- 6
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print("\n=== 6. 音频输入（真人录音 → STT → LLM → TTS）===")
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# 测试音频不在代码目录里，和模型放在一起（都是可重新下载的产物）。
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# 用模型目录反推，注意要 resolve()——服务器上模型目录是个软链，
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# 不解析的话 .parent 会指到 ~/ai-companion 而不是真实位置。
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wav_path = None
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for cand in (
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cfg.voice.model_dir.resolve().parent / "wavs" / "stt_1.wav",
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ROOT / "p0" / "stt_tts_bench" / "wavs" / "stt_1.wav",
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):
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if cand.is_file():
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wav_path = cand
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break
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if wav_path is not None:
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with wave.open(str(wav_path)) as w:
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samples = np.frombuffer(w.readframes(w.getnframes()),
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dtype=np.int16).astype(np.float32) / 32768.0
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ident.switch("u_ylhan", reason="回到主人")
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t0 = time.perf_counter()
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transcript, audios, events6 = [], [], []
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async for ev in engine.respond_to_audio(samples):
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events6.append(ev)
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if ev.kind == EventKind.TRANSCRIPT:
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transcript.append(ev.text)
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elif ev.kind == EventKind.SPEAK and ev.audio is not None:
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audios.append(ev.audio)
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got = "".join(transcript)
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m6 = [e for e in events6 if e.kind == EventKind.METRICS]
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print(f" STT: {got[:50]!r}")
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if m6 and m6[0].metrics:
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print(f" {m6[0].metrics.summary()}")
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check("音频链路跑通（能出转写）", bool(got.strip()))
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if audios:
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save_wav(OUT / "e2e_6_fromaudio.wav", np.concatenate(audios), tts.sample_rate)
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else:
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check("音频链路跑通", False, f"测试音频不存在: {wav_path}")
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# ---------------------------------------------------------- 7
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print("\n=== 7. 打断：取消正在进行的回复 ===")
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cancelled = "未取消"
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chunks_seen = 0
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async def long_turn():
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# 必须把整个流消费完才谈得上"取消"。
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# 第一版写成 `async for _ in ...: return "已开始"`——
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# 第一个事件就 return，生成器随之关闭，根本没有正在进行的回复可取消，
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# 测试于是永远"失败"，但失败原因跟取消能力毫无关系。
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nonlocal chunks_seen
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async for _ev in engine.respond_to_text("请详细讲一讲中国历史，越长越好。"):
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chunks_seen += 1
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return "已完成"
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task = asyncio.create_task(long_turn())
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await asyncio.sleep(0.7)
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task.cancel()
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try:
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await task
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except asyncio.CancelledError:
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cancelled = "已取消"
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check("长回复可被取消", cancelled == "已取消" and chunks_seen > 0,
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f"{cancelled}，取消前已收到 {chunks_seen} 个事件")
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await engine.end_session()
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await mem.close()
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await llm.aclose()
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shutil.rmtree(tmp, ignore_errors=True)
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print(f"\n合成音频已写入 {OUT}/ ，可人工试听：")
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for f in sorted(OUT.glob("e2e_*.wav")):
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print(f" {f.name}")
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print("\n" + "=" * 62)
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print(f"通过 {len(OK)} / {len(OK) + len(FAIL)}")
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for n in FAIL:
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print(f" 未通过: {n}")
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return 0 if not FAIL else 1
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if __name__ == "__main__":
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sys.exit(asyncio.run(main()))
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