[Wake/VAD/STT 与 Live runtime]：完成真实重复语音运行，包含云端ASR/TTS开关、run-live和临时上下文测试

openspec/changes/complete-live-repeat-voice-runtime/design.md

@@ -4,7 +4,7 @@
 
 `add-voice-pet-pipeline` 已归档并生成 `voice-pet-pipeline` 主规范。当前仓库已有 Python 包、CLI acceptance、`.env` 配置读取、Provider 协议、mock 测试、桌宠资产和 OpenSpec 主规范，但真实运行能力仍不完整：没有 `run-live` 常驻命令，没有真实麦克风输入和扬声器输出，没有本地模型下载/检查，没有证明同一运行进程内可以连续多轮携带历史。
 
-本设计聚焦第一版“无 GUI 的真实实时语音版”。GUI 桌宠窗口、长期记忆、播放中打断、跨设备 Transport 和云端 STT/TTS 均不在本变更范围内。
+本设计聚焦第一版“无 GUI 的真实实时语音版”。GUI 桌宠窗口、长期记忆、播放中打断和跨设备 Transport 不在本变更范围内；ASR/TTS 由 `.env` 的 `OWNER_SPEECH_PROVIDER=cloud|local` 控制，默认先走云端以保证真实可用。
 
 ## Goals
 
@@ -12,8 +12,8 @@
 2. 提供 `--once`，便于单轮真实验收和自动化测试。
 3. 使用 `.env` 作为默认配置来源，避免要求用户导出 shell 环境变量。
 4. 使用 `sounddevice` 真实读取本机麦克风。
-5. 使用 `sherpa-onnx` 本地 VAD/STT 模型，模型存放在项目 `models/`。
-6. 使用 macOS `say/afplay` 先保证本地 TTS 真实播出。
+5. 使用 `OWNER_SPEECH_PROVIDER=cloud|local` 明确选择语音模型路径。
+6. 默认 cloud 模式使用 NewAPI 的 `mimo-v2.5-asr` 和 `mimo-v2.5-tts`，local 模式保留 `sherpa-onnx` 和本地 TTS 路径。
 7. 在同一个 `run-live` 进程内保留最近 user/assistant 历史，进程退出即丢弃。
 8. 新增自动化测试证明两轮 runtime、临时上下文、进程隔离和错误恢复。
 
@@ -34,10 +34,10 @@ CLI
   -> LiveVoiceRuntimeFactory
     -> SoundDeviceAudioTransport
     -> WakeDetector("小杰小杰")
-    -> SherpaOnnxVadProvider
-    -> SherpaOnnxSttProvider
+    -> Energy/Sherpa VAD provider
+    -> CloudAsrSttProvider or SherpaOnnxSttProvider
     -> OpenAI/NewAPI LlmProvider
-    -> MacOsSayTtsProvider
+    -> CloudTtsProvider or MacOsSayTtsProvider
     -> ConversationContext(empty per process)
     -> TerminalRuntimeReporter
 
@@ -58,7 +58,7 @@ LiveVoiceRuntime.run()
 1. Parse CLI args.
 2. Load `.env` and merge only explicitly supported defaults.
 3. Validate LLM config.
-4. Validate models when live mode requires local VAD/STT.
+4. Validate speech provider mode; cloud mode validates credentials and model names, local mode validates project-local VAD/STT models.
 5. Validate audio devices.
 6. Create one `ConversationContext` for the process.
 7. Enter loop:
@@ -184,12 +184,13 @@ Test requirements:
 
 ## Wake/VAD/STT Design
 
-First implementation can use one of two local strategies:
+First implementation can use one of two strategies:
 
-1. Preferred: sherpa-onnx VAD for endpointing plus sherpa-onnx STT for short wake windows and user utterances.
-2. Future replacement: dedicated local KWS provider for “小杰小杰”.
+1. Default cloud mode: local VAD cuts speech segments, `mimo-v2.5-asr` transcribes wake and user segments through NewAPI-compatible `/v1/audio/transcriptions`.
+2. Local mode: sherpa-onnx VAD/STT uses project-local model assets under `models/`.
+3. Future replacement: dedicated local KWS provider for “小杰小杰”.
 
-The implementation must not send raw audio to the cloud for wake, VAD, or STT. If a dedicated wake model is unavailable, wake detection may transcribe short local segments and search for the normalized wake word.
+Cloud mode sends only VAD-cut speech segments to ASR, not the continuous microphone stream. Local mode does not send wake, VAD, or STT audio to the cloud. If a dedicated wake model is unavailable, wake detection may transcribe short speech segments and search for the normalized wake word.
 
 ## Model Download Design
 
@@ -241,4 +242,4 @@ No persistent data migration is needed. Existing `.env` remains valid. New optio
 
 1. Which exact sherpa-onnx Chinese STT model gives the best latency/accuracy tradeoff on this Mac.
 2. Whether future wake detection should use a dedicated KWS model instead of short-window STT.
-3. Whether TTS should later move from macOS `say` to sherpa-onnx TTS for consistent voice and offline packaging.
+3. Whether local TTS should later move from macOS `say` to sherpa-onnx TTS for consistent voice and offline packaging.