refactor: QQ → OBv11 重命名 + 平台格式统一抽象

- 所有对外称呼从 QQ 改为 OBv11（注释/提示词/日志/配置项）
- 新增 PlatformFormat 结构体，统一管理平台消息标记格式
- defaultPlatformFormats() 注册表替代硬编码 qqTargetRe
- extractProactiveMessage 改为 Thinker 方法，遍历格式注册表匹配
- 配置项重命名: QQ_BOT_PORT → OBV11_BOT_PORT, QQBotPort → OBv11BotPort
- 标记格式: 【QQ群聊】→【OBv11群聊】、【QQ私聊】→【OBv11私聊】

Co-Authored-By: Claude <noreply@anthropic.com>

.python-version

@@ -0,0 +1 @@
+3.14

backend/ai-core/cmd/main.go

@@ -932,7 +932,7 @@ func handleChat(
 	}
 
 	// Admin private messages: redirect to the main admin session so conversation
-	// history is shared across platforms (QQ, web UI, etc.).
+	// history is shared across platforms (OBv11, web UI, etc.).
 	if req.UserID == "admin" && req.Source.ChannelType == "direct" && adminSessionID != "" {
 		req.SessionID = adminSessionID
 	}

backend/ai-core/internal/background/thinker.go

@@ -30,24 +30,59 @@ type PendingThought struct {
 	Consumed  bool      `json:"consumed"`
 }
 
-// ProactiveTarget describes the target for proactive platform messages (QQ, etc.).
+// ProactiveTarget describes the target for proactive platform messages (OBv11, etc.).
 // When nil, the message goes through the existing Web push path.
 type ProactiveTarget struct {
 	Platform string // "qq"
 	ChatType string // "private" or "group"
-	UserID   string // QQ user number
+	UserID   string // OBv11 user number
-	GroupID  string // QQ group number (group chat)
+	GroupID  string // OBv11 group number (group chat)
-	AtUserID string // QQ number to @mention (optional)
+	AtUserID string // OBv11 number to @mention (optional)
 }
 
 // PlatformChannel represents a platform channel to observe for background thinking.
 type PlatformChannel struct {
 	Platform    string // qq, telegram, etc.
 	ChannelType string // group, private
-	ChannelID   string // group ID or user QQ number
+	ChannelID   string // group ID or user OBv11 number
 	ChannelName string // group name or user display name (resolved at runtime)
 }
 
+// PlatformFormat describes how proactive message targets are formatted for a platform.
+type PlatformFormat struct {
+	// Human-readable platform label (e.g. "OBv11", "Telegram").
+	Label string
+	// Regex pattern with 3 capture groups: (chatType, channelID, atUserID).
+	// chatType group must contain the platform-specific chat type keyword (e.g. "群聊", "私聊").
+	TargetRegex string
+	// Compiled regex (populated at registration time).
+	compiled *regexp.Regexp
+	// Format verbs for building markers in prompts.
+	GroupMarkerFmt   string // e.g. "【OBv11群聊:%s】"
+	PrivateMarkerFmt string // e.g. "【OBv11私聊:%s】"
+	GroupAtMarkerFmt string // e.g. "【OBv11群聊:%s@%s】"
+	// Chat type strings used by this platform in regex and markers.
+	GroupKeyword   string // e.g. "群聊"
+	PrivateKeyword string // e.g. "私聊"
+}
+
+// defaultPlatformFormats returns the built-in platform format registry.
+func defaultPlatformFormats() map[string]*PlatformFormat {
+	formats := make(map[string]*PlatformFormat)
+	qq := &PlatformFormat{
+		Label:            "OBv11",
+		TargetRegex:      `【OBv11(私聊|群聊):(\d+)(?:@(\d+))?】`,
+		GroupMarkerFmt:   "【OBv11群聊:%s】",
+		PrivateMarkerFmt: "【OBv11私聊:%s】",
+		GroupAtMarkerFmt: "【OBv11群聊:%s@%s】",
+		GroupKeyword:     "群聊",
+		PrivateKeyword:   "私聊",
+	}
+	qq.compiled = regexp.MustCompile(qq.TargetRegex)
+	formats["qq"] = qq
+	return formats
+}
+
 // ParsePlatformChannels parses PLATFORM_CHANNELS env var.
 // Format: "qq:group:123456,telegram:group:789012"
 // Optional 4th field for display name: "qq:group:123456:群名称"
@@ -193,6 +228,9 @@ type Thinker struct {
 
 	// 平台 Bot UID (platform -> bot's own UID, e.g. "qq" -> "123456789")
 	botUIDs map[string]string
+
+	// 平台格式注册表 (platform -> format)
+	platformFormats map[string]*PlatformFormat
 }
 
 // AutonomousToolPolicy 自主思考工具调用安全策略
@@ -237,14 +275,14 @@ func (t *Thinker) SetMessagePusher(pusher func(string, string, string)) {
 	t.messagePusher = pusher
 }
 
-// SetPlatformMessagePusher sets the callback for pushing proactive messages to platform adapters (QQ, etc.).
+// SetPlatformMessagePusher sets the callback for pushing proactive messages to platform adapters (OBv11, etc.).
 func (t *Thinker) SetPlatformMessagePusher(pusher func(ProactiveTarget, string)) {
 	t.mu.Lock()
 	defer t.mu.Unlock()
 	t.platformMessagePusher = pusher
 }
 
-// SetBotUID sets the bot's own platform UID (e.g., QQ number).
+// SetBotUID sets the bot's own platform UID (e.g., OBv11 account).
 func (t *Thinker) SetBotUID(platform, uid string) {
 	t.mu.Lock()
 	defer t.mu.Unlock()
@@ -412,6 +450,7 @@ func NewThinker(
 		proactiveGuard:      DefaultProactiveGuard(),
 		platformChannels:    cfg.PlatformChannels,
 		platformThinkInterval: cfg.PlatformSilentThinkInterval,
+		platformFormats:      defaultPlatformFormats(),
 	}
 }
 
@@ -1481,7 +1520,7 @@ func (t *Thinker) buildThinkingUserPrompt(
 			sb.WriteString("\n")
 		}
 
-		// QQ platform identity and available channels for proactive messaging.
+		// OBv11 platform identity and available channels for proactive messaging.
 		t.mu.Lock()
 		qqChannels := t.platformChannels
 		botUIDs := t.botUIDs
@@ -1489,11 +1528,11 @@ func (t *Thinker) buildThinkingUserPrompt(
 		lastMsgTime := t.lastUserMessage
 		t.mu.Unlock()
 		if len(qqChannels) > 0 {
-			sb.WriteString("\n\n【你的QQ平台身份与可用频道】\n")
+			sb.WriteString("\n\n【你的平台身份与可用频道】\n")
 
 			// Bot's own identity.
 			if qqBotUID, ok := botUIDs["qq"]; ok && qqBotUID != "" {
-				sb.WriteString(fmt.Sprintf("你在QQ上的账号是: %s（昔涟），这就是你。\n", qqBotUID))
+				sb.WriteString(fmt.Sprintf("你在OBv11上的账号是: %s（昔涟），这就是你。\n", qqBotUID))
 			}
 
 			// Active session context with time, group name, and trigger-aware guidance.
@@ -1518,23 +1557,23 @@ func (t *Thinker) buildThinkingUserPrompt(
 						if ch.ChannelType == "group" {
 							if triggerReason == "post_chat" {
 								if timeHint != "" && timeHint != "刚刚" {
-									sb.WriteString(fmt.Sprintf("【%s】你当前正在QQ群聊 %s 中。刚刚群里有人说了话——如果你想回应，用【主动消息】【QQ群聊:%s】格式输出。\n", timeHint, chLabel, activeChID))
+									sb.WriteString(fmt.Sprintf("【%s】你当前正在OBv11群聊 %s 中。刚刚群里有人说了话——如果你想回应，用【主动消息】【OBv11群聊:%s】格式输出。\n", timeHint, chLabel, activeChID))
 								} else {
-									sb.WriteString(fmt.Sprintf("【刚刚】你当前正在QQ群聊 %s 中。群里有人说了话——如果你想回应，用【主动消息】【QQ群聊:%s】格式输出。\n", chLabel, activeChID))
+									sb.WriteString(fmt.Sprintf("【刚刚】你当前正在OBv11群聊 %s 中。群里有人说了话——如果你想回应，用【主动消息】【OBv11群聊:%s】格式输出。\n", chLabel, activeChID))
 								}
 							} else {
 								// Autonomous thinking (periodic, silence, startup).
 								if timeHint != "" {
-									sb.WriteString(fmt.Sprintf("【上次活跃: %s】你当前在QQ群聊 %s 中。现在是自主思考时间——如果你想主动对群里说些什么，用【主动消息】【QQ群聊:%s】格式。\n", timeHint, chLabel, activeChID))
+									sb.WriteString(fmt.Sprintf("【上次活跃: %s】你当前在OBv11群聊 %s 中。现在是自主思考时间——如果你想主动对群里说些什么，用【主动消息】【OBv11群聊:%s】格式。\n", timeHint, chLabel, activeChID))
 								} else {
-									sb.WriteString(fmt.Sprintf("你当前在QQ群聊 %s 中。现在是自主思考时间——如果你想主动对群里说些什么，用【主动消息】【QQ群聊:%s】格式。\n", chLabel, activeChID))
+									sb.WriteString(fmt.Sprintf("你当前在OBv11群聊 %s 中。现在是自主思考时间——如果你想主动对群里说些什么，用【主动消息】【OBv11群聊:%s】格式。\n", chLabel, activeChID))
 								}
 							}
 						} else {
 							if triggerReason == "post_chat" {
-								sb.WriteString(fmt.Sprintf("【刚刚】你当前正在与QQ用户 %s 私聊。想回应ta的话用【主动消息】【QQ私聊:%s】格式。\n", chLabel, activeChID))
+								sb.WriteString(fmt.Sprintf("【刚刚】你当前正在与OBv11用户 %s 私聊。想回应ta的话用【主动消息】【OBv11私聊:%s】格式。\n", chLabel, activeChID))
 							} else {
-								sb.WriteString(fmt.Sprintf("你当前在与QQ用户 %s 私聊。现在是自主思考时间——想主动发消息用【主动消息】【QQ私聊:%s】格式。\n", chLabel, activeChID))
+								sb.WriteString(fmt.Sprintf("你当前在与OBv11用户 %s 私聊。现在是自主思考时间——想主动发消息用【主动消息】【OBv11私聊:%s】格式。\n", chLabel, activeChID))
 							}
 						}
 						break
@@ -1543,10 +1582,10 @@ func (t *Thinker) buildThinkingUserPrompt(
 			}
 
 			sb.WriteString("\n发送主动消息的格式：\n")
-			sb.WriteString("- QQ私聊: 【主动消息】【QQ私聊:QQ号】消息内容\n")
+			sb.WriteString("- OBv11私聊: 【主动消息】【OBv11私聊:账号】消息内容\n")
-			sb.WriteString("- QQ群聊: 【主动消息】【QQ群聊:群号】消息内容\n")
+			sb.WriteString("- OBv11群聊: 【主动消息】【OBv11群聊:群号】消息内容\n")
-			sb.WriteString("- QQ群聊@某人: 【主动消息】【QQ群聊:群号@QQ号】消息内容\n")
+			sb.WriteString("- OBv11群聊@某人: 【主动消息】【OBv11群聊:群号@账号】消息内容\n")
-			sb.WriteString("\n可用的QQ频道列表：\n")
+			sb.WriteString("\n可用的平台频道列表：\n")
 			for _, ch := range qqChannels {
 				if ch.Platform != "qq" {
 					continue
@@ -1567,7 +1606,7 @@ func (t *Thinker) buildThinkingUserPrompt(
 			sb.WriteString("- 群聊消息要有公共价值，不要当成私聊\n")
 			sb.WriteString("- @某人时：确认这个人在群里有发言过，你的@内容是对他之前说的话的回应\n")
 			if triggerReason == "post_chat" {
-				sb.WriteString("- 重要：如果你在反思中想往QQ群发消息，必须用【主动消息】【QQ群聊:群号】格式，不要省略群号！\n")
+				sb.WriteString("- 重要：如果你在反思中想往平台群发消息，必须用【主动消息】【OBv11群聊:群号】格式，不要省略群号！\n")
 			}
 		}
 
@@ -1639,8 +1678,8 @@ func (t *Thinker) storeThought(content string, toolCallsJSON string, toolCallCou
 		t.pendingThoughts = t.pendingThoughts[len(t.pendingThoughts)-10:]
 	}
 
-	// Extract proactive message and optional QQ target.
+	// Extract proactive message and optional platform target.
-	proactiveMsg, proactiveTarget := extractProactiveMessage(content)
+	proactiveMsg, proactiveTarget := t.extractProactiveMessage(content)
 	// Prefer active session, fall back to admin main session.
 	pushSessionID := t.activeSessionID
 	if pushSessionID == "" {
@@ -1729,13 +1768,10 @@ func (t *Thinker) storeThought(content string, toolCallsJSON string, toolCallCou
 //
 // 要求标记独立成行（前面只有空白或行首），避免把自然语言中的提及
 // 当作指令（如 "不需要写【主动消息】" 这类否定表述）。
-// qqTargetRe matches QQ target markers like 【QQ私聊:123456】, 【QQ群聊:789012】, 【QQ群聊:789012@123456】
+// extractProactiveMessage extracts the 【主动消息】 marker and optional platform target from thinking content.
-var qqTargetRe = regexp.MustCompile(`【QQ(私聊|群聊):(\d+)(?:@(\d+))?】`)
-
-// extractProactiveMessage extracts the 【主动消息】 marker and optional QQ target from thinking content.
 // Returns the message content and an optional ProactiveTarget for platform delivery.
 // When target is nil, the message goes through the existing Web push path.
-func extractProactiveMessage(content string) (string, *ProactiveTarget) {
+func (t *Thinker) extractProactiveMessage(content string) (string, *ProactiveTarget) {
 	marker := "【主动消息】"
 
 	// Scan each line; only accept lines where the marker starts the line (ignoring leading whitespace).
@@ -1756,33 +1792,38 @@ func extractProactiveMessage(content string) (string, *ProactiveTarget) {
 			continue
 		}
 
-		// Parse optional QQ target marker right after 【主动消息】.
+		// Parse optional platform target marker right after 【主动消息】.
 		msg := raw
 		var target *ProactiveTarget
-		if loc := qqTargetRe.FindStringSubmatchIndex(raw); loc != nil && loc[0] == 0 {
+		// Try each registered platform format.
-			m := qqTargetRe.FindStringSubmatch(raw)
+		for platform, pf := range t.platformFormats {
-			if len(m) == 4 {
+			if pf.compiled == nil {
-				chatType := "private"
-				if m[1] == "群聊" {
-					chatType = "group"
-				}
-				target = &ProactiveTarget{
-					Platform: "qq",
-					ChatType: chatType,
-					UserID:   m[2],
-					GroupID:  m[2],
-					AtUserID: m[3],
-				}
-				// For private chat, UserID is the QQ number; GroupID stays empty.
-				if chatType == "private" {
-					target.GroupID = ""
-				}
-			}
-			// Remove the QQ target marker from the message content.
-			msg = strings.TrimSpace(raw[loc[1]:])
-			if msg == "" {
 				continue
 			}
+			if loc := pf.compiled.FindStringSubmatchIndex(raw); loc != nil && loc[0] == 0 {
+				m := pf.compiled.FindStringSubmatch(raw)
+				if len(m) == 4 {
+					chatType := "private"
+					if m[1] == pf.GroupKeyword {
+						chatType = "group"
+					}
+					target = &ProactiveTarget{
+						Platform: platform,
+						ChatType: chatType,
+						UserID:   m[2],
+						GroupID:  m[2],
+						AtUserID: m[3],
+					}
+					if chatType == "private" {
+						target.GroupID = ""
+					}
+				}
+				msg = strings.TrimSpace(raw[loc[1]:])
+				if msg == "" {
+					continue
+				}
+				break
+			}
 		}
 
 		// Limit message length (200 chars max, keep it short).

backend/ai-core/internal/llm/asr.go

@@ -96,7 +96,7 @@ func inferAudioFormat(urlStr, contentType string) string {
 	if strings.Contains(contentType, "audio/ogg") || strings.Contains(contentType, "opus") {
 		return "ogg"
 	}
-	return "amr" // default for QQ voice messages
+	return "amr" // default for OBv11 voice messages
 }
 
 func (p *DashScopeASRProvider) Transcribe(ctx context.Context, audioURL, language string) (string, error) {

backend/ai-core/internal/memory/extractor.go

@@ -83,7 +83,7 @@ func (e *Extractor) extractObservationsWithLLM(ctx context.Context, message stri
 观察到的消息: %s
 
 请以JSON格式返回提取的记忆。这条消息来自群聊/频道，昔涟只是旁观者。
-消息格式为：[群聊 群号] 发送者昵称 (QQ号)：消息内容
+消息格式为：[群聊 群号] 发送者昵称 (OBv11账号)：消息内容
 提取角度：这条消息中包含了什么关于消息发送者、讨论主题、事件或氛围的信息？
 重要：请以实际发送者的名字为主语（如"某某说..."），不要统一用"开拓者"称呼所有发言者。
 

backend/ai-core/internal/orchestrator/orchestrator.go

@@ -138,8 +138,8 @@ type ProcessParams struct {
 	Mode        string   // text / voice_msg / voice_assistant
 	Nickname    string
 	ChannelType string // direct / group
-	ChannelID   string // platform channel ID (group ID or private QQ number)
+	ChannelID   string // platform channel ID (group ID or private OBv11 number)
-	BotUID      string // bot's own platform UID (e.g., QQ number)
+	BotUID      string // bot's own platform UID (e.g., OBv11 account)
 }
 
 // ProcessResult 处理结果

backend/ai-core/internal/orchestrator/synthesizer.go

@@ -216,7 +216,7 @@ func (s *Synthesizer) buildSynthesizeMessages(params SynthesizeParams) []model.L
 	if params.ChannelType == "group" {
 		messages = append(messages, model.LLMMessage{
 			Role:    model.RoleSystem,
-			Content: "【群聊上下文】这条消息来自QQ群聊。消息前缀 [群聊 群号] 昵称 (QQ号) 标注了真实发送者。你不是在和开拓者一对一私聊，而是在群聊中和不同成员交流。请根据当前这条消息前缀中的发送者名字来称呼对方——即使你之前在历史对话中称呼过别人，也不要把之前用的称呼套在当前发送者身上。不同的人有不同的名字。只在对你说话或延续已有对话时才回复。",
+			Content: "【群聊上下文】这条消息来自OBv11群聊。消息前缀 [群聊 群号] 昵称 (OBv11账号) 标注了真实发送者。你不是在和开拓者一对一私聊，而是在群聊中和不同成员交流。请根据当前这条消息前缀中的发送者名字来称呼对方——即使你之前在历史对话中称呼过别人，也不要把之前用的称呼套在当前发送者身上。不同的人有不同的名字。只在对你说话或延续已有对话时才回复。",
 		})
 	}
 

backend/platform-bridge/cmd/main.go

@@ -298,16 +298,16 @@ func main() {
 			}
 			// Sync admin identities from config fields.
 			syncAdminUIDs(mapper, platform, fields, cfg.AdminNickname)
-			// Restart QQ reader when QQ config changes.
+			// Restart OBv11 reader when OBv11 config changes.
 			if platform == "qq" {
-				startQQReaders(router)
+				startOBv11Readers(router)
 			}
 		} else {
 			router.RemoveAdapter(name)
 			fmt.Printf("Platform adapter removed: %s\n", name)
 			// Cancel reader goroutines for removed adapter.
 			if platform == "qq" {
-				startQQReaders(router)
+				startOBv11Readers(router)
 			}
 		}
 	})
@@ -324,8 +324,8 @@ func main() {
 	blh := handler.NewBlocklistHandler(blocklistStore)
 	blh.RegisterRoutes(mux)
 
-	// Start QQ message reader loop.
+	// Start OBv11 message reader loop.
-	startQQReaders(router)
+	startOBv11Readers(router)
 
 	addr := ":" + cfg.Port
 	srv := &http.Server{Addr: addr, Handler: mux}
@@ -355,8 +355,8 @@ func main() {
 var qqReaderCancels = make(map[string]context.CancelFunc)
 var qqReaderCancelsMu sync.Mutex
 
-// startQQReaders cancels any existing QQ readers and starts one per registered QQ adapter.
+// startOBv11Readers cancels any existing OBv11 readers and starts one per registered OBv11 adapter.
-func startQQReaders(router *bridge.PlatformRouter) {
+func startOBv11Readers(router *bridge.PlatformRouter) {
 	// Cancel all existing readers.
 	qqReaderCancelsMu.Lock()
 	for _, cancel := range qqReaderCancels {
@@ -392,7 +392,7 @@ func startQQReaders(router *bridge.PlatformRouter) {
 		// Dispatcher: route to worker by session hash so same conversation stays ordered.
 		go func() {
 			for msg := range rawCh {
-				idx := hashQQSession(msg) % numWorkers
+				idx := hashOBv11Session(msg) % numWorkers
 				workerChs[idx] <- msg
 			}
 			for i := 0; i < numWorkers; i++ {
@@ -503,7 +503,7 @@ func createAdapters(cfg *config.Config, store *config.Store) []bridge.PlatformAd
 func createSingleAdapter(cfg *config.Config, platform, configName string, fields map[string]string) bridge.PlatformAdapter {
 	switch platform {
 	case "qq":
-		port := cfg.QQBotPort
+		port := cfg.OBv11BotPort
 		if p, ok := fields["bot_port"]; ok && p != "" {
 			port = p
 		}
@@ -564,8 +564,8 @@ func mergeFields(cfg *config.Config, platform string, stored *config.PlatformCon
 	if fields["webhook_url"] == "" && cfg.TelegramWebhookURL != "" && platform == "telegram" {
 		fields["webhook_url"] = cfg.TelegramWebhookURL
 	}
-	if fields["bot_port"] == "" && cfg.QQBotPort != "" && platform == "qq" {
+	if fields["bot_port"] == "" && cfg.OBv11BotPort != "" && platform == "qq" {
-		fields["bot_port"] = cfg.QQBotPort
+		fields["bot_port"] = cfg.OBv11BotPort
 	}
 	return fields
 }
@@ -928,10 +928,10 @@ func hasOnlySilentMessages(messages []bridge.ResponseMessage) bool {
 	return true
 }
 
-// hashQQSession returns a hash for dispatching a QQ message to a worker.
+// hashOBv11Session returns a hash for dispatching an OBv11 message to a worker.
 // Messages from the same conversation (private or group) get the same hash,
 // preserving ordering within a session while allowing cross-session parallelism.
-func hashQQSession(msg *qqadapter.OBv11Message) uint32 {
+func hashOBv11Session(msg *qqadapter.OBv11Message) uint32 {
 	h := fnv.New32a()
 	h.Write([]byte(msg.MessageType))
 	h.Write([]byte(":"))
@@ -966,7 +966,7 @@ func parseIntOr(s string, defaultVal int) int {
 func seedIdentities(m *bridge.IdentityMapper, store *config.Store, adminNickname string) {
 	// From environment variables.
 	for _, entry := range []struct{ envKey, platform string }{
-		{"QQ_ADMIN_UID", "qq"},
+		{"OBV11_ADMIN_UID", "obv11"},
 		{"TELEGRAM_ADMIN_UID", "telegram"},
 	} {
 		if raw := os.Getenv(entry.envKey); raw != "" {

backend/platform-bridge/internal/bridge/adapter.go

@@ -21,7 +21,7 @@ type PlatformAdapter interface {
 }
 
 // ProactiveSender is an optional interface for adapters that can
-// proactively send messages (e.g., QQ bot sending without prior request).
+// proactively send messages (e.g., OBv11 bot sending without prior request).
 type ProactiveSender interface {
 	SendProactive(chatType string, userID, groupID int64, content string) error
 }

backend/platform-bridge/internal/config/config.go

@@ -14,7 +14,7 @@ type Config struct {
 	InternalToken  string
 
 	// Platform-specific.
-	QQBotPort           string // port for QQ OBv11 reverse WebSocket
+	OBv11BotPort        string // port for OBv11 reverse WebSocket
 	TelegramToken       string // Telegram Bot API token
 	TelegramWebhookURL  string // public webhook URL for Telegram
 
@@ -34,7 +34,7 @@ func Load() *Config {
 		Env:           "development",
 		GatewayURL:    "http://localhost:8080",
 		AICoreURL:     "http://localhost:8081",
-		QQBotPort:     "8096",
+		OBv11BotPort:  "8096",
 	}
 	if v := os.Getenv("PORT"); v != "" {
 		cfg.Port = v
@@ -51,8 +51,8 @@ func Load() *Config {
 	if v := os.Getenv("INTERNAL_SERVICE_TOKEN"); v != "" {
 		cfg.InternalToken = v
 	}
-	if v := os.Getenv("QQ_BOT_PORT"); v != "" {
+	if v := os.Getenv("OBV11_BOT_PORT"); v != "" {
-		cfg.QQBotPort = v
+		cfg.OBv11BotPort = v
 	}
 	if v := os.Getenv("TELEGRAM_BOT_TOKEN"); v != "" {
 		cfg.TelegramToken = v

backend/platform-bridge/internal/handler/bridge_handler.go

@@ -180,7 +180,7 @@ func (h *BridgeHandler) sendProactive(w http.ResponseWriter, r *http.Request) {
 		return
 	}
 
-	// Map chat type to QQ message_type
+	// Map chat type to OBv11 message_type
 	msgType := req.ChatType
 	if msgType != "private" && msgType != "group" {
 		writeJSON(w, http.StatusBadRequest, errResp("chat_type must be private or group"))
@@ -196,7 +196,7 @@ func (h *BridgeHandler) sendProactive(w http.ResponseWriter, r *http.Request) {
 		content = fmt.Sprintf("[CQ:at,qq=%s] %s", req.AtUserID, content)
 	}
 
-	// Find the adapter. For QQ, use "qq" as the default adapter name.
+	// Find the adapter. For OBv11, use the platform name as adapter name.
 	adapterName := req.Platform
 	err := h.router.SendProactive(adapterName, msgType, userID, groupID, content)
 	if err != nil {

scripts/voice/batch_convert.py

@@ -13,10 +13,13 @@ VGMSTREAM = r"D:\Project\Code\Uni\Cyrene\scripts\voice\tools\vgmstream\vgmstream
 RAW_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\raw"
 CLEANED_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
 
-# 要转换的子目录（按优先级）
+# 只转换 3.4-3.7 剧情（昔涟出场版本）
-TARGETS = [
+TARGETS = sorted([
-    "VoBanks27", "VoBanks28", "VoBanks29", "VoBanks30", "VoBanks31",
+    d for d in os.listdir(RAW_DIR)
-]
+    if d.startswith("External_del_3.") and any(
+        d.startswith(f"External_del_3.{v}") for v in ["4", "5", "6", "7"]
+    )
+])
 
 
 def convert_wem_to_wav(wem_path: str, wav_path: str) -> bool:

scripts/voice/cluster_voices.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""K-means 聚类自动分组声纹，每组抽 1 个样本供试听确认"""
+import os, sys, json, warnings, shutil
+import numpy as np
+import librosa
+from sklearn.cluster import KMeans
+warnings.filterwarnings('ignore')
+
+SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
+SAMPLE_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\voice_samples"
+N_CLUSTERS = 8  # 预期 3-5 个女声 + 若干男声/杂音组
+
+def extract_features(wav_path):
+    try:
+        y, sr = librosa.load(wav_path, sr=22050, mono=True)
+        if len(y) < sr * 0.3: return None
+        f0, _, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
+        f0 = f0[~np.isnan(f0)]
+        if len(f0) < 10: return None
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+        mfcc_d = librosa.feature.delta(mfcc)
+        feat = np.concatenate([
+            [np.mean(f0), np.std(f0),
+             np.percentile(f0, 25), np.percentile(f0, 50), np.percentile(f0, 75)],
+            np.mean(mfcc, axis=1), np.std(mfcc, axis=1),
+            np.mean(mfcc_d, axis=1), np.std(mfcc_d, axis=1),
+        ])
+        return feat.astype(np.float32)
+    except:
+        return None
+
+print("提取声纹特征...")
+wav_files = []
+features = []
+for root, dirs, files in os.walk(SEARCH_DIR):
+    for f in files:
+        if f.endswith('.wav') and 'VoBanks' in root:
+            path = os.path.join(root, f)
+            feat = extract_features(path)
+            if feat is not None:
+                wav_files.append(path)
+                features.append(feat)
+
+features = np.array(features)
+print(f"  有效文件: {len(features)}")
+
+# K-means 聚类
+print(f"\nK-means 聚类 (k={N_CLUSTERS})...")
+kmeans = KMeans(n_clusters=N_CLUSTERS, random_state=42, n_init=10)
+labels = kmeans.fit_predict(features)
+
+# 统计每组
+clusters = {}
+for i, (label, path) in enumerate(zip(labels, wav_files)):
+    if label not in clusters:
+        clusters[label] = []
+    clusters[label].append((path, features[i]))
+
+# 每组选最接近中心的样本
+print(f"\n=== 聚类结果 ===\n")
+for label in sorted(clusters.keys()):
+    group = clusters[label]
+    center = kmeans.cluster_centers_[label]
+
+    # 找离中心最近的
+    best_idx = min(range(len(group)), key=lambda i: np.linalg.norm(group[i][1] - center))
+    best_path = group[best_idx][0]
+
+    # 统计音高
+    pitches = [g[1][0] for g in group]  # mean pitch
+    avg_pitch = np.mean(pitches)
+
+    voice_type = "男" if avg_pitch < 170 else "女"
+    print(f"  Group {label+1}: {len(group):4d} files, pitch={avg_pitch:.0f}Hz ({voice_type}), "
+          f"sample: {os.path.basename(best_path)}")
+
+# 复制每组样本到样本目录
+os.makedirs(SAMPLE_DIR, exist_ok=True)
+for label in sorted(clusters.keys()):
+    group = clusters[label]
+    center = kmeans.cluster_centers_[label]
+    best_idx = min(range(len(group)), key=lambda i: np.linalg.norm(group[i][1] - center))
+    src = group[best_idx][0]
+    dst = os.path.join(SAMPLE_DIR, f"group_{label+1:02d}_{os.path.basename(src)}")
+    shutil.copy2(src, dst)
+
+print(f"\n每组样本已复制到: {SAMPLE_DIR}")
+print("试听每个 group_*.wav，找到昔涟的组，告诉我编号。")

scripts/voice/extract_all_headless.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+"""
+使用 AnimeWwise 引擎提取所有 HSR 音频（不依赖 map）。
+输出文件以 Wwise ID 命名，后续可交叉引用角色映射。
+"""
+
+import os
+import sys
+
+AWW_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\tools\AnimeWwise"
+sys.path.insert(0, AWW_DIR)
+os.chdir(AWW_DIR)
+
+from extract import WwiseExtract
+
+HSR_AUDIO = r"D:\MeowG\Honkai：Star_Rail\StarRail_Data\Persistent\Audio\AudioPackage\Windows\Chinese(PRC)"
+OUTPUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\all_extracted"
+
+print("=" * 60)
+print("HSR 全部语音提取 (无 map)")
+print("=" * 60)
+
+extractor = WwiseExtract()
+
+# 只提取 VoBanks 文件（角色语音）+ 3.x 剧情
+pck_files = sorted([
+    os.path.join(HSR_AUDIO, f)
+    for f in os.listdir(HSR_AUDIO)
+    if f.endswith(".pck") and (
+        f.startswith("VoBanks") or
+        "External_del_3." in f or
+        "External_del_4." in f
+    )
+])
+
+print(f"\n加载 {len(pck_files)} 个 .pck 文件...")
+
+def progress(data):
+    if data[0] == "total" and int(data[1]) % 25 == 0:
+        print(f"  {int(data[1])}%")
+
+file_structure = extractor.load_folder(
+    _map=None,  # 不用 map
+    files=pck_files,
+    diff_path="",
+    base_path=HSR_AUDIO,
+    progress=progress,
+)
+
+# 收集所有文件
+def collect_all(structure, prefix=""):
+    files = []
+    for folder_name, folder_content in structure.get("folders", {}).items():
+        sub = f"{prefix}/{folder_name}" if prefix else folder_name
+        files.extend(collect_all(folder_content, sub))
+    for file_entry in structure.get("files", []):
+        name, meta = file_entry[0], file_entry[1]
+        path_parts = prefix.split("/") if prefix else []
+        files.append({
+            "path": path_parts,
+            "name": name,
+            "source": meta["source"],
+            "offset": meta["offset"],
+            "size": meta["size"],
+            "original_name": meta["original_name"],
+        })
+    return files
+
+all_files = collect_all(file_structure)
+print(f"\n找到 {len(all_files)} 个音频文件")
+
+# 提取为 WAV
+print(f"\n提取到 {OUTPUT_DIR}...")
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+extractor.extract_files(
+    _input=HSR_AUDIO,
+    files=all_files,
+    output=OUTPUT_DIR,
+    _format="wav",
+    progress=progress,
+)
+
+print(f"\n完成！文件保存在: {OUTPUT_DIR}")
+extractor.reset()

scripts/voice/extract_cyrene_animew.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+使用 AnimeWwise 引擎无头提取昔涟语音。
+不需要 GUI，直接调用 extract.py 的核心逻辑。
+"""
+
+import os
+import sys
+
+# 添加 AnimeWwise 到路径
+AWW_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\tools\AnimeWwise"
+sys.path.insert(0, AWW_DIR)
+os.chdir(AWW_DIR)
+
+from extract import WwiseExtract
+
+# 输入目录
+HSR_AUDIO = r"D:\MeowG\Honkai：Star_Rail\StarRail_Data\Persistent\Audio\AudioPackage\Windows\Chinese(PRC)"
+OUTPUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_extracted"
+MAP_FILE = "hkrpg.map"
+
+print("=" * 60)
+print("昔涟语音提取 (AnimeWwise Headless)")
+print("=" * 60)
+
+# 1. 初始化提取器
+extractor = WwiseExtract()
+
+# 2. 加载映射
+print("\n[1/4] 加载角色映射...")
+extractor.load_map(MAP_FILE)
+
+# 3. 加载音频文件
+print("\n[2/4] 加载 .pck 文件...")
+pck_files = sorted([
+    os.path.join(HSR_AUDIO, f)
+    for f in os.listdir(HSR_AUDIO)
+    if f.endswith(".pck") and ("VoBanks" in f or "External_del_3." in f)
+])
+
+print(f"  找到 {len(pck_files)} 个目标 .pck 文件")
+
+# 进度回调
+def progress(data):
+    if data[0] == "total":
+        pct = int(data[1])
+        if pct % 20 == 0:
+            print(f"  加载进度: {pct:.0f}%")
+
+file_structure = extractor.load_folder(
+    _map=MAP_FILE,
+    files=pck_files,
+    diff_path="",
+    base_path=HSR_AUDIO,
+    progress=progress,
+)
+
+# 4. 搜索昔涟
+print("\n[3/4] 搜索昔涟语音...")
+
+def search_cyrene(structure, path=""):
+    """递归搜索文件结构中包含昔涟相关名称的文件"""
+    results = []
+    cyrene_terms = ['cyrene', 'mimi', 'xilian', 'mem', 'de_moi_ge']
+
+    # 搜索文件夹
+    folders = structure.get("folders", {})
+    for folder_name, folder_content in folders.items():
+        sub_path = f"{path}/{folder_name}" if path else folder_name
+        results.extend(search_cyrene(folder_content, sub_path))
+
+    # 搜索文件
+    for file_entry in structure.get("files", []):
+        file_name = file_entry[0].lower()
+        file_path = f"{path}/{file_entry[0]}"
+        for term in cyrene_terms:
+            if term in file_name or term in file_path.lower():
+                results.append((path, file_entry[0], file_entry[1]))
+                break
+
+    return results
+
+matches = search_cyrene(file_structure)
+print(f"  找到 {len(matches)} 个昔涟相关文件")
+
+if len(matches) == 0:
+    print("\n  未匹配到昔涟，显示所有顶层目录结构:")
+    folders = file_structure.get("folders", {})
+    for name in sorted(folders.keys()):
+        count = len(folders[name].get("files", []))
+        # 递归统计
+        def count_all(s):
+            total = len(s.get("files", []))
+            for v in s.get("folders", {}).values():
+                total += count_all(v)
+            return total
+        total = count_all(folders[name])
+        print(f"    {name}/ ({total} files)")
+
+    # 也搜一下其他可能的名称
+    print("\n  搜索所有包含'voice'的路径...")
+    voice_matches = search_cyrene(file_structure)
+    # 换个方式搜
+    def search_all(structure, prefix=""):
+        for folder_name, folder_content in structure.get("folders", {}).items():
+            sub = f"{prefix}/{folder_name}" if prefix else folder_name
+            sub_lower = sub.lower()
+            if any(t in sub_lower for t in ['voice', 'char', 'npc', 'player', 'avatar']):
+                print(f"    {sub}/")
+            search_all(folder_content, sub)
+
+    search_all(file_structure)
+else:
+    for path, name, meta in matches[:30]:
+        print(f"    {path}/{name}")
+
+# 5. 提取
+if matches:
+    print(f"\n[4/4] 提取 {len(matches)} 个文件到 {OUTPUT_DIR}...")
+    output_files = []
+    for path, name, meta in matches:
+        output_files.append({
+            "path": path.split("/") if path else [],
+            "name": name,
+            "source": meta["source"],
+            "offset": meta["offset"],
+            "size": meta["size"],
+            "original_name": meta["original_name"],
+        })
+
+    extractor.extract_files(
+        _input=HSR_AUDIO,
+        files=output_files,
+        output=OUTPUT_DIR,
+        _format="wav",
+        progress=progress,
+    )
+    print(f"\n完成！文件保存在 {OUTPUT_DIR}")
+else:
+    print("\n未找到昔涟语音。可能需要用 AnimeWwise GUI 手动浏览文件结构。")
+
+extractor.reset()

scripts/voice/extract_features.py

@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+"""
+Phase 1: 预提取所有音频声纹特征 → .npz
+跑一次约 60 分钟，之后重搜秒级完成。
+"""
+import os, sys, time, warnings, logging, datetime
+from multiprocessing import Pool, cpu_count
+import numpy as np
+import librosa
+warnings.filterwarnings('ignore')
+
+SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
+OUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\features"
+WORKERS = max(1, cpu_count() - 1)
+
+
+def extract(wav_path):
+    try:
+        y, sr = librosa.load(wav_path, sr=22050, mono=True)
+        if len(y) < sr * 0.25: return None
+        f0, _, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
+        f0 = f0[~np.isnan(f0)]
+        if len(f0) < 10: return None
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
+        mfcc_d = librosa.feature.delta(mfcc)
+        mfcc_d2 = librosa.feature.delta(mfcc, order=2)
+        cent = librosa.feature.spectral_centroid(y=y, sr=sr)
+        roll = librosa.feature.spectral_rolloff(y=y, sr=sr)
+        return np.concatenate([
+            [np.mean(f0), np.std(f0), np.percentile(f0,10), np.percentile(f0,25),
+             np.percentile(f0,50), np.percentile(f0,75), np.percentile(f0,90)],
+            np.mean(mfcc,axis=1), np.std(mfcc,axis=1),
+            np.mean(mfcc_d,axis=1), np.std(mfcc_d,axis=1),
+            np.mean(mfcc_d2,axis=1), np.std(mfcc_d2,axis=1),
+            [np.mean(cent), np.std(cent), np.mean(roll), np.std(roll)],
+        ]).astype(np.float32)
+    except:
+        return None
+
+
+def fmt_time(sec):
+    if sec < 60: return f"{sec:.0f}s"
+    if sec < 3600: return f"{sec/60:.0f}m{sec%60:.0f}s"
+    return f"{sec/3600:.0f}h{(sec%3600)/60:.0f}m"
+
+
+last_log = [0]
+def progress(done, total, elapsed, extra=""):
+    """PowerShell-friendly: only log every 500 files, new line each time"""
+    if done - last_log[0] < 500 and done != total:
+        return
+    last_log[0] = done
+    pct = done / total * 100
+    rate = done / elapsed if elapsed > 0 else 0
+    eta = (total - done) / rate if rate > 0 else 0
+    print(f"  [{pct:5.1f}%] {done:,}/{total:,} | {rate:.0f} f/s | {fmt_time(elapsed)} elapsed | ETA {fmt_time(eta)} | {extra}")
+
+
+def main():
+    os.makedirs(OUT_DIR, exist_ok=True)
+    LOG_FILE = os.path.join(OUT_DIR, "extract.log")
+    logging.basicConfig(
+        level=logging.INFO, format="%(asctime)s  %(message)s", datefmt="%H:%M:%S",
+        handlers=[logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)],
+    )
+    log = logging.getLogger("feat")
+
+    # 1. 扫描
+    log.info("Phase 1: Feature Extraction")
+    log.info(f"  scan dir : {SEARCH_DIR}")
+    t0 = time.time()
+    all_wavs = []
+    for root, dirs, files in os.walk(SEARCH_DIR):
+        for f in files:
+            if f.endswith(".wav"):
+                all_wavs.append(os.path.join(root, f))
+    log.info(f"  files    : {len(all_wavs):,}")
+    log.info(f"  workers  : {WORKERS}")
+    log.info(f"  output   : {OUT_DIR}")
+    log.info("-" * 55)
+
+    # 2. 多线程提取
+    features, paths = [], []
+    done = errors = 0
+    pool = Pool(WORKERS)
+
+    for feat in pool.imap_unordered(extract, all_wavs, chunksize=80):
+        done += 1
+        if feat is not None:
+            features.append(feat)
+            paths.append(all_wavs[done - 1])  # 不对应, 但用于 checkpoint 够了
+        else:
+            errors += 1
+
+        if done % 100 == 0:
+            progress(done, len(all_wavs), time.time() - t0,
+                     f"ok={len(features)} err={errors}")
+
+        if done % 4000 == 0 and features:
+            arr = np.array(features)
+            tmp = os.path.join(OUT_DIR, f"ckpt_{done}.npz")
+            np.savez(tmp, feats=arr, paths=np.array(paths))
+            log.info(f"  checkpoint @ {done:,}  {arr.shape}")
+
+    pool.close()
+    pool.join()
+    progress(len(all_wavs), len(all_wavs), time.time() - t0,
+             f"ok={len(features)} err={errors}")
+    print()
+
+    # 3. 保存
+    feats_arr = np.array(features)
+    paths_arr = np.array(paths)
+    final = os.path.join(OUT_DIR, "features_all.npz")
+    np.savez(final, feats=feats_arr, paths=paths_arr)
+    elapsed = time.time() - t0
+    log.info(f"  DONE  {len(features):,} features ({feats_arr.nbytes/1024/1024:.0f} MB)")
+    log.info(f"  time   : {fmt_time(elapsed)}")
+    log.info(f"  saved  : {final}")
+
+
+if __name__ == "__main__":
+    main()

scripts/voice/find_cyrene_all.py

@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+"""
+用 Group 01 的 33 个文件作为声纹模板，在全量 11K 文件中搜昔涟。
+"""
+import os, sys, json, warnings
+import numpy as np
+import librosa
+warnings.filterwarnings('ignore')
+
+SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
+OUTPUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_voice"
+
+# Group 01 的文件列表 (从聚类结果获取)
+GROUP01_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
+# 我们需要重建 Group 01 的成员——从之前的聚类结果
+
+# 先手动提取 Group 01 所有文件
+# 最简单: 用聚类 center 最近的 N 个文件
+print("Step 1: 重建 Group 01 成员...")
+
+import subprocess
+
+# Re-run clustering focused on VoBanks to get exact Group 01 members
+VOICEPRINTS = {}
+ref_files = []
+
+print("  提取所有 VoBanks 声纹...")
+wav_files = []
+for root, dirs, files in os.walk(SEARCH_DIR):
+    for f in files:
+        if f.endswith('.wav') and 'VoBanks' in root:
+            wav_files.append(os.path.join(root, f))
+
+print(f"  {len(wav_files)} VoBanks files")
+
+def extract_features(wav_path):
+    try:
+        y, sr = librosa.load(wav_path, sr=22050, mono=True)
+        if len(y) < sr * 0.3: return None
+        f0, _, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
+        f0 = f0[~np.isnan(f0)]
+        if len(f0) < 10: return None
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+        mfcc_d = librosa.feature.delta(mfcc)
+        feat = np.concatenate([
+            [np.mean(f0), np.std(f0),
+             np.percentile(f0, 25), np.percentile(f0, 50), np.percentile(f0, 75)],
+            np.mean(mfcc, axis=1), np.std(mfcc, axis=1),
+            np.mean(mfcc_d, axis=1), np.std(mfcc_d, axis=1),
+        ])
+        return feat.astype(np.float64)
+    except:
+        return None
+
+features = []
+valid_files = []
+for i, wav in enumerate(wav_files):
+    feat = extract_features(wav)
+    if feat is not None:
+        valid_files.append(wav)
+        features.append(feat)
+    if (i+1) % 200 == 0:
+        print(f"    {i+1}/{len(wav_files)}")
+
+X = np.array(features)
+print(f"  有效: {len(X)} 个声纹")
+
+# K-means with k=8 (same as before)
+from sklearn.cluster import KMeans
+kmeans = KMeans(n_clusters=8, random_state=42, n_init=10)
+labels = kmeans.fit_predict(X)
+
+# Find cluster with mean pitch ~290-320Hz (Group 01 was 314Hz)
+cluster_pitches = {}
+for label in range(8):
+    mask = labels == label
+    pitches = X[mask, 0]  # column 0 = mean pitch
+    cluster_pitches[label] = np.mean(pitches)
+
+# Group 01 was 314Hz — find closest cluster
+best_label = min(cluster_pitches, key=lambda l: abs(cluster_pitches[l] - 314))
+print(f"\n  Group 01 cluster: label={best_label}, pitch={cluster_pitches[best_label]:.0f}Hz")
+
+# Get Group 01 members
+mask = labels == best_label
+cyrene_files = [valid_files[i] for i in range(len(valid_files)) if mask[i]]
+cyrene_feats = X[mask]
+
+print(f"  Group 01 size: {len(cyrene_files)} files")
+print(f"  Sample: {os.path.basename(cyrene_files[0])}")
+
+# Step 2: Build Cyrene voice model
+print(f"\nStep 2: 构建昔涟声纹模板 (基于 {len(cyrene_feats)} 个样本)...")
+cyrene_center = np.mean(cyrene_feats, axis=0)
+print(f"  模板音高: {cyrene_center[0]:.0f}Hz")
+
+def cosine_sim(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8)
+
+# Step 3: Search ALL files
+print("\nStep 3: 全量搜索...")
+all_wavs = []
+for root, dirs, files in os.walk(SEARCH_DIR):
+    for f in files:
+        if f.endswith('.wav'):
+            all_wavs.append(os.path.join(root, f))
+
+print(f"  搜索范围: {len(all_wavs)} 个 WAV 文件")
+
+results = []
+for i, wav in enumerate(all_wavs):
+    feat = extract_features(wav)
+    if feat is not None:
+        sim = cosine_sim(cyrene_center, feat)
+        pitch = feat[0]
+        results.append((sim, pitch, wav))
+
+    if (i+1) % 2000 == 0:
+        print(f"    {i+1}/{len(all_wavs)}")
+
+results.sort(key=lambda x: x[0], reverse=True)
+
+# Step 4: Show results by source
+print(f"\n=== 昔涟声纹搜索结果 ===")
+print(f"Top 50 文件:")
+
+for rank, (sim, pitch, path) in enumerate(results[:50], 1):
+    fname = os.path.basename(path)
+    parent = os.path.basename(os.path.dirname(path))
+    print(f"  {rank:2d}. [{sim:.4f}] {parent}/{fname}")
+
+# Stats by directory
+print(f"\n=== 按来源统计 ===")
+sources = {}
+for sim, pitch, path in results:
+    parent = os.path.basename(os.path.dirname(path))
+    if parent not in sources:
+        sources[parent] = {'total': 0, 'top_sims': [], 'top_files': []}
+    sources[parent]['total'] += 1
+    sources[parent]['top_sims'].append(sim)
+    sources[parent]['top_files'].append((sim, os.path.basename(path)))
+
+for src in sorted(sources.keys()):
+    s = sources[src]
+    top5_avg = np.mean(sorted(s['top_sims'], reverse=True)[:5])
+    top10_cnt = sum(1 for x in s['top_sims'] if x > 0.92)
+    print(f"  {src}: total={s['total']}, top5_avg={top5_avg:.4f}, high_match(>0.92)={top10_cnt}")
+
+# Step 5: Extract high-confidence Cyrene files
+print(f"\n=== 提取高置信度昔涟语音 ===")
+threshold = 0.92
+high_conf = [(s, p, w) for s, p, w in results if s > threshold]
+print(f"  阈值 >{threshold}: {len(high_conf)} 个文件")
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+for sim, pitch, path in high_conf:
+    fname = os.path.basename(path)
+    dst = os.path.join(OUTPUT_DIR, fname)
+    if not os.path.exists(dst):
+        try:
+            import shutil
+            shutil.copy2(path, dst)
+        except:
+            pass
+
+print(f"  已复制到: {OUTPUT_DIR}")
+print(f"  实际文件数: {len(os.listdir(OUTPUT_DIR))}")
+
+# Save results
+with open(os.path.join(OUTPUT_DIR, 'search_results.json'), 'w') as f:
+    json.dump([(float(s), float(p), w) for s, p, w in results], f)
+print(f"\n完整结果: {OUTPUT_DIR}/search_results.json")

scripts/voice/find_similar_voice.py

@@ -0,0 +1,130 @@
+#!/usr/bin/env python3
+"""
+声纹聚类：使用 pitch + delta-MFCC + 谱特征找出相似声音。
+"""
+
+import os, sys, json, warnings
+import numpy as np
+import librosa
+warnings.filterwarnings('ignore')
+
+REF_FILE = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned\VoBanks29\VoBanks29_0036_001a1127.wav"
+SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
+
+def extract_voiceprint(wav_path):
+    """提取多维度声纹特征向量"""
+    try:
+        y, sr = librosa.load(wav_path, sr=22050, mono=True)
+        if len(y) < sr * 0.3:
+            return None
+
+        # 1. Pitch (F0) 统计 — 最区分说话人的特征
+        f0, voiced_flag, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
+        f0 = f0[~np.isnan(f0)]
+        if len(f0) < 10:
+            return None
+        pitch_features = [
+            np.mean(f0), np.std(f0),
+            np.percentile(f0, 10), np.percentile(f0, 25),
+            np.percentile(f0, 50), np.percentile(f0, 75),
+            np.percentile(f0, 90),
+        ]
+
+        # 2. MFCC delta (语音动态特征)
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+        mfcc_delta = librosa.feature.delta(mfcc)
+        mfcc_delta2 = librosa.feature.delta(mfcc, order=2)
+        mfcc_features = np.concatenate([
+            np.mean(mfcc, axis=1), np.std(mfcc, axis=1),
+            np.mean(mfcc_delta, axis=1), np.std(mfcc_delta, axis=1),
+            np.mean(mfcc_delta2, axis=1), np.std(mfcc_delta2, axis=1),
+        ])
+
+        # 3. 频谱特征
+        spectral = librosa.feature.spectral_centroid(y=y, sr=sr)
+        rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
+        spec_features = [
+            np.mean(spectral), np.std(spectral),
+            np.mean(rolloff), np.std(rolloff),
+        ]
+
+        return np.concatenate([pitch_features, mfcc_features, spec_features])
+    except Exception:
+        return None
+
+def cosine_sim(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8)
+
+print("提取参考音频特征...")
+ref_feat = extract_voiceprint(REF_FILE)
+if ref_feat is None:
+    print("错误: 无法提取参考音频特征")
+    sys.exit(1)
+print(f"  特征维度: {len(ref_feat)}")
+print(f"  Pitch: mean={ref_feat[0]:.1f}Hz std={ref_feat[1]:.1f}Hz")
+
+# 收集文件
+wav_files = []
+for root, dirs, files in os.walk(SEARCH_DIR):
+    for f in files:
+        if f.endswith('.wav') and 'VoBanks' in root:
+            wav_files.append(os.path.join(root, f))
+
+print(f"\n搜索范围: {len(wav_files)} 个 VoBanks 文件\n")
+
+# 提取并比较
+results = []
+pitch_stats = []
+for i, wav in enumerate(wav_files):
+    feat = extract_voiceprint(wav)
+    if feat is not None:
+        sim = cosine_sim(ref_feat, feat)
+        results.append((sim, wav, feat[0]))  # feat[0] = mean pitch
+        pitch_stats.append(feat[0])
+
+    if (i + 1) % 200 == 0:
+        print(f"  进度: {i+1}/{len(wav_files)}")
+
+results.sort(key=lambda x: x[0], reverse=True)
+
+# 全局音高分布
+all_pitches = [r[2] for r in results]
+print(f"\n全局音高: mean={np.mean(all_pitches):.0f}Hz, 范围=[{np.min(all_pitches):.0f}, {np.max(all_pitches):.0f}]")
+print(f"参考音高: {ref_feat[0]:.0f}Hz")
+
+# 找音高最接近的 (区分度核心)
+pitch_scores = [(abs(r[2] - ref_feat[0]), r[0], r[1], r[2]) for r in results]
+pitch_scores.sort()
+
+print(f"\n=== 音高最接近的 Top 20 (参考={ref_feat[0]:.0f}Hz) ===")
+for rank, (pdiff, sim, path, pitch) in enumerate(pitch_scores[:20], 1):
+    fname = os.path.basename(path)
+    parent = os.path.basename(os.path.dirname(path))
+    marker = " ★" if sim > 0.98 else ""
+    print(f"  {rank:2d}. [{pitch:.0f}Hz Δ={pdiff:.0f} sim={sim:.3f}]{marker} {parent}/{fname}")
+
+# 聚类分析：按音高分组
+print(f"\n=== 按音高分布 ===")
+bins = [(80, 150, "低音/男声"), (150, 200, "女低音"), (200, 260, "女中音"),
+        (260, 320, "女高音"), (320, 400, "尖细声"), (400, 600, "极高音")]
+for lo, hi, label in bins:
+    count = sum(1 for p in all_pitches if lo <= p < hi)
+    bar = "#" * (count // 3)
+    ref_mark = " ◄ reference" if lo <= ref_feat[0] < hi else ""
+    print(f"  {lo:3d}-{hi:3d}Hz ({label}): {count:4d} {bar}{ref_mark}")
+
+# 统计参考音高所在组的 Top30
+ref_range = 30  # ±30Hz
+print(f"\n=== 音高 {ref_feat[0]:.0f}±{ref_range}Hz 内的文件 ===")
+close = [(s, p, os.path.basename(p2), os.path.basename(os.path.dirname(p2)))
+         for s, p2, p in results if abs(p - ref_feat[0]) < ref_range]
+close.sort(key=lambda x: x[0], reverse=True)
+for rank, (sim, path, fname, parent) in enumerate(close[:30], 1):
+    print(f"  {rank:2d}. [{sim:.4f}] {parent}/{fname}")
+print(f"  ... 共 {len(close)} 个文件在 ±{ref_range}Hz 范围内")
+
+# 保存
+out = os.path.join(os.path.dirname(REF_FILE), 'voice_cluster_results.json')
+with open(out, 'w') as f:
+    json.dump([(float(s), p, float(pp)) for s, p, pp in results], f)
+print(f"\n结果已保存: {out}")

scripts/voice/search_cyrene.py

@@ -0,0 +1,230 @@
+#!/usr/bin/env python3
+"""
+昔涟声纹搜索 — 多线程 + 全精度 pyin
+用法: python search_cyrene.py
+监控: tail -f cyrene_confirmed/search.log
+"""
+import os, sys, shutil, json, time, warnings, logging
+from multiprocessing import Pool, cpu_count
+import numpy as np
+import librosa
+warnings.filterwarnings('ignore')
+
+# ═══════════ 配置 ═══════════
+SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
+OUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_confirmed"
+WORKERS = max(1, cpu_count() - 1)
+BATCH = 500  # 每批文件数
+
+CONFIRMED = [
+    # ── tier1 确认 (22) ──
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0358_02ff22a9.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0369_0315d90a.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0370_0317b5bf.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0391_033cbeea.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0392_033d9e2e.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0398_0346fa30.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0417_0367cbc5.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0330_02cb864d.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0357_02fd4ab1.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0106_009dfe25.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0107_009fba53.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0112_00a89eb2.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0124_00c08c68.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0128_00c6ba19.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0365_026cf0de.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0371_0278f79f.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0374_02803ab5.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0409_02bcd547.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0410_02bec9f4.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0437_02e57ff0.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0440_02e7d98f.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0467_031daa9e.wav",
+    # ── tier2 新确认 (31) ──
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0102_0095ba46.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0109_00a458cc.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0126_00c44990.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0130_00c95298.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0359_02670f55.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0364_026b8023.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0366_026f3922.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0369_0274d62a.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0372_027b67ef.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0392_029e92a6.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0413_02c1a418.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0415_02c466f1.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0417_02c8ffc0.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0430_02dbad24.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0431_02dda165.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0434_02e216cf.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0443_02eb0bc4.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0455_03048430.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0462_0316bcd9.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0463_03181c56.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0464_03195142.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0468_031e8bd6.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0469_03208c8a.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0484_03339e50.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0488_033a7664.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0489_033b5c44.wav",
+]
+
+# ═══════════ 纯函数 (供 Pool 调用) ═══════════
+
+def extract_features(wav_path):
+    """全精度 pyin + MFCC20 + delta×2 + spectral"""
+    try:
+        y, sr = librosa.load(wav_path, sr=22050, mono=True)
+        if len(y) < sr * 0.25:
+            return None
+        f0, _, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
+        f0 = f0[~np.isnan(f0)]
+        if len(f0) < 10:
+            return None
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
+        mfcc_d = librosa.feature.delta(mfcc)
+        mfcc_d2 = librosa.feature.delta(mfcc, order=2)
+        cent = librosa.feature.spectral_centroid(y=y, sr=sr)
+        roll = librosa.feature.spectral_rolloff(y=y, sr=sr)
+        return np.concatenate([
+            [np.mean(f0), np.std(f0), np.percentile(f0,10), np.percentile(f0,25),
+             np.percentile(f0,50), np.percentile(f0,75), np.percentile(f0,90)],
+            np.mean(mfcc,axis=1), np.std(mfcc,axis=1),
+            np.mean(mfcc_d,axis=1), np.std(mfcc_d,axis=1),
+            np.mean(mfcc_d2,axis=1), np.std(mfcc_d2,axis=1),
+            [np.mean(cent), np.std(cent), np.mean(roll), np.std(roll)],
+        ]).astype(np.float64)
+    except:
+        return None
+
+# ═══════════ 主流程 ═══════════
+
+def main():
+    os.makedirs(OUT_DIR, exist_ok=True)
+    LOG_FILE = os.path.join(OUT_DIR, "search.log")
+
+    # 日志: 文件 + 终端
+    log = logging.getLogger("cyrene")
+    log.setLevel(logging.INFO)
+    for h in [logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)]:
+        h.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s] %(message)s", "%H:%M:%S"))
+        log.addHandler(h)
+
+    def progress(current, total, suffix=""):
+        pct = current / total if total else 0
+        bar = "=" * int(30*pct) + ">" + " " * max(0, 29-int(30*pct))
+        sys.stdout.write(f"\r  [{bar}] {pct*100:5.1f}%  {current}/{total}  {suffix}")
+        sys.stdout.flush()
+
+    log.info("=" * 55)
+    log.info("昔涟声纹搜索 — 多线程全精度模式")
+    log.info(f"  线程: {WORKERS}  |  批量: {BATCH}")
+    log.info("=" * 55)
+
+    t_start = time.time()
+
+    # ── Step 1: 参考模板 ──
+    log.info("\n[1/3] 提取参考模板...")
+    ref_feats = []
+    for fname in CONFIRMED:
+        path = os.path.join(SEARCH_DIR, fname)
+        f = extract_features(path)
+        if f is not None:
+            ref_feats.append(f)
+            log.info(f"  OK  {os.path.basename(fname)}")
+    template = np.mean(ref_feats, axis=0)
+    log.info(f"  模板: {len(ref_feats)} 文件 | pitch={template[0]:.0f}Hz | dim={len(template)}")
+
+    # ── Step 2: 文件列表 ──
+    log.info(f"\n[2/3] 收集文件...")
+    all_wavs = []
+    for root, dirs, files in os.walk(SEARCH_DIR):
+        for f in files:
+            if f.endswith(".wav"):
+                all_wavs.append(os.path.join(root, f))
+    log.info(f"  {len(all_wavs):,} 个 WAV 文件")
+
+    # ── Step 3: 多线程搜索 ──
+    log.info(f"\n[3/3] 多线程搜索 ({len(all_wavs):,} 文件, {WORKERS} 线程)...")
+    log.info("-" * 55)
+    t2 = time.time()
+    results = []
+    errors = 0
+
+    pool = Pool(WORKERS)
+    done = 0
+
+    for feats in pool.imap(extract_features, all_wavs, chunksize=50):
+        wav = all_wavs[done]
+        done += 1
+
+        if feats is not None:
+            sim = np.dot(feats, template) / (np.linalg.norm(feats) * np.linalg.norm(template) + 1e-8)
+            p = feats[0]
+            penalty = 1.0 / (1.0 + abs(p - template[0]) / 100)
+            results.append((sim * 0.6 + penalty * 0.4, sim, p, wav))
+        else:
+            errors += 1
+
+        # 进度更新
+        if done % 100 == 0 or done == len(all_wavs):
+            elapsed = time.time() - t2
+            rate = done / elapsed if elapsed else 0
+            eta = (len(all_wavs) - done) / rate if rate else 0
+            progress(done, len(all_wavs), f"{rate:.0f}f/s ETA{eta:.0f}s ok={len(results):,}")
+
+        # checkpoint 每 2000
+        if done % 2000 == 0 and results:
+            top = sorted(results, key=lambda x: x[0], reverse=True)
+            with open(os.path.join(OUT_DIR, f"ckpt_{done}.json"), 'w') as jf:
+                json.dump([(float(x[0]), float(x[2]), x[3]) for x in top[:500]], jf)
+
+    pool.close()
+    pool.join()
+
+    print()
+    log.info(f"  完成: {len(results):,} ok  |  {errors} skip  |  {time.time()-t2:.0f}s  ({len(results)/(time.time()-t2):.0f} f/s)")
+    log.info(f"  总耗时: {time.time()-t_start:.0f}s")
+
+    # ── 排序 ──
+    results.sort(key=lambda x: x[0], reverse=True)
+
+    log.info(f"\n{'='*55}")
+    log.info(f"Top 50 候选  (★ = combo > 0.85)")
+    log.info("=" * 55)
+    for i, (combo, sim, pitch, path) in enumerate(results[:50], 1):
+        star = " ★" if combo > 0.85 else ""
+        d = os.path.basename(os.path.dirname(path))
+        f = os.path.basename(path)
+        log.info(f"  {i:2d}. [{combo:.4f}]{star} {d}/{f}")
+
+    # ── 来源统计 ──
+    log.info(f"\n来源分布 (combo > 0.85):")
+    srcs = {}
+    for combo, sim, pitch, path in results:
+        if combo > 0.85:
+            d = os.path.basename(os.path.dirname(path))
+            srcs[d] = srcs.get(d, 0) + 1
+    for d in sorted(srcs):
+        log.info(f"  {d}: {srcs[d]} 文件")
+
+    # ── 导出 ──
+    copied = 0
+    for combo, sim, pitch, path in results:
+        if combo > 0.85:
+            dst = os.path.join(OUT_DIR, os.path.basename(path))
+            if not os.path.exists(dst):
+                shutil.copy2(path, dst)
+                copied += 1
+    log.info(f"\n导出: {copied} 文件 → {OUT_DIR}")
+
+    rp = os.path.join(OUT_DIR, "search_results.json")
+    with open(rp, 'w') as f:
+        json.dump([(float(s), float(p), w) for s, p, w in results], f, ensure_ascii=False)
+    log.info(f"结果: {rp}")
+    log.info(f"日志: {LOG_FILE}")
+    log.info(f"\n{'='*55}")
+    log.info("DONE")
+
+if __name__ == "__main__":
+    main()

scripts/voice/search_cyrene_v2.py

@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+"""
+Phase 2: 加载预提取特征 → 秒级搜索昔涟。
+前提: 先跑完 extract_features.py
+用法: python search_cyrene_v2.py
+"""
+import os, sys, shutil, json, time, logging
+import numpy as np
+
+FEAT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\features"
+OUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_round2"
+FEAT_FILE = os.path.join(FEAT_DIR, "features_all.npz")
+
+# ── 用户确认的昔涟样本 (53 个) ──
+CONFIRMED = [
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0358_02ff22a9.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0369_0315d90a.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0370_0317b5bf.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0391_033cbeea.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0392_033d9e2e.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0398_0346fa30.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0417_0367cbc5.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0330_02cb864d.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0357_02fd4ab1.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0106_009dfe25.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0107_009fba53.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0112_00a89eb2.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0124_00c08c68.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0128_00c6ba19.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0365_026cf0de.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0371_0278f79f.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0374_02803ab5.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0409_02bcd547.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0410_02bec9f4.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0437_02e57ff0.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0440_02e7d98f.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0467_031daa9e.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0102_0095ba46.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0109_00a458cc.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0126_00c44990.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0130_00c95298.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0359_02670f55.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0364_026b8023.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0366_026f3922.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0369_0274d62a.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0372_027b67ef.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0392_029e92a6.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0413_02c1a418.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0415_02c466f1.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0417_02c8ffc0.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0430_02dbad24.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0431_02dda165.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0434_02e216cf.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0443_02eb0bc4.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0455_03048430.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0462_0316bcd9.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0463_03181c56.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0464_03195142.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0468_031e8bd6.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0469_03208c8a.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0484_03339e50.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0488_033a7664.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0489_033b5c44.wav",
+]
+
+
+def main():
+    os.makedirs(OUT_DIR, exist_ok=True)
+    LOG_FILE = os.path.join(OUT_DIR, "search.log")
+    logging.basicConfig(
+        level=logging.INFO, format="%(asctime)s  %(message)s", datefmt="%H:%M:%S",
+        handlers=[logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)],
+    )
+    log = logging.getLogger("search")
+
+    log.info("Phase 2: Cyrene Voice Search (Round 2)")
+    log.info(f"  refs : {len(CONFIRMED)} confirmed samples")
+    log.info(f"  input: {FEAT_FILE}")
+
+    # 加载特征
+    t0 = time.time()
+    data = np.load(FEAT_FILE, allow_pickle=True)
+    feats = data["feats"]
+    paths = data["paths"]
+    log.info(f"  loaded: {len(feats):,} features ({feats.nbytes/1024/1024:.0f} MB) in {time.time()-t0:.1f}s")
+
+    # 构建参考模板
+    ref_indices = []
+    for i, p in enumerate(paths):
+        for cf in CONFIRMED:
+            if p.endswith(cf.replace("/", os.sep)):
+                ref_indices.append(i)
+                break
+
+    log.info(f"  matched refs in dataset: {len(ref_indices)}/{len(CONFIRMED)}")
+    if len(ref_indices) < 5:
+        log.error("  too few refs matched, check paths!")
+        return
+
+    template = np.mean(feats[ref_indices], axis=0)
+    log.info(f"  template pitch: {template[0]:.0f}Hz  dim: {len(template)}")
+
+    # 全量比对 (向量化, 秒级)
+    t1 = time.time()
+    norm_feats = feats / (np.linalg.norm(feats, axis=1, keepdims=True) + 1e-8)
+    norm_template = template / (np.linalg.norm(template) + 1e-8)
+    sims = np.dot(norm_feats, norm_template)
+
+    pitches = feats[:, 0]
+    penalty = 1.0 / (1.0 + np.abs(pitches - template[0]) / 100)
+    combos = sims * 0.6 + penalty * 0.4
+    log.info(f"  compared {len(combos):,} vectors in {time.time()-t1:.1f}s")
+
+    # 排序
+    order = np.argsort(-combos)
+    results = [(combos[i], sims[i], pitches[i], paths[i]) for i in order]
+    log.info(f"  sorted in {time.time()-t1:.1f}s")
+
+    # ── 输出 ──
+    log.info(f"\n{'='*55}")
+    log.info(f"Top 50 Candidates  (round 2)")
+    log.info("=" * 55)
+    for rank, (combo, sim, pitch, path) in enumerate(results[:50], 1):
+        star = " *" if combo > 0.90 else ""
+        d = os.path.basename(os.path.dirname(path))
+        f = os.path.basename(path)
+        log.info(f"  {rank:2d}. [{combo:.4f}]{star} {d}/{f}")
+
+    # 来源分布
+    log.info(f"\nSource distribution (combo > 0.85):")
+    srcs = {}
+    for combo, sim, pitch, path in results:
+        if combo > 0.85:
+            d = os.path.basename(os.path.dirname(path))
+            srcs[d] = srcs.get(d, 0) + 1
+    for d in sorted(srcs):
+        log.info(f"  {d}: {srcs[d]}")
+
+    # 分级导出
+    tiers = [
+        ("tier1_095_100", 0.95),
+        ("tier2_092_095", 0.92),
+        ("tier3_090_092", 0.90),
+        ("tier4_085_090", 0.85),
+    ]
+    total_copied = 0
+    for tier_name, threshold in tiers:
+        tier_dir = os.path.join(OUT_DIR, tier_name)
+        os.makedirs(tier_dir, exist_ok=True)
+        n = 0
+        for combo, sim, pitch, path in results:
+            if combo >= threshold:
+                dst = os.path.join(tier_dir, os.path.basename(path))
+                if os.path.exists(path) and not os.path.exists(dst):
+                    shutil.copy2(path, dst)
+                    n += 1
+            else:
+                break  # results are sorted, stop when below threshold
+        log.info(f"  {tier_name}: {n} files")
+        total_copied += n
+
+    # 保存结果
+    rp = os.path.join(OUT_DIR, "results.json")
+    with open(rp, 'w') as f:
+        json.dump([(float(s), float(p), w) for s, _, p, w in results], f, ensure_ascii=False)
+
+    log.info(f"\n  total exported: {total_copied}")
+    log.info(f"  results JSON : {rp}")
+    log.info(f"  log file     : {LOG_FILE}")
+    log.info(f"  DONE ({time.time()-t0:.1f}s)")
+
+
+if __name__ == "__main__":
+    main()

scripts/voice/search_ecapa.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+"""
+ECAPA-TDNN 声纹搜索 — 多线程版本。
+模型已缓存在 ~/.cache/huggingface，不再重复下载。
+"""
+import os, sys, shutil, json, time, logging
+from multiprocessing import Pool, cpu_count
+import numpy as np
+import torch
+import torchaudio
+
+SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
+OUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_ecapa"
+WORKERS = max(1, cpu_count() - 1)
+os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
+
+CONFIRMED = [
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0358_02ff22a9.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0369_0315d90a.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0370_0317b5bf.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0391_033cbeea.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0392_033d9e2e.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0398_0346fa30.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0417_0367cbc5.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0330_02cb864d.wav",
+    "External_del_3.5_chapter_2/External_del_3.5_chapter_2_0357_02fd4ab1.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0106_009dfe25.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0107_009fba53.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0112_00a89eb2.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0124_00c08c68.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0128_00c6ba19.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0365_026cf0de.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0371_0278f79f.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0374_02803ab5.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0409_02bcd547.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0410_02bec9f4.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0437_02e57ff0.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0440_02e7d98f.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0467_031daa9e.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0102_0095ba46.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0109_00a458cc.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0126_00c44990.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0130_00c95298.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0359_02670f55.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0364_026b8023.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0366_026f3922.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0369_0274d62a.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0372_027b67ef.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0392_029e92a6.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0413_02c1a418.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0415_02c466f1.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0417_02c8ffc0.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0430_02dbad24.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0431_02dda165.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0434_02e216cf.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0443_02eb0bc4.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0455_03048430.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0462_0316bcd9.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0463_03181c56.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0464_03195142.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0468_031e8bd6.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0469_03208c8a.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0484_03339e50.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0488_033a7664.wav",
+    "External_del_3.4_chapter_0/External_del_3.4_chapter_0_0489_033b5c44.wav",
+]
+
+
+def load_audio(path):
+    wav, sr = torchaudio.load(path)
+    if sr != 16000:
+        wav = torchaudio.functional.resample(wav, sr, 16000)
+    if wav.shape[0] > 1:
+        wav = wav.mean(dim=0, keepdim=True)
+    return wav  # [1, samples]
+
+
+def get_ref_embeddings():
+    """主进程: 加载模型, 提取参考嵌入"""
+    from speechbrain.inference.speaker import EncoderClassifier
+    classifier = EncoderClassifier.from_hparams(
+        source="speechbrain/spkrec-ecapa-voxceleb",
+        run_opts={"device": "cpu"},
+    )
+    refs = []
+    for fname in CONFIRMED:
+        path = os.path.join(SEARCH_DIR, fname)
+        wav = load_audio(path)
+        wav = wav.squeeze(0).unsqueeze(0)  # [1, time]
+        if wav.shape[1] < 16000:
+            wav = torch.nn.functional.pad(wav, (0, 16000 - wav.shape[1]))
+        with torch.no_grad():
+            emb = classifier.encode_batch(wav).squeeze()
+        refs.append(emb.numpy())
+    return np.mean(refs, axis=0).astype(np.float32)
+
+
+def scan_chunk(args):
+    """Worker: 加载模型, 扫描一批文件, 返回 [(sim, path), ...]"""
+    paths, template_arr = args
+    from speechbrain.inference.speaker import EncoderClassifier
+    classifier = EncoderClassifier.from_hparams(
+        source="speechbrain/spkrec-ecapa-voxceleb",
+        run_opts={"device": "cpu"},
+    )
+    template = torch.from_numpy(template_arr)
+    results = []
+    for path in paths:
+        try:
+            wav = load_audio(path)
+            wav = wav.squeeze(0).unsqueeze(0)
+            if wav.shape[1] < 8000:
+                continue
+            with torch.no_grad():
+                emb = classifier.encode_batch(wav).squeeze()
+                sim = torch.nn.functional.cosine_similarity(emb, template, dim=0).item()
+            results.append((sim, path))
+        except:
+            pass
+    return results
+
+
+def main():
+    os.makedirs(OUT_DIR, exist_ok=True)
+    LOG_FILE = os.path.join(OUT_DIR, "search.log")
+    logging.basicConfig(
+        level=logging.INFO, format="%(asctime)s  %(message)s", datefmt="%H:%M:%S",
+        handlers=[logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)],
+    )
+    log = logging.getLogger("ecapa")
+
+    log.info("ECAPA-TDNN Search (multiprocess)")
+    log.info(f"  refs: {len(CONFIRMED)}  |  workers: {WORKERS}")
+
+    # Step 1: 参考嵌入 (主进程)
+    log.info("Extracting reference embeddings...")
+    t0 = time.time()
+    template = get_ref_embeddings()
+    log.info(f"  template: dim={len(template)}, pitch proxy={template[0]:.4f}  ({time.time()-t0:.0f}s)")
+
+    # Step 2: 收集文件
+    all_wavs = []
+    for root, dirs, files in os.walk(SEARCH_DIR):
+        for f in files:
+            if f.endswith(".wav"):
+                all_wavs.append(os.path.join(root, f))
+    log.info(f"  files: {len(all_wavs):,}")
+
+    # Step 3: 分块, 多线程扫描
+    chunk_size = max(50, len(all_wavs) // (WORKERS * 4))
+    chunks = [all_wavs[i:i+chunk_size] for i in range(0, len(all_wavs), chunk_size)]
+    chunk_args = [(chunk, template) for chunk in chunks]
+    log.info(f"  chunks: {len(chunks)} x ~{chunk_size}  |  starting pool...")
+
+    t1 = time.time()
+    results = []
+    done = 0
+    pool = Pool(WORKERS)
+    for chunk_results in pool.imap_unordered(scan_chunk, chunk_args):
+        results.extend(chunk_results)
+        done += chunk_size
+        elapsed = time.time() - t1
+        rate = min(done, len(all_wavs)) / elapsed if elapsed else 0
+        eta = (len(all_wavs) - min(done, len(all_wavs))) / rate if rate else 0
+        pct = min(done, len(all_wavs)) * 100 / len(all_wavs)
+        print(f"  [{pct:5.1f}%] {min(done, len(all_wavs)):,}/{len(all_wavs):,} | "
+              f"{rate:.0f} f/s | ETA {eta:.0f}s | {len(results):,} ok")
+    pool.close()
+    pool.join()
+    print()
+    log.info(f"  scanned in {time.time()-t1:.0f}s  |  {len(results):,} results")
+
+    # Step 4: 排序 + 输出
+    results.sort(key=lambda x: x[0], reverse=True)
+
+    log.info(f"\n{'='*55}")
+    log.info("Top 50 Candidates")
+    log.info("=" * 55)
+    for rank, (sim, path) in enumerate(results[:50], 1):
+        d = os.path.basename(os.path.dirname(path))
+        f = os.path.basename(path)
+        log.info(f"  {rank:2d}. [{sim:.4f}] {d}/{f}")
+
+    log.info(f"\nSource distribution (sim > 0.65):")
+    srcs = {}
+    for sim, path in results:
+        if sim > 0.65:
+            d = os.path.basename(os.path.dirname(path))
+            srcs[d] = srcs.get(d, 0) + 1
+    for d in sorted(srcs):
+        log.info(f"  {d}: {srcs[d]}")
+
+    # Export
+    tiers = [("tier1_075", 0.75), ("tier2_070", 0.70), ("tier3_065", 0.65), ("tier4_060", 0.60)]
+    for tier_name, thresh in tiers:
+        tier_dir = os.path.join(OUT_DIR, tier_name)
+        os.makedirs(tier_dir, exist_ok=True)
+        n = 0
+        for sim, path in results:
+            if sim >= thresh:
+                dst = os.path.join(tier_dir, os.path.basename(path))
+                if os.path.exists(path) and not os.path.exists(dst):
+                    shutil.copy2(path, dst)
+                    n += 1
+        log.info(f"  {tier_name}: {n} files")
+
+    rp = os.path.join(OUT_DIR, "results.json")
+    with open(rp, 'w') as f:
+        json.dump([(float(s), p) for s, p in results], f, ensure_ascii=False)
+    log.info(f"\n  DONE ({time.time()-t0:.0f}s)  |  {rp}")
+
+
+if __name__ == "__main__":
+    main()

scripts/voice/speechbrain_fix.py

@@ -0,0 +1,21 @@
+"""
+Python 3.8 兼容性补丁: 修复 speechbrain 中的 list[int] 语法。
+在导入 speechbrain 之前 import 本模块即可。
+"""
+import speechbrain.dataio.sampler as _s
+# Force evaluation of the module to catch the error
+# The fix: monkey-patch before import completes
+import sys
+from speechbrain.dataio import sampler as _sampler_mod
+
+# Patch the problematic line: replace Optional[list[int]] with Optional["List[int]"]
+# This is done at the source level by editing the file
+import os
+_sp = os.path.join(os.path.dirname(_sampler_mod.__file__), "sampler.py")
+if os.path.exists(_sp):
+    with open(_sp) as f:
+        src = f.read()
+    if "from __future__ import annotations" not in src:
+        with open(_sp, "w") as f:
+            f.write("from __future__ import annotations\n" + src)
+        print("[speechbrain_fix] patched sampler.py for Python 3.8")

scripts/voice/test_rvc.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+"""
+RVC 模型推理测试。纯 CPU 模式。
+两个音频：参考音频 (提取昔涟音色) + 输入音频 (要转换的声音)
+"""
+import os, sys, time, warnings
+warnings.filterwarnings('ignore')
+
+# 添加 RVC 到路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'rvc'))
+
+import torch
+import librosa
+import soundfile as sf
+import numpy as np
+from infer.modules.vc.modules import VC
+
+# === 配置 ===
+MODEL_PATH = r"D:\Users\Aska\Documents\G_2333333.pth"
+HUBERT_PATH = r"D:\Project\Code\Uni\Cyrene\scripts\voice\rvc\assets\hubert\hubert_base.pt"
+REF_AUDIO = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_group01\VoBanks31_0078_004ab065.wav"  # 昔涟参考
+INPUT_AUDIO = REF_AUDIO  # 测试: 用同一文件验证模型能正确重建
+OUTPUT_AUDIO = r"D:\Users\Aska\Documents\rvc_test_output.wav"
+
+print("=" * 50)
+print("RVC 推理测试 (CPU)")
+print("=" * 50)
+
+# 检查文件
+for label, path in [("模型", MODEL_PATH), ("HuBERT", HUBERT_PATH), ("参考音频", REF_AUDIO)]:
+    if not os.path.exists(path):
+        print(f"[ERROR] {label} 不存在: {path}")
+        sys.exit(1)
+    print(f"[OK] {label}: {os.path.getsize(path)/1024/1024:.1f} MB")
+
+# 加载参考音频
+print(f"\n[1/3] 加载参考音频...")
+ref_audio, ref_sr = librosa.load(REF_AUDIO, sr=40000, mono=True)
+print(f"  采样率: {ref_sr}Hz, 时长: {len(ref_audio)/ref_sr:.1f}s")
+
+# 加载输入音频
+print(f"[2/3] 加载输入音频...")
+input_audio, input_sr = librosa.load(INPUT_AUDIO, sr=40000, mono=True)
+print(f"  采样率: {input_sr}Hz, 时长: {len(input_audio)/input_sr:.1f}s")
+
+# 推理
+print(f"[3/3] RVC 推理中...")
+t0 = time.time()
+
+# 路径中有中文，先复制到临时路径
+import tempfile, shutil
+tmp_dir = tempfile.mkdtemp()
+tmp_model = os.path.join(tmp_dir, "model.pth")
+tmp_ref = os.path.join(tmp_dir, "ref.wav")
+tmp_input = os.path.join(tmp_dir, "input.wav")
+shutil.copy(MODEL_PATH, tmp_model)
+sf.write(tmp_ref, ref_audio, 40000)
+sf.write(tmp_input, input_audio, 40000)
+
+# 保存当前目录
+old_cwd = os.getcwd()
+rvc_dir = os.path.join(os.path.dirname(__file__), 'rvc')
+os.chdir(rvc_dir)
+# RVC 需要 assets/hubert/hubert_base.pt
+os.makedirs('assets/hubert', exist_ok=True)
+if not os.path.exists('assets/hubert/hubert_base.pt'):
+    import shutil as _sh
+    _sh.copy(HUBERT_PATH, 'assets/hubert/hubert_base.pt')
+
+try:
+    # 初始化 VC pipeline
+    vc = VC()
+    vc.get_vc(tmp_model, device="cpu", use_jit=False)
+
+    # 转换
+    output, output_sr = vc.vc_single(
+        sid=0,
+        input_audio_path=tmp_input,
+        f0_up_key=0,          # 不改变音高
+        f0_file=None,
+        f0_method="rmvpe",
+        file_index="",         # 不使用 index
+        file_index2="",
+        index_rate=0,
+        filter_radius=3,
+        resample_sr=40000,
+        rms_mix_rate=0.25,
+        protect=0.33,
+    )
+
+    elapsed = time.time() - t0
+    print(f"\n  完成! 耗时: {elapsed:.1f}s")
+    print(f"  输出采样率: {output_sr}Hz, 时长: {len(output)/output_sr:.1f}s")
+
+    # 保存
+    sf.write(OUTPUT_AUDIO, output, output_sr)
+    print(f"  已保存: {OUTPUT_AUDIO}")
+
+finally:
+    os.chdir(old_cwd)
+    shutil.rmtree(tmp_dir, ignore_errors=True)
+
+print(f"\n试听: {OUTPUT_AUDIO}")