diff --git a/app/src/main/java/com/k2fsa/sherpa/onnx/MainActivity.kt b/app/src/main/java/com/k2fsa/sherpa/onnx/MainActivity.kt
index 7001eac..3001ac0 100644
--- a/app/src/main/java/com/k2fsa/sherpa/onnx/MainActivity.kt
+++ b/app/src/main/java/com/k2fsa/sherpa/onnx/MainActivity.kt
@@ -159,12 +159,11 @@ class MainActivity : AppCompatActivity() {
     // Embedding model candidates, tried in order. Place one in the external
     // models dir (preferred) or at the ROOT of assets/. Download:
     // https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-recongition-models
+    // 仅 CAM++(2026-09-16 用户拍板:eres2net 系识别效果太差,整体退役)。
+    // 单模型单空间:滚动/快检/权威判定共用 CAM++;fastReady()=false 时所有
+    // fast 路径自动回落准确注册表(阈值 0.5/0.42,更严更稳)
     private val speakerModels = listOf(
-        // 主选:CAM++ 7.1M,中英训练,3D-Speaker 榜单 EER 优于 eres2net 系,
-        // v1.13 声纹提取器架构无关(读 ONNX 元数据),换文件即生效
         "3dspeaker_speech_campplus_sv_zh_en_16k-common_advanced.onnx",
-        "3dspeaker_speech_eres2netv2_sv_zh-cn_16k-common.onnx",        // accurate fallback
-        "3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx",  // smaller/faster
     )
     private var activeSpeakerModel: String? = null
     private val speakerThreshold = 0.5f
@@ -464,9 +463,6 @@ class MainActivity : AppCompatActivity() {
                 initOfflineRecognizer()
                 setStatus(getString(R.string.loading_speaker))
                 initSpeakerIdentification()
-                if (speakersEnabled) {
-                    initFastSpeakerModel()
-                }
                 runOnUiThread {
                     modelsLoaded = true
                     if (!historyMode) recordButton.isEnabled = true
@@ -1232,23 +1228,6 @@ class MainActivity : AppCompatActivity() {
         activeSpeakerModel = null
     }
 
-    // E:轻量声纹模型(eres2net base,列表末位)专供滚动换人检测,推理快 ~4 倍。
-    // 找不到该文件(或准确模型本来就是 base)时跳过,滚动检测自动回退准确模型。
-    // 注:换 CAM++ 主选后 fast 空间暂仍用 base(独立 registry 镜像,互不污染);
-    // CAM++ 足够小,后续想让它兼职 fast 只需把末位换成同文件。
-    private fun initFastSpeakerModel() {
-        val fastName = speakerModels.last()
-        if (activeSpeakerModel == fastName) return   // 准确模型已是 base
-        try {
-            SpeakerRecognition.initFastExtractor(
-                null, speakerModelFile(fastName).absolutePath
-            )
-            Log.i(TAG, "fast speaker model ready: $fastName")
-        } catch (t: Throwable) {
-            Log.i(TAG, "fast model '$fastName' not found, rolling falls back to v2")
-        }
-    }
-
     // 点击行首说话人标签(ClickableSpan)→ 命名或改名。
     // internalKey:该行的内部声纹键("S0"/真名);null=归属未定,提示稍后再命名。
     private fun showNameSpeakerDialog(internalKey: String?) {
@@ -1860,9 +1839,11 @@ class MainActivity : AppCompatActivity() {
                 uiHandler.post { adoptSwitchLabel(serial, name) }
             }
             name == null || score < verdictFallbackMin -> {
-                // 确认无人:发新号 + 双空间注册(纯净素材)
-                val fastEmb = SpeakerRecognition.computeEmbeddingFast(
-                    samples, sampleRateInHz)
+                // 确认无人:发新号注册(纯净素材);fastReady=false(单 CAM++)时
+                // 镜像无意义,跳过额外计算
+                val fastEmb = if (SpeakerRecognition.fastReady()) {
+                    SpeakerRecognition.computeEmbeddingFast(samples, sampleRateInHz)
+                } else null
                 val label = SpeakerRecognition.nextAutoLabel().also {
                     SpeakerRecognition.registry.addAuto(it, acc)
                     if (fastEmb != null) {
diff --git a/app/src/main/java/com/k2fsa/sherpa/onnx/SpeakerEmbedding.kt b/app/src/main/java/com/k2fsa/sherpa/onnx/SpeakerEmbedding.kt
index 270c2cf..889f3e3 100644
--- a/app/src/main/java/com/k2fsa/sherpa/onnx/SpeakerEmbedding.kt
+++ b/app/src/main/java/com/k2fsa/sherpa/onnx/SpeakerEmbedding.kt
@@ -221,9 +221,10 @@ class SpeakerRegistry {
 // Singleton holder shared by the UI thread, recording thread and the rolling
 // speaker-check thread.
 //
-// 双模型方案(E):准确模型(eres2netv2)负责 endpoint 权威判定与注册;
-// 轻量模型(eres2net base,可选)只负责滚动换人检测(推理快 ~4 倍)。
-// ⚠ 两个模型的 embedding 不互通:各自配一份 registry,注册时双写镜像。
+// 单模型方案(2026-09-16 用户拍板):仅 CAM++;eres2net 系整体退役。
+// fastReady()=false 时:computeEmbeddingFast 回落 CAM++,滚动检索直接走
+// 准确注册表(speakerThreshold 0.5 / verdictFallbackMin 0.42)。镜像注册
+// 机制保留但不再有调用者(fastEmb 门控后恒 null)。
 // ---------------------------------------------------------------------------
 object SpeakerRecognition {
     @Volatile private var _extractor: SpeakerEmbeddingExtractor? = null