diff --git a/.github/workflows/product-site.yml b/.github/workflows/product-site.yml
index 93bda3250..b7edf8e7d 100644
--- a/.github/workflows/product-site.yml
+++ b/.github/workflows/product-site.yml
@@ -91,6 +91,7 @@ jobs:
           tests/test_python_api_docs_contract.py
           tests/test_streaming_vad_docs.py
           tests/test_kws_docs.py
+          tests/test_speaker_emotion_docs.py
           tests/test_service_docs_contract.py
           tests/test_generated_api_docs.py
           tests/test_api_signatures.py
diff --git a/docs/README.md b/docs/README.md
index e4412aa08..c672eb65b 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -30,6 +30,7 @@
 | Compare CPU, GPU, realtime and API runtimes | [Deployment matrix](deployment_matrix.md) / [中文](deployment_matrix_zh.md) |
 | Handle streaming speech boundaries and finalization | [Streaming VAD](streaming_vad.md) / [中文](streaming_vad_zh.md) |
 | Detect keywords and manage utterance-end KWS results | [Keyword spotting](keyword_spotting.md) / [中文](keyword_spotting_zh.md) |
+| Distinguish speaker vectors, anonymous labels and emotion tags | [Speakers and emotion tags](speaker_emotion.md) / [中文](speaker_emotion_zh.md) |
 | Transcribe and diarize with third-party MOSS | [MOSS](moss_transcribe_diarize.md) / [中文](moss_transcribe_diarize_zh.md) |
 | Accelerate with the FunASR vLLM split engine | [vLLM](vllm_guide.md) / [中文](vllm_guide_zh.md) |
 | Evaluate native vLLM serving | [Validation record](vllm_native_funasr_validation.md) |
diff --git a/docs/index.rst b/docs/index.rst
index 3e00f16b6..d89e2a15b 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -44,6 +44,8 @@ entry to these guides. Source Markdown remains in this repository.
    streaming_vad_zh
    keyword_spotting
    keyword_spotting_zh
+   speaker_emotion
+   speaker_emotion_zh
    deployment_matrix
    deployment_matrix_zh
    ./moss_transcribe_diarize.md
diff --git a/docs/speaker_emotion.md b/docs/speaker_emotion.md
new file mode 100644
index 000000000..2c199328f
--- /dev/null
+++ b/docs/speaker_emotion.md
@@ -0,0 +1,205 @@
+[简体中文](speaker_emotion_zh.md) | English
+
+# Speakers and Emotion Tags
+
+Choose the result you need before choosing the model. A speaker embedding,
+an anonymous cluster label, an enrolled person's identity and an emotion tag
+are different outputs. None can be substituted for another.
+
+## Choose a Task
+
+| Task | Path |
+| --- | --- |
+| Extract one speaker vector from selected speech | CAMPPlus or ERes2NetV2; example below |
+| Attribute transcript segments to anonymous speakers | [ASR/VAD/speaker pipeline](python_api.md#vad-timestamps-and-speakers) |
+| Preserve transcription, emotion and event tags | SenseVoiceSmall; example below |
+| Joint transcription and anonymous diarization | Third-party [OpenMOSS MOSS guide](moss_transcribe_diarize.md) |
+
+CAMPPlus and ERes2NetV2 return `spk_embedding`, a Tensor, not a person's name,
+match decision or general ASR `text` field. A single input normally has one row;
+its embedding dimension comes from the checkpoint configuration, not a universal
+192-element API guarantee. Use `batch_size=1` here: some batching paths return
+multiple embedding rows in one dictionary, not one keyed result per input.
+
+Embeddings alone do not establish identity. Enrollment, matching, threshold
+calibration, consent and evaluation on the intended population are separate
+application work. A clustering label such as `spk=0` or MOSS's `S01` is anonymous
+within a recording, not a stable person ID across recordings. A requested
+`spk_embedding_center` is a cluster mean, not completed enrollment.
+
+## Prepare a Local Checkpoint
+
+Complete the [installation checks](installation/installation.md) and use a
+complete local snapshot with its configuration, frontend, tokenizer where
+applicable, and weights. Review each model's license and record the resolved
+revision or file hashes, SDK version, imported module path and source commit.
+This guide follows the implementations linked below; it does not claim that
+every older wheel, export or model variant has the same interface.
+
+- **CAMPPlus (`embedding`):** `iic/speech_campplus_sv_zh-cn_16k-common`.
+  Its ModelScope alias is `cam++`.
+- **ERes2NetV2 (`embedding`):** `iic/speech_eres2netv2_sv_zh-cn_16k-common`.
+- **SenseVoice (`sensevoice`):** `iic/SenseVoiceSmall`.
+
+The `embedding` and `sensevoice` choices belong to this example program, not new
+FunASR model aliases or CLI subcommands. Do not point `embedding` at an ASR
+checkpoint or generalize ERes2NetV2 behavior to every ERes2Net variant.
+Check [Model Zoo](../model_zoo/readme.md) for other model paths.
+
+Use nonempty mono 16 kHz WAV audio. The program reads normalized `float32`
+samples and rejects stereo, empty or differently sampled audio instead of
+silently transforming it. For embeddings, select a single person's speech;
+mixtures, silence and extremely short clips are not reliable identity evidence.
+The input checks below are format checks, not a speech-quality detector.
+
+## Save the SDK Result Without Losing Tags
+
+This standalone program takes `task`, `model_dir`, `audio` and a new JSON output
+path. For example, run it with `embedding /models/campplus speaker.wav vector.json`
+or `sensevoice /models/sensevoice utterance.wav tags.json` after the script name.
+Both modes process a complete clip on CPU, without VAD, punctuation, a companion
+speaker model or streaming cache. There are no model downloads in the program.
+
+```python
+import argparse
+import json
+import os
+from pathlib import Path
+import soundfile as sf
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+
+def embedding_record(results):
+    if not results:
+        raise ValueError("No result from the speaker model")
+    if len(results) != 1:
+        raise ValueError("Expected one speaker result for one input")
+    vector = results[0]["spk_embedding"]
+    if vector.ndim != 2 or vector.shape[0] != 1 or vector.shape[1] == 0:
+        raise ValueError("Expected a nonempty single-row speaker embedding")
+    return {"spk_embedding": vector.detach().cpu().tolist()}
+
+
+def tagged_records(results):
+    if not results:
+        raise ValueError("No result from SenseVoice")
+    records = []
+    for item in results:
+        raw = item["text"]
+        if not isinstance(raw, str):
+            raise ValueError("Expected SenseVoice text with its original tags")
+        records.append({
+            "key": item.get("key"), "raw_tagged_text": raw,
+            "display_text": rich_transcription_postprocess(raw),
+        })
+    return records
+
+
+def write_result(path, record):
+    payload = json.dumps(record, ensure_ascii=False, allow_nan=False, indent=2)
+    fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600)
+    with os.fdopen(fd, "w", encoding="utf-8") as stream:
+        stream.write(payload + "\n")
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("task", choices=["embedding", "sensevoice"])
+parser.add_argument("model_dir")
+parser.add_argument("audio")
+parser.add_argument("output")
+args = parser.parse_args()
+model_dir = Path(args.model_dir).expanduser().resolve(strict=True)
+if not model_dir.is_dir():
+    raise ValueError("Expected a complete local model directory")
+speech, sample_rate = sf.read(args.audio, dtype="float32")
+if sample_rate != 16000 or speech.ndim != 1 or len(speech) == 0:
+    raise ValueError("Expected nonempty mono 16 kHz audio")
+model = AutoModel(
+    model=str(model_dir), device="cpu", ncpu=1, disable_update=True,
+    trust_remote_code=False, vad_model=None, punc_model=None, spk_model=None,
+)
+if args.task == "embedding":
+    results = model.generate(input=speech, fs=sample_rate, batch_size=1)
+    result = embedding_record(results)
+else:
+    results = model.generate(
+        input=speech, fs=sample_rate, batch_size=1,
+        language="auto", use_itn=True, output_timestamp=False,
+    )
+    result = tagged_records(results)
+write_result(args.output, {
+    "task": args.task, "model_dir": str(model_dir),
+    "sample_rate": sample_rate, "result": result,
+})
+```
+
+The JSON envelope, `raw_tagged_text` and `display_text` are application-owned
+fields created by this example, not new SDK response fields. The vector is
+explicitly detached, moved to CPU and converted to a nested list for JSON.
+Nonfinite numeric values are rejected before creating a file. Existing output
+files are never overwritten; select a new path for each run. On POSIX the file
+is created with owner-only mode; use appropriate directory permissions and
+platform ACLs too. Voice-derived vectors and transcripts can be sensitive:
+retain only what the application needs and do not post private samples publicly.
+
+## Read SenseVoice Tags Correctly
+
+The raw tagged string is normally in `result["text"]`, not a guaranteed
+`raw_text`, `emotion` or `emotion_score` field. Tags are case-sensitive, for
+example `<|zh|>`, `<|HAPPY|>`, `<|Speech|>` and `<|withitn|>`. Their presence and
+meaning depend on the checkpoint. A presentation mapping table is not a promise
+that every model emits every mapped tag.
+
+`rich_transcription_postprocess` is lossy display processing: it removes tags,
+chooses display emotion by occurrence counts and merges repeated display
+markers. It also performs text replacements. It is neither a structured tag
+parser nor a probability calculator; preserve the original string first.
+Do not infer confidence, psychological state or diagnosis from a display symbol
+or model label. Silence and short/noisy clips are not reliable emotional evidence.
+`emotion2vec` is a separate model/interface, not an alias for SenseVoice tags.
+
+The example chooses `language="auto"`, `use_itn=True` and
+`output_timestamp=False` explicitly; these are not all mandatory parameters.
+SenseVoice language hints use `auto/zh/en/yue/ja/ko`. `use_itn` controls inverse
+text normalization, not emotion recognition, and explicit `text_norm` takes
+precedence. Repeat request options on each call. This is whole-clip inference,
+not the KWS EOS protocol or a live emotional-state monitor.
+
+## VAD, Diarization and Service Boundaries
+
+Standalone embedding extraction and short-clip SenseVoice inference do not
+require VAD. Generic AutoModel speaker clustering instead lives in its VAD
+pipeline: setting only `spk_model` or `return_spk_res=True` does not add
+diarization to direct inference. VAD boundaries are not guaranteed speaker
+changes. Long-audio segmentation also changes the context used for tags.
+Follow the separate [SDK pipeline](python_api.md#vad-timestamps-and-speakers)
+for compatible ASR/VAD/punctuation/speaker models. `sentence_info[].spk` is
+anonymous and its SDK `start/end` fields use milliseconds; NumPy cluster-center
+arrays need explicit serialization just like tensors.
+
+Third-party OpenMOSS MOSS-Transcribe-Diarize supplies its own joint transcription
+and diarization path without an external `vad_model` or `spk_model`. Reuse the
+[MOSS guide](moss_transcribe_diarize.md) for backend, memory and response details;
+do not attach a second clustering pipeline or advertise known-person identity.
+
+The current built-in [HTTP transcription service](../examples/openai_api/README.md)
+configures SenseVoice with VAD and its fallback strips rich tags from top-level
+and segment text. It does not add emotion score fields. Its `spk=true` path is
+a service-specific pipeline, and HTTP segment `start/end` use seconds. Moving
+this SDK example to `/v1/audio/transcriptions` is not a promise to preserve its
+tags or parameters. Check the [deployment matrix](deployment_matrix.md) rather
+than assuming vLLM, llama.cpp, ONNX or WebSocket exports share Python outputs.
+
+## Source and Validation
+
+Contracts: [CAMPPlus](../funasr/models/campplus/model.py),
+[ERes2NetV2](../funasr/models/eres2net/model.py),
+[SenseVoice](../funasr/models/sense_voice/model.py),
+[postprocessing](../funasr/utils/postprocess_utils.py),
+[AutoModel](../funasr/auto/auto_model.py), and
+[HTTP adaptation](../funasr/bin/_server_app.py).
+The [guide tests](../tests/test_speaker_emotion_docs.py) execute the published
+program with recording SDK doubles and the real presentation helper. They check
+field preservation, serialization and invalid input handling, not acoustic
+quality, identity matching, demographic performance or emotion accuracy.
diff --git a/docs/speaker_emotion_zh.md b/docs/speaker_emotion_zh.md
new file mode 100644
index 000000000..bcba32d02
--- /dev/null
+++ b/docs/speaker_emotion_zh.md
@@ -0,0 +1,179 @@
+简体中文 | [English](speaker_emotion.md)
+
+# 说话人与情感标签
+
+先确定需要的结果，再选择模型。声纹向量、匿名聚类编号、已注册人员身份与情感标签
+是四类不同输出，不能互相替代。
+
+## 按任务选择
+
+| 任务 | 路径 |
+| --- | --- |
+| 从选定语音提取一个说话人向量 | CAMPPlus 或 ERes2NetV2；见下方示例 |
+| 为转写片段分配匿名说话人 | [ASR/VAD/说话人流水线](python_api_zh.md#vad时间戳与说话人) |
+| 保留转写、情感和音频事件标签 | SenseVoiceSmall；见下方示例 |
+| 联合转写与匿名说话人分离 | 第三方 [OpenMOSS MOSS 指南](moss_transcribe_diarize_zh.md) |
+
+CAMPPlus 与 ERes2NetV2 返回 `spk_embedding` Tensor，不返回人的姓名、匹配结论
+或通用 ASR `text` 字段。单条输入通常对应一行，向量维度由检查点配置决定，
+不是接口保证永远只有 192 维。本例使用 `batch_size=1`；部分批处理路径会在一个
+字典内返回多行向量，而非每个输入对应一个带 `key` 的结果。
+
+向量本身不等于身份确认。注册、匹配、阈值校准、授权及面向目标人群的评测需要
+应用单独设计。`spk=0` 或 MOSS 的 `S01` 是录音内的匿名标签，不是跨录音稳定的人员 ID。
+`spk_embedding_center` 是聚类均值，不表示已经完成身份注册。
+
+## 准备本地检查点
+
+完成[安装检查](installation/installation_zh.md)，准备包含配置、前端、适用时的
+tokenizer 及权重的完整本地快照。阅读各模型许可证，记录解析后的 revision 或文件哈希、
+SDK 版本、实际导入模块路径与源码 commit。本指南对应文末链接的实现，不保证所有旧版
+wheel、导出后端或模型变体接口一致。
+
+- **CAMPPlus（`embedding`）：** `iic/speech_campplus_sv_zh-cn_16k-common`。
+  ModelScope 别名为 `cam++`。
+- **ERes2NetV2（`embedding`）：** `iic/speech_eres2netv2_sv_zh-cn_16k-common`。
+- **SenseVoice（`sensevoice`）：** `iic/SenseVoiceSmall`。
+
+`embedding` 和 `sensevoice` 选项只属于下方示例程序，不是新增的 FunASR 模型别名或 CLI 子命令。
+不要把 ASR 检查点传给 `embedding`，也不要将 ERes2NetV2 的行为推广到所有 ERes2Net 变体。
+其他模型请查看 [Model Zoo](../model_zoo/readme_zh.md)。
+
+音频使用非空、单声道、16 kHz WAV。程序读取归一化 `float32` 波形，拒绝立体声、空音频
+或其他采样率，不隐式转换。提取声纹时使用选定的单人有效语音；混合说话人、静音或极短
+片段不能提供可靠身份依据。下方校验仅检查格式，不是语音质量检测器。
+
+## 保存结果并保留原始标签
+
+独立程序接收 `task`、`model_dir`、`audio` 和新的 JSON 输出路径。例如在脚本名后传入
+`embedding /models/campplus speaker.wav vector.json`，或
+`sensevoice /models/sensevoice utterance.wav tags.json`。
+两个模式都在 CPU 上处理完整片段，不添加 VAD、标点、配套说话人模型或流式缓存，
+程序内部不下载模型。
+
+```python
+import argparse
+import json
+import os
+from pathlib import Path
+import soundfile as sf
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+
+def embedding_record(results):
+    if not results:
+        raise ValueError("No result from the speaker model")
+    if len(results) != 1:
+        raise ValueError("Expected one speaker result for one input")
+    vector = results[0]["spk_embedding"]
+    if vector.ndim != 2 or vector.shape[0] != 1 or vector.shape[1] == 0:
+        raise ValueError("Expected a nonempty single-row speaker embedding")
+    return {"spk_embedding": vector.detach().cpu().tolist()}
+
+
+def tagged_records(results):
+    if not results:
+        raise ValueError("No result from SenseVoice")
+    records = []
+    for item in results:
+        raw = item["text"]
+        if not isinstance(raw, str):
+            raise ValueError("Expected SenseVoice text with its original tags")
+        records.append({
+            "key": item.get("key"), "raw_tagged_text": raw,
+            "display_text": rich_transcription_postprocess(raw),
+        })
+    return records
+
+
+def write_result(path, record):
+    payload = json.dumps(record, ensure_ascii=False, allow_nan=False, indent=2)
+    fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600)
+    with os.fdopen(fd, "w", encoding="utf-8") as stream:
+        stream.write(payload + "\n")
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("task", choices=["embedding", "sensevoice"])
+parser.add_argument("model_dir")
+parser.add_argument("audio")
+parser.add_argument("output")
+args = parser.parse_args()
+model_dir = Path(args.model_dir).expanduser().resolve(strict=True)
+if not model_dir.is_dir():
+    raise ValueError("Expected a complete local model directory")
+speech, sample_rate = sf.read(args.audio, dtype="float32")
+if sample_rate != 16000 or speech.ndim != 1 or len(speech) == 0:
+    raise ValueError("Expected nonempty mono 16 kHz audio")
+model = AutoModel(
+    model=str(model_dir), device="cpu", ncpu=1, disable_update=True,
+    trust_remote_code=False, vad_model=None, punc_model=None, spk_model=None,
+)
+if args.task == "embedding":
+    results = model.generate(input=speech, fs=sample_rate, batch_size=1)
+    result = embedding_record(results)
+else:
+    results = model.generate(
+        input=speech, fs=sample_rate, batch_size=1,
+        language="auto", use_itn=True, output_timestamp=False,
+    )
+    result = tagged_records(results)
+write_result(args.output, {
+    "task": args.task, "model_dir": str(model_dir),
+    "sample_rate": sample_rate, "result": result,
+})
+```
+
+外层 JSON、`raw_tagged_text` 和 `display_text` 是示例应用创建的字段，不是 SDK 新增返回字段。
+向量先显式 detach、移到 CPU，再转换为嵌套列表。非有限数值会在创建文件前被拒绝。
+程序不会覆盖已有输出，每次运行使用新路径。POSIX 上以仅所有者可访问的模式创建文件；
+仍需配置目录权限及对应平台 ACL。声纹向量和转写可能涉及敏感信息，应按需保留，
+不要公开私人音频或结果。
+
+## 正确读取 SenseVoice 标签
+
+原始带标签字符串通常在 `result["text"]`，不能假设存在 `raw_text`、`emotion` 或
+`emotion_score`。标签区分大小写，例如 `<|zh|>`、`<|HAPPY|>`、`<|Speech|>`、
+`<|withitn|>`；是否出现及其含义取决于检查点。展示映射表不保证所有模型都会输出其中全部标签。
+
+`rich_transcription_postprocess` 是有损展示函数：删除标签、按出现次数选择展示情感、
+合并重复展示标记，还会做文本替换。它不是结构化标签解析器或概率计算器，必须先保留原文。
+不要从展示符号或模型标签推导置信概率、真实心理状态或诊断结论。静音、短片段和噪声音频
+不能提供可靠情感依据。`emotion2vec` 是另一类模型及接口，不是 SenseVoice 标签的别名。
+
+本例显式选择 `language="auto"`、`use_itn=True`、`output_timestamp=False`，并非这些参数
+都必填。SenseVoice 语言提示使用 `auto/zh/en/yue/ja/ko`。
+`use_itn` 控制逆文本规范化，不是情感识别开关，显式 `text_norm` 的优先级更高。
+每次调用重复传入需要固定的选项。本例是完整片段推理，不是 KWS 的 EOS 协议或实时情绪监控。
+
+## VAD、说话人分离与服务边界
+
+独立声纹提取和短片段 SenseVoice 推理不要求 VAD。通用 AutoModel 说话人聚类则位于
+VAD 流水线中，只设置 `spk_model` 或 `return_spk_res=True` 不会给直接推理增加说话人分离。
+VAD 边界不保证对应说话人切换；长音频分段也会改变情感标签所依据的上下文。
+组合兼容的 ASR/VAD/标点/说话人模型时，遵循独立的
+[SDK 流水线](python_api_zh.md#vad时间戳与说话人)。
+`sentence_info[].spk` 是匿名编号，SDK `start/end` 单位为毫秒；
+NumPy 聚类中心数组也需要像 Tensor 一样显式序列化。
+
+第三方 OpenMOSS MOSS-Transcribe-Diarize 有自己的联合转写与分离路径，不需要外部
+`vad_model` 或 `spk_model`。后端、显存和返回格式复用现有
+[MOSS 指南](moss_transcribe_diarize_zh.md)，不要叠加第二套聚类或宣称已识别已知人员身份。
+
+当前内置 [HTTP 转写服务](../examples/openai_api/README_zh.md) 的 SenseVoice 配置附带 VAD，
+fallback 会剥离顶层及分段文本中的富标签，不会补建情感分数字段。
+其 `spk=true` 属于服务自己的流水线，HTTP 分段 `start/end` 单位为秒。
+把此 SDK 示例换成 `/v1/audio/transcriptions` 不代表仍能读取同样的标签或使用同样的参数。
+请查阅[部署矩阵](deployment_matrix_zh.md)，不要假设 vLLM、llama.cpp、ONNX 或 WebSocket
+导出路径都保留 Python 返回内容。
+
+## 源码与验证
+
+契约来源：[CAMPPlus](../funasr/models/campplus/model.py)、
+[ERes2NetV2](../funasr/models/eres2net/model.py)、
+[SenseVoice](../funasr/models/sense_voice/model.py)、
+[后处理](../funasr/utils/postprocess_utils.py)、
+[AutoModel](../funasr/auto/auto_model.py) 与 [HTTP 适配](../funasr/bin/_server_app.py)。
+[指南测试](../tests/test_speaker_emotion_docs.py) 使用记录调用的 SDK 替身和真实展示函数执行
+公开程序，验证字段保留、序列化和错误输入，不代表声学质量、身份匹配、不同人群表现或情感准确率评测。
diff --git a/model_zoo/readme.md b/model_zoo/readme.md
index b42dad748..3da96f4d7 100644
--- a/model_zoo/readme.md
+++ b/model_zoo/readme.md
@@ -57,6 +57,10 @@ interchangeable ASR checkpoint.
 
 ## Pipeline components
 
+For speaker vectors and SenseVoice raw/display tags, use the
+[speaker and emotion guide](../docs/speaker_emotion.md). For utterance-end KWS,
+use the separate [keyword spotting guide](../docs/keyword_spotting.md).
+
 | Component | Alias | Model cards | What it does not do |
 | --- | --- | --- | --- |
 | Voice activity detection | `fsmn-vad` | [ModelScope](https://modelscope.cn/models/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) / [HF](https://huggingface.co/funasr/fsmn-vad) | Does not transcribe speech or identify a speaker. |
diff --git a/model_zoo/readme_zh.md b/model_zoo/readme_zh.md
index 9cc02c776..744f3114f 100644
--- a/model_zoo/readme_zh.md
+++ b/model_zoo/readme_zh.md
@@ -54,6 +54,9 @@ print(result[0]["text"])
 
 ## 流水线组件
 
+声纹向量及 SenseVoice 原始／展示标签见[说话人与情感指南](../docs/speaker_emotion_zh.md)。
+句末关键词检测见独立的 [KWS 指南](../docs/keyword_spotting_zh.md)。
+
 | 组件 | 别名 | 模型卡 | 不包含的能力 |
 | --- | --- | --- | --- |
 | 语音活动检测 | `fsmn-vad` | [ModelScope](https://modelscope.cn/models/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) / [HF](https://huggingface.co/funasr/fsmn-vad) | 不转写语音，也不识别人名。 |
diff --git a/tests/test_speaker_emotion_docs.py b/tests/test_speaker_emotion_docs.py
new file mode 100644
index 000000000..20ff4ea3b
--- /dev/null
+++ b/tests/test_speaker_emotion_docs.py
@@ -0,0 +1,159 @@
+"""Execute the bilingual recipe and the real rich-text presentation helper."""
+
+import ast
+import json
+import os
+import runpy
+import sys
+import types
+from pathlib import Path
+
+import pytest
+from bs4 import BeautifulSoup
+from markdown import markdown
+
+ROOT = Path(__file__).resolve().parents[1]
+GUIDES = ["speaker_emotion.md", "speaker_emotion_zh.md"]
+
+
+def test_bilingual_task_guides_exist():
+    for name in GUIDES:
+        assert (ROOT / "docs" / name).is_file()
+
+
+@pytest.fixture(params=GUIDES)
+def recipe(request):
+    path = ROOT / "docs" / request.param
+    soup = BeautifulSoup(markdown(path.read_text(), extensions=["fenced_code"]), "html.parser")
+    blocks = soup.select("code.language-python")
+    assert len(blocks) == 1
+    ast.parse(blocks[0].get_text())
+    return compile(blocks[0].get_text(), str(path), "exec")
+
+
+class Waveform:
+    ndim = 1
+
+    def __init__(self, length=48000, ndim=1):
+        self.length, self.ndim = length, ndim
+
+    def __len__(self):
+        return self.length
+
+
+class Embedding:
+    ndim = 2
+
+    def __init__(self, rows=None):
+        self.rows = [[0.25, -0.5, 0.75]] if rows is None else rows
+        self.shape = (len(self.rows), len(self.rows[0]) if self.rows else 0)
+        self.detached = self.on_cpu = False
+
+    def detach(self):
+        self.detached = True
+        return self
+
+    def cpu(self):
+        self.on_cpu = True
+        return self
+
+    def tolist(self):
+        assert self.detached and self.on_cpu
+        return self.rows
+
+
+def run_recipe(recipe, monkeypatch, tmp_path, task="embedding", results=None,
+               sample_rate=16000, length=48000, ndim=1, output=None):
+    model_dir = tmp_path / "checkpoint"
+    model_dir.mkdir(exist_ok=True)
+    constructors, calls = [], []
+    if results is None:
+        results = [{"spk_embedding": Embedding()}] if task == "embedding" else [
+            {"key": "sample", "text": "<|zh|><|HAPPY|><|Speech|><|withitn|>你好"}
+        ]
+
+    class Model:
+        def __init__(self, **kwargs):
+            constructors.append(kwargs)
+
+        def generate(self, **kwargs):
+            calls.append(kwargs)
+            return results
+
+    helper = runpy.run_path(str(ROOT / "funasr/utils/postprocess_utils.py"))
+    postprocess = types.ModuleType("funasr.utils.postprocess_utils")
+    postprocess.rich_transcription_postprocess = helper["rich_transcription_postprocess"]
+    monkeypatch.setitem(sys.modules, "funasr", types.SimpleNamespace(AutoModel=Model))
+    monkeypatch.setitem(sys.modules, "funasr.utils", types.ModuleType("funasr.utils"))
+    monkeypatch.setitem(sys.modules, "funasr.utils.postprocess_utils", postprocess)
+    monkeypatch.setitem(sys.modules, "soundfile", types.SimpleNamespace(
+        read=lambda *a, **k: (Waveform(length, ndim), sample_rate)))
+    output = output or tmp_path / "result.json"
+    monkeypatch.setattr(sys, "argv", ["attributes.py", task, str(model_dir), "speech.wav", str(output)])
+    scope = {"__name__": "__main__"}
+    exec(recipe, scope)
+    return json.loads(output.read_text()), calls, constructors
+
+
+def test_embedding_is_a_finite_cpu_serializable_vector_not_identity(recipe, monkeypatch, tmp_path):
+    data, calls, constructors = run_recipe(recipe, monkeypatch, tmp_path)
+    assert data["result"] == {"spk_embedding": [[0.25, -0.5, 0.75]]}
+    assert "name" not in data["result"] and "text" not in data["result"]
+    assert calls[0]["batch_size"] == 1 and calls[0]["fs"] == 16000
+    assert "cache" not in calls[0] and "language" not in calls[0]
+    assert constructors[0]["device"] == "cpu"
+    assert constructors[0]["trust_remote_code"] is False
+    assert all(constructors[0][p] is None for p in ("vad_model", "punc_model", "spk_model"))
+    if os.name == "posix":
+        assert (tmp_path / "result.json").stat().st_mode & 0o777 == 0o600
+
+
+def test_raw_tags_survive_real_lossy_display_processing(recipe, monkeypatch, tmp_path):
+    raw = "<|zh|><|HAPPY|><|Speech|><|withitn|>你好"
+    data, calls, _ = run_recipe(recipe, monkeypatch, tmp_path, task="sensevoice", results=[{"key": "sample", "text": raw}])
+    assert data["result"] == [{"key": "sample", "raw_tagged_text": raw, "display_text": "你好😊"}]
+    assert calls[0]["language"] == "auto"
+    assert calls[0]["use_itn"] is True and calls[0]["output_timestamp"] is False
+    assert "return_raw_text" not in calls[0]
+
+
+def test_multiple_sensevoice_results_are_preserved_in_order(recipe, monkeypatch, tmp_path):
+    raw = ["<|en|><|NEUTRAL|><|Speech|><|withitn|>Hello", "<|zh|><|SAD|><|Speech|><|withitn|>再见"]
+    data, _, _ = run_recipe(recipe, monkeypatch, tmp_path, task="sensevoice", results=[{"text": text} for text in raw])
+    assert [item["raw_tagged_text"] for item in data["result"]] == raw
+    assert all(item["key"] is None for item in data["result"])
+
+
+@pytest.mark.parametrize("rate,length,ndim", [(8000, 48000, 1), (16000, 0, 1), (16000, 48000, 2)])
+def test_invalid_audio_rejected(recipe, monkeypatch, tmp_path, rate, length, ndim):
+    with pytest.raises(ValueError, match="nonempty mono 16 kHz"):
+        run_recipe(recipe, monkeypatch, tmp_path, sample_rate=rate, length=length, ndim=ndim)
+    assert not (tmp_path / "result.json").exists()
+
+
+@pytest.mark.parametrize("rows", [[], [[]], [[], []], [[1.0], [2.0]], [[float("nan")]]])
+def test_invalid_embedding_is_not_published_as_json(recipe, monkeypatch, tmp_path, rows):
+    with pytest.raises(ValueError):
+        run_recipe(recipe, monkeypatch, tmp_path, results=[{"spk_embedding": Embedding(rows)}])
+    assert not (tmp_path / "result.json").exists()
+
+
+@pytest.mark.parametrize("task", ["embedding", "sensevoice"])
+def test_empty_result_is_not_invented(recipe, monkeypatch, tmp_path, task):
+    with pytest.raises(ValueError, match="No result"):
+        run_recipe(recipe, monkeypatch, tmp_path, task=task, results=[])
+    assert not (tmp_path / "result.json").exists()
+
+
+def test_existing_output_is_not_overwritten(recipe, monkeypatch, tmp_path):
+    path = tmp_path / "prior.json"
+    path.write_text("existing result")
+    with pytest.raises(FileExistsError):
+        run_recipe(recipe, monkeypatch, tmp_path, output=path)
+    assert path.read_text() == "existing result"
+
+
+def test_malformed_rich_text_is_not_silently_discarded(recipe, monkeypatch, tmp_path):
+    with pytest.raises(ValueError, match="Expected SenseVoice text"):
+        run_recipe(recipe, monkeypatch, tmp_path, task="sensevoice", results=[{"text": 123}])
+    assert not (tmp_path / "result.json").exists()
diff --git a/web-pages/product-site/data/documentation.json b/web-pages/product-site/data/documentation.json
index e3ad24e2a..f20c63a90 100644
--- a/web-pages/product-site/data/documentation.json
+++ b/web-pages/product-site/data/documentation.json
@@ -19,6 +19,7 @@
     {"slug": "model-zoo", "group": "models", "zh": "Model Zoo", "en": "Model Zoo", "source_zh": "model_zoo/readme_zh.md", "source_en": "model_zoo/readme.md"},
     {"slug": "streaming-vad", "group": "models", "zh": "流式语音活动检测", "en": "Streaming voice activity detection", "source_zh": "docs/streaming_vad_zh.md", "source_en": "docs/streaming_vad.md"},
     {"slug": "keyword-spotting", "group": "models", "zh": "关键词检测", "en": "Keyword spotting", "source_zh": "docs/keyword_spotting_zh.md", "source_en": "docs/keyword_spotting.md"},
+    {"slug": "speaker-emotion", "group": "models", "zh": "说话人与情感标签", "en": "Speakers and emotion tags", "source_zh": "docs/speaker_emotion_zh.md", "source_en": "docs/speaker_emotion.md"},
     {"slug": "training", "group": "train", "zh": "微调与评测", "en": "Fine-tuning & evaluation", "source_zh": "docs/training_zh.md", "source_en": "docs/training.md"},
     {"slug": "model-registration", "group": "train", "zh": "自定义模型注册", "en": "Custom model registration", "source_zh": "docs/model_registration_zh.md", "source_en": "docs/model_registration.md"},
     {"slug": "docker", "group": "deploy", "zh": "容器环境与镜像选择", "en": "Containers & image selection", "source_zh": "docs/installation/docker_zh.md", "source_en": "docs/installation/docker.md"},
diff --git a/web-pages/product-site/tests/browser/speaker-emotion.spec.ts b/web-pages/product-site/tests/browser/speaker-emotion.spec.ts
new file mode 100644
index 000000000..d54ef35a3
--- /dev/null
+++ b/web-pages/product-site/tests/browser/speaker-emotion.spec.ts
@@ -0,0 +1,35 @@
+import { expect, test } from '@playwright/test';
+
+for (const width of [390, 1440]) {
+  for (const language of ['zh', 'en']) {
+    test(`Speaker and emotion guide: ${language} ${width}px`, async ({ page, context }, testInfo) => {
+      const prefix = language === 'en' ? '/en' : '';
+      const title = language === 'en' ? 'Speakers and emotion tags' : '说话人与情感标签';
+      const errors: string[] = [];
+      page.on('pageerror', error => errors.push(error.message));
+      await context.grantPermissions(['clipboard-read', 'clipboard-write']);
+      await page.setViewportSize({ width, height: 900 });
+      await page.goto(`${prefix}/docs/`);
+      await page.locator('[data-doc-search] input').fill(title);
+      await page.locator(`.search-results a[href="${prefix}/docs/speaker-emotion.html"]`).click();
+      await expect(page.locator('.docs-title h1')).toHaveText(title);
+      expect(await page.evaluate(() => document.documentElement.scrollWidth - innerWidth)).toBeLessThanOrEqual(1);
+      await page.screenshot({ path: testInfo.outputPath('initial.png') });
+      const checkpoints = page.locator('.docs-article ul').first();
+      await expect(checkpoints.locator('li')).toHaveCount(3);
+      await checkpoints.scrollIntoViewIfNeeded();
+      await page.screenshot({ path: testInfo.outputPath('checkpoints.png') });
+      const block = page.locator('.docs-article pre').filter({ hasText: 'def embedding_record' });
+      await expect(block).toHaveCount(1);
+      const expected = await block.locator('code').innerText();
+      await block.locator('button').click();
+      expect((await page.evaluate(() => navigator.clipboard.readText())).trim()).toBe(expected.trim());
+      await page.screenshot({ path: testInfo.outputPath('code.png') });
+      await page.goto(`${prefix}/docs/model-zoo.html`);
+      await page.locator(`.docs-article a[href="${prefix}/docs/speaker-emotion.html"]`).click();
+      await expect(page).toHaveURL(new RegExp(`${prefix}/docs/speaker-emotion.html$`));
+      expect(await page.evaluate(() => document.documentElement.scrollWidth - innerWidth)).toBeLessThanOrEqual(1);
+      expect(errors).toEqual([]);
+    });
+  }
+}