feat(moss): add AutoModel runtime adapter

.github/workflows/test-moss-adapter.yml

@@ -0,0 +1,53 @@
+name: Validate MOSS adapter
+
+on:
+  pull_request:
+    paths:
+      - "funasr/models/moss_transcribe_diarize/**"
+      - "funasr/auto/auto_model.py"
+      - "docs/moss_transcribe_diarize*.md"
+      - "tests/test_moss_transcribe_diarize_*.py"
+      - ".github/workflows/test-moss-adapter.yml"
+  push:
+    branches:
+      - main
+    paths:
+      - "funasr/models/moss_transcribe_diarize/**"
+      - "funasr/auto/auto_model.py"
+      - "docs/moss_transcribe_diarize*.md"
+      - "tests/test_moss_transcribe_diarize_*.py"
+      - ".github/workflows/test-moss-adapter.yml"
+
+permissions:
+  contents: read
+
+jobs:
+  contract:
+    runs-on: ubuntu-latest
+    timeout-minutes: 20
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+          cache: pip
+      - name: Install contract-test dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install \
+            torch --index-url https://download.pytorch.org/whl/cpu
+          python -m pip install \
+            pytest black==25.1.0 numpy soundfile requests packaging \
+            omegaconf hydra-core kaldiio librosa tqdm pyyaml
+      - name: Check formatting and syntax
+        run: |
+          python -m black --check \
+            funasr/models/moss_transcribe_diarize/model.py \
+            tests/test_moss_transcribe_diarize_model.py \
+            tests/test_moss_transcribe_diarize_docs.py
+          python -m py_compile funasr/models/moss_transcribe_diarize/model.py
+      - name: Run adapter and documentation contracts
+        run: |
+          python -m pytest -q \
+            tests/test_moss_transcribe_diarize_model.py \
+            tests/test_moss_transcribe_diarize_docs.py

docs/moss_transcribe_diarize.md

@@ -5,8 +5,9 @@
 This guide connects the third-party
 [OpenMOSS/MOSS-Transcribe-Diarize](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize)
 model to the FunASR deployment ecosystem. The model is published by OpenMOSS
-under Apache-2.0; it is not a FunASR model and is not registered in FunASR
-`AutoModel`.
+under Apache-2.0; it is not a FunASR model. FunASR provides an adapter for its
+public Transformers and vLLM interfaces while retaining the OpenMOSS model
+name, license, and upstream revision.
 
 MOSS-Transcribe-Diarize jointly generates transcription, timestamps, and
 speaker labels such as `[S01]`. An application therefore does not need to
@@ -25,6 +26,65 @@ This guide was verified against:
 Pin all three revisions. The model uses `trust_remote_code`; do not execute a
 floating model revision in a production service.
 
+## FunASR AutoModel contract
+
+Use an isolated Python 3.10+ environment with Transformers 5.6 or newer for
+the local backend. MOSS performs long-form transcription and speaker
+diarization in one generation, so do **not** pass `vad_model` or `spk_model`.
+External VAD segmentation would break the model's global speaker identity
+across chunks.
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(
+    model="OpenMOSS-Team/MOSS-Transcribe-Diarize",
+    model_revision="e8681d68e7042738ffca8ac8212bc8fcb1131ab8",
+    backend="hf",
+    device="cuda:0",
+    dtype="bf16",
+    attn_implementation="sdpa",
+    disable_update=True,
+)
+
+result = model.generate("audio.wav", max_new_tokens=5120)[0]
+print(result["text"])
+for segment in result["sentence_info"]:
+    print(segment["start"], segment["end"], segment["spk"], segment["text"])
+```
+
+The adapter preserves the raw tagged generation in `raw_text` and returns the
+common FunASR fields:
+
+- `text`: readable transcript without MOSS control tags;
+- `timestamp`: segment-level `[start_ms, end_ms]` pairs;
+- `sentence_info`: `start`, `end`, `text`, `sentence`, `spk`, and `timestamp`;
+- `raw_text`: exact `[start][Sxx]text[end]` generation for auditing.
+
+If the parser cannot prove the tagged structure, it leaves the model text
+visible in `text` and `raw_text` and returns empty timestamp/segment arrays
+instead of silently inventing speaker metadata.
+
+The same result contract can wrap an already running vLLM service without
+downloading local weights:
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(
+    model="OpenMOSS-Team/MOSS-Transcribe-Diarize",
+    backend="vllm",
+    vllm_base_url="http://127.0.0.1:8898/v1",
+    vllm_model="moss-transcribe-diarize",
+    disable_update=True,
+)
+result = model.generate("audio.wav")[0]
+```
+
+The vLLM adapter sends the documented OpenAI-compatible multipart request and
+normalizes the returned raw text. Authentication can be supplied with
+`vllm_api_key`; keep it in a secret store rather than source code.
+
 ## Choose a serving path
 
 | Path | Environment | Response contract | Use when |
@@ -105,6 +165,14 @@ This proves the pinned API and speaker-return contract on those inputs. It is
 not a diarization accuracy result, overlap test, throughput benchmark, or
 production capacity claim.
 
+The FunASR adapter was also tested on both `backend="hf"` and
+`backend="vllm"` with model revision
+`e8681d68e7042738ffca8ac8212bc8fcb1131ab8`. The 15.1685-second two-speaker
+probe (`43dccc068506439cb633b382b6b98185baa837363d08cc5f7152ca89b0fdc3c8`)
+returned two monotonic segments labelled `S01` and `S02` through the common
+`AutoModel` result contract. The temporary vLLM worker was stopped after the
+test; this is a contract smoke test, not an accuracy benchmark.
+
 ## SGLang Omni
 
 Follow the pinned upstream SGLang Omni installation guide for CUDA 13, then

docs/moss_transcribe_diarize_zh.md

@@ -5,7 +5,8 @@
 本文把第三方
 [OpenMOSS/MOSS-Transcribe-Diarize](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize)
 模型接入 FunASR 部署生态。模型由 OpenMOSS 以 Apache-2.0 发布，不是 FunASR
-自有模型，也没有注册到 FunASR `AutoModel`。
+自有模型。FunASR 只为其公开的 Transformers 与 vLLM 接口提供适配器，并保留
+OpenMOSS 模型名称、许可证和上游 revision。
 
 MOSS-Transcribe-Diarize 会联合生成转写、时间戳和 `[S01]` 等说话人标签，应用侧
 不必再拼接外部 VAD、ASR 和 diarization 管线。这里描述的是部署形态，不表示模型
@@ -22,6 +23,59 @@ MOSS-Transcribe-Diarize 会联合生成转写、时间戳和 `[S01]` 等说话
 
 三者都要固定。模型依赖 `trust_remote_code`，生产服务不要执行浮动的模型 revision。
 
+## FunASR AutoModel 契约
+
+本地后端应使用隔离的 Python 3.10+ 环境，并安装 Transformers 5.6 或更新版本。
+MOSS 在一次生成中完成长音频转写与说话人识别，因此不要传 `vad_model` 或
+`spk_model`。外部 VAD 会把长音频切开，并破坏跨分块的全局说话人身份。
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(
+    model="OpenMOSS-Team/MOSS-Transcribe-Diarize",
+    model_revision="e8681d68e7042738ffca8ac8212bc8fcb1131ab8",
+    backend="hf",
+    device="cuda:0",
+    dtype="bf16",
+    attn_implementation="sdpa",
+    disable_update=True,
+)
+
+result = model.generate("audio.wav", max_new_tokens=5120)[0]
+print(result["text"])
+for segment in result["sentence_info"]:
+    print(segment["start"], segment["end"], segment["spk"], segment["text"])
+```
+
+适配器在 `raw_text` 中保留原始标签生成，并返回 FunASR 通用字段：
+
+- `text`：移除 MOSS 控制标签后的可读转写；
+- `timestamp`：segment 级 `[start_ms, end_ms]`；
+- `sentence_info`：包含 `start`、`end`、`text`、`sentence`、`spk` 和 `timestamp`；
+- `raw_text`：用于审计的原始 `[start][Sxx]text[end]` 生成。
+
+当解析器无法确认标签结构时，会保留 `text` 与 `raw_text`，并返回空时间戳和分段，
+不会静默编造说话人信息。
+
+同一结果契约也可以包装已经启动的 vLLM 服务，并且不会下载本地权重：
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(
+    model="OpenMOSS-Team/MOSS-Transcribe-Diarize",
+    backend="vllm",
+    vllm_base_url="http://127.0.0.1:8898/v1",
+    vllm_model="moss-transcribe-diarize",
+    disable_update=True,
+)
+result = model.generate("audio.wav")[0]
+```
+
+vLLM 适配器发送官方文档中的 OpenAI-compatible multipart 请求，并统一解析返回文本。
+认证可通过 `vllm_api_key` 提供；请放在 secret store，不要写入源码。
+
 ## 选择服务后端
 
 | 路径 | 环境 | 响应契约 | 适用场景 |
@@ -98,6 +152,12 @@ FunASR 生态验证使用 vLLM `0.23.1rc1.dev949+g68b4a1d58`、Torch
 这证明固定版本对这些输入满足 API 和说话人返回契约，不是 diarization 精度、重叠语音、
 吞吐或生产容量结论。
 
+FunASR 适配器还分别使用 `backend="hf"` 和 `backend="vllm"`，按模型 revision
+`e8681d68e7042738ffca8ac8212bc8fcb1131ab8` 做了真实测试。15.1685 秒的双说话人
+样例（`43dccc068506439cb633b382b6b98185baa837363d08cc5f7152ca89b0fdc3c8`）通过
+统一 `AutoModel` 结果契约返回两个单调时间分段和 `S01`、`S02`。测试完成后已停止
+临时 vLLM worker；这只是契约冒烟测试，不是精度基准。
+
 ## SGLang Omni
 
 按固定上游 SGLang Omni 安装指南准备 CUDA 13 环境，然后下载不可变模型快照并从本地目录启动：

funasr/auto/auto_model.py

@@ -542,6 +542,12 @@ def build_model(**kwargs):
         if kwargs["model"] in {"silero-vad", "silero_vad"}:
             kwargs.setdefault("model_conf", {})
             kwargs["model"] = "SileroVad"
+        if kwargs["model"] in {
+            "MOSS-Transcribe-Diarize",
+            "OpenMOSS-Team/MOSS-Transcribe-Diarize",
+        }:
+            kwargs.setdefault("model_conf", {})
+            kwargs.setdefault("model_path", kwargs["model"])
         if "model_conf" not in kwargs:
             logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
             kwargs = download_model(**kwargs)

funasr/models/moss_transcribe_diarize/__init__.py

@@ -0,0 +1,5 @@
+"""OpenMOSS MOSS-Transcribe-Diarize integration."""
+
+from .model import MossTranscribeDiarize
+
+__all__ = ["MossTranscribeDiarize"]