feat: support SenseVoice speaker diarization (#2706)

- Auto-enable output_timestamp when spk_model is present in inference_with_vad
- Add SenseVoice + SPK demo and update README news/quick-start

README.md

@@ -34,7 +34,7 @@
 
 ## What's new:
 
-- 2026/05/19: Fun-ASR-Nano now supports speaker diarization. Use with `vad_model` + `spk_model` + `punc_model` to get per-sentence speaker labels. See [demo](examples/industrial_data_pretraining/fun_asr_nano/demo_spk.py).
+- 2026/05/19: Fun-ASR-Nano and SenseVoice now support speaker diarization. Use with `vad_model` + `spk_model` + `punc_model` to get per-sentence speaker labels. See [Fun-ASR-Nano demo](examples/industrial_data_pretraining/fun_asr_nano/demo_spk.py), [SenseVoice demo](examples/industrial_data_pretraining/sense_voice/demo_spk.py).
 - 2025/12/15: [Fun-ASR-Nano-2512](https://github.com/FunAudioLLM/Fun-ASR) is an end-to-end speech recognition large model trained on tens of millions of hours real speech data. It supports low-latency real-time transcription and covers 31 languages.
 - 2024/10/29: Real-time Transcription Service 1.12 released, The 2pass-offline mode supports the SensevoiceSmal model；([docs](runtime/readme.md));
 - 2024/10/10：Added support for the Whisper-large-v3-turbo model, a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. It can be downloaded from the [modelscope](examples/industrial_data_pretraining/whisper/demo.py), and [openai](examples/industrial_data_pretraining/whisper/demo_from_openai.py).
@@ -231,6 +231,27 @@ Parameter Description:
 - `merge_vad`: Whether to merge short audio fragments segmented by the VAD model, with the merged length being `merge_length_s`, in seconds (s).
 - `ban_emo_unk`: Whether to ban the output of the `emo_unk` token.
 
+#### SenseVoice with Speaker Diarization
+
+```python
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(
+    model="iic/SenseVoiceSmall",
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    spk_model="cam++",
+    punc_model="ct-punc",
+    device="cuda:0",
+)
+res = model.generate(input="example.wav", cache={}, language="auto", use_itn=True,
+                     batch_size_s=60, merge_vad=True, merge_length_s=15)
+# Result contains sentence_info with speaker labels
+print(res[0]["sentence_info"])
+# [{'text': '...', 'start': 750, 'end': 5550, 'timestamp': [...], 'spk': 0}]
+```
+
 #### Paraformer
 
 ```python

README_zh.md

@@ -34,7 +34,7 @@ FunASR 希望在语音识别的学术研究和工业应用之间架起一座桥
 
 ## 最新动态
 
-- 2026/05/19: Fun-ASR-Nano 现已支持说话人分离。配合 `vad_model` + `spk_model` + `punc_model` 使用，可获得带说话人标签的逐句结果。参见 [demo](examples/industrial_data_pretraining/fun_asr_nano/demo_spk.py)。
+- 2026/05/19: Fun-ASR-Nano 和 SenseVoice 现已支持说话人分离。配合 `vad_model` + `spk_model` + `punc_model` 使用，可获得带说话人标签的逐句结果。参见 [Fun-ASR-Nano demo](examples/industrial_data_pretraining/fun_asr_nano/demo_spk.py)、[SenseVoice demo](examples/industrial_data_pretraining/sense_voice/demo_spk.py)。
 - 2025/12/15: [Fun-ASR-Nano-2512](https://github.com/FunAudioLLM/Fun-ASR) 是一款基于数千万小时真实语音数据训练的端到端语音识别大模型。它支持低延迟实时转写，并涵盖 31 种语言识别功能。
 - 2024/10/29: 中文实时语音听写服务 1.12 发布，2pass-offline 模式支持 SensevoiceSmall 模型；详细信息参阅([部署文档](runtime/readme_cn.md))
 - 2024/10/10：新增加 Whisper-large-v3-turbo 模型支持，多语言语音识别/翻译/语种识别，支持从 [modelscope](examples/industrial_data_pretraining/whisper/demo.py)仓库下载，也支持从 [openai](examples/industrial_data_pretraining/whisper/demo_from_openai.py)仓库下载模型。

examples/industrial_data_pretraining/sense_voice/demo_spk.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+"""
+SenseVoice with Speaker Diarization
+
+This demo shows how to use SenseVoice with VAD + Speaker Model + Punctuation Model
+to get per-sentence speaker labels.
+"""
+
+import torch
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+
+def main():
+    model_dir = "iic/SenseVoiceSmall"
+    device = (
+        "cuda:0"
+        if torch.cuda.is_available()
+        else "mps" if torch.backends.mps.is_available() else "cpu"
+    )
+
+    model = AutoModel(
+        model=model_dir,
+        vad_model="fsmn-vad",
+        vad_kwargs={"max_single_segment_time": 30000},
+        spk_model="cam++",
+        punc_model="ct-punc",
+        device=device,
+    )
+
+    wav_path = f"{model.model_path}/example/zh.mp3"
+    res = model.generate(
+        input=wav_path,
+        cache={},
+        language="auto",
+        use_itn=True,
+        batch_size_s=60,
+        merge_vad=True,
+        merge_length_s=15,
+    )
+
+    # Print full text
+    text = rich_transcription_postprocess(res[0]["text"])
+    print("Text:", text)
+    print()
+
+    # Print per-sentence results with speaker labels
+    print("Speaker Diarization Results:")
+    for sent in res[0]["sentence_info"]:
+        sent_text = rich_transcription_postprocess(sent["text"])
+        print(
+            f"  Speaker {sent['spk']}: [{sent['start']}ms - {sent['end']}ms] {sent_text}"
+        )
+
+
+if __name__ == "__main__":
+    main()

funasr/auto/auto_model.py

@@ -442,6 +442,8 @@ class AutoModel:
 
     def inference_with_vad(self, input, input_len=None, **cfg):
         self._reset_runtime_configs()
+        if self.spk_model is not None and "output_timestamp" not in cfg:
+            cfg["output_timestamp"] = True
         kwargs = self.kwargs
         # step.1: compute the vad model
         deep_update(self.vad_kwargs, cfg)