update docs and readme

README.md

@@ -28,6 +28,7 @@
 
 <a name="whats-new"></a>
 ## What's new: 
+- 2023/10/10: The ASR-SpeakersDiarization combined pipeline [speech_campplus_speaker-diarization_common](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr_vad_spk/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/demo.py) is now released. Experience the model to get recognition results with speaker information.
 - 2023/10/07: [FunCodec](https://github.com/alibaba-damo-academy/FunCodec): A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec.
 - 2023/09/01: The offline file transcription service 2.0 (CPU) of Mandarin has been released, with added support for ffmpeg, timestamp, and hotword models. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial.md)).
 - 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to ([Deployment documentation](funasr/runtime/docs/SDK_tutorial_online.md)).

README_zh.md

@@ -31,8 +31,9 @@ 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
 
 <a name="最新动态"></a>
 ## 最新动态
+- 2023.10.10: [Paraformer-long-Spk](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr_vad_spk/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/demo.py)模型发布，支持在长语音识别的基础上获取每句话的说话人标签。
 - 2023.10.07: [FunCodec](https://github.com/alibaba-damo-academy/FunCodec): FunCodec提供开源模型和训练工具，可以用于音频离散编码，以及基于离散编码的语音识别、语音合成等任务。
-- 2023.09.01：中文离线文件转写服务2.0 CPU版本发布，新增ffmpeg、时间戳与热词模型支持，详细信息参阅([一键部署文档](funasr/runtime/docs/SDK_tutorial_zh.md))
+- 2023.09.01: 中文离线文件转写服务2.0 CPU版本发布，新增ffmpeg、时间戳与热词模型支持，详细信息参阅([一键部署文档](funasr/runtime/docs/SDK_tutorial_zh.md))
 - 2023.08.07: 中文实时语音听写服务一键部署的CPU版本发布，详细信息参阅([一键部署文档](funasr/runtime/docs/SDK_tutorial_online_zh.md))
 - 2023.07.17: BAT一种低延迟低内存消耗的RNN-T模型发布，详细信息参阅（[BAT](egs/aishell/bat)）
 - 2023.07.03: 中文离线文件转写服务一键部署的CPU版本发布，详细信息参阅([一键部署文档](funasr/runtime/docs/SDK_tutorial_zh.md))

docs/model_zoo/modelscope_models.md

@@ -17,7 +17,8 @@ Here we provided several pretrained models on different datasets. The details of
 |                                                                     Model Name                                                                     | Language |          Training Data           | Vocab Size | Parameter | Offline/Online | Notes                                                                                                                           |
 |:--------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:--------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
 |        [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)        | CN & EN  | Alibaba Speech Data (60000hours) |    8404    |   220M    |    Offline     | Duration of input wav <= 20s                                                                                                    |
-| [Paraformer-large-long](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) | CN & EN  | Alibaba Speech Data (60000hours) |    8404    |   220M    |    Offline     | Which would deal with arbitrary length input wav                                                                                 |
+| [Paraformer-large-long](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) | CN & EN  | Alibaba Speech Data (60000hours) |    8404    |   220M    |    Offline     | Which would deal with arbitrary length input wav |
+| [Paraformer-large-Spk](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) | CN & EN  | Alibaba Speech Data (60000hours) |    8404    |   220M    |    Offline     | Supporting speaker diarizatioin for ASR results based on paraformer-large-long |
 | [Paraformer-large-contextual](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary) | CN & EN  | Alibaba Speech Data (60000hours) |    8404    |   220M    |    Offline     | Which supports the hotword customization based on the incentive enhancement, and improves the recall and precision of hotwords. |
 |              [Paraformer](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)              | CN & EN  | Alibaba Speech Data (50000hours) |    8358    |    68M    |    Offline     | Duration of input wav <= 20s                                                                                                    |
 |           [Paraformer-online](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary)           | CN & EN  | Alibaba Speech Data (50000hours) |    8404    |    68M    |     Online     | Which could deal with streaming input                                                                                           |

docs/model_zoo/modelscope_models_zh.md

@@ -17,7 +17,8 @@
 |                                                                     模型名字                                                                     |    语言    |         训练数据          |       词典大小        | 参数量  | 非实时/实时  | 备注                         |
 |:--------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:-----------------:|:----:|:-------:|:---------------------------|
 |        [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)        |  中文和英文   |    阿里巴巴语音数据（60000小时）  |       8404        | 220M |   非实时   | 输入wav文件持续时间不超过20秒          |
-| [Paraformer-large长音频版本](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) |  中文和英文   |   阿里巴巴语音数据（60000小时）   |       8404        | 220M |   非实时   || 能够处理任意长度的输入wav文件                                                                                |
+| [Paraformer-large长音频版本](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) |  中文和英文   |   阿里巴巴语音数据（60000小时）   |       8404        | 220M |   非实时   | 能够处理任意长度的输入wav文件         |
+| [Paraformer-large-Spk](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) |  中文和英文   |   阿里巴巴语音数据（60000小时）   |       8404        | 220M |   非实时   | 在长音频功能的基础上添加说话人识别功能         |
 |     [Paraformer-large热词](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary)      |         中文和英文         | 阿里巴巴语音数据（60000小时） | 8404 |  220M   | 非实时                        | 基于激励增强的热词定制支持，可以提高热词的召回率和准确率，输入wav文件持续时间不超过20秒  |
 |       [Paraformer](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)                     |   中文和英文  |   阿里巴巴语音数据（50000小时）   |       8358        | 68M  |   离线    | 输入wav文件持续时间不超过20秒          |
 |               [Paraformer实时](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary)                | 中文和英文  | 阿里巴巴语音数据 (50000hours) |       8404        | 68M  | 实时  | 能够处理流式输入                   |

egs_modelscope/asr/TEMPLATE/README.md

@@ -99,6 +99,28 @@ print(rec_result)
 ```
 The decoding mode of `fast` and `normal` is fake streaming, which could be used for evaluating of recognition accuracy.
 Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151)
+
+#### [Paraformer-Spk](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary)
+This model allows user to get recognition results which contain speaker info of each sentence. Refer to [CAM++](https://modelscope.cn/models/damo/speech_campplus_speaker-diarization_common/summary) for detailed information about speaker diarization model.
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+if __name__ == '__main__':
+    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_speaker_demo.wav'
+    output_dir = "./results"
+    inference_pipeline = pipeline(
+        task=Tasks.auto_speech_recognition,
+        model='damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn',
+        model_revision='v0.0.2',
+        vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+        punc_model='damo/punc_ct-transformer_cn-en-common-vocab471067-large',
+        output_dir=output_dir,
+    )
+    rec_result = inference_pipeline(audio_in=audio_in, batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000)
+    print(rec_result)
+```
+
 #### [RNN-T-online model]()
 Undo
 

egs_modelscope/asr/TEMPLATE/README_zh.md

@@ -100,6 +100,29 @@ print(rec_result)
 fast 和 normal 的解码模式是假流式解码，可用于评估识别准确性。
 演示的完整代码，请参见 [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151)
 
+#### [Paraformer-Spk model](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary)
+返回识别结果的同时返回每个子句的说话人分类结果。关于说话人日志模型的详情请见[CAM++](https://modelscope.cn/models/damo/speech_campplus_speaker-diarization_common/summary)。
+
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+if __name__ == '__main__':
+    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_speaker_demo.wav'
+    output_dir = "./results"
+    inference_pipeline = pipeline(
+        task=Tasks.auto_speech_recognition,
+        model='damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn',
+        model_revision='v0.0.2',
+        vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+        punc_model='damo/punc_ct-transformer_cn-en-common-vocab471067-large',
+        output_dir=output_dir,
+    )
+    rec_result = inference_pipeline(audio_in=audio_in, batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000)
+    print(rec_result)
+```
+
+
 #### [RNN-T-online 模型]()
 Undo
 

egs_modelscope/asr_vad_spk/TEMPLATE

@@ -0,0 +1 @@
+../asr/TEMPLATE