update modelscope model zoo

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-en-long](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) | EN  | Alibaba Speech Data (50000hours) |    10020    |   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                                                                                                    |

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-en长音频版本](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) |  英文   |   阿里巴巴语音数据（50000小时）   |       10020        | 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          |