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ERes2Net模型结合全局特征和局部特征,从而提高说话人识别性能。局部特征融合将一个单一残差块内的特征融合提取局部信号;全局特征融合使用不同层级输出的不同尺度声学特征聚合全局信号。ERes2Net-Base是参数量较小的ERes2Net模型,可实现快速训练和推理,在参数量为4.6M的条件下,在3D-Speaker各测试集中,识别性能超越ECAPA-TDNN。
本模型使用达摩院开源数据集3D-Speaker数据集进行训练,包含约10k个说话人,可以对16k采样率的中文音频进行识别。
模型来源: https://modelscope.cn/models/damo/speech_eres2net_base_sv_zh-cn_3dspeaker_16k/summary
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
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Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》