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Notice: In order to resolve issues more efficiently, please raise issue following the template. (注意:为了更加高效率解决您遇到的问题,请按照模板提问,补充细节)
在runtime环境下使用speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404-onnx模型。添加如下热词表时感觉热词直接会有相互干扰。比如 针灸铜人 80 久通 80
测试可能会出现针灸通人、久铜等结果。请问添加热词是单独提高token概率吗。如果是全词匹配的话,按说wfst里影响不会这么大。有无办法解决?
pip
The text was updated successfully, but these errors were encountered:
runtime中的热词分两部分,首先是基于clas的nn热词,这个阶段是通过attention进行热词与decoder信息的匹配的 有热词冲突会导致attention机制产生错误的相关性,没有很好的解法 可能的解决方法是拆解长热词或者把短热词补长
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Notice: In order to resolve issues more efficiently, please raise issue following the template.
(注意:为了更加高效率解决您遇到的问题,请按照模板提问,补充细节)
🐛 Bug
在runtime环境下使用speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404-onnx模型。添加如下热词表时感觉热词直接会有相互干扰。比如
针灸铜人 80
久通 80
测试可能会出现针灸通人、久铜等结果。请问添加热词是单独提高token概率吗。如果是全词匹配的话,按说wfst里影响不会这么大。有无办法解决?
Environment
pip
, source):The text was updated successfully, but these errors were encountered: