fix ERNIE-SAT README, test=doc (#2392)

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# ERNIE-SAT with AISHELL3 dataset
# ERNIE-SAT with VCTK dataset
ERNIE-SAT speech-text joint pretraining framework, which achieves SOTA results in cross-lingual multi-speaker speech synthesis and cross-lingual speech editing tasks, It can be applied to a series of scenarios such as Speech Editing, personalized Speech Synthesis, and Voice Cloning.
ERNIE-SAT 是可以同时处理中英文的跨语言的语音-语言跨模态大模型,其在语音编辑、个性化语音合成以及跨语言的语音合成等多个任务取得了领先效果。可以应用于语音编辑、个性化合成、语音克隆、同传翻译等一系列场景,该项目供研究使用。
## 模型框架
ERNIE-SAT 中我们提出了两项创新:
- 在预训练过程中将中英双语对应的音素作为输入,实现了跨语言、个性化的软音素映射
- 采用语言和语音的联合掩码学习实现了语言和语音的对齐
## Model Framework
In ERNIE-SAT, we propose two innovations:
- In the pretraining process, the phonemes corresponding to Chinese and English are used as input to achieve cross-language and personalized soft phoneme mapping
- The joint mask learning of speech and text is used to realize the alignment of speech and text
<p align="center">
<img src="https://user-images.githubusercontent.com/24568452/186110814-1b9c6618-a0ab-4c0c-bb3d-3d860b0e8cc2.png" />

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# ERNIE-SAT with AISHELL3 and VCTK dataset
# ERNIE-SAT with VCTK dataset
ERNIE-SAT speech-text joint pretraining framework, which achieves SOTA results in cross-lingual multi-speaker speech synthesis and cross-lingual speech editing tasks, It can be applied to a series of scenarios such as Speech Editing, personalized Speech Synthesis, and Voice Cloning.
ERNIE-SAT 是可以同时处理中英文的跨语言的语音-语言跨模态大模型,其在语音编辑、个性化语音合成以及跨语言的语音合成等多个任务取得了领先效果。可以应用于语音编辑、个性化合成、语音克隆、同传翻译等一系列场景,该项目供研究使用。
## 模型框架
ERNIE-SAT 中我们提出了两项创新:
- 在预训练过程中将中英双语对应的音素作为输入,实现了跨语言、个性化的软音素映射
- 采用语言和语音的联合掩码学习实现了语言和语音的对齐
## Model Framework
In ERNIE-SAT, we propose two innovations:
- In the pretraining process, the phonemes corresponding to Chinese and English are used as input to achieve cross-language and personalized soft phoneme mapping
- The joint mask learning of speech and text is used to realize the alignment of speech and text
<p align="center">
<img src="https://user-images.githubusercontent.com/24568452/186110814-1b9c6618-a0ab-4c0c-bb3d-3d860b0e8cc2.png" />

@ -1,11 +1,10 @@
# ERNIE-SAT with VCTK dataset
ERNIE-SAT speech-text joint pretraining framework, which achieves SOTA results in cross-lingual multi-speaker speech synthesis and cross-lingual speech editing tasks, It can be applied to a series of scenarios such as Speech Editing, personalized Speech Synthesis, and Voice Cloning.
ERNIE-SAT 是可以同时处理中英文的跨语言的语音-语言跨模态大模型,其在语音编辑、个性化语音合成以及跨语言的语音合成等多个任务取得了领先效果。可以应用于语音编辑、个性化合成、语音克隆、同传翻译等一系列场景,该项目供研究使用。
## 模型框架
ERNIE-SAT 中我们提出了两项创新:
- 在预训练过程中将中英双语对应的音素作为输入,实现了跨语言、个性化的软音素映射
- 采用语言和语音的联合掩码学习实现了语言和语音的对齐
## Model Framework
In ERNIE-SAT, we propose two innovations:
- In the pretraining process, the phonemes corresponding to Chinese and English are used as input to achieve cross-language and personalized soft phoneme mapping
- The joint mask learning of speech and text is used to realize the alignment of speech and text
<p align="center">
<img src="https://user-images.githubusercontent.com/24568452/186110814-1b9c6618-a0ab-4c0c-bb3d-3d860b0e8cc2.png" />

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