Jackiexiao/MTTS 227
A Demo of Mandarin/Chinese TTS frontend
obsidian中文帮助手册
Jackiexiao/10-minutes-to-SpaceVim 157
10-minutes-to-SpaceVim
A demo of zh/Chinese Text to Speech system run on CPU in real time. 中文实时语音合成系统Demo
Jackiexiao/foam-mkdocs-template 21
using mkdocs / mkdocs-material / mkdocs-roamlinks-plugin
Jackiexiao/docker-TensorFlowTTS-zh 4
Docker image for TensorflowTTS baker server
Jackiexiao/10-minutes-to-foam 2
A quick start guide for foam, a personal knowledge management and sharing system
Jackiexiao/mkdocs-roamlinks-plugin 2
An MkDocs plugin that automagically generates relative links between markdown pages
利用Python进行数据分析 第二版 (2017) 中文翻译笔记
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issue commentliusongxiang/efficient_tts
Reproducing good results (as claimed in paper)
@liusongxiang I train the model and found that the learning rate is random instead of the value set in config.yaml. Then I set "scheduler" as None in the trainning script, then lr is ok. I wonder what is the real learning rate you used when traing the Baibei dataset? Is the lr constant or decay while trainning?
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This is a pytorch implementation of the paper: StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
https://arxiv.org/abs/1806.02169
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issue commentliusongxiang/efficient_tts
Reproducing good results (as claimed in paper)
@Liujingxiu23 Thanks for your attention. I haven't try the end2end training yet since I have been stuck in other things. If you are interested, I think you could try by combining this repo with the ParallelWaveGAN repo.
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issue commentliusongxiang/efficient_tts
Reproducing good results (as claimed in paper)
@liusongxiang Thank you for your reply. The results of Biaobei is good. The inference speed is really fast. Have you test end-to-end training, "EFTS-Wav" in the paper which combine acoustic model and vocoder model as one model?
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End-to-End Speech Processing Toolkit
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