For money, who can show how to train a model in DiNet? about dinet

[mushanwei]

I know how to train custom data, just follow the readme steps.

[Inferencer]

Bit more difficult than that, loss convergence etc.. I tried to eyeball the results before moving onto each stage and the results did not match the work I had to put into collecting datasets etc, there are plenty of issues in this repo regarding similar issues which is why op has rather cleverly tried to avoid the headache/ learning curve.

[mushanwei]

Bit more difficult than that, loss convergence etc.. I tried to eyeball the results before moving onto each stage and the results did not match the work I had to put into collecting datasets etc, there are plenty of issues in this repo regarding similar issues which is why op has rather cleverly tried to avoid the headache/ learning curve.

I don't know what is your data looks like, but in my data, it seems work, athough this is some loss convergence issue, but it seems results not bad.

[Inferencer]

Bit more difficult than that, loss convergence etc.. I tried to eyeball the results before moving onto each stage and the results did not match the work I had to put into collecting datasets etc, there are plenty of issues in this repo regarding similar issues which is why op has rather cleverly tried to avoid the headache/ learning curve.

I don't know what is your data looks like, but in my data, it seems work, athough this is some loss convergence issue, but it seems results not bad.

Mine was a single person dataset so perhaps this was some over fitting etc, the dataset itself was high quality about 3 hours of front facing studio lighting, the results where a higher chin+cheek fidelity and less bounding box but jittery lips. Perhaps a more diverse dataset would have resolved this, I saw a lot of people asking about syncnet training and wasn't 100% aware of what that was

[NaMoCv]

Bit more difficult than that, loss convergence etc.. I tried to eyeball the results before moving onto each stage and the results did not match the work I had to put into collecting datasets etc, there are plenty of issues in this repo regarding similar issues which is why op has rather cleverly tried to avoid the headache/ learning curve.比这更困难的是，损失收敛等。我试图在进入每个阶段之前观察结果，结果与我必须投入收集数据集等的工作不匹配，这个回购协议中有很多关于类似问题的问题这就是为什么 op 相当聪明地试图避免头痛/学习曲线。

I don't know what is your data looks like, but in my data, it seems work, athough this is some loss convergence issue, but it seems results not bad.我不知道你的数据是什么样的，但在我的数据中，它似乎有效，虽然这是一些损失收敛问题，但看起来结果还不错。

有尝试过中文的效果吗？

[Inferencer]

Bit more difficult than that, loss convergence etc.. I tried to eyeball the results before moving onto each stage and the results did not match the work I had to put into collecting datasets etc, there are plenty of issues in this repo regarding similar issues which is why op has rather cleverly tried to avoid the headache/ learning curve.比这更困难的是，损失收敛等。我试图在进入每个阶段之前观察结果，结果与我必须投入收集数据集等的工作不匹配，这个回购协议中有很多关于类似问题的问题这就是为什么 op 相当聪明地试图避免头痛/学习曲线。

I don't know what is your data looks like, but in my data, it seems work, athough this is some loss convergence issue, but it seems results not bad.我不知道你的数据是什么样的，但在我的数据中，它似乎有效，虽然这是一些损失收敛问题，但看起来结果还不错。

有尝试过中文的效果吗？

No but others have, but deepspeech wasn't trained on Chinese so the lip movements won't be fully accurate to Chinese audio

[NaMoCv]

Bit more difficult than that, loss convergence etc.. I tried to eyeball the results before moving onto each stage and the results did not match the work I had to put into collecting datasets etc, there are plenty of issues in this repo regarding similar issues which is why op has rather cleverly tried to avoid the headache/ learning curve.比这更困难的是，损失收敛等。我试图在进入每个阶段之前观察结果，结果与我必须投入收集数据集等的工作不匹配，这个回购协议中有很多关于类似问题的问题这就是为什么 op 相当聪明地试图避免头痛/学习曲线。

I don't know what is your data looks like, but in my data, it seems work, athough this is some loss convergence issue, but it seems results not bad.我不知道你的数据是什么样的，但在我的数据中，它似乎有效，虽然这是一些损失收敛问题，但看起来结果还不错。

有尝试过中文的效果吗？

No but others have, but deepspeech wasn't trained on Chinese so the lip movements won't be fully accurate to Chinese audio

I tried to use the examples in the README for training and reasoning, but the lip tremors were severe, and the mouth still retained its original movements when there was no sound. So I think this project is not very good.

I'm not working for money, I'm working for fun. So you can try my project here it will be included training dinet full pipeline in several days..
https://github.com/primepake/better_wav2lip

[Chechgroup]

I know how to train custom data, just follow the readme steps.

how to contact you? I would pay I need your help

[Chechgroup]

Bit more difficult than that, loss convergence etc.. I tried to eyeball the results before moving onto each stage and the results did not match the work I had to put into collecting datasets etc, there are plenty of issues in this repo regarding similar issues which is why op has rather cleverly tried to avoid the headache/ learning curve.

I don't know what is your data looks like, but in my data, it seems work, athough this is some loss convergence issue, but it seems results not bad.

Mine was a single person dataset so perhaps this was some over fitting etc, the dataset itself was high quality about 3 hours of front facing studio lighting, the results where a higher chin+cheek fidelity and less bounding box but jittery lips. Perhaps a more diverse dataset would have resolved this, I saw a lot of people asking about syncnet training and wasn't 100% aware of what that was

how to contact you? I would pay I need your help

[tailangjun]

t full pipeline in several days..

#New Features: DINet full pipeline training
Very much looking forward to it!