have someone ever tried this repo on other languages and got good performance？

[MisakaMikoto96]

have someone ever tried this repo on other languages and got good performance？ 50 hours of toy data seem didn't get intelligibility.

[airpdev]

Hi, @MisakaMikoto96 Sorry but could you get the good result with English? I can not generate audio by using trained model with some data. Result is just noise not voice. Looking forward to your reply. Regards! Petar

[MisakaMikoto96]

Hi, @MisakaMikoto96 Sorry but could you get the good result with English? I can not generate audio by using trained model with some data. Result is just noise not voice. Looking forward to your reply. Regards! Petar

I only tried it on a 1-hour nano mandarin data, and I set the input prompts to be itself(do not use self.sample_prompts in data.py), I got the human voice as its overfit in my dataset (test by input a transcription and its related audio, it is able to reconstruct the audio). The reproduction of @enhuiz ‘s work seems some different from the paper. May I ask why the sample_prompt is in data processing and only choose the qnt not the <phn, qnt>?

And also in the infere nce stage, the paper prefers to input "text_prompt" + "text_to_be_gen" + "audio_prompt", is any explanation in your code？

Really thanks for your work!

[airpdev]

Hi, @MisakaMikoto96 Thanks for your message. Sorry but would you like to share the code? (data.py, config.py, ar.yml) Looking forward to hearing from you. Best Regards! Petar

[skysbird]

i've the same confusion. But i found that this kind of work can let us infer from the target text, but not concat from the prefix text+target text like the described in paper

[skysbird]

And also in the infere nce stage, the paper prefers to input "text_prompt" + "text_to_be_gen" + "audio_prompt", is any explanation in your code？

this can be lead a problem when your acoustic prompt are not consistent to the prefix text