刘博您好，看到这里说Source codes should be modified correspondingly for VC applications

[wyt1234]

Thank you for the questions.
For Q1:
I adapted espnet a lot; it seems that espnet asr models always downsample the encoder input along the temporal axis more than 4x and do not support phoneme as output symbols. Source codes should be modified correspondingly for VC applications. But the basic steps for the training process is very similar to those presented in espnet asr recipes, including the data preparation, files organization. The run.sh should be modified a little bit, e.g., the language model can be skipped. Sufficient familiarity of espnet source code should be necessary if you want to train a content encoder using your own data.

For Q2:
Please refer to this paper for your questions: TTS Skins: Speaker Conversion via ASR.
Good VC performance validate the speaker independence property of the bottle neck feature obtained in this way. The paper listed above says that BNF is better than PPG features, but this could really be a model selection thing.

Hope this can help.
Songxiang Liu

Originally posted by @liusongxiang in #4 (comment)

刘博您好，看到这里说Source codes should be modified correspondingly for VC applications，也看到代码里提供了一份en_conformer_ctc_att的espnet训练配置，请问一下直接使用这个config是否可以训练ppg部分？