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View Code? Open in Web Editor NEWAn implement of "Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training"
License: MIT License
An implement of "Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training"
License: MIT License
Hello, could you release pretrained model?
Hi ,I want to reproduce the effect of this paper. Will the dataset or model be released?
Hi! Thank you for your great work! I have small question about voice conversion using inference.py
or model itself in python shell
In the demo (https://hhguo.github.io/DemoEASVC/) there are the best coversions made with pitch shifting. Assume I have a trained model checkpoint. What should I do to produce different conversed audios corresponding to alpha parameter (like in the end of demo: Pitch Control
section)?
Also, the same question to Timbre Transfer
section.
I appreciate your help very much!
Hello! I made some preprocessing to get features of wavs in dataset for training EA-SVC. Actually, I get the following features:
pyannote.audio
I tried training for first 2 stages (i.e. without adversarial generator training and then with it) on both LibriSpeech dev-clean and NUS48E singing. Disentaglement loss wasn't used in experiment. So, for the 1st stage loss_g
(g_mag
+ g_sc
) is about 1.0; for the 2nd: loss_g
increased to 5.0 (g_mag
+ g_sc
+ g_adv
+ g_feat
), loss_d
is about 3.0e-01 (d_real
+ d_fake
). Model wasn't trained for 3rd stage. In both dataset experiments results are quite the same.
Because generated audio on both stages are not good, I wonder if I made a mistake in training process or something. I believe losses values above will give you a better view of this situation.
P.S. Number of stage refers to such parameter in config:
"adv_ag": false
, "adv_fd": false
"adv_ag": true
, "adv_fd": false
"adv_ag": true
, "adv_fd": true
greate job!
Can you give some introduction to the pitch model?
Great work! How to make PPG features? Speaker embedding? F0 features?
In configs there are stage
parameter in each config with value 0, 1, 2. It is not used in the main train
function of train.py
file, but also fails with any command:
CUDA_VISIBLE_DEVICES=0 python train.py -c configs/stage1.json
with traceback:
Traceback (most recent call last):
File "train.py", line 283, in <module>
train(num_gpus, args.rank, args.group_name, **train_config)
TypeError: train() got an unexpected keyword argument 'stage'
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