Comments (3)
MOS is 5-scale (1-bad, 2-poor, 3-fair, 4-good, 5-excellent), each listener gives the point for each wav, then we can obtain a series of points given by all listeners for each VC system, the points of each VC system are averaged to obtain the final MOS.
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Okay so a listener gives score points out of 5 manually upon listening converted audios. There isn't any other accuracy, precision or something else calculated for comparison?
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Apart from giving the subjective MOS, we also used ASR to test the CER/WER of converted voice and F0-PCC to measure the pitch variation consistency between the converted voice and source voice, more details can be found in our paper.
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Related Issues (20)
- about the model question HOT 4
- Where can I get the silence trimmed VCTK corpus? HOT 2
- Other vocoder HOT 1
- How can I calculate suitable parameters? HOT 1
- NameError: name 'amp' is not defined . File "train.py", line 407, in train_model HOT 3
- Docker deploy keeps saying {"message":"Could not convert file input input_source to Path"}
- Error: ValueError: Buffer has wrong number of dimensions (expected 1, got 2) HOT 2
- How to slove this problem? HOT 9
- In convert.py subprocess.call(['cp', src_wav_path, out_dir]) What does' CP 'mean? HOT 2
- What is the "parallel-wavegan-decode" in cmd = ['parallel-wavegan-decode', '--checkpoint',...] ,it is a folder??? HOT 2
- what does gamma: 0.5 in config/training/cpc.ymal mean? HOT 3
- How to solve this problem? HOT 2
- Mel stats and Vocoder HOT 2
- How to solve this problem? HOT 3
- lf0 question about convert phase HOT 3
- Training Loss Abnormal HOT 3
- What do z_dim and c_dim stand forοΌ HOT 4
- Training for Indian Multi-Speaker/Multi-lingual VC HOT 1
- voice conversion not happens after fine-tuned with pretrained model HOT 1
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