Comments (2)
There are many reasons why you could be getting a bad performance on your Chinese corpus. The first obvious reason is that you could have very few recordings in your corpus. Also, it could be that the recordings in your corpus have different noise types or are just generally not clean/crisp.
Testing this system on a Chinese corpus is definitely interesting as the authors of the paper tested the system on English US speech. I am not sure how would this StarGAN perform given that Chinese speech is very musical and subtle differences in the phonemes could mean completely different things.. In case you run into intelligibility problems, you could try to increase cycle consistency loss parameter when training, maybe that would work. I haven't tried it myself. I am speculating it would as the network would give more weight to preserving the linguistic component of the conversion. If you do try it, please try it and let us know, I am interested in the results. 😃
from stargan-voice-conversion.
In my experience, StarGAN-VC/CycleGAN-VC is also strongly affected by speaker.
When I trained my StarGAN-VC implementation on Japanese corpus with 3 speaker (A, B, C), B2A and C2A work well but A2B, A2C, B2C is not good.
Anyway, As mentioned above, Chinese (and Japanese also) have different acoustic characters compared with English, so if @leokwu will try fine-tune the model, I am strongly interested in the results.
from stargan-voice-conversion.
Related Issues (20)
- I cannot run the code. HOT 24
- Do you have file ./models\200000-G.ckpt ? I want to download it. Thank you
- preprocessing.py possible sox issue HOT 4
- Id mapping loss HOT 1
- Loss function meanings HOT 4
- Suggestions for documentation
- Number of Mel-cpestral coefficients (MCEPs)
- Why g_loss is lack of g_loss_identity
- not find gated cnn
- How to fine-tune StarGAN-VC model?
- model is not stargan-vc HOT 7
- Error in training with more than 4 speakers
- D/loss_real: -0.0000
- Inference time HOT 3
- Can implementation of the author share 200000 iteration model for comparative study? HOT 5
- A question about the adversarial loss.
- Python 3.5 HOT 1
- run Convert.py wrong HOT 1
- How should I take it?Thank you! HOT 2
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from stargan-voice-conversion.