Comments (3)
Hi @sunil3590 no answer but the paper said "(3)is able to generate converted speech signals quickly enough to allow real-time implementations and", sadly did not mention the hardware required to achieve that.
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@carlfm01 did you get an answer? I have the same question.
from stargan-voice-conversion.
Hi, i just came by this issue recently. As i inspected carefully, the model generates the signal pretty fast (milliseconds), however, the total inference time is not real-time at all.
The bottleneck actually comes from the WORLD decomposing step of turning raw input audio into F0s, sp, ap. The time taken corresponds to the length of the audio. In my case, the input length of 14.6s would result in nearly 5s of inferencing.
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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
- Can implementation of the author share 200000 iteration model for comparative study? HOT 5
- A question about the adversarial loss.
- Are there any requirements for training datasets? HOT 2
- Python 3.5 HOT 1
- run Convert.py wrong HOT 1
- How should I take it?Thank you! HOT 2
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