Comments (4)
What matters is the model's required inputs. The model requires source cepstrum, source cepstrum lengths, masks made from source ceptrum lengths, and target speaker embedding. That's what the dictionary provides. You don't need to use a dictionary as long as you have the required inputs.
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Thanks! I think, I may be missing something basic. Not clear on what to do next after training the model?
How to create or define the sources speakers and target speakers? how to get the converted samples? Thanks for your help!
from autopst.
You are welcome. For the basics, please refer to the paper. https://arxiv.org/abs/2106.08519
from autopst.
thanks, let me check again. I was hoping to get more straight forward direction and commands to run the code for the conversion by providing any set of source and target speakers through command line or modifying the demo code. Got lost on it after training the model.
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Related Issues (17)
- ModuleNotFoundError: No module named 'onmt' HOT 1
- KeyError when run prepare_train_data.py HOT 2
- How to solve SEA model problem
- the speech content of converted voice with my own trained model changed HOT 2
- SpeechSplit actually better than AutoPST for seen speakers? HOT 1
- Error while running demo.ipynd
- Issue with stop prediction for longer utterances. HOT 1
- test_vctk.meta HOT 5
- Unable to reproduce results HOT 1
- License of this repository and model HOT 3
- How to test AutoPST in onother languages? HOT 6
- How to train SEA model HOT 14
- How to make 'mfcc_stats.pkl' and 'spk2emb_82.pkl'? HOT 3
- How to find mean and std of MFCC? HOT 8
- 請問我該如何解決 repeats has to be Long tensor 的問題?(How to solve a problem) HOT 2
- Inference with new input audio HOT 4
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