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README.md

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# Voice Conversion with Non-Parallel Data
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## Subtitle: Speaking like Kate Winslet
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>* This is the first draft.
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>* Authors: Dabi Ahn([email protected]), [Kyubyong Park](https://github.com/Kyubyong)([email protected])
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>* We always welcome any questions, new ideas, or contributions.
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## Samples
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It's not perfect yet, but listen to [them](https://soundcloud.com/andabi/sets/voice-style-transfer-to-kate-winslet-with-deep-neural-networks).
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[Here](https://soundcloud.com/andabi/sets/voice-style-transfer-to-kate-winslet-with-deep-neural-networks).
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## Intro
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What if you could imitate a famous celebrity's voice or sing like a famous singer?
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* IMHO, the accuracy of Net1(phoneme classification) does not need to be so perfect.
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* Net2 can reach to near optimal when Net1 accuracy is correct to some extent.
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## Future Works
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* Adversarial training
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* Expecting to generate sharper and cleaner voice.
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* Cross lingual
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## Ultimate Goals
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* Many-to-Many(Multi target speaker) voice conversion system
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* VC without training set of target voice, but only small set of target voice (1 min)
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* (On going)
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## References
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* ["Phonetic posteriorgrams for many-to-one voice conversion without parallel data training"](https://www.researchgate.net/publication/307434911_Phonetic_posteriorgrams_for_many-to-one_voice_conversion_without_parallel_data_training), 2016 IEEE International Conference on Multimedia and Expo (ICME)
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* ["TACOTRON: TOWARDS END-TO-END SPEECH SYNTHESIS"](https://arxiv.org/abs/1703.10135), Submitted to Interspeech 2017
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* ["TACOTRON: TOWARDS END-TO-END SPEECH SYNTHESIS"](https://arxiv.org/abs/1703.10135), Submitted to Interspeech 2017

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