KV

KVoiceWalk – Voice cloning for Kokoro TTS using random walk algorithms

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KVoiceWalk – Voice cloning for Kokoro TTS using random walk algorithms

I was blown away by Kokoro and what it managed to do with such little space. I became curious if it would be possible to create new voices by direct manipulation of the style tensors. After many failed attempts I finally landed on a method that properly scores the similarity of two audio segments that works well enough to random walk similar voices for Kokoro. I plan on using this scoring as part of a genetic algorithm, but wanted to baseline test it with this code. The scoring mechanism using Resemblyzer to calculate similarity to target audio and similarity to another segment of audio it generates itself, self similarity. This self similarity was key in keeping the model stable and the audio consistent across inputs. But it was not enough to prevent over fitting to Resemblyzer. I had to create a third metric which uses a normalized difference of a variety of audio features compared to the target features. Summing those I get a feature similarity metric which is useful in keeping audio quality from degrading too much and prevents over fitting. The last challenge was weighting the score while keeping it flexible enough to explore the complex text to speech style space. Using a weighted harmonic mean allowed for back sliding on some metrics for significant improvement in others, which reduced stagnation and worked well enough for the random walk to work. The results are fairly good. I would say it ends up in the uncanny valley of similarity rather than producing a proper clone of the target voice. It sounds like it might be the target voice, but does well enough to improve similarity from 70% to around 90%. There are probably limitations to the architecture of Kokoro in how close it can possibly sound to other voices, but there is probably some more progress to be made using a more advanced genetic algorithm. Check out the code, make some new voices, and let me know if you have any ideas on ways to improve.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, using · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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