Name: Daxter (From Jak and Daxter) | AI RVC Model
Brand: Weights
Rating: 4.6 (14 reviews)

 Introducing the advanced Weights's "Daxter" AI Voice Model, designed specifically for Retrieval-Based Voice Conversion (RVC) technology. Harnessing the power of artificial intelligence, this innovative model allows you to convert speech between different speakers while maintaining naturalness and similarity in voice quality. Ideally suited for applications such as AI music creation, producing high-quality AI covers, and even text-to-speech conversion tasks, the Daxter model delivers unparalleled performance. With its seamless integration into various systems, creating your next AI cover has never been easier! To get started, try out our FREE AI tools today at Weights and experience the future of voice conversion firsthand. Optimized using cutting-edge SEO practices, discover how 'Weights Daxter RVC Model,' 'AI Music Cover Creator,' and 'Text-to-Speech AI Tools' drive user engagement and conversions across platforms.

With pitch guidance: Without pitch guidance:

Daxter (From Jak and Daxter)

.albinator

Daxter (From Jak and Daxter) (RVC v2) 500 Epoch

Now you should do the rest of the Jak characters :^)

Margen67

ccateni 28 (Connery Cateni)

Don't know if I mislabeled the model version without pitch guidance but the first sample was unexpected to say the least.

I will do it if i can find out a method to extract voices from OpenGOAL i just found some Daxter voice clips on youtube for this one

This is hilarious  He sounds soo drunk without pitch guidance but also kinda good

Oh... Never knew about that site thanks. I could probably make something out of that but i think i would need more voice samples to make it sound good.

Trained on a 10 minutes & 35 seconds 40khz lossless dataset with RMVPE-Titan with batch size 8. There are more epochs to download if you want that. Model: More stuff: Dataset Sources: A Hard Day's Night Movie (30 Seconds with Adobe Podcast) Help Movie (21 Seconds with Adobe Podcast) Magical Mystery Tour Movie (11 seconds) Live at BBC Albums (1 12 Minutes) Christmas Albums (2 32 minutes) Bootlegs (5 47 minutes) It's not necessary but feel free to tag me on YouTube @Albinator_ so i can hear the stuff that you make

Paul McCartney (Talking / 1960's)

Paul McCartney (Talking / 1960's) (RVC V2) 550 Epochs

Trained on a 6 minutes & 48 seconds 40khz lossless dataset with RMVPE-Titan with batch size 8. There are more epochs to download if you want that. Model: More stuff: Dataset Sources: A Hard Day's Night Movie (14 Seconds with Adobe Podcast) Help Movie (23 Seconds with Adobe Podcast) Magical Mystery Tour Movie (1 second) Live at BBC Albums (1 17 Minutes) Christmas Albums (2 03 minutes) Bootlegs (2 48 minutes) It's not necessary but feel free to tag me on YouTube @Albinator_ so i can hear the stuff that you make

George Harrison (Talking / 1960's)

George Harrison (Talking / 1960's) (RVC V2) 430 Epochs

Trained on a 5 min 40khz lossless dataset with RMVPE-Titan with batch size 8. Dataset Sources: A Hard Day's Night Movie (26 Seconds with Adobe Podcast) Help Movie (49 Seconds with Adobe Podcast) Magical Mystery Tour Movie (16 seconds) Live at BBC Albums (20 Seconds) Christmas Albums (1 42 minutes) Bootlegs (1 23 minutes)

Ringo Starr (Talking / 1960's)

Ringo Starr (Talking / 1960's) (RVC V2) 440 Epochs

Trained on a 12 min 40khz lossless dataset with RMVPE-Titan with batch size 8. Model: Dataset Sources: A Hard Day's Night Movie (23 Seconds with Adobe Podcast) Help Movie (30 Seconds with Adobe Podcast) Magical Mystery Tour Movie (2 09 minutes) Live at BBC Albums (59 Seconds) Christmas Albums (3 33 minutes) Bootlegs (4 21 minutes)

John Lennon (Talking / 1960's)

John Lennon (Talking / 1960's) (RVC V2) 450 Epochs

Trained on a 6.29 min dataset of dirty and low quality audio with V2 RVMPE-Titan 40 khz 500 Epochs and batch size 8. Some info about the group: Sample: Model: More stuff:

Daxter (From Jak and Daxter)

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