README.md

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license: openrail
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Test RVC models on the DDLC character Monika, via various hyperparams and datasets.

# monika-test-0 (~07/2023)
* Trained on augmented dataset of ~10 10 second clips
* Trained for ~100 epochs
* RVC1
* "Version 1" ("0" in the old numbering)
  
# monika-test-2 (~07/2023)
* Trained on augmented dataset of ~10 10 second clips (augmented via tortoise tts)
* Trained for 100 epochs
* RVC1
  
# monika-test-4 (~07/2023)
* Trained on smaller but better dataset of ~2 10 second clips (augmented via 11labs)
* Trained for 150 epochs
* RVC1

# monika-test-7 (08/22/2023)
* Trained on augmented dataset of ~10+ 10 second clips (augmented via tortoise tts)
* Trained for 60 epochs (720 steps)
* Better quality than others
* "Version 2" ("1" in old numbering)
* RVC2

# monika-test-8 (08/22/2023)
* Trained on smaller but better dataset of ~5 10 second clips (some augmented via 11labs)
* Trained for 60 epochs (660 steps)
* Even clearer quality but with slightly more artifacting than monika-test-7 (still better than pre 7th ones)
* "Version 2a" ("1a" in old numbering)
* RVC2

# ct-m3 (~10/2023)
* Trained on preprocessed version of dataset of ~5 10 second clips
* Trained for ~100 epochs
* Test model
* RVC1

# ct-m4 (~10/2023)
* Trained on preprocessed version of dataset of ~5 10 second clips
* Trained for ~200 epochs
* Test model
* RVC1

# ct-m4a (~10/2023)
* Trained on preprocessed version of dataset of ~5 10 second clips
* Trained for ~200 epochs
* "Version 4" ("3" in old numbering)
* RVC2

# fused2 (~02/2024)
* Merge between ct-m3 and another model ("Sayori"-based model, with ratio of 75% to 25%)
* Somewhat clearer quality
* Yet another test model
* RVC2

# fused5 (~01/2025)
* 50-50 merge between ct-m5 (~06/2024) and fused2
* In addition to merging, experiment with fine-tuning on more synthetic data which doubled preprocessed dataset size
* Sliiightly better than fused2; seems not to "break" where fused2 does, otherwise they seem almost the same
* RVC2