Description
Before Start : I wanted to test the RIN E3 pre-training model again but this time with a very short but very good quality dataset (these are audios that were on an old external hard drive I can't find the source anymore but it's not from a game). This model is therefore more "experimental" I no longer have access to ElevenLabs "pro" so I had to refer to the "free" trial version which gives a more robotic TTS (less practical for testing) and don't count on my lousy English to test directly with my voice :kekw:. Last Update : <t:1707531572:R> Model URL : Version firev2: RVC V2.0 - RIN E3 Pitch Extraction Algorithm : RMVPE Epochs - Steps : 800 - 15.2k ~ 00:03:48 [ Duke Nukem Studio Voice - ??? ] Yes the dataset is very short Recommended Usage : Speech Search Feature Ratio : 0.75 Pitch : ( -12 10 = Man ) - Depend Base Voice Voice is Deep You can adjust if you find better version Previews : Preview_TTS.wav : Provide : ElevenLabs Free Demo - Joseph EN Contains External Effects : No Pitch 12 Feature Ratio : 0.75 Info : As mentioned above the tts is less expressive so the voice is just as expressive when applied with Applio. Preview_Cover.wav : Provide : Mustard - Ballin [ My DIY Acapella ] Contains External Effects : Yes Pitch 12 Feature Ratio : 0.75 Info : For Fun Important (Need Feedback) : I would need some feedback (not about the quality of the model itself) but about its fidelity because I know (having done some tests) that RIN E3 often provides a very good hearing quality but each time I have this strange feeling that the voice is different (it may be just an impression) but i need to be sure before continue to use this pretrain ! :nerddog:
Comments
🎸 🎸 🎸 🎸 🎸 🎸
my balls itch like fuck
🎸 🎸 🎸 🎸 🎸 🎸 🎸 🎸 🎸 🎸 🎸 🎸 🎸 🎸 🔥 🔥🔥🔥🔥🔥

i kick gum and chew ass. and i'm all out of ass.

well this does sound alright in terms of audio quality, however there is some very minimal amount of noise but thats not bc of the pre train really. your best bet would be to train the model with 40k base and compare them, also would u mind sharing the g/total, grad_g and grad_d graphs? make sure your smoothing is set to 0.987 and have ignore outliners on.
most datasets use around 10 mins of audio
eh whatever you say
but semi-unrelated the new pretrains are doing ai hub wonders rn
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