tokenizer
hey bro, what the tokenizer you use for rus, Is it the default tokeniser provided by mrq?
Hi, @yeserumo11! Yes, I didn't do any changes to the tokeniser. My understanding is that fine-tuning wouldn't really be possible if I changed the tokeniser. The original can already produce results in Russian, though with a strong accent.
thank you, one more question, im not sure if im understandling this correctly, the training audio is single-speaker audio clip, so when generate with the trained model, it can generate the voice like the guy and speak textual content that has not appeared in the training data. But it may performace bad if i want to generate another person's voice using this model, is that right? So i should train a model for every single speaker.
thank you, one more question, im not sure if im understandling this correctly, the training audio is single-speaker audio clip, so when generate with the trained model, it can generate the voice like the guy and speak textual content that has not appeared in the training data. But it may performace bad if i want to generate another person's voice using this model, is that right? So i should train a model for every single speaker.
thank you, one more question, im not sure if im understandling this correctly, the training audio is single-speaker audio clip, so when generate with the trained model, it can generate the voice like the guy and speak textual content that has not appeared in the training data. But it may performace bad if i want to generate another person's voice using this model, is that right? So i should train a model for every single speaker.
no , because
"Tortoise is a text-to-speech program built with the following priorities:
- Strong multi-voice capabilities.
- Highly realistic prosody and intonation.
This repo contains all the code needed to run Tortoise TTS in inference mode."
https://github.com/neonbjb/tortoise-tts
you can see it support strong multi-voice speaker
In case your model run wrong, can't clone another voice, maybe you training model wrong way