![]() These reference clips are recordings of a speaker that you provide to guide speech generation. It accomplishes this by consulting reference clips. Tortoise was specifically trained to be a multi-speaker model. tts_with_preset( "your text here", reference_clips, preset = 'fast') Voice customization guide Next, install TorToiSe and it's dependencies: Will spend a lot of time chasing dependency problems. I have been told that if you do not do this, you On Windows, I highly recommend using the Conda installation path. If you want to use this on your own computer, you must have an NVIDIA GPU.įirst, install pytorch using these instructions. I've put together a notebook you can use here: ![]() Usage guide ColabĬolab is the easiest way to try this out. See this page for a large list of example outputs. On a K80, expect to generate a medium sized sentence every 2 minutes. It leverages both an autoregressive decoder and a diffusion decoder both known for their low Tortoise is a bit tongue in cheek: this model I'm naming my speech-related repos after Mojave desert flora and fauna. Added ability to use your own pretrained models.Added ability to download voice conditioning latent via a script, and then use a user-provided conditioning latent.Added ability to produce totally random voices.Wrap the text you want to use to prompt the model but not be spoken in brackets. Added several new voices from the training set.Improvements to read.py and do_tts.py (new options).New CLVP-large model for further improved decoding guidance.Add better debugging support existing tools now spit out debug files which can be used to reproduce bad runs.Found that it does not, in fact, make an appreciable difference in the output. This repo contains all the code needed to run Tortoise TTS in inference mode. Highly realistic prosody and intonation.Tortoise is a text-to-speech program built with the following priorities:
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