![]() The results are quite fascinating and I recommend you play around with it! These voices don't actually exist and will be random every time you run I've included a feature which randomly generates a voice. The reference clip is also used to determine non-voice related aspects of the audio output like volume, background noise, recording quality and reverb. These clips are used to determine many properties of the output, such as the pitch and tone of the voice, speaking speed, and even speaking defects like a lisp or stuttering. 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. Pcm_audio = tts.tts_with_preset( "your text here", reference_clips, preset= 'fast') Tortoise can be used programmatically, like so: reference_clips = You can re-generate any bad clips by re-running read.py with the -regenerate Once all the clips are generated, it will combine them into a single file and This will break up the textfile into sentences, and then convert them to speech one at a time. python tortoise/read.py -textfile -voice random This script provides tools for reading large amounts of text. python tortoise/do_tts.py -text "I'm going to speak this" -voice random -preset fast This script allows you to speak a single phrase with one or more voices. ![]() If you are on windows, you will also need to install pysoundfile: conda install -c conda-forge pysoundfile Next, install TorToiSe and it's dependencies: git clone 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: ![]() See this page for a large list of example outputs.Ĭolab is the easiest way to try this out. 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.
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