And guess what, I can write in completely random, informal Swiss German dialect and ChatGPT understands everything, but answers in standard German. It's a mostly undocumented/unofficial writing system. There is no orthography (writing rules), no grammar rules etc. For those who don't know: Swiss German is a dialect continuum, very very different from standard German to an extend, that most untrained German speakers don't understand us. That's revolutionary for minority languages without a lot of learning material available online.Īlso I am a native Swiss German speaker. For example when you ask it to translate "I love you" into Thai, it mentions, that normally you would not say this in the same circumstances as you would say it to your lover in the West, correctly explaining in what circumstances people would really use it, and what to use instead. It even takes cultural differences into account. That's better than any machine translation I've ever tried so far. If you're not interested in building or maintaining your own, you can use our API! I'd be happy to help.ĬhatGPT is so crazy it even works in fluent Thai. As long as you have a GPU, you're good to go. In any case, these models are solid choices for building consumer apps. We can also do the hosting for you if that's not your desire or forte. If you want to train your own voice using your own collected sample data, you can experiment with it on Google Colab and on FakeYou, then reuse the same model file by hosting it in a cloud GPU instance. Your custom voice can adapt across languages and speaking styles, and is perfect for adding a one-of-a-kind voice to your Text-to-Speech solutions.
REALISTIC TEXT TO SPEECH FOR MAC HOW TO
FakeYou's Discord has a bunch of people that can show you how to train these models, and there are other Discord communities that offer the same assistance. Overview Welcome to the Custom Neural Voice portal Custom Neural Voice (CNV) lets you create a natural-sounding synthetic voice that is trained on human voice recordings. These three models are faster than real time, and there's a lot of information available and a big community built up around them. You can mimic singing and emotion pretty easily.
TalkNet is also popular when a secondary reference pitch signal is supplied. Input text => Text pre-processing => Synthesizer => Vocoder => => Output audio Your pipeline looks like this at a high level:
You'll want to pair it with the Hifi-Gan vocoder to get end-to-end text to speech. You're looking for Tacotron 2 or one of its offshoots that add multi-speaker, TorchMoji, etc. It's good for creatives making one-off deepfake YouTube videos, and that's about it. Tortoise produces quality results with limited training data, but is an extremely slow model that is not suitable for real time use cases. I'm the author of and can speak to Tortoise and the TTS field.
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