Clone high-quality voices that are 99% accurate to their real human voices. No need for expensive equipment or complicated software. Perfect for content creators, podcasters, and businesses looking to add a personal touch to their audio projects.
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called VALL-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems. VALL-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that VALL-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find VALL-E could preserve the speaker’s emotion and acoustic
We built VALL-E upon the Flow Matching model, which is Meta’s latest advancement on non-autoregressive generative models that can learn highly non-deterministic mapping between text and speech. Non-deterministic mapping is useful because it enables VALL-E to learn from varied speech data without those variations having to be carefully labeled. This means VALL-E can train on more diverse data and a much larger scale of data.
We created a Token for the project
Collecting and analyzing data on technique and experimentation on more than 450 different sounds.
Create the software blueprint needed to create the application and link it to Meta cloud services
Creating and testing interfaces for the application.
Creating and offering VET for pre-sale
Create social media accounts
Increase the interactions in the community and promote the idea
Offering VET for trading on a platform (Binance)
Upload the application after its completion on Google Play and App Store
Work on marketing the application .
It is difficult at the present time to dispense with the applications of artificial intelligence in our daily lives
Like other technologies such as DALL-E and chatGPT , it is necessary to use technologies that use voice
If you are a content writer, voice commentator, or TV presenter, then the VALL-E application is directed directly to you
Like all AI applications, there are good uses and bad uses for the application.
With the exception of votes within the market, identity verification will be used when raising any vote in order to ensure that the person selling is the same person
We will seriously consider any complaint in this regard
Regardless of the level of security in dealing with digital currencies, but the idea that the prices of votes will not be unified, the price of Elon Musk’s voice will not be like the price of Mike’s voice (for example).
Therefore, the process of pricing voices will be subject to supply and demand, and therefore its price will be determined with the price of a currency
Buying the token in the pre-sale stage enables you to obtain a larger number of coins and thus the opportunity to obtain higher celebrity votes, and you can also resell it on the trading platform to the rest of the users at a higher price.