![]() ![]() The platform also provides additional features such as a voice recorder, video converter, speech-to-text, and audio editor.ģ200+ voices: rappers, YouTubers, characters, and iconic figures like Trump. VoxBox stands out as the premier AI voice generator for presidential voices, allowing you to produce text-to-speech voiceovers in the styles of Trump, Biden, and Obama using their respective AI voices.įurthermore, beyond the realm of AI presidential voices, VoxBox offers an extensive library of over 3200 voices, spanning celebrities, singers, fictional characters, and more. Part 2: President Voice Text to Speech - Top 3 President AI Voice Generators Some of the popular voice editing features offered by Voicemod include satanic agents, chipmunk, Xbox gamer girl voice changer, and alien voices. The interface of Voicemod is user-friendly and enables users to maintain full control over their voice editing. While a free trial version of the tool is available, it has limited functions, so users may want to purchase the pro version to access all of its features. As we can see, WaveNets reduce the gap between the state of the art and human-level performance by over 50% for both US English and Mandarin Chinese.įor both Chinese and English, Google’s current TTS systems are considered among the best worldwide, so improving on both with a single model is a major achievement.Voicemod provides users with around 60 voice filters to modify their voice. MOS are a standard measure for subjective sound quality tests, and were obtained in blind tests with human subjects (from over 500 ratings on 100 test sentences). The following figure shows the quality of WaveNets on a scale from 1 to 5, compared with Google’s current best TTS systems ( parametric and concatenative), and with human speech using Mean Opinion Scores (MOS). We trained WaveNet using some of Google’s TTS datasets so we could evaluate its performance. ![]() Building up samples one step at a time like this is computationally expensive, but we have found it essential for generating complex, realistic-sounding audio. This value is then fed back into the input and a new prediction for the next step is made. At each step during sampling a value is drawn from the probability distribution computed by the network. After training, we can sample the network to generate synthetic utterances. It is a fully convolutional neural network, where the convolutional layers have various dilation factors that allow its receptive field to grow exponentially with depth and cover thousands of timesteps.Īt training time, the input sequences are real waveforms recorded from human speakers. The above animation shows how a WaveNet is structured. As well as yielding more natural-sounding speech, using raw waveforms means that WaveNet can model any kind of audio, including music. WaveNet changes this paradigm by directly modelling the raw waveform of the audio signal, one sample at a time. Existing parametric models typically generate audio signals by passing their outputs through signal processing algorithms known as vocoders. So far, however, parametric TTS has tended to sound less natural than concatenative. This has led to a great demand for parametric TTS, where all the information required to generate the data is stored in the parameters of the model, and the contents and characteristics of the speech can be controlled via the inputs to the model. This makes it difficult to modify the voice (for example switching to a different speaker, or altering the emphasis or emotion of their speech) without recording a whole new database. ![]() However, generating speech with computers - a process usually referred to as speech synthesis or text-to-speech (TTS) - is still largely based on so-called concatenative TTS, where a very large database of short speech fragments are recorded from a single speaker and then recombined to form complete utterances. The ability of computers to understand natural speech has been revolutionised in the last few years by the application of deep neural networks (e.g., Google Voice Search). Talking MachinesĪllowing people to converse with machines is a long-standing dream of human-computer interaction. We also demonstrate that the same network can be used to synthesize other audio signals such as music, and present some striking samples of automatically generated piano pieces. We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems, reducing the gap with human performance by over 50%. This post presents WaveNet, a deep generative model of raw audio waveforms. ![]()
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