![]() Saurous, Yannis Agiomyrgiannakis, Yonghui Wu, Sound Understanding team, TTS Research team, and TensorFlow team. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ Skerry-Ryan, Rif A. Each of these is an interesting research problem on its own. Furthermore, we cannot yet control the generated speech, such as directing it to sound happy or sad. ![]() Also, our system cannot yet generate audio in realtime. (Diddy) Ha, sicker than your average, Poppa twist cabbage off instinct. For example, our system has difficulties pronouncing complex words (such as “ decorum” and “ merlot”), and in extreme cases it can even randomly generate strange noises. Uh, uh, ( Uh, come on) Verse 1: The Notorious B.I.G. While our samples sound great, there are still some difficult problems to be tackled. In an evaluation where we asked human listeners to rate the naturalness of the generated speech, we obtained a score that was comparable to that of professional recordings. You can listen to some of the Tacotron 2 audio samples that demonstrate the results of our state-of-the-art TTS system. For technical details, please refer to the paper. The lower half of the image describes the sequence-to-sequence model that maps a sequence of letters to a spectrogram. Finally these features are converted to a 24 kHz waveform using a WaveNet-like architecture.Ī detailed look at Tacotron 2's model architecture. These features, an 80-dimensional audio spectrogram with frames computed every 12.5 milliseconds, capture not only pronunciation of words, but also various subtleties of human speech, including volume, speed and intonation. Instead, we generate human-like speech from text using neural networks trained using only speech examples and corresponding text transcripts.Ī full description of our new system can be found in our paper “ Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions.” In a nutshell it works like this: We use a sequence-to-sequence model optimized for TTS to map a sequence of letters to a sequence of features that encode the audio. Our approach does not use complex linguistic and acoustic features as input. Incorporating ideas from past work such as Tacotron and WaveNet, we added more improvements to end up with our new system, Tacotron 2. There has been great progress in TTS research over the last few years and many individual pieces of a complete TTS system have greatly improved. Bang every MC easily, busily Recently niggas frontin ain't sayin' nuttin' (nope) so I just Speak my piece, (c'mon) keep my piece Cubans with the Jesus piece (thank you God), with my peeps Packin', askin' who want it, you got it nigga flaunt it That Brooklyn bullshit, we on it Biggie Biggie Biggie can't you see Sometimes your words just hypnotiz. Generating very natural sounding speech from text (text-to-speech, TTS) has been a research goal for decades. Posted by Jonathan Shen and Ruoming Pang, Software Engineers, on behalf of the Google Brain and Machine Perception Teams
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