The paper introduces VITS2, a single-stage text-to-speech mannequin that synthesizes extra pure speech by enhancing varied facets of earlier fashions. The mannequin addresses points like intermittent unnaturalness, computational effectivity, and dependence on phoneme conversion. The proposed strategies improve naturalness, speech attribute similarity in multi-speaker fashions, and coaching and inference effectivity.
The sturdy dependence on phoneme conversion in earlier works is considerably decreased, permitting for a completely end-to-end single-stage strategy.
Earlier Strategies:
Two-Stage Pipeline Methods: These techniques divided the method of producing waveforms from enter texts into two cascaded levels. The primary stage produced intermediate speech representations like mel-spectrograms or linguistic options from the enter texts. The second stage then generated uncooked waveforms based mostly on these intermediate representations. These techniques had limitations corresponding to error propagation from the primary stage to the second, reliance on human-defined options like mel-spectrogram, and the computation required to generate intermediate options.
Single-Stage Fashions: Current research have actively explored single-stage fashions that straight generate waveforms from enter texts. These fashions haven’t solely outperformed the two-stage techniques but in addition demonstrated the flexibility to generate high-quality speech almost indistinguishable from human speech.
Conditional variational autoencoder with adversarial studying for end-to-end text-to-speech by J. Kim, J. Kong, and J. Son was a big prior work within the area of single-stage text-to-speech synthesis. This earlier single-stage strategy achieved nice success however had a number of issues, together with intermittent unnaturalness, low effectivity of the period predictor, advanced enter format, inadequate speaker similarity in multi-speaker fashions, sluggish coaching, and robust dependence on phoneme conversion.
The present paper’s foremost contribution is to deal with the problems discovered within the earlier single-stage mannequin, significantly the one talked about within the above profitable mannequin, and introduce enhancements to realize higher high quality and effectivity in text-to-speech synthesis.
Deep neural network-based text-to-speech has seen important developments. The problem lies in changing discontinuous textual content into steady waveforms, guaranteeing high-quality speech audio. Earlier options divided the method into two levels: producing intermediate speech representations from texts after which producing uncooked waveforms based mostly on these representations. Single-stage fashions have been actively studied and have outperformed two-stage techniques. The paper goals to deal with points present in earlier single-stage fashions.
The paper describes enhancements in 4 areas: period prediction, augmented variational autoencoder with normalizing flows, alignment search, and speaker-conditioned textual content encoder. A stochastic period predictor is proposed, educated via adversarial studying. The Monotonic Alignment Search (MAS) is used for alignment, with modifications for high quality enchancment. The mannequin introduces a transformer block into the normalizing flows for capturing long-term dependencies. A speaker-conditioned textual content encoder is designed to higher mimic the varied speech traits of every speaker.
Experiments have been performed on the LJ Speech dataset and the VCTK dataset. The research used each phoneme sequences and normalized texts as mannequin inputs. Networks have been educated utilizing the AdamW optimizer, and the coaching was performed on NVIDIA V100 GPUs.Crowdsourced imply opinion rating (MOS) assessments have been performed to judge the naturalness of the synthesized speech. The proposed methodology confirmed important enchancment within the high quality of synthesized speech in comparison with earlier fashions. Ablation research have been performed to confirm the validity of the proposed strategies.
Lastly, the authors demonstrated the validity of their proposed strategies via experiments, high quality analysis, and computation pace measurement however conveyed that varied issues nonetheless exist within the area of speech synthesis that have to be addressed, and hope that their work could be a foundation for future analysis.
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I’m Mahitha Sannala, a Pc Science Grasp’s scholar on the College of California, Riverside. I maintain a Bachelor’s diploma in Pc Science and Engineering from the Indian Institute of Expertise, Palakkad. My foremost areas of curiosity lie in Synthetic Intelligence and Machine studying. I’m significantly keen about working with medical knowledge and to derive invaluable insights from them . As a devoted learner, I’m keen to remain up to date with the newest developments within the fields of AI and ML.