In recent times, text-to-speech (TTS) know-how has made vital strides, but quite a few challenges nonetheless stay. Autoregressive (AR) programs, whereas providing numerous prosody, are inclined to endure from robustness points and gradual inference speeds. Non-autoregressive (NAR) fashions, then again, require specific alignment between textual content and speech throughout coaching, which may result in unnatural outcomes. The brand new Masked Generative Codec Transformer (MaskGCT) addresses these points by eliminating the necessity for specific text-speech alignment and phone-level length prediction. This novel method goals to simplify the pipeline whereas sustaining and even enhancing the standard and expressiveness of generated speech.
MaskGCT is a brand new open-source, state-of-the-art TTS mannequin accessible on Hugging Face. It brings a number of thrilling options to the desk, reminiscent of zero-shot voice cloning and emotional TTS, and might synthesize speech in each English and Chinese language. The mannequin was educated on an intensive dataset of 100,000 hours of in-the-wild speech knowledge, enabling it to generate long-form and variable-speed synthesis. Notably, MaskGCT encompasses a totally non-autoregressive structure. This implies the mannequin doesn’t depend on iterative prediction, leading to quicker inference occasions and a simplified synthesis course of. With a two-stage method, MaskGCT first predicts semantic tokens from textual content and subsequently generates acoustic tokens conditioned on these semantic token.
MaskGCT makes use of a two-stage framework that follows a “mask-and-predict” paradigm. Within the first stage, the mannequin predicts semantic tokens based mostly on the enter textual content. These semantic tokens are extracted from a speech self-supervised studying (SSL) mannequin. Within the second stage, the mannequin predicts acoustic tokens conditioned on the beforehand generated semantic tokens. This structure permits MaskGCT to totally bypass text-speech alignment and phoneme-level length prediction, distinguishing it from earlier NAR fashions. Furthermore, it employs a Vector Quantized Variational Autoencoder (VQ-VAE) to quantize the speech representations, which minimizes data loss. The structure is extremely versatile, permitting for the technology of speech with controllable velocity and length, and helps functions like cross-lingual dubbing, voice conversion, and emotion management, all in a zero-shot setting.
MaskGCT represents a big leap ahead in TTS know-how as a result of its simplified pipeline, non-autoregressive method, and strong efficiency throughout a number of languages and emotional contexts. Its coaching on 100,000 hours of speech knowledge, protecting numerous audio system and contexts, offers it unparalleled versatility and naturalness in generated speech. Experimental outcomes exhibit that MaskGCT achieves human-level naturalness and intelligibility, outperforming different state-of-the-art TTS fashions on key metrics. For instance, MaskGCT achieved superior scores in speaker similarity (SIM-O) and phrase error fee (WER) in comparison with different TTS fashions like VALL-E, VoiceBox, and NaturalSpeech 3. These metrics, alongside its high-quality prosody and adaptability, make MaskGCT a perfect device for functions that require each precision and expressiveness in speech synthesis.
MaskGCT pushes the boundaries of what’s attainable in text-to-speech know-how. By eradicating the dependencies on specific text-speech alignment and length prediction and as an alternative utilizing a completely non-autoregressive, masked generative method, MaskGCT achieves a excessive stage of naturalness, high quality, and effectivity. Its flexibility to deal with zero-shot voice cloning, emotional context, and bilingual synthesis makes it a game-changer for varied functions, together with AI assistants, dubbing, and accessibility instruments. With its open availability on platforms like Hugging Face, MaskGCT isn’t just advancing the sphere of TTS but in addition making cutting-edge know-how extra accessible for builders and researchers worldwide.
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