Latest developments in LLMs, such because the GPT sequence and rising “o1” fashions, spotlight the advantages of scaling coaching and inference-time computing. Whereas scaling throughout coaching—by rising mannequin measurement and dataset quantity—has been a well-established technique, current findings emphasize some great benefits of inference-time scaling, the place further computational sources throughout testing enhance output high quality and job complexity dealing with. This precept has been extensively explored in text-based fashions however stays underutilized in speech synthesis. Present text-to-speech (TTS) methods usually make use of multi-stage architectures, combining LLMs with diffusion fashions or different processing modules, complicating scaling selections. Not like textual content fashions, which comply with a standardized Transformer framework that enables systematic scaling investigations, TTS analysis has largely centered on architectural enhancements moderately than optimizing inference-time computation.
A shift towards single-stage TTS architectures addresses the inefficiencies of multi-stage pipelines by instantly modeling discrete speech tokens as a substitute of counting on intermediate acoustic representations. This method reduces complexity, enhances scalability, and permits large-scale coaching with out important reminiscence constraints. Evaluations of such architectures reveal state-of-the-art efficiency in zero-shot speech synthesis, cross-lingual adaptation, and emotion preservation, surpassing conventional multi-stage fashions. Moreover, integrating scaling methods improves ASR accuracy, bridging the hole between text- and speech-based LLM purposes. By adopting a unified, compute-efficient framework, current developments in TTS align extra carefully with the scalable methodologies seen in textual content LLMs, enabling extra versatile and high-quality speech synthesis options.
Researchers from ASLP Lab at Northwestern Polytechnical College, College of Science and Expertise Beijing, College of Surrey, Chinese language College of Hong Kong, Hong Kong Baptist College, College of Rochester, and Shanghai Mobvoi Data Expertise introduce Llasa, a Transformer-based TTS mannequin aligned with customary LLM architectures. Scaling train-time computing improves speech naturalness and prosody, whereas inference-time computing, with speech understanding verifiers, enhances emotional expressiveness, timbre consistency, and content material accuracy. Evaluations on a number of datasets present state-of-the-art outcomes, and the mannequin and code are publicly obtainable to encourage additional TTS analysis.
The TTS framework aligns with the usual textual content LLM paradigm, utilizing a tokenizer and a Transformer-based LLM. It employs Xcodec2, a speech tokenizer that encodes waveforms into discrete tokens and decodes them into high-quality audio. The mannequin learns the joint distribution of textual content and speech tokens, optimizing the conditional chance of producing speech tokens based mostly on textual content enter. The speech tokenizer integrates semantic and acoustic options utilizing a twin encoder system. The method scales coaching information and mannequin measurement to enhance efficiency and evaluates train-time and inference-time compute methods, specializing in textual content understanding and in-context studying capabilities.
The research compares the proposed speech tokenizer with current codecs and evaluates its efficiency in TTS methods. The speech tokenizer is examined in opposition to varied fashions utilizing metrics similar to Phrase Error Fee (WER), Perceptual Analysis of Speech High quality (PESQ), and speaker similarity (SPK SIM). Outcomes present that the tokenizer performs effectively at low token charges, attaining higher speech high quality than different codecs. The TTS fashions, evaluated for his or her textual content understanding and in-context studying talents, enhance with scaling mannequin measurement and coaching information. Inference-time compute scaling additionally enhances efficiency, balancing speaker similarity and transcription accuracy.
In conclusion, the research introduces Llasa, a scalable TTS system that makes use of a single Transformer mannequin and tokenizer, aligning with text-based LLMs. The research explores train-time and inference-time compute scaling, exhibiting that bigger fashions and datasets enhance speech naturalness, prosody, and comprehension. Moreover, utilizing speech understanding fashions as verifiers, inference-time scaling enhances speaker similarity, emotional expressiveness, and accuracy. Llasa’s experiments reveal state-of-the-art efficiency with sturdy zero-shot TTS capabilities. The authors launch their fashions and coaching codes to encourage additional analysis within the subject.
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Sana Hassan, a consulting intern at Marktechpost and dual-degree pupil at IIT Madras, is keen about making use of know-how and AI to handle real-world challenges. With a eager curiosity in fixing sensible issues, he brings a contemporary perspective to the intersection of AI and real-life options.