CLaM-TTS represents a leap forward in zero-shot Text-to-Speech (TTS) synthesis, integrating neural audio codecs with language models.

• Utilizes probabilistic residual vector quantization, leading to superior compression and simultaneous multiple token generation.
• Shows higher performance and faster inference than current neural codec-based TTS models in multiple evaluations.
• Examines the impact of pretraining extents and text tokenization strategies on TTS performance.

This advancement opens new avenues for creating natural-sounding synthetic speech, enhancing the accessibility and expressiveness of AI voice technologies. Read the detailed report.