- SparkTTS (https://sparkaudio.github.io/spark-tts/) is presented as a new, compact (~2.5 GB unquantized) LLM-based text-to-speech system that encodes all speaker attributes—accent, emotion, room tone, etc.—in the first 32 tokens fed to the audio decoder, termed “time-invariant acoustic characteristics.”  
- Discussion notes that SparkTTS interprets Chinese-accented training data as a variety of English accents, raising the possibility of using speech models for automatic accent classification via their latent embeddings.  
- Participants observe that SparkTTS is text-only at inference time, missing the opportunity to condition on audio context; contrast is drawn with Sesame’s conversational speech system, which feeds both full audio and text to a transformer, enabling the model to perceive user emotion and side-channel cues while the text LLM remains “oblivious” to them.  
- Practical advice for audiobook generation: segment audio at quote marks or use an LLM to insert explicit scene-direction tokens so the TTS can reset speaker characteristics per scene.  
- Overall, the logs highlight an emerging split between text-only TTS pipelines and richer multimodal speech systems that jointly model linguistic and paralinguistic information.