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StepAudio 2.5 is a unified audio-language foundation model for ASR, TTS, and realtime spoken interaction with task-tailored RLHF and specialized decoding.

BEAVER is a training-free hierarchical prompt compression framework for long-context LLM inference with strong benchmark performance and 26.4x lower latency on 128k contexts.

Mio is an end-to-end multimodal interactive digital-human framework that combines reasoning and real-time embodiment with state-of-the-art performance.

We introduce a novel token-based text-to-speech (TTS) model with 0.8B parameters, trained on a mix of real and synthetic data totaling 650k hours, to address issues like pronunciation accuracy and style consistency. This model integrates a latent variable sequence with enhanced acoustic information into the TTS system, reducing errors and style changes. Our training includes data augmentation for improved timbre consistency, and we use a few-shot voice conversion model to generate diverse voices. This approach enables learning of one-to-many mappings in speech, ensuring both diversity and timbre consistency. Our model outperforms VALL-E in pronunciation, style maintenance, and timbre continuity.