We present LiveBand, a real-time system that generates high-fidelity music accompaniments to live audio input, respecting strict causal constraints. Our method trains a causal transformer generator in the continuous latent space of a pre-trained causal audio autoencoder, using adversarial sequence-level supervision from a discriminator. At each timestep, the generator receives only the causally available mix context and Gaussian noise, and predicts accompaniment latents without access to future mix frames or ground-truth target latents. Training is performed in a single parallel forward pass under causal masking, while streaming inference proceeds autoregressively with a rolling attention state. The model's training and inference computations are matched by design, eliminating teacher forcing and the associated exposure bias. On a multi-instrument music accompaniment benchmark, LiveBand improves over prior work on objective measures of audio quality, beat alignment, and mix adherence, while enabling real-time streaming generation without lookahead into the future on consumer hardware.

翻译：我们提出LiveBand，一个在严格因果约束下对现场音频输入生成高保真音乐伴奏的实时系统。该方法在预训练因果音频自编码器的连续潜空间中训练因果Transformer生成器，并利用鉴别器提供的对抗性序列级监督。在每个时间步，生成器仅接收因果可用的混音上下文与高斯噪声，预测伴奏潜变量，且不访问未来混音帧或真实目标潜变量。训练在因果掩码下通过单次并行前向传播完成，而流式推理则以滚动注意力状态自回归进行。模型训练与推理的计算过程被设计为完全匹配，从而消除了教师强制及其相关的曝光偏差。在多乐器音乐伴奏基准测试中，LiveBand在音频质量、节拍对齐与混音一致性等客观指标上优于先前工作，并能在消费级硬件上实现无需前瞻的实时流式生成。