AudioCraft

AudioLM is a pure audio language model that generates high‑fidelity, long‑term coherent speech and piano music by learning from raw audio alone, without requiring any text transcripts or symbolic representations. It represents audio hierarchically using two types of discrete tokens, semantic tokens extracted from a self‑supervised model to capture phonetic or melodic structure and global context, and acoustic tokens from a neural codec to preserve speaker characteristics and fine waveform details, and chains three Transformer stages to predict first semantic tokens for high‑level structure, then coarse and finally fine acoustic tokens for detailed synthesis. The resulting pipeline allows AudioLM to condition on a few seconds of input audio and produce seamless continuations that retain voice identity, prosody, and recording conditions in speech or melody, harmony, and rhythm in music. Human evaluations show that synthetic continuations are nearly indistinguishable from real recordings.

Seed-Music is a unified framework for high-quality and controlled music generation and editing, capable of producing vocal and instrumental works from multimodal inputs such as lyrics, style descriptions, sheet music, audio references, or voice prompts, and of supporting post-production editing of existing tracks by allowing direct modification of melodies, timbres, lyrics, or instruments. It combines autoregressive language modeling with diffusion approaches and a three-stage pipeline comprising representation learning (which encodes raw audio into intermediate representations, including audio tokens, symbolic music tokens, and vocoder latents), generation (which transforms these multimodal inputs into music representations), and rendering (which converts those representations into high-fidelity audio). The system supports lead-sheet to song conversion, singing synthesis, voice conversion, audio continuation, style transfer, and fine-grained control over music structure.