Audio Generation Models
Audio and Music Generation Models — Neural audio generation produces realistic speech, music, and sound effects by modeling complex temporal patterns in waveforms, spectrograms, and symbolic representations.
Autoregressive Waveform Models — WaveNet introduced dilated causal convolutions for sample-by-sample audio generation, achieving unprecedented speech quality but requiring slow sequential inference. WaveRNN reduced computational costs using single-layer recurrent networks with dual softmax outputs. SampleRNN operated at multiple temporal resolutions, with higher-level modules conditioning lower-level sample generation. These models capture fine-grained acoustic details but face inherent speed limitations from autoregressive generation.
Non-Autoregressive Synthesis — WaveGlow combines flow-based generative models with WaveNet-style architectures for parallel waveform synthesis. Diffusion-based vocoders like DiffWave and WaveGrad iteratively denoise Gaussian noise into high-fidelity audio, offering quality comparable to autoregressive models with faster generation. HiFi-GAN uses multi-scale and multi-period discriminators to train efficient generator networks that produce high-quality audio in real time on consumer hardware.
Music Generation Systems — Jukebox from OpenAI generates music with singing in raw audio space using hierarchical VQ-VAE representations. MusicLM from Google conditions generation on text descriptions, enabling natural language control over musical output. MuseNet and Music Transformer model symbolic music as token sequences, capturing long-range musical structure including harmony, rhythm, and form. Diffusion models adapted for music generate spectrograms that are converted to audio through neural vocoders.
Text-to-Speech Advances — Tacotron and FastSpeech architectures convert text to mel-spectrograms, which vocoders then synthesize into waveforms. VALL-E treats TTS as a language modeling task over neural audio codec codes, enabling zero-shot voice cloning from short reference clips. Bark and Tortoise TTS leverage large-scale training for expressive, natural-sounding synthesis with emotional control and multilingual capabilities.
Audio generation models have reached a remarkable inflection point where synthesized speech and music are increasingly indistinguishable from human-produced audio, opening transformative applications while raising important questions about authenticity and misuse.