RAVE: A variational autoencoder for fast and high-quality neural audio synthesis (2111.05011v2)

Abstract: Deep generative models applied to audio have improved by a large margin the state-of-the-art in many speech and music related tasks. However, as raw waveform modelling remains an inherently difficult task, audio generative models are either computationally intensive, rely on low sampling rates, are complicated to control or restrict the nature of possible signals. Among those models, Variational AutoEncoders (VAE) give control over the generation by exposing latent variables, although they usually suffer from low synthesis quality. In this paper, we introduce a Realtime Audio Variational autoEncoder (RAVE) allowing both fast and high-quality audio waveform synthesis. We introduce a novel two-stage training procedure, namely representation learning and adversarial fine-tuning. We show that using a post-training analysis of the latent space allows a direct control between the reconstruction fidelity and the representation compactness. By leveraging a multi-band decomposition of the raw waveform, we show that our model is the first able to generate 48kHz audio signals, while simultaneously running 20 times faster than real-time on a standard laptop CPU. We evaluate synthesis quality using both quantitative and qualitative subjective experiments and show the superiority of our approach compared to existing models. Finally, we present applications of our model for timbre transfer and signal compression. All of our source code and audio examples are publicly available.

Analysis of the RAVE Model for Neural Audio Synthesis

The paper "RAVE: A variational autoencoder for fast and high-quality neural audio synthesis" by Antoine Caillon and Philippe Esling introduces the RAVE model, a Variational AutoEncoder (VAE) approach for synthesizing audio waveforms in a way that is computationally efficient and maintains high quality. In contrast to the complexity and the high computational demands of existing audio generative models, RAVE presents an innovative training architecture that leverages both multiband decomposition and a novel two-stage training procedure to achieve remarkable results in audio synthesis.

Core Contributions and Methodology

The principal contribution of the RAVE model lies in addressing the balance between synthesis quality and speed. Traditional approaches, such as autoregressive models like WaveNet, while effective, are computationally intensive and restrict real-time applications due to their high sampling rates. RAVE circumvents this limitation through a two-stage training approach that separates representation learning from adversarial fine-tuning.

1. Two-Stage Training Procedure: The training process of RAVE is divided into two distinct phases. Initially, the VAE is trained for effective representation learning, which allows the model to capture high-level audio features without penalizing low-level phase variations. This phase utilizes a multiscale spectral distance approach to evaluate waveform similarity, thereby aiding in the accurate learning of audio representations without focusing on irrelevant phase details.
2. Adversarial Fine-Tuning: In the second training stage, the model focuses solely on enhancing audio quality through adversarial tuning, while freezing the encoder parameters learned in the first stage. This is crucial as it shifts the objective towards refining the synthesis quality by training the decoder with a GAN-based mechanism, ensuring the generated audio signals maintain subjective quality closest to true audio samples.
3. Multiband Decomposition: RAVE employs a 16-band decomposition of raw audio waveforms, significantly reducing dimensionality, which in turn provides fast synthesis without sacrificing audio fidelity. This multiband approach not only optimizes computational load but also scales up to synthesize high fidelity 48kHz audio, showcasing low latency performance by running 20 times faster than real-time on standard laptops.
4. Latent Space Analysis: Post-training, RAVE implements a singular value decomposition method for understanding and controlling the latent spaces, allowing a dynamic trade-off between representation compactness and audio reconstruction fidelity. The introduction of a fidelity parameter ($f$) allows tuning of this trade-off by selecting informative latent dimensions, dismissing uninformative ones, and thus maintaining high performance with reduced latent size.

Experimental Results and Implications

The experimental evaluations underscore the effectiveness of RAVE by comparing it against state-of-the-art models like NSynth and SING. Results demonstrate superior audio reconstruction quality with RAVE, achieving significant reductions in parameter count and computational demands. Notably, RAVE outperforms in both qualitative assessments of audio quality and quantitative measures of synthesis speed, reinforcing its suitability for real-time audio applications.

The implications of this research are significant, particularly for applications that require both speed and high quality, such as real-time musical performances, immersive virtual environments, and mobile audio applications. Furthermore, this work sets a precedent for efficient yet accurate unsupervised audio representation learning, which can potentially guide future advancements in audio signal processing.

Future Prospects

RAVE opens several avenues for further exploration and enhancement in AI-driven audio synthesis. Potential developments could include incorporating domain adaptation techniques to enhance cross-domain synthesis capabilities, extending the model's applicability to various audio signal types beyond music and speech. Additionally, advancing the model's capacity for parameter elasticity without performance loss could be explored, further enhancing real-time applications' robustness.

In conclusion, RAVE represents a significant stride in neural audio synthesis, providing a compelling solution to the challenge of balancing quality and computational efficiency. Its innovative approach and demonstrable advantages will likely serve as a foundation for future research in this domain. The publication of source code and audio examples further underlines the commitment to transparency and reproducibility, paving the way for continued contributions and innovation in the field.