Spectrogram Patch Codec: A 2D Block-Quantized VQ-VAE and HiFi-GAN for Neural Speech Coding

Abstract

We present a neural speech codec that challenges the need for complex residual vector quantization (RVQ) stacks by introducing a simpler, single-stage quantization approach. Our method operates directly on the mel-spectrogram, treating it as a 2D data and quantizing non-overlapping 4x4 patches into a single, shared codebook. This patchwise design simplifies the architecture, enables low-latency streaming, and yields a discrete latent grid. To ensure high-fidelity synthesis, we employ a late-stage adversarial fine-tuning for the VQ-VAE and train a HiFi-GAN vocoder from scratch on the codec's reconstructed spectrograms. Operating at approximately 7.5 kbits/s for 16 kHz speech, our system was evaluated against several state-of-the-art neural codecs using objective metrics such as STOI, PESQ, MCD, and ViSQOL. The results demonstrate that our simplified, non-residual architecture achieves competitive perceptual quality and intelligibility, validating it as an effective and open foundation for future low-latency codec designs.