# BigCodec: Pushing the Limits of Low-Bitrate Neural Speech Codec

arXiv: https://arxiv.org/abs/2409.05377 · HTML: https://mlsanity.com/paper/2409.05377/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2024.

## Built on by (most cited first, 30 of 46)

- [On The Landscape of Spoken Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2504.08528/) (2025)
- [Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens](https://mlsanity.com/paper/2503.01710/) (2025)
- [Step-Audio 2 Technical Report](https://mlsanity.com/paper/2507.16632/) (2025)
- [WavChat: A Survey of Spoken Dialogue Models](https://mlsanity.com/paper/2411.13577/) (2024)
- [Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis](https://mlsanity.com/paper/2502.04128/) (2025)
- [MiMo-Audio: Audio Language Models are Few-Shot Learners](https://mlsanity.com/paper/2512.23808/) (2025)
- [Recent Advances in Discrete Speech Tokens: A Review](https://mlsanity.com/paper/2502.06490/) (2025)
- [XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs](https://mlsanity.com/paper/2506.23325/) (2025)
- [FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks](https://mlsanity.com/paper/2502.04465/) (2025)
- [Ming-UniAudio: Speech LLM for Joint Understanding, Generation and Editing with Unified Representation](https://mlsanity.com/paper/2511.05516/) (2025)
- [TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling](https://mlsanity.com/paper/2504.07053/) (2025)
- [MOSS-TTS Technical Report](https://mlsanity.com/paper/2603.18090/) (2026)
- [TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling](https://mlsanity.com/paper/2508.16790/) (2025)
- [FocalCodec-Stream: Streaming Low-Bitrate Speech Coding via Causal Distillation](https://mlsanity.com/paper/2509.16195/) (2025)
- [MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation](https://mlsanity.com/paper/2506.00385/) (2025)
- [UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models](https://mlsanity.com/paper/2601.01373/) (2026)
- [DM-Codec: Distilling Multimodal Representations for Speech Tokenization](https://mlsanity.com/paper/2410.15017/) (2024)
- [SecoustiCodec: Cross-Modal Aligned Streaming Single-Codecbook Speech Codec](https://mlsanity.com/paper/2508.02849/) (2025)
- [Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization](https://mlsanity.com/paper/2601.23174/) (2026)
- [TTS-1 Technical Report](https://mlsanity.com/paper/2507.21138/) (2025)
- [LongCat-AudioDiT: High-Fidelity Diffusion Text-to-Speech in the Waveform Latent Space](https://mlsanity.com/paper/2603.29339/) (2026)
- [UniTTS: An end-to-end TTS system without decoupling of acoustic and semantic information](https://mlsanity.com/paper/2505.17426/) (2025)
- [Finite Scalar Quantization Enables Redundant and Transmission-Robust Neural Audio Compression at Low Bit-rates](https://mlsanity.com/paper/2509.09550/) (2025)
- [UniSRCodec: Unified and Low-Bitrate Single Codebook Codec with Sub-Band Reconstruction](https://mlsanity.com/paper/2601.02776/) (2026)
- [Scaling Speech Tokenizers with Diffusion Autoencoders](https://mlsanity.com/paper/2602.06602/) (2026)
- [Reconstruct! Don't Encode: Self-Supervised Representation Reconstruction Loss for High-Intelligibility and Low-Latency Streaming Neural Audio Codec](https://mlsanity.com/paper/2603.05887/) (2026)
- [Eliminating stability hallucinations in llm-based tts models via attention guidance](https://mlsanity.com/paper/2509.19852/) (2025)
- [U-Codec: Ultra Low Frame-rate Neural Speech Codec for Fast High-fidelity Speech Generation](https://mlsanity.com/paper/2510.16718/) (2025)
- [The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs](https://mlsanity.com/paper/2602.15491/) (2026)
- [TASTE-Streaming: Towards Streamable Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling](https://mlsanity.com/paper/2603.12350/) (2026)

## Abstract (arXiv)

We present BigCodec, a low-bitrate neural speech codec. While recent neural speech codecs have shown impressive progress, their performance significantly deteriorates at low bitrates (around 1 kbps). Although a low bitrate inherently restricts performance, other factors, such as model capacity, also hinder further improvements. To address this problem, we scale up the model size to 159M parameters that is more than 10 times larger than popular codecs with about 10M parameters. Besides, we integrate sequential models into traditional convolutional architectures to better capture temporal dependency and adopt low-dimensional vector quantization to ensure a high code utilization. Comprehensive objective and subjective evaluations show that BigCodec, with a bitrate of 1.04 kbps, significantly outperforms several existing low-bitrate codecs. Furthermore, BigCodec achieves objective performance comparable to popular codecs operating at 4-6 times higher bitrates, and even delivers better subjective perceptual quality than the ground truth.