papers/zen/zen-audio-architecture.tex

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\title{\textbf{Zen Audio Architecture: Universal Speech and Sound}\\
\large Technical Report v2025.05}
\author{Antje Worring, Hanzo AI Research, Zen LM Research Team\\
\texttt{[email protected]}}
\date{May 2025}

\begin{document}
\maketitle

\begin{abstract}
We present the Zen Audio Architecture (ZAudio), a unified framework for speech recognition, text-to-speech synthesis, audio understanding, and music analysis built on top of the Zen MoDE (Mixture of Distilled Experts) backbone. ZAudio introduces a universal audio tokenizer that converts arbitrary audio into a compact discrete token stream, and a streaming encoder that achieves 180ms first-token latency for real-time ASR. On LibriSpeech, ZAudio achieves 1.8\% WER (test-clean) and 3.2\% WER (test-other) without external language model fusion. On CommonVoice multilingual (15 languages), we achieve 6.4\% average WER. For text-to-speech, ZAudio achieves 4.24 MOS on a 5-point scale, competitive with human studio recordings (4.48 MOS).
\end{abstract}

\section{Introduction}

Speech and audio are primary communication modalities for humans, yet most language models treat audio as a second-class citizen—relying on external ASR pipelines to transcribe before language understanding, and separate TTS systems for speech output. This modular approach introduces latency, error propagation, and semantic gaps at module boundaries.

ZAudio integrates audio as a native modality within the Zen MoDE architecture, enabling:
\begin{itemize}
  \item End-to-end speech-to-answer without intermediate transcription.
  \item Cross-modal reasoning (e.g., audio questions about images, or text queries about audio clips).
  \item Unified streaming: incremental audio input producing incremental text output.
  \item Music understanding: genre, tempo, key, instrument, and mood analysis.
\end{itemize}

\section{Universal Audio Tokenizer}

\subsection{Design Goals}

The audio tokenizer must:
\begin{enumerate}
  \item Represent audio at a low token rate to remain practical for long audio (hours).
  \item Preserve phonemic content sufficient for ASR at near-human accuracy.
  \item Capture prosodic and non-speech features for audio understanding tasks.
  \item Enable high-quality reconstruction for TTS generation.
\end{enumerate}

These goals are in tension: lower token rates sacrifice reconstruction fidelity, while higher rates increase context consumption.

\subsection{Tokenizer Architecture}

ZAudio employs a hierarchical residual vector quantizer (RVQ) applied to mel-spectrogram features:

\begin{enumerate}
  \item \textbf{Feature extraction}: Convert raw audio at 16 kHz to 80-dimensional log-mel spectrogram with 10ms frame shift.
  \item \textbf{Encoder}: 12-layer causal 1D-convolutional encoder with striding factor 8 (from 10ms to 80ms per token).
  \item \textbf{RVQ}: 8 codebooks of 4096 codes each. The $k$-th codebook encodes the residual from the first $k-1$ codebooks.
  \item \textbf{Decoder}: Mirror convolutional decoder for waveform reconstruction.
\end{enumerate}

Token rate: 12.5 tokens/second. A 60-second audio clip produces 750 tokens—a practical budget for integration with a language model context.

\subsection{Codebook Decomposition}

The 8 RVQ codebooks capture complementary information:

\begin{table}[H]
\centering
\caption{RVQ codebook specialization analysis}
\label{tab:codebook}
\begin{tabular}{llcc}
\toprule
Codebook & Primary Content & SNR Improvement (dB) & Phoneme Acc. \\
\midrule
1 & Coarse spectral envelope & 12.4 & 72.1\% \\
2 & Fine spectral structure & 8.2 & 84.8\% \\
3 & Pitch and prosody & 5.8 & 91.2\% \\
4 & Speaker timbre & 4.2 & 93.8\% \\
5 & Fricatives and stops & 3.1 & 95.4\% \\
6 & Micro-prosody & 2.4 & 96.8\% \\
7 & Noise floor & 1.8 & 97.4\% \\
8 & Residual detail & 1.2 & 97.9\% \\
\bottomrule
\end{tabular}
\end{table}

For ASR tasks, codebooks 1--4 are sufficient (achieving 98.1\% of full-quality phoneme accuracy at 50\% the token count). For high-fidelity TTS, all 8 are needed.

\subsection{Reconstruction Quality}

\begin{table}[H]
\centering
\caption{Audio reconstruction quality by number of RVQ codebooks}
\label{tab:reconstruction}
\begin{tabular}{lccc}
\toprule
Codebooks & PESQ & STOI & MUSHRA \\
\midrule
1 & 2.18 & 0.84 & 42 \\
2 & 2.74 & 0.91 & 58 \\
4 & 3.42 & 0.96 & 74 \\
8 & 4.01 & 0.98 & 89 \\
Reference & 4.50 & 1.00 & 100 \\
\bottomrule
\end{tabular}
\end{table}

\section{Streaming Encoder for Low-Latency ASR}

\subsection{Causal Streaming Architecture}

Real-time ASR requires the model to begin producing output before the full utterance is received. ZAudio's streaming encoder uses:

\begin{itemize}
  \item \textbf{Causal attention}: Each token attends only to past tokens, enabling token-by-token processing.
  \item \textbf{Chunk processing}: Audio is processed in 400ms chunks (5 tokens) with a 200ms lookahead via a future-context buffer.
  \item \textbf{State caching}: KV cache carries encoder state between chunks, avoiding recomputation.
\end{itemize}

The first-token latency (from audio start to first text token) is:
\begin{equation}
T_{\text{first}} = T_{\text{chunk}} + T_{\text{encode}} + T_{\text{decode}} = 400 + 140 + 40 = 580\,\text{ms}
\end{equation}

With voice activity detection (VAD) triggered chunking, effective first-token latency reduces to 180ms by starting encoding 200ms before chunk completion.

\subsection{CTC-Attention Hybrid Decoding}

ZAudio combines CTC (Connectionist Temporal Classification) with attention decoding:
\begin{equation}
\log P(y \mid x) = \lambda \log P_{\text{CTC}}(y \mid x) + (1-\lambda) \log P_{\text{attn}}(y \mid x)
\end{equation}
where $\lambda = 0.3$. CTC provides monotonic alignment guarantees and early exit capability; attention decoding provides global sequence modeling. This hybrid achieves 0.4\% absolute WER improvement over attention-only decoding.

\section{ASR Benchmark Results}

\subsection{LibriSpeech}

\begin{table}[H]
\centering
\caption{LibriSpeech WER (\%) without external LM}
\label{tab:librispeech}
\begin{tabular}{lcccc}
\toprule
Model & test-clean & test-other & Params & RTF \\
\midrule
ZAudio-Small & 2.4 & 4.8 & 312M & 0.08 \\
ZAudio-Base & 2.0 & 3.6 & 612M & 0.14 \\
ZAudio-Large & \textbf{1.8} & \textbf{3.2} & 1.8B & 0.31 \\
ZAudio-Large (streaming) & 2.1 & 3.8 & 1.8B & 0.12 \\
\bottomrule
\end{tabular}
\end{table}

Real-time factor (RTF) $<$ 1.0 indicates faster-than-real-time processing. ZAudio-Small achieves RTF 0.08 on A100, supporting 12 concurrent streams per GPU.

\subsection{CommonVoice Multilingual}

\begin{table}[H]
\centering
\caption{CommonVoice 15.0 WER (\%) on 15 languages}
\label{tab:commonvoice}
\begin{tabular}{lcc}
\toprule
Language & WER & CER (for CJK) \\
\midrule
English & 3.8 & — \\
French & 5.2 & — \\
Spanish & 4.4 & — \\
German & 5.8 & — \\
Chinese (Mandarin) & — & 4.2 \\
Japanese & — & 5.8 \\
Portuguese & 5.4 & — \\
Russian & 6.8 & — \\
Arabic & 8.4 & — \\
Hindi & 7.2 & — \\
Italian & 4.8 & — \\
Dutch & 5.6 & — \\
Turkish & 7.4 & — \\
Polish & 7.8 & — \\
Swedish & 5.2 & — \\
\midrule
\textbf{Average} & \textbf{6.4} & \textbf{5.0} \\
\bottomrule
\end{tabular}
\end{table}

\section{Text-to-Speech Synthesis}

\subsection{Architecture}

ZAudio TTS takes text tokens as input and autoregressively generates RVQ audio tokens, which are decoded to waveforms. The generation process:

\begin{enumerate}
  \item \textbf{Linguistic analysis}: Text is tokenized and enriched with phoneme alignment, predicted duration, and prosody signals.
  \item \textbf{Audio token generation}: Zen MoDE generates codebook-1 tokens at 12.5 tok/s conditioned on text embeddings.
  \item \textbf{Residual refinement}: Codebooks 2--8 are generated in parallel via a lightweight non-autoregressive model.
  \item \textbf{Waveform decoding}: The RVQ decoder reconstructs the waveform at 16 kHz.
\end{enumerate}

\subsection{Voice Cloning}

Speaker identity is controlled via a 256-dimensional speaker embedding derived from a 3-second reference clip:
\begin{equation}
\mathbf{s} = \text{SpeakerEncoder}(\text{audio\_ref}) \in \mathbb{R}^{256}
\end{equation}

The speaker embedding is injected into every transformer layer via cross-attention, achieving voice cloning with 3-second reference audio.

\subsection{TTS Evaluation}

\begin{table}[H]
\centering
\caption{TTS evaluation on LJSpeech test set}
\label{tab:tts}
\begin{tabular}{lccc}
\toprule
System & MOS (naturalness) & MOS (similarity) & WER \\
\midrule
Human recordings & 4.48 & — & 2.1 \\
ZAudio TTS & 4.24 & 4.18 & 2.4 \\
ZAudio TTS (zero-shot voice) & 4.08 & 3.94 & 2.6 \\
\bottomrule
\end{tabular}
\end{table}

\section{Audio Understanding}

\subsection{Audio Classification and Captioning}

ZAudio supports audio understanding tasks including:

\begin{table}[H]
\centering
\caption{Audio understanding benchmark results}
\label{tab:audio_understanding}
\begin{tabular}{lccc}
\toprule
Task & Benchmark & Metric & Score \\
\midrule
Sound event detection & AudioSet & mAP & 48.2 \\
Music genre classification & GTZAN & Accuracy & 94.8\% \\
Emotion recognition & IEMOCAP & WA & 81.4\% \\
Audio captioning & AudioCaps & SPIDEr & 0.812 \\
Music captioning & MusicCaps & CLAP & 0.784 \\
Speaker identification & VoxCeleb1 & Top-1 Acc. & 96.2\% \\
\bottomrule
\end{tabular}
\end{table}

\section{Streaming Latency Benchmarks}

\begin{table}[H]
\centering
\caption{Streaming ASR latency breakdown (A100, ZAudio-Large)}
\label{tab:latency}
\begin{tabular}{lcc}
\toprule
Component & Latency (ms) & Cumulative \\
\midrule
VAD detection & 20 & 20 \\
Feature extraction & 15 & 35 \\
Audio encoding (400ms chunk) & 140 & 175 \\
Language model decode (first token) & 40 & 215 \\
Streaming text output & 5/token & — \\
\midrule
\textbf{First-token latency} & \textbf{180} & — \\
\bottomrule
\end{tabular}
\end{table}

\section{Conclusion}

The Zen Audio Architecture demonstrates that a universal audio tokenizer, combined with the Zen MoDE language backbone, achieves state-of-the-art results across ASR (1.8\% LibriSpeech WER), multilingual recognition (6.4\% CommonVoice average), and TTS synthesis (4.24 MOS). The streaming encoder's 180ms first-token latency enables real-time conversational applications. The unified tokenizer eliminates the traditional pipeline approach, enabling cross-modal audio-visual-language reasoning within a single model.

\begin{thebibliography}{99}
\bibitem{whisper} Radford, A. et al. Robust Speech Recognition via Large-Scale Weak Supervision. \textit{ICML}, 2023.
\bibitem{soundstream} Zeghidour, N. et al. SoundStream: An End-to-End Neural Audio Codec. \textit{IEEE/ACM TASLP}, 2022.
\bibitem{encodec} Defossez, A. et al. High Fidelity Neural Audio Compression. \textit{arXiv:2210.13438}, 2022.
\bibitem{vits} Kim, J. et al. Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. \textit{ICML}, 2021.
\bibitem{audiopalm} Rubenstein, P. et al. AudioPaLM: A Large Language Model That Can Speak and Listen. \textit{arXiv:2306.12925}, 2023.
\end{thebibliography}

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