Search arXivSearch

arXiv · 2608.21378

sanoTTS: The Smallest Real-Time Neural TTS on a General-Purpose Microcontroller

Abstract

This paper describes an audited neural text-to-speech stack that runs from phoneme IDs to 22.05-kHz PCM on general-purpose microcontrollers. Its deployed graph has 567,008 parameters, and its two int8 blobs occupy 679,832 bytes. On an ESP32-S3, the complete duration-acoustic-inverse-STFT path generates 4.54 s of speech in 1.02 s (0.22x real time) without a neural accelerator. The same portable C core runs offline at 5.72x real time on an FPU-less ESP32-C3. To our knowledge, this is the smallest complete phoneme-to-waveform neural TTS graph demonstrated in real time on a general-purpose microcontroller without a neural accelerator. We derive the students from the conditional-VAE objective of their Piper/VITS teachers and state the duration, latent-interface, waveform, adversarial, and joint-distillation losses used in training. The size and speed come with an audible cost: on unseen text, the embedded stack distilled from en_US-kristin-medium scores 2.54 SCOREQ and 2.80 UTMOS, compared with 4.68 and 4.42 for its teacher. A separate English quality package uses the stronger en_US-amy-medium teacher. Its 1,454,284-parameter Pareto point scores 4.13 SCOREQ and 4.10 UTMOS; a 1,834,380-parameter variant scores 4.16 SCOREQ. A controlled capacity study with Kristin identifies the decoder, rather than the output representation, as the main constraint. Two evaluation failures also affected the work: a narrow, templated test set overstated one early student's SCOREQ by 1.35, and aggregate quality predictors missed a sibilant failure that was evident in listening and in a phoneme-resolved spectral probe. Checksums cover the reported model blobs, runtime ports, and golden vectors.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ashish Thapa. 2026-07-14. sanoTTS: The Smallest Real-Time Neural TTS on a General-Purpose Microcontroller. https://arxiv.org/abs/2608.21378

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Streaming Generation for Music Accompaniment

Music generation models can produce high-fidelity coherent accompaniment given complete audio input, but are limited to editing and loop-based workflows. We study real-time audio-to-audio accompaniment: as a model hears an input audio stream (e.g., a singer singing), it has to also simultaneously generate in real-time a coherent accompanying stream (e.g., a guitar accompaniment). In this work, we propose a model design considering inevitable system delays in practical deployment with two design variables: future visibility $t_f$, the offset between the output playback time and the latest input time used for conditioning, and output chunk duration $k$, the number of frames emitted per call. We train Transformer decoders across a grid of $(t_f,k)$ and show two consistent trade-offs: increasing effective $t_f$ improves coherence by reducing the recency gap, but requires faster inference to stay within the latency budget; increasing $k$ improves throughput but results in degraded accompaniment due to a reduced update rate. Finally, we observe that naive maximum-likelihood streaming training is insufficient for coherent accompaniment where future context is not available, motivating advanced anticipatory and agentic objectives for live jamming.

cs.SD

M-CIF: Multi-Scale Alignment For CIF-Based Non-Autoregressive ASR

The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanism creates a smooth and monotonic mapping from acoustic features to target tokens, achieving performance on Mandarin competitive with other NAR approaches. However, without finer-grained guidance, its stability degrades in some languages such as English and French. In this paper, we propose Multi-scale CIF (M-CIF), which performs multi-level alignment by integrating character and phoneme level supervision progressively distilled into subword representations, thereby enhancing robust acoustic-text alignment. Experiments show that M-CIF reduces WER compared to the Paraformer baseline, especially on CommonVoice by 4.21% in German and 3.05% in French. To further investigate these gains, we define phonetic confusion errors (PE) and space-related segmentation errors (SE) as evaluation metrics. Analysis of these metrics across different M-CIF settings reveals that the phoneme and character layers are essential for enhancing progressive CIF alignment.

cs.SD

Decoding Order Matters in Autoregressive Speech Synthesis

Autoregressive speech synthesis often adopts a left-to-right order, yet generation order is a modelling choice. We investigate decoding order through masked diffusion framework, which progressively unmasks positions and allows arbitrary decoding orders during training and inference. By interpolating between identity and random permutations, we show that randomness in decoding order affects speech quality. We further compare fixed strategies, such as \texttt{l2r} and \texttt{r2l} with adaptive ones, such as Top-$K$, finding that fixed-order decoding, including the dominating left-to-right approach, is suboptimal, while adaptive decoding yields better performance. Finally, since masked diffusion requires discrete inputs, we quantise acoustic representations and find that even 1-bit quantisation can support reasonably high-quality speech.

cs.SD