Gradium has open-sourced Phonon, a family of small text-to-speech (TTS) models designed to run on-device.
Key points
- Gradium open-sourced Phonon, a family of on-device TTS models.
- Phonon 31m English model is 64.5 MB and Apache 2.0 licensed.
- Models run on CPUs, phones, Raspberry Pi 5, browsers, and GPUs.
- Phonon uses a causal transformer backbone and flow-matching head.
- Models were distilled from a larger Phonon teacher.
The Phonon on-device TTS model is available in 31 million and 40 million parameter sizes. The English Phonon 31m model, released under the Apache 2.0 license, is contained within a 64.5 MB file and allows developers to generate natural speech without requiring cloud calls, GPUs, or incurring per-character fees.
The Phonon 31m model is English-only and includes 37 built-in voices, distributed under Apache 2.0. The Phonon 40m models come in two variants: one model per language (English, French, German, Spanish, Portuguese) under CC BY-NC 4.0, and a multilingual model supporting all five languages with 102 voices, also under CC BY-NC 4.0.
By the numbers
- Parameter size of Phonon model
- 31M
- File size of English Phonon 31m
- 64.5 MB
- Audio output sample rate
- 24 kHz
Phonon utilizes a causal transformer backbone with a prefix combining text and speaker embeddings. It employs a SentencePiece tokenizer with a 4,000-token vocabulary for raw text input, bypassing phoneme conversion. The backbone generates latent vectors every 80 ms, which are then processed by a flow-matching head to produce an audio embedding in a single step.
A variant of Kyutai’s Mimi decoder then generates 24 kHz PCM audio, with a separate end-of-speech head determining the output's termination.
The Phonon 31m architecture consists of 6 transformer layers with a width of 512 and 8 attention heads, while the Phonon 40m uses 9 layers at the same width. Both models were distilled from a larger Phonon teacher model. The training process was conducted on an NVIDIA H100 cluster, leveraging NCCL and CUDA Graphs, and builds upon Kyutai's prior work.
Sources and references
Original report
Gradium Open-Sources Phonon, a 31M-Parameter On-Device TTS Model Under Apache 2.0
MarkTechPost
Read on MarkTechPost (opens in a new tab)Linked in the original report
- Linkgradium.ai/blog/release-phonon-open-weight (opens in a new tab)
- Modelhuggingface.co/gradium/phonon-31m-en (opens in a new tab)
- Linkgradium.ai/blog/gradium-releases-phonon (opens in a new tab)
- Linkgradium.ai/phonon (opens in a new tab)
- Modelhuggingface.co/kyutai/mimi (opens in a new tab)
- Paperarxiv.org/abs/2509.06926 (opens in a new tab)
Ref 00E5JUPZ
Cite this
APA
Interestana AI Editorial. (2026, October 22). Gradium Open-Sources Phonon TTS Model Under Apache 2.0. Interestana. https://interestana.com/articles/gradium-open-sources-phonon-a-31m-parameter-on-device-tts-model-under-apache-20-00e5jupz
MLA
“Gradium Open-Sources Phonon TTS Model Under Apache 2.0.” Interestana, 22 Oct. 2026, interestana.com/articles/gradium-open-sources-phonon-a-31m-parameter-on-device-tts-model-under-apache-20-00e5jupz.
Chicago
Interestana AI Editorial. “Gradium Open-Sources Phonon TTS Model Under Apache 2.0.” Interestana, October 22, 2026. https://interestana.com/articles/gradium-open-sources-phonon-a-31m-parameter-on-device-tts-model-under-apache-20-00e5jupz.
BibTeX
@misc{interestana_00e5jupz,
author = {{Interestana AI Editorial}},
title = {{Gradium Open-Sources Phonon TTS Model Under Apache 2.0}},
howpublished = {Interestana},
year = {2026},
month = oct,
url = {https://interestana.com/articles/gradium-open-sources-phonon-a-31m-parameter-on-device-tts-model-under-apache-20-00e5jupz},
note = {Summary of reporting by MarkTechPost, https://www.marktechpost.com/2026/10/22/gradium-phonon-31m-on-device-tts/}
}Credit the source
Original reporting: MarkTechPost, “Gradium Open-Sources Phonon, a 31M-Parameter On-Device TTS Model Under Apache 2.0”, https://www.marktechpost.com/2026/10/22/gradium-phonon-31m-on-device-tts/(opens in a new tab)
Follow this story
Get Computing & Chips in your inbox — new stories in 40 words or fewer, once a week.
Subscribe