Hugging Face releases Phonon TTS with on-device speech and open weights
Hugging Face launched Phonon, a text-to-speech model designed to run entirely on a user’s device without cloud dependency. The release includes two models: Phonon 31m (Apache 2.0 license, supports English) and Phonon 40m (CC BY-NC, supports five languages: English, French, German, Spanish, and Portuguese). Both are under 75 MB and feature 100+ built-in voices, with custom voice cloning via…
Key points
- Phonon runs offline on devices like phones, laptops, and browsers with no cloud dependency
- Two models released: **Phonon 31m** (English, Apache 2.0) and **Phonon 40m** (5 languages, CC BY-NC)
- Tops benchmarks for intelligibility and speed, supports 100+ voices and custom cloning
The model tops benchmarks on intelligibility (first in German/Spanish, top 3 across languages) and speed (fastest CPU streaming). Hugging Face built a new inference framework (ptts) for cross-device compatibility, including support for CPU, GPU, and NPU backends. Phonon integrates with Hugging Face Transformers (v5.21+) and has been downloaded 200,000+ times in its first two weeks. The company also introduced the Open TTS Leaderboard, a new tool for comparing open TTS models across 23 languages.
Model page: Phonon 31m →
The story so far
2 episodes →- Hugging Face releases Phonon TTS with on-device speech and open weightsthis story
A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.
Hugging Face · 5 November 2026
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This text was published by Hugging Face and written by Olesia Krizhevich. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
Coverage and discussion
2sources- Gradium’s On-Device Phonon TTS Passes 200,000 Downloads in Two WeeksPress · Unite.AI ·
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
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