Gradium

Lysandre Verified account @LysandreJik · 3d

A small, on-device TTS models is competing with models up to 100× their size, and taking first place on specific benchmarks!

@GradiumAI's new Phonon model offers a concrete example.

On the Open TTS Leaderboard, Gradium reports:

- First place on intelligibility in German and Spanish.
- Top-three intelligibility in every language it evaluated.
- Leading CPU streaming results on time-to-first-audio.
These results also show why it’s useful to compare TTS models on more than a single overall ranking.

For the Open TTS Leaderboard, @EricBezzam separates several dimensions:

- Intelligibility, measured through word/character error rates.
- Generation throughput and time-to-first-audio.
- Predicted naturalness scores.
- Speaker similarity for voice cloning.

Leading on intelligibility in a particular language is a specific result, and so is leading on CPU response latency; but neither establishes that a model is best across every use case.

For on-device applications, Phonon’s combination of size, intelligibility, and CPU latency seems really relevant. For other (heavier?) workloads, language coverage, throughput, voice similarity, or delivery may be more important.

Leaderboard:
huggingface.co/spaces/hf-audi…

Gradium’s post: huggingface.co/blog/phonon-tts

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