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LatestAratakov2
AratakoText → Speech

Irodori-TTS v2 Offers Open Japanese Speech Synthesis

The 500-million-parameter model from researcher Aratako provides a high-quality, single-speaker voice under a permissive MIT license.

Mar 23, 2026
UpdateMIT
Irodori-TTS-500M v2

Independent AI researcher Aratako has released the second version of Irodori-TTS, a 500-million-parameter text-to-speech model designed for the Japanese language. The project provides a capable and efficient tool for generating a female voice, filling a need for high-quality, open-source models in languages other than English.

The model is built on the VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) architecture, a popular framework known for producing natural-sounding speech. It was trained on the JSUT dataset, a public Japanese speech corpus from a single female speaker. The full model is now available on the Hugging Face Hub for community use.

By releasing Irodori-TTS under the permissive MIT license, Aratako enables developers and researchers to freely integrate Japanese speech synthesis into their applications or use it as a foundation for further research. While many advanced text-to-speech systems remain proprietary, this release underscores the vital role of independent contributors in building a more open and multilingual AI ecosystem.

Sources

  • Aratako/Irodori-TTS-500M-v2

    Hugging Face

    Visit
OlderGAIR Releases daVinci-MagiHuman for Video GenerationGAIR · Image → Video · 7 months agoNewerCohere Releases Top-Ranked Multilingual Transcription ModelCohere · Speech → Text · 7 months ago

Get the model

Hugging Face

Specs

Parameters500M
Sample rate48 kHz
LanguagesJapanese
Size2 GB
PrecisionFP32
LicenseMIT
Likes142

Can you run it?

Runs on just about anything.

  • FP32 (as published)

    Any modern laptop · 8 GB RAM

    3 GB
  • 16-bit

    Any modern laptop · 8 GB RAM

    2 GB
  • FP8

    Any modern laptop · 8 GB RAM

    1.5 GB
Your machine

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text → Speech

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