hexgrad/Kokoro-82M

Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects.

🐈 GitHub: https://github.com/hexgrad/kokoro

🚀 Demo: https://hf.co/spaces/hexgrad/Kokoro-TTS

• As of April 2025, the market rate of Kokoro served over API is under $1 per million characters of text input, or under $0.06 per hour of audio output. (On average, 1000 characters of input is about 1 minute of output.) Sources: ArtificialAnalysis/Replicate at 65 cents per M chars and DeepInfra at 80 cents per M chars.
• This is an Apache-licensed model, and Kokoro has been deployed in numerous projects and commercial APIs. We welcome the deployment of the model in real use cases.

• Fake websites like kokorottsai_com (snapshot: https://archive.ph/nRRnk) and kokorotts_net (snapshot: https://archive.ph/60opa) are likely scams masquerading under the banner of a popular model.
• Any website containing "kokoro" in its root domain (e.g. kokorottsai_com, kokorotts_net) is NOT owned by and NOT affiliated with this model page or its author, and attempts to imply otherwise are red flags.

• Releases
• Usage
• EVAL.md ↗️
• SAMPLES.md ↗️
• VOICES.md ↗️
• Model Facts
• Training Details
• Creative Commons Attribution
• Acknowledgements

Releases

| Model | Published | Training Data | Langs & Voices | SHA256 | | ----- | --------- | ------------- | -------------- | ------ | | v1.0 | 2025 Jan 27 | Few hundred hrs | 8 & 54 | 496dba11 | | v0.19 | 2024 Dec 25 | =0.9.2 soundfile !apt-get -qq -y install espeak-ng > /dev/null 2>&1 from kokoro import KPipeline from IPython.display import display, Audio import soundfile as sf import torch pipeline = KPipeline(lang_code='a') text = ''' Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects. ''' generator = pipeline(text, voice='af_heart') for i, (gs, ps, audio) in enumerate(generator): print(i, gs, ps) display(Audio(data=audio, rate=24000, autoplay=i==0)) sf.write(f'{i}.wav', audio, 24000) `` Under the hood, kokoro uses misaki`, a G2P library at https://github.com/hexgrad/misaki

Model Facts

Architecture: - StyleTTS 2: https://arxiv.org/abs/2306.07691 - ISTFTNet: https://arxiv.org/abs/2203.02395 - Decoder only: no diffusion, no encoder release

Architected by: Li et al @ https://github.com/yl4579/StyleTTS2

Trained by: @rzvzn on Discord

Languages: Multiple

Model SHA256 Hash: 496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4

Training Details

Data: Kokoro was trained exclusively on permissive/non-copyrighted audio data and IPA phoneme labels. Examples of permissive/non-copyrighted audio include: - Public domain audio - Audio licensed under Apache, MIT, etc - Synthetic audio[1] generated by closed[2] TTS models from large providers

[1] https://copyright.gov/ai/ai_policy_guidance.pdf

[2] No synthetic audio from open TTS models or "custom voice clones"

Total Dataset Size: A few hundred hours of audio

Total Training Cost: About $1000 for 1000 hours of A100 80GB vRAM

Creative Commons Attribution

The following CC BY audio was part of the dataset used to train Kokoro v1.0.

| Audio Data | Duration Used | License | Added to Training Set After | | ---------- | ------------- | ------- | --------------------------- | | Koniwa tnc |