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Kokoro 82M Kannada

Fast, small, open Kannada TTS: Kokoro-82M (distilled StyleTTS2) fine-tuned for Kannada with a trained diffusion prosody sampler — the component stock Kokoro drops, and the difference between robotic and natural prosody.

Sourcehuggingface.co/Sammy-Mohan/Kokoro-82M-Kannada/tree/a473ec5a48e61c0dc375d0463a3bf7a0e15e5467

TypeOtherVersionRevision a473ec5CClasse

Source du modèle

Type: Other
Taille du fichier: 714 MB
Version: vRevision a473ec5
Mis à jour: 19 oct. 2026
Téléchargements: 0
Source: Hugging Face

Description de la source

Kokoro-82M · Kannada (ಕನ್ನಡ)

Fast, small, open Kannada TTS: Kokoro-82M (distilled StyleTTS2) fine-tuned for Kannada with a trained diffusion prosody sampler — the component stock Kokoro drops, and the difference between robotic and natural prosody.

🔊 Samples: https://huggingface.co/spaces/Sammy-Mohan/kannada-tts
📖 Code, recipe & the full journey (all findings): https://github.com/sammy4321/Kokoro-Indic-Fine-Tuning

Files

file	purpose
`kokoro_kannada_00005_inference.pth`	model weights (optimizer stripped, inference-ready)
`config_kn.yml`	StyleTTS2 architecture/config
`ref_spk9.wav`	the style-reference clip = the shipped voice (full-bandwidth speaker — important, see repo)

Usage

Use the inference script (handles the fork's architecture code + diffusion sampling + best-of-N):

git clone https://github.com/sammy4321/Kokoro-Indic-Fine-Tuning
cd Kokoro-Indic-Fine-Tuning && pip install -r demo/requirements.txt huggingface_hub
python inference/synthesize.py --text "ನಮಸ್ಕಾರ!" --out out.wav --best-of 3

Details

82M params · 24 kHz · trained on ~11 h public Kannada speech (SPRINGLab IndicTTS + AI4Bharat Rasa, 17 speakers) for <$100 of compute. ASR round-trip CER 0.00 on clean text. Known gaps: numerals-as-digits and heavy English code-switching need a text-normalization frontend (roadmap).

License: code Apache-2.0; weights are derived from the datasets above — check their terms before commercial use; attribution required. Built on StyleTTS2 (MIT), Kokoro-82M (Apache-2.0), and the kikiri-tts recipe.

Sources

1 source

Hugging FacePrincipalVersion a473ec5a48e61c0dc375d0463a3bf7a0e15e5467

huggingface.co/Sammy-Mohan/Kokoro-82M-Kannada/tree/a473ec5a48e61c0dc375d0463a3bf7a0e15e5467

Vérifié 19 oct.

Artefacts du modèle

1 artefact

kokoro_kannada_00005_inference.pth

pth · 714 MB · SHA-256 542798b1805a…c8ca · Hugging Face

Download file

Extraits de sources

3 extraits

Readme3 aoûthuggingface.co/Sammy-Mohan/Kokoro-82M-Kannada

--- language: kn license: apache-2.0 tags: [text-to-speech, tts, kannada, kokoro, styletts2] pipeline_tag: text-to-speech --- # Kokoro-82M · Kannada (ಕನ್ನಡ) Fast, small, open Kannada TTS: [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) (distilled StyleTTS2) fine-tuned for Kannada **with a trained diffusion prosody sampler** — the component stock Kokoro drops, and the difference between robotic and natural prosody. - 🔊 **Samples:** https://huggingface.co/spaces/Sammy-Mohan/kannada-tts - 📖 **Code, recipe & the full journey (all findings):** https://github.com/sammy4321/Kokoro-Indic-Fine-Tuning ## Files | file | purpose | |---|---| | `kokoro_kannada_00005_inference.pth` | model weights (optimizer stripped, inference-ready) | | `config_kn.yml` | StyleTTS2 architecture/config | | `ref_spk9.wav` | the style-reference clip = the shipped voice (full-bandwidth speaker — important, see repo) | ## Usage Use the inference script (handles the fork's architecture code + diffusion sampling + best-of-N): ```bash git clone https://github.com/sammy4321/Kokoro-Indic-Fine-Tuning cd Kokoro-Indic-Fine-Tuning && pip install -r demo/requirements.txt huggingface_hub python inference/synthesize.py --text "ನಮಸ್ಕಾರ!" --out out.wav --best-of 3 ``` ## Details 82M params · 24 kHz · trained on ~11 h public Kannada speech ([SPRINGLab IndicTTS](https://huggingface.co/datasets/SPRINGLab/IndicTTS_Kannada) + [AI4Bharat Rasa](https://huggingface.co/datasets/ai4bharat/Rasa), 17 speakers) for <$100 of compute. ASR round-trip CER 0.00 on clean text. Known gaps: numerals-as-digits and heavy English code-switching need a text-normalization frontend (roadmap). **License:** code Apache-2.0; weights are derived from the datasets above — check their terms before commercial use; attribution required. Built on StyleTTS2 (MIT), Kokoro-82M (Apache-2.0), and the [kikiri-tts](https://github.com/semidark/kikiri-tts) recipe.

Note19 oct.huggingface.co/Sammy-Mohan/Kokoro-82M-Kannada

Source context: 0 downloads · 2 likes · Pipeline text-to-speech · Repo Sammy-Mohan/Kokoro-82M-Kannada

Note3 aoûthuggingface.co/Sammy-Mohan/Kokoro-82M-Kannada

Source context: 0 downloads · 1 likes · Pipeline text-to-speech · Repo Sammy-Mohan/Kokoro-82M-Kannada