Kokoro
Kokoro-82M
Kokoro is an open-weight text-to-speech model published by hexgrad on Hugging Face. The upstream card presents it as an 82M-parameter model focused on lightweight, fast, cost-efficient speech generation while still being practical for local and production use.
LA Studio installs Kokoro as a virtual model under hexgrad/Kokoro-82M. The downloadable GGUF artifacts are hosted in conversion repositories:
cstr/kokoro-82m-GGUFfor the Kokoro backbone GGUF.cstr/kokoro-voices-GGUFfor preset speaker voice packs.
Links
- Upstream model card: https://huggingface.co/hexgrad/Kokoro-82M
- Upstream GitHub: https://github.com/hexgrad/kokoro
- Upstream demo: https://hf.co/spaces/hexgrad/Kokoro-TTS
- Samples: https://huggingface.co/hexgrad/Kokoro-82M/blob/main/SAMPLES.md
- Voices: https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md
Model Facts
- Task: Text-to-Speech
- Parameters: 82M
- License: Apache-2.0
- Current upstream release noted by the model card: v1.0, published 2025-01-27
- Languages and voices noted for v1.0: 8 languages and 54 voices
- Architecture: StyleTTS 2 with an iSTFTNet-derived, decoder-only TTS stack
- Upstream SHA256 for v1.0:
496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4
Training Notes
The upstream card states that Kokoro was trained on permissive or non-copyrighted audio data with IPA phoneme labels. It lists public-domain audio, permissively licensed audio, and synthetic audio generated by closed-source TTS models as examples of training data sources. The card estimates the total training set at a few hundred hours and reports roughly 1000 A100 80GB GPU-hours across v0.19 and v1.0.
LA Studio Notes
Kokoro in LA Studio is a preset-voice TTS workflow. It needs one backbone model file and one installed voice pack:
- Default backbone:
kokoro-82m-f16.gguf - Default voice pack:
kokoro-voice-af_heart.gguf
The CrispASR Kokoro runtime depends on espeak-ng for phonemizer support. Kokoro voice cloning from reference audio is not enabled in LA Studio; use the packaged voice GGUF files instead.
Cấu hình các tham số tùy chỉnh này để tinh chỉnh quy trình tiếng nói và giọng nói.
Controls randomness of speech generation. Higher values increase variety.
Random seed for reproducibility. Set to -1 for random.
Maximum decoding steps for the neural voice audio synthesizer.