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KittenTTS
KittenTTS is an ONNX-based text-to-speech library for generating speech on a CPU without requiring a GPU.
KittenML documents several small model variants, built-in voices and WAV output. It remains a developer preview whose APIs may change. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
Text-to-speech library
A Python application supplies text and a voice, then receives audio samples or writes an audio file.
Why it stands out
Several small model packages
The repository lists mini, micro and nano variants, including a quantized nano package. Model size and numeric format are separate choices.
Availability
Local library and online demo
GitHub provides installation and usage examples; the project's Hugging Face demo offers a browser route to hear sample output.
Why it matters
What makes it useful
A developer can turn a short script into a WAV file using a named voice and adjustable speech speed. The examples show both returning audio samples to an application and saving speech directly to a file.
What to know
Where it fits
KittenTTS is the speech-generation component inside a Python workflow. The application still handles text selection, playback and any larger narration or assistant interface.
Notable points
What stands out
The nano variants have the same 15 million parameter count but different listed disk sizes: 56 MB for fp32 and 25 MB for int8. A smaller download can come from quantization rather than fewer parameters; those sizes are not complete runtime memory estimates.
Before using
What to review
The documented local path needs Python and its dependencies; a GPU is optional.
KittenML notes reports of problems with the nano int8 variant and asks affected users to open an issue. Its APIs remain a developer preview and may change.
Multilingual TTS and a mobile SDK are listed as roadmap items, not delivered features in the documented library.
Reader fit
Who may find it relevant
Developers adding speech output to a local Python application or testing small voice models on CPU.
Someone who wants to hear it first can use the demo; integrating it into an application still requires programming and listening checks.
Editorial note
Why LifeHubber lists it
Text preprocessing has different defaults in the documented APIs: generate leaves it off, while generate_to_file enables it. That can change how a price, date or abbreviation is spoken. When comparing sample output with saved narration, match the preprocessing setting as well as the voice.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Choose the speech component for your audio task.
KittenTTS supplies speech from text. If your project also needs transcription, voice direction or live conversation, continue with the different audio jobs before choosing the rest of the stack.
More in Speech Models
Keep browsing this category
Explore more speech model resources.
Fish Audio S2 Pro
fishaudio/s2-pro
A text-to-speech model with detailed control over prosody and emotional delivery.
AuK
Tencent-Hunyuan/AuK
A 1.5B speech model for instruction-guided text-to-speech, content and acoustic editing, paralinguistic changes, speech enhancement, and source separation, with public code, weights, demos, ComfyUI nodes, and fine-tuning materials.
TADA
HumeAI/tada
A speech-language model that aligns speech and text into a single synchronized stream.
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