Kitten TTS is an open-source text-to-speech library. Its flagship model, KittenTTS 2, is a
1.7B-parameter 1-bit speech language model with in-context voice cloning and expression control: give it
five seconds of anyone's voice and it speaks your text in that voice. It runs realtime on a CPU!
The library also ships the original lightweight legacy models,
15M-80M parameters, which run on CPU without a GPU. Both families load through the same
KittenTTS(...) constructor.
Commercial support is available. For integration assistance, custom voices, or enterprise licensing, contact us.
A CUDA GPU with roughly 8 GB free, or a CPU with about 6 GB of RAM
About 1 GB of disk space for the model, or 506 MiB with the smaller weights
Installation
pip install kittenml
That is the whole install. KittenTTS 2 is the default model, voice cloning is included, and no
Hugging Face login is needed -- every weight the model uses ships in its own repository. It also
pulls the small ONNX runtime, so the lightweight models work from the
same install.
Basic usage
from kittenml import KittenTTS
import soundfile as sf
m = KittenTTS("KittenML/kitten-tts-2")
audio = m.generate("One day, a little girl named Lily found a needle in her room.",
voice="Bruno")
sf.write("output.wav", audio, m.sample_rate)
To run Kitten-TTS 2 on a CPU, please refer to Running on CPU
m.available_voices lists all 47 built-in voices, described in
voices and expression. Bella, Jasper, Luna, Bruno, Rosie, Hugo,
Kiki and Leo are the same speakers as in KittenTTS 0.8, so code written against the ONNX models
keeps working.
The weights come in two sizes. The default is 947 MiB and lossless; weights="emb4" is 506 MiB
because it quantises the token embedding, which costs a little quality. Only the one you ask for
is downloaded.
m = KittenTTS("KittenML/kitten-tts-2", weights="emb4") # half the download
# Trade stability against expressiveness
audio = m.generate("Hello, world.", voice="Luna", preset="expressive")
# Save directly to a file
m.generate_to_file("Hello, world.", "output.wav", voice="Bruno")
Voice cloning
Pass reference= instead of voice= — same method, the recording just replaces the built-in
speaker. Give it 5-30 seconds of a single speaker. The transcript is part of the prompt, but you
do not have to type it; Whisper fills it in when omitted.
audio = m.generate("This is my own voice, cloned.", reference="my_voice.wav")
# Supplying the transcript skips the Whisper pass
audio = m.generate("This is my own voice.", reference="my_voice.wav",
reference_text="what is actually said in the clip")
The reference feeds the model by two independent routes -- a speaker embedding through the
model's projection head, and the clip itself as codec tokens in the prompt -- so identity
survives even when one route is weak.
Measured on the built-in voices: a generated clip scores 0.49-0.72 speaker similarity against its
own reference and 0.01-0.18 against the other 37, and cloning an unseen recording scores 0.81
against that recording.
Expression controls
Beta. Emotion control steers delivery rather than guaranteeing it, and the effect varies
by voice and by sentence.
audio = m.generate(
"[joyful] We actually won the grant <laugh> I can (((hardly))) believe it!",
voice="Kiki",
preset="expressive",
)
A leading [emotion] tag, inline <event> tags and (((emphasis))) spans reach the model as
markup rather than being spoken, and automatically enable its expression conditioning. Ten
emotions and ten vocal events are recognised -- see
voices and expression for the full lists and what is not
covered.
Running on CPU
Kitten-TTS 2 runs on CPU out of the box — device is auto-detected — but the fastest way is
kitten-tts-2-cpp, our llama.cpp fork. It reads
the GGUF weights in the model repository's cpp/ directory. You can use the Python bindings too:
pip install kitten_tts_cpp
from kitten_tts_cpp import KittenTTS
model = KittenTTS("KittenML/kitten-tts-2", decoder="student_w4", threads=8)
model.generate_to_file("One day, a little girl named Lily found a needle in her room.", "hello.wav", voice="Bruno")
print(model.last_report) # Generation timings and token IDs.
Kitten-TTS 2 C++ is in beta. Please report any issues you encounter.
Running with vLLM
For GPU inference with vLLM on Linux and an NVIDIA CUDA GPU, install the optional backend:
pip install "kittenml[vllm]"
Select backend="vllm":
from kittenml import KittenTTS
def main():
m = KittenTTS("KittenML/kitten-tts-2", backend="vllm")
m.generate_to_file("Hello there.", "output.wav", voice="Bruno")
print("Saved output.wav")
if __name__ == "__main__":
main()
Keep the main guard when running a script: vLLM starts a worker process. The same
voices, decoders, weight choices and generation arguments apply. See the
vLLM guide for memory settings and benchmarks.
Long text and streaming
Long input is split on sentence boundaries and synthesized chunk by chunk, then joined with
silence trimming and short edge fades so the seams are inaudible. This is automatic: the model is
reliable on short inputs but truncates or drifts into repetition when asked for a whole script in
one pass.
To start playing before the whole thing is ready, stream it:
for chunk in m.generate_stream(long_text, voice="Luna"):
play(chunk) # each chunk is a numpy array at m.sample_rate
It takes the same arguments as generate, so reference= streams a cloned voice too.
Streaming is chunk-level, not token-level: a chunk is generated and vocoded in full before it
is yielded, so the first chunk still costs its own generation time. On an A100, a 936-character
passage yielded its first 21 s of audio after 17 s and finished 54 s of audio in 42 s of wall
clock -- so playback keeps ahead of generation, but there is a real initial delay.
Two consequences worth knowing:
Short text does not stream. Input that fits in one chunk (under roughly 380 characters,
and short trailing pieces get merged into their neighbour) yields exactly one chunk, so
generate_stream behaves like generate.
Chunks are yielded raw.generate post-processes the seams -- trimming each segment's edge
silence, adding short fades and one consistent pause -- which a streaming caller cannot do
without waiting for the next chunk. Concatenating streamed chunks directly gives slightly
rougher joins than generate on the same text.
Documentation
| | |
|---|---|
| API reference | Every argument to generate, streaming, and the advanced knobs |
| Voices and expression | The 47 voices, emotion and vocal-event tags, the ten languages |
| Decoders | How audio is decoded, and the smaller quantised decoders |
| Text normalization | How written text becomes spoken text |
| Architecture | What the model is, package layout, vendored components |
| Lightweight ONNX models | The CPU models, 15M-80M parameters, and their API |
System Requirements
KittenTTS 2
Operating system: Linux, Windows or Mac
Python: 3.9 or later
A virtual environment (conda, venv, or similar) is recommended to avoid dependency conflicts.
Commercial Support
We offer commercial support for teams integrating Kitten TTS into their products. This includes integration assistance, custom voice development, and enterprise licensing.
Contact us or email info@stellonlabs.com to discuss your requirements.
This project is licensed under the Apache License 2.0. That covers the code in this
repository.
The models are licensed separately and their terms may differ. Each model repository carries
its own licensing, so check the one you intend to use before relying on it — do not assume the
code's license extends to the weights.