Kitten-TTS 2 C++

CPU-focused C++ inference for KittenTTS2, with built-in voices, expression controls, and prepared reference voices. The runtime downloads prepared model assets and runs synthesis natively in C++. Users do not need to run the Python exporter.

The speech language model runs through llama.cpp/GGML, text normalization uses the kitten-text-processing submodule, and the S3 waveform decoder uses CPU LibTorch. Only KittenTTS2 checkpoints are supported.

Features

• Built-in voices, including Bruno, Bella, Jasper, Luna, Rosie, Hugo, Kiki, and Leo.
• Emotion tags, vocal events, and emphasis: [joyful], <laugh>, and (((really))).
• Stable and expressive sampling presets, sentence-aware chunking, and audio joins.
• A 1.03 GB GGUF with lossless packing of the checkpoint's ternary weights, plus an FP16 reference export.
• Default S3 and optional student_w4 / student_w8 decoder exports.
• Mono float32 WAV output at 24 kHz.

Build

Requires a C++20 compiler, CMake, and CPU LibTorch. A CPU PyTorch installation can provide LibTorch's headers and libraries. Use the same LibTorch version for export and inference, with compatible torch/torchaudio versions for asset preparation.

git submodule update --init vendor/kitten-text-processing

cmake -S . -B build \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_CXX_STANDARD=20 \
  -DCMAKE_CXX_STANDARD_REQUIRED=ON \
  -DGGML_CUDA=OFF -DGGML_METAL=OFF \
  -DLLAMA_BUILD_KITTEN_TTS=ON \
  -DLLAMA_BUILD_SERVER=OFF \
  -DLLAMA_BUILD_TESTS=OFF \
  -DLLAMA_BUILD_EXAMPLES=OFF \
  -DCMAKE_PREFIX_PATH="$(python -c 'import torch; print(torch.utils.cmake_prefix_path)')"


cmake --build build --target kitten-tts -j 8

The normalizer prepares its grammar data using Python at build time. To use existing grammar data, configure with -DKITTEN_DATA_DIR=/path/to/data.

Download and run

The runtime fetches config.json from KittenML/kitten-tts-2 first, then downloads its GGUF, selected TorchScript decoder, and matching voice conditioning. Assets are cached under $XDG_CACHE_HOME/kitten-tts (or ~/.cache/kitten-tts).

build/bin/kitten-tts --text 'One day, a little girl named Lily found a needle in her room.' --output hello.wav
build/bin/kitten-tts --decoder student_w4 --text 'One day, a little girl named Lily found a needle in her room.' --output w4.wav

Use --repo OWNER/REPO to select another KittenTTS2 repository, --revision COMMIT to pin a model version, --cache-dir DIR to change the cache location, and --offline to use cached files without network access. HF_TOKEN supports private repositories. --download-only --decoder student_w4 prepares the cache without synthesis. Only assets needed for the selected mode are downloaded.

The model repository must publish a cpp manifest and prepared files as described in the publisher guide. Local exports and model repository directories remain supported through --assets DIR; --model FILE overrides the GGUF.

The default GGUF uses this fork's TQ2_1 format: 256 ternary weights packed into 64 bytes, plus two FP16 scales for the checkpoint's 128-weight groups. The exporter requires exact reconstruction. This reduces the model from 1.45 GB with Q4_0 to 1.03 GB. Embeddings remain FP16 and one-dimensional tensors remain FP32. GGML's quantized activation arithmetic can still introduce small logit differences.

TQ2_1 requires this fork's CPU runtime; it is not standard TQ2_0 and stock llama.cpp cannot load it. Use --outtype ternary-q4_0 for the previous standard Q4_0 storage format, or --outtype f16 for the reference export.

On supported Intel CPUs, the runtime losslessly repacks TQ2_1 weights into the existing AMX Q4_0 layout at load time. The GGUF stays 1.03 GB, but runtime transformer-weight storage grows from about 357 MiB to 756 MiB. Use --no-repack to keep the compact CPU representation. CPUs without AMX use the existing compact kernels.

See the detailed guide for FP16 exports, student decoders, reference voice preparation, and the normalizer submodule setup.

Generate speech

build/bin/kitten-tts \
  --text 'Hello there. This is Kitten T T S running on the CPU.' \
  --voice Bruno --threads 8 --seed 1234 \
  --output hello.wav --report hello.json

For expressive speech:

build/bin/kitten-tts \
  --text '[joyful] We won the grant <laugh> I can (((hardly))) believe it!' \
  --voice Kiki --preset expressive \
  --output expressive.wav

Use --help for sampling, repetition penalties, chunking, and decoder thread settings. --tokens-only skips waveform decoding; --repeat 3 benchmarks repeated synthesis with the model and decoder kept loaded.

Current scope

Sampled output is not bit-identical to Python: BF16 arithmetic, GGML activation quantization, and sampling RNGs differ. Matching seeds do not guarantee matching generated tokens across runtimes.

Voice cloning requires an offline Python preparation step and a reference transcript. The runtime is a batch CLI; Python's streaming API is not implemented. Waveform decoding currently depends on CPU LibTorch.

Credits

KittenTTS2 and kitten-text-processing are from KittenML. See the implementation guide for decoder attribution and license details.

Built on DeepGrove's llama.cpp fork.