Instructions to use Luigi/PrimeTTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Luigi/PrimeTTS with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Luigi/PrimeTTS:F32 # Run inference directly in the terminal: llama cli -hf Luigi/PrimeTTS:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Luigi/PrimeTTS:F32 # Run inference directly in the terminal: llama cli -hf Luigi/PrimeTTS:F32
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Luigi/PrimeTTS:F32 # Run inference directly in the terminal: ./llama-cli -hf Luigi/PrimeTTS:F32
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Luigi/PrimeTTS:F32 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Luigi/PrimeTTS:F32
Use Docker
docker model run hf.co/Luigi/PrimeTTS:F32
- LM Studio
- Jan
- Ollama
How to use Luigi/PrimeTTS with Ollama:
ollama run hf.co/Luigi/PrimeTTS:F32
- Unsloth Studio
How to use Luigi/PrimeTTS with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Luigi/PrimeTTS to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Luigi/PrimeTTS to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Luigi/PrimeTTS to start chatting
- Docker Model Runner
How to use Luigi/PrimeTTS with Docker Model Runner:
docker model run hf.co/Luigi/PrimeTTS:F32
- Lemonade
How to use Luigi/PrimeTTS with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Luigi/PrimeTTS:F32
Run and chat with the model
lemonade run user.PrimeTTS-F32
List all available models
lemonade list
- Atomic Chat
PrimeTTS v2-Stream — token-level streaming zh-TW/English TTS
A streaming MB-iSTFT-VITS (Xinran voice, 16 kHz): band-attention encoder + causal FFN + causal MB-iSTFT vocoder, so audio can be emitted incrementally in fixed chunks. Bit-exact between whole-utterance and chunked inference.
Streaming params: chunk = 24 frames (~384 ms), left cache = 64, right lookahead = 4 (1 frame = 256 samples @ 16 kHz). First-audio ≈ enc(once) + one chunk.
This is a streaming-capable variant of PrimeTTS v2 (a distinct checkpoint, not v2 run in a streaming loop). The causal + limited-lookahead architecture trades some quality for streamability vs the non-streaming v2.
Files
| file | purpose |
|---|---|
v2stream_enc.onnx |
text (x,tone,lang,x_lengths,noise_scale,length_scale) → z[1,192,T]. Run once. |
v2stream_dec.onnx |
z[1,192,Tc] → wav[1,1,Tc·256]. Run per chunk (overlap-save). |
primetts_v2stream_xinran_f32.gguf |
single-graph gguf for RapidSpeech.cpp ggml (MBISTFT_STREAM=1). |
onnx_stream.py |
reference streaming runner (ORT). |
meta.json |
params + frontend convention. |
Run A — ONNX Runtime (Python, torch-free)
from onnx_stream import StreamingTTS # in this repo
tts = StreamingTTS("v2stream_enc.onnx", "v2stream_dec.onnx")
z = tts.encode(phone_ids, tone_ids, lang_ids) # once
for pcm_chunk in tts.stream(z): # per 24-frame chunk
play(pcm_chunk) # 16 kHz float32
phone_ids/tone_ids/lang_ids come from the frontend (text → ids): g2pw
(bopomofo, Taiwan readings) + g2p_en (arpabet), 88 syms / 6 tones / 2 langs,
with add_blank (0 interleaved between tokens). See frontend_bopomofo in the
demo Space.
Run B — sherpa-onnx fork (C++/ORT)
OfflineTtsMbistftStreamModel(enc=..., dec=..., num_threads=2).generate(x,tone,lang, callback=...) emits audio per chunk via the callback. Branch:
vieenrose/sherpa-onnx @ mbistft-streaming-tts.
Run C — RapidSpeech.cpp (ggml, GPU/Jetson)
MBISTFT_STREAM=1 MBISTFT_STREAM_LOOKAHEAD=5 mbistft-parity primetts_v2stream_xinran_f32.gguf inputs.json out --stream-chunks. Branch:
vieenrose/RapidSpeech.cpp @ jetson-nano-gen1.
Validated on Jetson Nano (Tegra X1): parity cosine > 0.99, first-audio ~285 ms.
License: Apache-2.0. Part of the Luigi/PrimeTTS family.