rapidspeech-metal 1.2.0

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RapidSpeech.cpp 🎙️

Python-friendly local ASR and TTS, powered by a native C++ GGUF runtime.

RapidSpeech.cpp gives Python developers a simple API for local speech recognition, text-to-speech, VAD, speaker embedding, and voice cloning. Under the hood it uses a pure C/C++ engine, ggml backends, and a unified GGUF model format, so you get native performance without running a speech server.

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Python In 60 Seconds

Install

pip install rapidspeech

GPU wheels:

pip install rapidspeech-metal   # macOS / Apple Silicon
pip install rapidspeech-cuda    # Linux / NVIDIA

Text to speech

python python-api-examples/tts/tts-offline.py \
  --model /path/to/omnivoice-f16.gguf \
  --text "Hello, welcome to RapidSpeech." \
  --output output.wav

Speech to text

python python-api-examples/asr/asr-offline.py \
  --model /path/to/funasr-nano-fp16.gguf \
  --audio /path/to/audio.wav

Python API

import rapidspeech

tts = rapidspeech.tts_synthesizer("/path/to/omnivoice-f16.gguf")
tts.set_params(instruct="male, young adult", language="English", seed=42)
pcm = tts.synthesize("Hello from a native speech engine.")
sample_rate = tts.get_sample_rate()

import rapidspeech

asr = rapidspeech.asr_offline("/path/to/funasr-nano-fp16.gguf")
sample_rate = asr.get_model_meta()["audio_sample_rate"]
pcm = ...  # 1-D float32 mono PCM at sample_rate
asr.push_audio(pcm)
asr.process()
print(asr.get_text())

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Why RapidSpeech.cpp

• Python API, native core: write Python, run a C++/ggml engine underneath.
• One model format: ASR, TTS, VAD, and speaker models use GGUF.
• NumPy in, NumPy out: ASR takes float32 PCM; TTS returns float32 PCM.
• Local by default: no cloud API, no speech server, no Python model stack.
• Edge-first backends: CPU, Metal, CUDA, Vulkan, CANN, OpenCL, and WebGPU.

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Performance Snapshot

Test environment: Apple M1 Pro, funasr-nano-fp16.gguf, 15s audio.

Configuration	RTF	Wall Time	Notes
CPU -t 4	0.465	12.4s	CPU-only inference
GPU -t 4	0.170	5.2s	Metal acceleration
GPU -t 4 Q4_K	0.756	-	Quantized model: GPU dequant overhead
CPU -t 4 Q4_K	0.530	-	Quantized model CPU inference, 596 MB (3.3x compression)

RTF is processing time divided by audio duration. Lower is faster; RTF < 1 is faster than real time.

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Supported Today

Task	Models	Status
ASR	SenseVoice-small, FunASR-nano	Stable
VAD	Silero VAD, FireRedVAD	Stable
TTS	OmniVoice, OpenVoice2, Kokoro	Active
Speaker	CAMPPlus	Stable

In Progress

CosyVoice3, Qwen3-ASR, Qwen3-TTS.

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Documentation

• Python examples
• Technical Notes: architecture, design tradeoffs, backends, model conversion, and binding surfaces.
• Browser / WASM examples
• Node.js example

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Native C++ CLI

Download Models

Models are available on:

• 🤗 Hugging Face: https://huggingface.co/RapidAI/RapidSpeech
• ModelScope: https://www.modelscope.cn/models/RapidAI/RapidSpeech

Build from Source

git clone https://github.com/RapidAI/RapidSpeech.cpp
cd RapidSpeech.cpp
git submodule sync && git submodule update --init --recursive
cmake -B build
cmake --build build --config Release

Build artifacts are located in the build/ directory:

• rs-asr-offline — Offline ASR command-line tool
• rs-asr-vad-online — VAD-segmented quasi-streaming ASR command-line tool
• rs-tts-offline — Offline TTS command-line tool
• rs-quantize — Model quantization tool

Core Commands

Offline ASR

./build/rs-asr-offline \
  -m /path/to/funasr-nano-fp16.gguf \
  -w /path/to/audio.wav \
  -t 4 \
  --gpu true

VAD-segmented ASR

./build/rs-asr-offline \
  -m /path/to/funasr-nano-fp16.gguf \
  -v /path/to/silero_vad_v6.gguf \
  -w /path/to/audio.wav \
  -t 4 \
  --vad-threshold 0.5 \
  --silence-ms 600

Text to speech

./build/rs-tts-offline \
  -m /path/to/omnivoice-f16.gguf \
  -t "Hello, welcome to RapidSpeech!" \
  --instruct "male, young adult, moderate pitch" \
  --lang English \
  --n-steps 32 \
  -o output.wav

Quantization

./build/rs-quantize /path/to/input-f16.gguf /path/to/output-q4_k.gguf q4_k

Python

See Python examples for offline ASR, streaming ASR, offline TTS, streaming TTS, VAD, and voice cloning.

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🤝 Contributing

If you are interested in the following areas, we welcome your PRs or participation in discussions:

• Adapting more models to the framework.
• Refining and optimizing the project architecture.
• Improving inference performance.

Acknowledgements

1. Fun-ASR
2. llama.cpp
3. ggml
4. cppjieba — Chinese word segmentation
5. WeText — text normalization (ITN/TN)
6. miniaudio — single-file audio I/O