audio.cpp is a high-performance C++ audio inference framework built on top of ggml, designed to make modern local audio models practical, portable, and fast.
Tired of juggling a dozen Conda environments, hundreds of Python packages, and dependency conflicts just to try a few audio models? audio.cpp gives those paths a shared native runtime instead.
Important
CUDA performance headline: multiple TTS paths already run 1.8x to up to 8x faster than their Python reference paths while cutting end-to-end latency by 45%-85%.
GGUF performance: all released model families support GGUF loading, and tested Q8 packages can run up to 1.53x faster while reducing peak VRAM by up to about 37% on routes such as Higgs Audio, Fish Audio, and Voxtral. See the GGUF guide for support status and the Q8 performance report for 16-bit vs Q8 measurements.
VibeVoice 1.5B: generates a 93.9-minute podcast in 18.2 minutes with 10 diffusion steps and without quantization, running about 5.15x faster than real time.
Supertonic 3: generates about 10 hours of audio in 3 minutes on RTX5090. Up to 200x+ real-time on CUDA, 6x+ real-time on CPU, and 47 ms TTFT in CUDA streaming mode. Demo: 10 hours of audio generated in 3 minutes.
Real-world ASR win: In TranscrIA benchmark on messy French meeting audio, audio.cpp’s Nemotron 3.5 ASR matched the same WER as other implementations while using about 1/4 of the wall time.
It is built for real end-to-end execution rather than one-off model demos: the same runtime powers TTS, voice cloning, voice conversion, ASR, diarization, VAD, source separation, alignment, codec-style models, and higher-level workflows through a common framework surface.
Highlights:
- Parity. Strong parity tooling against Python reference paths.
- Performance. Performance-focused execution, reusable sessions, and batch-style offline inference. Optimized for CUDA.
- Portability. A portable native stack centered on
ggml, with CLI and server entry points instead of Python-only deployment paths. - Pipelines. Experimental JSON pipeline support for higher-level multi-step workflows.
- Audio Utilities. Built-in denoise, enhancement, resampling, and STFT/ISTFT utilities for real production-style task paths.
The goal of the framework is to provide highly optimized, reusable building blocks for audio-related models, so new model integrations can be brought up faster, shared components can be improved once and benefit many families, and real end-to-end inference paths can stay efficient, maintainable, and portable.
audio.cpp would not be moving this quickly without generous contributors bringing in real fixes, new capabilities, and careful polish. See CONTRIBUTING.md for how to contribute and for a shout-out to the people already helping shape the project.
Tip
Contribution focus: the most helpful contributions right now are improvements to the UI, API server, and pipeline/workflow subsystems. These areas make the existing model surface easier to use, serve, compose, and validate. See CONTRIBUTING.md for more details.
New model PRs: before starting a new model port, please check the supported model table because several families are already implemented or under testing. New ports should start under the community models surface, where review is lighter than core models but still needs reproducible validation. Please follow the measurement style in PR #19 and PR #63: exact build/run commands, model paths or package ids, generated outputs, parity or path-test results, and relevant performance or memory notes.
Important
2026-07-23 - Release 0.4: This release brings audio.cpp to 35 model families listed across the supported and community tables and adds Higgs Audio v3 TTS 4B, Fish Audio S2 Pro, and Voxtral Realtime ASR, with GGUF-first CUDA validation for the new paths. On warmed TTS requests, Higgs Audio Q8_0 runs about 8.8x-10.1x faster than real time, while Fish Audio Q8_0 runs about 3.1x-3.4x faster than real time. Voxtral adds offline and streaming ASR, with Q8_0 GGUF around 15.7x faster than real time and about 171 ms streaming TTFT.
Also new: community models OuteTTS and VieNeu-TTS, the WebUI surface thanks to @kigner and @patrickjchen, broader standalone GGUF/package-spec support, and reusable framework work for vocoders, sampling, attention, Whisper frontend, and model metadata.
2026-07-14 - Release 0.3: This release added IndexTTS2, Irodori-TTS, MOSS-TTS-Nano, MOSS-TTS-Local, Supertonic 3, Chatterbox voice conversion, and the first broad GGUF loading/conversion wave. Thanks to @justinjohn0306 for MOSS-TTS-Local and @mirek190 for driving GGUF forward.
2026-06-25 to 2026-07-08: audio.cpp grew from the first released model wave into broad TTS, ASR, music generation, source separation, VAD, diarization, codec, and voice-conversion coverage, with VibeVoice 1.5B/7B, LoRA adapter loading, initial streaming support, and major CUDA Conv1DTransp speedups.
Task tags: TTS text to speech, Clone voice cloning, VC voice conversion, ASR speech recognition, Align forced alignment, VAD voice activity detection, Diar speaker diarization, Codec audio codec, Sep source separation, Music music/song generation, SFX sound effects, Edit audio/music editing, Design voice design, Dialogue multi-speaker dialogue TTS, Ctrl TTS/clone voice control such as emotion, style, instruction, caption, or non-verbal tag control.
Runtime tags: safetensors is the default model loading path. GGUF 16/Q8 means both 16-bit and q8_0 GGUF paths are tested; GGUF Q8 means only q8_0 is tested; GGUF F32 means the original-F32 GGUF path is tested. See docs/gguf.md for precision/status details. Bundled means the tiny runtime asset ships under assets/framework/models and needs no separate model download. Stream means the family exposes a streaming server/session path.
| Family | Task | Lang | Variants | Runtime |
|---|---|---|---|---|
| ace_step | Music, Edit | 50+ langs | ACE-Step 1.5 Turbo and Base with acestep-5Hz-lm-1.7B | GGUF 16/Q8 |
| chatterbox | TTS, Clone, VC | ar, da, de, el, en, es, fi, fr, hi, it, ko, ms, nl, no, pl, pt, sv, sw, tr | Chatterbox with 0.5B backbone | GGUF 16/Q8 |
| citrinet_asr | ASR | en | Citrinet-256 | GGUF Q8 |
| fish_audio | TTS, Clone, Ctrl | auto, en, zh | Fish Audio S2 Pro | GGUF 16/Q8 |
| heartmula | Music | zh, en, ja, ko, es | HeartMuLa-oss-3B with HeartCodec-oss | GGUF 16/Q8 |
| higgs_audio_stt | ASR | en | Higgs Audio v3 STT | GGUF 16/Q8, Stream |
| higgs_audio_tts | TTS, Clone, Ctrl | auto | Higgs Audio v3 TTS 4B | GGUF 16/Q8 |
| htdemucs | Sep | lang agnostic | HTDemucs, HTDemucs_ft | GGUF 16/Q8 |
| hviske_asr | ASR | da | Hviske v5.3 | GGUF Q8 |
| marblenet_vad | VAD | lang agnostic | MarbleNet VAD | Bundled |
| mel_band_roformer | Sep | lang agnostic | Mel-Band RoFormer MLX vocal separation variants | GGUF 16/Q8 |
| miocodec | Codec, VC | lang agnostic | MioCodec v2, 25 Hz, 44.1 kHz | GGUF 16/Q8 |
| miotts | TTS, Clone | en, ja | MioTTS-1.7B | GGUF 16/Q8 |
| omnivoice | TTS, Clone, Design, Ctrl | 646+ langs | OmniVoice, Qwen3-0.6B based | GGUF 16/Q8, Stream |
| pocket_tts | TTS, Clone | en, de, it, pt, es | PocketTTS-100M | GGUF 16/Q8 |
| nemotron_asr | ASR | 100+ ASR prompt codes incl. auto | Nemotron 3.5 ASR Streaming 0.6B | GGUF 16/Q8, Stream |
| qwen3_asr | ASR | zh, en, yue, ar, de, fr, es, pt, id, it, ko, ru, th, vi, ja, tr, hi, ms, nl, sv, da, fi, pl, cs, fil, fa, el, ro, hu, mk | Qwen3-ASR-0.6B, Qwen3-ASR-1.7B-hf | GGUF 16/Q8 |
| qwen3_forced_aligner | Align | zh, yue, en, de, es, fr, it, pt, ru, ko, ja | Qwen3-ForcedAligner-0.6B | GGUF 16/Q8 |
| qwen3_tts | TTS, Clone, Design, Ctrl | zh, en, fr, de, it, ja, ko, pt, ru, es | Qwen3-TTS-12Hz-0.6B-Base, Qwen3-TTS-12Hz-1.7B-Base, Qwen3-TTS-12Hz-1.7B-CustomVoice, Qwen3-TTS-12Hz-1.7B-VoiceDesign | GGUF 16/Q8 |
| seed_vc | VC | lang agnostic | SeedVC XLS-R + HiFT, SeedVC Whisper-small + BigVGAN | GGUF 16/Q8 |
| silero_vad | VAD | lang agnostic | Silero VAD | Bundled, Stream |
| sortformer_diar | Diar | en | Sortformer-4spk-v1 | - |
| stable_audio | Music, SFX, Edit | en | Stable Audio 3 Small Music, Stable Audio 3 Small SFX, Stable Audio 3 Medium | GGUF 16/Q8 |
| vevo2 | TTS, Music, VC, Edit | en, zh | Vevo2 with Qwen2.5-0.5B AR model | GGUF 16 |
| vibevoice | TTS, Dialogue | en, zh | VibeVoice-1.5B, VibeVoice-7B | GGUF 16/Q8 |
| vibevoice_asr | ASR | auto | VibeVoice ASR | GGUF 16/Q8 |
| voxtral_realtime | ASR | auto | Voxtral-Mini-4B-Realtime-2602 | GGUF 16/Q8, Stream |
| voxcpm2 | TTS, Clone, Design, Ctrl | ar, da, de, el, en, es, fi, fr, he, hi, id, it, ja, km, ko, lo, ms, my, nl, no, pl, pt, ru, sv, sw, th, tl, tr, vi, zh | VoxCPM2-2B, 48 kHz | GGUF 16/Q8, Stream |
| index_tts2 | TTS, Clone, Ctrl | zh, en | IndexTTS-2 | GGUF 16/Q8 |
| irodori_tts | TTS, Clone, Design, Ctrl | ja | Irodori-TTS-500M-v3, Irodori-TTS-600M-v3-VoiceDesign | GGUF 16/Q8 |
| moss_tts_nano | TTS, Clone | auto | MOSS-TTS-Nano-100M | GGUF 16/Q8 |
| moss_tts_local | TTS, Clone, Ctrl | auto, optional language hint | MOSS-TTS-Local-Transformer-v1.5 | GGUF 16/Q8 |
| supertonic | TTS | en, ko, ja, ar, bg, cs, da, de, el, es, et, fi, fr, hi, hr, hu, id, it, lt, lv, nl, pl, pt, ro, ru, sk, sl, sv, tr, uk, vi, na | Supertonic 3 | GGUF F32, Stream |
Community model ports live under community_models to make the ownership boundary clear while keeping them available through the normal audio.cpp CLI and server paths. Some community-contributed models graduate into the core model tree when they become part of the main release surface. Huge thanks to the contributors who bring these models in, test them, and keep pushing the framework into new territory. See docs/community_models/models.md for community-model expectations and current entries.
| Family | Task | Lang | Runtime | Contributor | What They Added |
|---|---|---|---|---|---|
| glm_tts | TTS, Clone | zh, en | GGUF | Mirek @mirek190 | GLM-TTS zero-shot synthesis and voice cloning support |
| moss_tts_local | TTS, Clone, Ctrl | auto, optional language hint | GGUF | @justinjohn0306 | MOSS-TTS-Local Transformer v1.5 support |
| outetts | TTS, Clone | en, ar, zh, nl, fr, de, it, ja, ko, lt, ru, es, pt, be, bn, ka, hu, lv, fa, pl, sw, ta, uk | GGUF | Mirek @mirek190 | Llama-OuteTTS-1.0-1B TTS and voice cloning support |
| vietneu_tts | TTS, Clone | vi, en | GGUF | Phuoc @phuocnguyen90 | VieNeu-TTS-v3-Turbo TTS and voice cloning support |
PocketTTS language selection is a model-load option. When the model path points at the PocketTTS root, the loader uses english unless you pass --load-option language=<name>. Kyutai's normal non-English PocketTTS releases are smaller distilled language models intended for the fast PocketTTS path. The _24l variants are larger 24-layer, undistilled preview models that can sound better but are slower. Kyutai currently publishes French only as french_24l, not as a normal distilled french language directory, so French is not listed as a normal PocketTTS language here.
Docker CPU and CUDA images are available for both CLI and server use. See Docker.md for build commands and working Docker examples.
The repository still ships a Python model manager at tools/model_manager.py for
downloading supported packages into the expected models/ layout.
This path is gradually becoming legacy as the project moves toward standalone GGUF packages. If a model has a ready-to-use GGUF package, prefer that route first.
GGUF downloads:
- Released model packages: audio-cpp/audio.cpp-gguf
- Community model package: mirek190/audio.cpp
See the Model Manager guide for model-manager usage and package notes.
audio.cpp includes a Gradio WebUI for trying local models from the browser, managing downloads, and running common TTS/ASR/audio workflows without writing CLI commands.
The WebUI lives in webui/. See webui/README.md for setup, launch commands, and model-download notes.
Huge thanks to @kigner for the original audio.cpp-webui, and to @patrickjchen for porting and integrating it into audio.cpp.
| OS | Requirements |
|---|---|
| Linux | GCC 13 or newer, CMake, backend toolchain for CUDA or Vulkan builds |
| Windows | Visual Studio Build Tools 2022 or newer with C++ desktop workload, MSVC x64 compiler, Windows SDK, CMake, Ninja, MSVC OpenMP components; official NVIDIA CUDA Toolkit for CUDA builds |
| macOS | Xcode or Xcode Command Line Tools with the Metal compiler available through xcrun |
On macOS, audio.cpp can be installed from the Homebrew tap:
brew tap 0xShug0/audio-cpp
brew trust 0xShug0/audio-cpp
brew install audio-cppFor Nix and NixOS builds, see docs/build/nixos.md.
Use the Linux helper script for CPU, CUDA, or Vulkan builds:
scripts/build_linux.sh --backend cuda --target audiocpp_cli --target audiocpp_server
scripts/build_linux.sh --backend vulkan --target audiocpp_cli --target audiocpp_server
scripts/build_linux.sh --backend cpu --target audiocpp_cli --target audiocpp_serverThe script writes to aligned build directories such as build/linux-cuda-release, build/linux-vulkan-release, and build/linux-cpu-release.
For portable CPU kernels on machines where native ISA flags are not suitable:
scripts/build_linux.sh --backend cuda --native-cpu OFF --target audiocpp_cli --target audiocpp_serverFor deployment builds with compiled package specs:
scripts/build_linux.sh --backend cuda --deployment-build --target audiocpp_cli --target audiocpp_serverFor direct CMake commands, see docs/build/linux.md.
Use the Windows PowerShell build script:
powershell.exe -NoProfile -ExecutionPolicy Bypass -File .\scripts\build_windows.ps1Common presets:
.\scripts\build_windows.ps1 -Preset windows-cuda-release -Target audiocpp_cli
.\scripts\build_windows.ps1 -Preset windows-cpu-release -Target audiocpp_cli
.\scripts\build_windows.ps1 -Target audiocpp_server -Jobs 16From cmd.exe, use the wrapper:
scripts\build_windows.cmdFor deployment builds with compiled package specs:
.\scripts\build_windows.ps1 -DeploymentBuild -Target audiocpp_cliFor requirements, CPU profiles, CUDA packaging, and release zips, see docs/build/windows.md.
On macOS, use the Metal helper script to build against ggml's Metal backend:
scripts/build_metal.sh --target audiocpp_cliThe script configures build/macos-metal-release by default, enables ENGINE_ENABLE_METAL=ON, disables CUDA and Vulkan, embeds the Metal shader library, and builds static libraries plus the requested target.
Useful variants:
scripts/build_metal.sh --target audiocpp_server
scripts/build_metal.sh --build-type Release --archs arm64 --target audiocpp_cli
scripts/build_metal.sh --with-tests --target audio_dsp_test
scripts/build_metal.sh --openmp auto --target audiocpp_cli
scripts/build_metal.sh --native-cpu OFF --target audiocpp_cli
scripts/build_metal.sh --deployment-build --target audiocpp_cliThe built CLI is written to:
build/macos-metal-release/bin/audiocpp_cli| Option | Meaning | Default |
|---|---|---|
ENGINE_ENABLE_CUDA |
Enable the ggml CUDA backend. Required for --backend cuda. |
OFF |
ENGINE_ENABLE_VULKAN |
Enable the ggml Vulkan backend. Required for --backend vulkan. |
OFF |
ENGINE_ENABLE_METAL |
Enable the ggml Metal backend. Required for --backend metal. |
OFF on most platforms, ON on Apple |
ENGINE_ENABLE_LLAMAFILE |
Enable llamafile SGEMM support in ggml CPU builds. | ON |
ENGINE_ENABLE_CUDA_GRAPHS |
Enable ggml CUDA graphs support when CUDA is enabled. | ON |
ENGINE_ENABLE_NATIVE_CPU |
Build ggml CPU kernels with native host ISA flags such as -march=native. Disable this for portable CPU kernels or toolchains that reject generated CPU instructions. |
ON |
ENGINE_ENABLE_OPENMP |
Enable OpenMP for host-side parallel work. | ON |
ENGINE_BUILD_EXAMPLES |
Build example binaries. | OFF |
ENGINE_BUILD_TESTS |
Build framework unit tests. | OFF |
ENGINE_BUILD_WARMBENCH |
Build warmbench helper binaries. | OFF |
AUDIOCPP_DEPLOYMENT_BUILD |
Compile package specs into CLI/server binaries for standalone GGUF and package-spec fallback loading. Script builds expose this as --deployment-build on Linux/macOS and -DeploymentBuild on Windows. |
OFF |
For full setup, CLI, server, and workflow examples, see docs/usage.md.
The main CLI binary is:
build/bin/audiocpp_cliHigh-level command shape:
audiocpp_cli --task <task> --model <path> [--family <family>] [--backend <backend>] [--mode <mode>] [options]Core selectors:
--task vad|asr|diar|sep|gen|tts|clon|vc|s2s|align|vdes|spk|svc--model <path>--family <name>optionally narrows model-loader selection when a model path could match more than one family--backend cpu|cuda|vulkan|metal|best--mode offline|streaming; streaming is available for models whose docs list streaming support
Common interface options:
--load-option key=valuepasses model-load options, such as PocketTTS language selection--session-option key=valuepasses session/runtime options, such as backend-specific weight controls--request-option key=valuepasses per-request model options--config <id>selects a discovered config asset--weight <id>selects a discovered weight asset--device <n>selects the backend device--threads <n>sets backend and OpenMP worker threads
Examples:
Text-to-speech:
build/bin/audiocpp_cli \
--task tts \
--family pocket_tts \
--model /path/to/model \
--backend cuda \
--text "audio.cpp is running PocketTTS locally." \
--voice-ref assets/resources/sample.wav \
--out build/out/pocket_tts.wavPocketTTS with another language and a built-in voice:
build/bin/audiocpp_cli \
--task tts \
--family pocket_tts \
--model /path/to/models/pocket-tts \
--backend cuda \
--load-option language=spanish \
--text "Hola, esta es una prueba corta de Pocket TTS." \
--voice-id alba \
--out build/out/pocket_tts_spanish.wavASR:
build/bin/audiocpp_cli \
--task asr \
--family qwen3_asr \
--model /path/to/model \
--backend cuda \
--audio assets/resources/sample_16k.wavVoice conversion:
build/bin/audiocpp_cli \
--task vc \
--family seed_vc \
--model /path/to/model \
--backend cuda \
--audio assets/resources/a.wav \
--voice-ref assets/resources/b.wav \
--out build/out/seed_vc.wavUseful CLI features:
--helpwith--taskshows task-oriented help--helpwith--model <path>and optional--family <family>shows model-owned request, session, and load options--inspectprints discovered configs, weights, and capabilities--list-loadersprints registered model families (--jsonfor the machine-readable contract)python tools/model_manager.py list --jsonprints installable packages; keep it synced with loaders (docs/maintainers/loader_and_catalog.md)--batch-text-file <txt>runs one offline request per non-empty line--batch-text-dir <dir>runs one offline request per.txt,.md, or.jsonfile, normalizing each file as one paragraph--batch-audio-dir <dir>runs one offline request per.wav--audio-chunk-mode autolets ASR/alignment models choose their safe long-audio policy; expert users can override withfixed,vad, ornonewhere supported--request-sequence <json>runs a multi-request offline session--batch-merge-audio none|concatcontrols batch audio merge behavior--batch-manifest-out <json>writes a batch output manifest--pipeline <json>runs a workflow instead of a raw task--list-pipelinesprints registered workflows--workflow-input key=valueoverrides pipeline inputs--logstreams framework logs to stdout--log-file <path>streams framework logs to a file in real time--segments-out,--turns-out, and--words-outwrite structured JSON outputs--vad-chunks-outwrites offline VAD-based chunk windows; tune them with--vad-chunk-max-seconds,--vad-chunk-merge-gap-seconds, and--vad-chunk-padding-seconds
The server binary is:
build/bin/audiocpp_serverBuild:
cmake --build build -j$(nproc) --target audiocpp_serverCreate a config file with your own model paths:
cat > server.json <<'JSON'
{
"host": "127.0.0.1",
"port": 8080,
"backend": "cuda",
"device": 0,
"threads": 1,
"lazy_load": true,
"models": [
{
"id": "pocket-tts",
"family": "pocket_tts",
"path": "/path/to/models/pocket-tts",
"task": "tts",
"mode": "offline",
"load_options": {
"language": "english"
},
"session_options": {
"language": "english"
}
},
{
"id": "qwen3-asr",
"family": "qwen3_asr",
"path": "/path/to/models/Qwen3-ASR-0.6B",
"task": "asr",
"mode": "offline"
}
]
}
JSONSet "lazy_load": true to register configured model ids at startup while loading each model only on first use. Use per-model "lazy": true or "lazy": false to override that default.
Set top-level "backend" to "cuda", "cpu", "vulkan", or "metal". CUDA is the optimized path for audio.cpp; CPU, Vulkan, and Metal are intended for portability and testing when the binary is built with that backend, but performance and model coverage may be lower.
Warning
Lazy loading does not unload models after a request. Once a model is first used, the server keeps that model and session in memory for reuse until the server exits.
Start:
build/bin/audiocpp_server --config server.jsonThe server exposes:
GET /healthGET /v1/modelsPOST /v1/audio/speechPOST /v1/audio/transcriptionsPOST /v1/tasks/run
More server examples are in app/server/README.md.
Pipelines are an experimental JSON workflow feature for chaining multiple model and audio-processing steps behind one CLI command. A pipeline can define default inputs, let users override them with --workflow-input key=value, split long media into model-sized chunks, merge text or audio outputs back together, write intermediate artifacts under --out-dir, and copy the declared final_audio to --out.
This is the higher-level layer for production-style audio jobs: redubbing, batch cleanup, long-form narration, voice conversion, source-separation workflows, transcription-plus-alignment, and future workflows that combine translation, diarization, denoise, enhancement, or review steps as those model surfaces are wired into the framework.
The included same-language speech redub pipeline transcribes long speech in chunks with Qwen3 ASR, merges the transcript, then regenerates the speech in a target reference voice with Qwen3 TTS. The default test input assets/resources/speech.wav is about 418 seconds long and was generated from an 8,091-character speech text, so it exercises long-audio split and merge behavior rather than a short one-shot request:
build/bin/audiocpp_cli \
--pipeline assets/pipeline/speech_redub.json \
--backend cuda \
--out-dir build/out/speech_redub_pipeline \
--out build/out/speech_redub_pipeline.wavOverride the source speech or target voice without editing the JSON:
build/bin/audiocpp_cli \
--pipeline assets/pipeline/speech_redub.json \
--backend cuda \
--workflow-input source_audio=/path/to/speech.wav \
--workflow-input target_voice=/path/to/voice.wav \
--workflow-input language=English \
--out-dir build/out/speech_redub_pipeline \
--out build/out/speech_redub_pipeline.wavThe repository includes both framework-level parity validation and app-level end-to-end path checks. At a high level, the flow is:
The main harness under tests/ is tests/warmbench.py. It is used for long-lived multi-request validation, parity checks against Python references, and performance-oriented session reuse scenarios. The tests/ tree also contains model-specific C++ and Python warmbench entrypoints that warmbench.py coordinates.
The main app-facing test tooling under tools/ is tools/audiocpp_cli/run_audiocpp_cli_path_tests.py. It drives audiocpp_cli through cataloged offline and streaming cases, verifies expected outputs such as audio or JSON artifacts, and is useful for checking real user-facing request paths rather than just lower-level model components. Streaming coverage is model-specific and applies to models documented with streaming support.
The Python-reference side of these tests usually requires more time-consuming setup than the C++ path because different models rely on different Python reference repos and dependency stacks. In practice, the framework-side tooling is fast to iterate on once models are installed, while Python parity runs often need extra environment preparation before they are ready.
- TranscrIA is a self-hosted meeting transcription platform with diarization and local LLM correction. audio.cpp is integrated as a first-class STT engine in the product.
- Pocket TTS Browser Engine uses audio.cpp to bring fully local PocketTTS voices into Chrome and Edge through the browser TTS API.
- GuideAnts uses audio.cpp as the default local AI stack path for basic ASR and TTS, with planned reusable skills for audio.cpp scenarios and model configurations.
Warning
These Python-relative numbers were measured for the initial release. Several model paths have improved substantially since then, so the figures below should be read as the original release baseline rather than the latest peak performance.
All performance metrics in this section were measured on Ubuntu with the CUDA backend on an NVIDIA GeForce RTX 5090. The Python-relative one-shot and long-lived-session comparisons come from direct framework/runtime API benchmark calls, not from audiocpp_cli; CLI path tests are separate and include app-layer request parsing, output writing, and other user-facing overhead.
Absolute RTF depends on the GPU and system setup, but the Python-relative speedups are real because audio.cpp and the matching Python reference paths were measured on the same CUDA setup.
audio.cpp already shows some genuinely exciting wins against the matching Python reference paths, especially on the TTS side, even when using the original model weights without quantization. The headline win is wall time: several TTS paths run 1.8x to up to 10x faster than Python while cutting end-to-end latency by 45%-90%.
- In one-shot runs, several TTS-family models already land far ahead of Python:
vevo2: 5.03x faster with 80.11% less wall timepocket tts: 3.68x faster with 72.80% less wall timemiotts: 2.73x faster with 63.39% less wall timemoss_tts_local: 2.33x faster with 57.07% less wall timeqwen3 tts: 1.83x faster with 45.34% less wall timevibevoice: 1.40x faster with 28.75% less wall time
- In long-lived-session runs, where the same loaded session serves multiple requests in sequence, the gains stay strong:
pocket tts: 3.22x faster with 68.91% less wall timeqwen3 tts: 2.74x faster with 63.47% less wall timemoss_tts_local: 2.66x faster with 62.35% less wall timemiotts: 2.28x faster with 56.22% less wall timevibevoice: 1.77x faster with 43.55% less wall timevevo2: 1.75x faster with 42.72% less wall time
- In long-form runs on the shared 6,026-character, 1,028-word passage, the strongest Python-relative wins still show up clearly:
pocket tts: 3.15x faster with 68.23% less wall timeqwen3 tts: 3.06x faster with 67.33% less wall timevibevoice: 2.86x faster with 65.07% less wall timevevo2: 1.77x faster with 43.51% less wall timechatterbox: 1.58x faster with 36.83% less wall time
- These long-lived-session numbers are especially important for real applications, because they reflect the common case where model load, cached state, and reusable runtime setup are amortized across many requests.
- Bars below the 1.0x line are useful too: they spotlight exactly where more optimization work is still worth doing.
The figures report Python wall time / audio.cpp wall time. The 1.0x line means equal wall time; bars above 1.0x mean audio.cpp is faster than Python, and bars below 1.0x mean it is slower.
For TTS-family models, the measured one-shot RTF is:
| model | audio len (s) | wall time (s) | RTF | x faster than real time |
|---|---|---|---|---|
| chatterbox | 9.72 | 2.45 | 0.252 | 3.97x |
| miotts | 20.40 | 3.30 | 0.162 | 6.18x |
| moss_tts_local | 9.60 | 0.97 | 0.101 | 9.91x |
| omnivoice | 9.00 | 1.32 | 0.146 | 6.84x |
| pocket tts | 8.08 | 0.26 | 0.032 | 31.09x |
| qwen3 tts | 11.44 | 4.46 | 0.390 | 2.56x |
| vevo2 | 8.66 | 2.47 | 0.285 | 3.51x |
| vibevoice | 11.07 | 5.02 | 0.454 | 2.20x |
| voxcpm2 | 5.60 | 3.09 | 0.551 | 1.81x |
For long-form TTS tests, each run uses the same 6,026-character, 1,028-word input text (vibevoice uses 106,310 chars, 18,052 words, 4 speakers). Rows are CUDA unless marked CPU. The measured RTF is:
| model | audio len (s) | wall time (s) | RTF | x faster than real time |
|---|---|---|---|---|
| chatterbox | 391.24 | 58.57 | 0.150 | 6.68x |
| index tts2 | 422.12 | 139.95 | 0.332 | 3.02x |
| miotts | 399.16 | 66.59 | 0.167 | 5.99x |
| moss_tts_nano | 391.20 | 43.16 | 0.110 | 9.06x |
| moss_tts_local | 375.44 | 73.84 | 0.197 | 5.08x |
| omnivoice | 357.00 | 17.77 | 0.050 | 20.09x |
| pocket tts | 353.12 | 7.30 | 0.021 | 48.40x |
| qwen3 tts | 327.60 | 72.65 | 0.222 | 4.51x |
| supertonic | 379.32 | 2.02 | 0.005 | 187.62x |
| supertonic (CPU) | 379.40 | 61.40 | 0.162 | 6.18x |
| vevo2 | 457.68 | 52.47 | 0.115 | 8.72x |
| voxcpm2 | 315.84 | 72.70 | 0.230 | 4.34x |
| vibevoice | 5615.73 | 1376.84 | 0.245 | 4.08x |
Some models expose memory-saver session options such as ace_step.mem_saver=true, heartmula.mem_saver=true, stable_audio.mem_saver=true, omnivoice.mem_saver=true, and voxcpm2.mem_saver=true. These options keep the default output path unchanged while reducing graph workspace VRAM or releasing staged graph/cache state after request phases; later requests may rebuild released graphs.
Many model sessions expose quantization through --session-option <family>.weight_type=<mode>, and some families also expose more specific knobs such as ...conv_weight_type, ...talker_weight_type, or ...speech_decoder_weight_type. The exact supported modes are model-specific rather than global.
audio.cpp also supports standalone GGUF packages.
In practice, lower precision and quantized modes should be treated as model- and route-specific optimizations rather than universally safe defaults.
-
Safety. Quantization may not be safe on every path even when a model parser accepts the option. For example, in our ACE-Step 1.5 checks, lower-precision runs could fail at runtime with
ACE-Step planner masked decode found no valid tokenwhile higher-precision settings completed normally. -
Quality Drop. Output quality can drop a lot. In our VeVo2 checks, non-
fp32outputs showed noticeably weaker similarity to thefp32reference under the repo's existing waveform and log-mel comparison metrics, and even output length could shift. -
Performance Gain. The performance gain may be minor relative to that quality risk. For example,
q8_0was faster than the default setting by only around 3.8% on Qwen3-TTS and around 3.6% on VeVo2. Other models may benefit more, but the tradeoff should be validated per model and per route rather than assumed. -
Memory Benefit. Lower precision and quantized weights can still be useful for reducing weight memory footprint and making larger models easier to fit within device limits. For example, in our Qwen3-TTS checks, switching from the default setting to
q8_0reduced peak RAM by about 3.7% and peak VRAM by about 25.0%. That benefit is real, but it should be evaluated together with runtime stability, output quality, and end-to-end speed rather than assumed from precision alone.
- The repo supports multiple backends, but backend and model coverage are model-dependent.
- GGUF is a container, not a universal architecture adapter. Existing llama.cpp or whisper.cpp GGUF files are not automatically compatible unless their tensor names and model metadata are mapped to the audio.cpp family implementation.
Build_xcframework.shis outdated; Metal and Apple XCFramework packaging still need to be retested after the framework refactor.