qvac-ext-lib-whisper.cpp

On-device speech and audio AI in pure C++ on ggml: speech-to-text, speaker diarization, end-of-utterance detection, text-to-speech, voice cloning, speech enhancement, and music generation.

Property	Value
CMake project	`qvac-speech` (feature-gated superbuild over `third_party/` + `engines/`)
Runtime dependencies	ggml only. No Python, PyTorch, or ONNX Runtime at inference time
Engines	`third_party/whisper.cpp`, `engines/parakeet`, `engines/tts`, `engines/audiogen`
Models	every model loads from GGUF (see Supported models)
Desktop	Linux, macOS, Windows
Mobile	Android (arm64-v8a), iOS (arm64)
Backends	CPU, Metal, Vulkan, OpenCL (Adreno), CUDA, Apple Core ML (encoder sidecar)
Quantization	`f32`, `f16`, `bf16`, `q8_0`, `q6_k`, `q5_0`, `q5_1`, `q4_0`, `q4_k_m` (per model, see tables)
Shared ggml	one `ggml-speech` vcpkg port, built from qvac-ext-ggml@speech
Language	C++17

Architecture

+-----------------------------+  +-----------------------------+
| third_party/whisper.cpp     |  | engines/parakeet            |
| speech-to-text              |  | ASR + diarization + EOU     |
+-----------------------------+  +-----------------------------+
+-----------------------------+  +-----------------------------+
| engines/tts                 |  | engines/audiogen            |
| TTS + cloning + enhancement |  | text-to-music               |
+-----------------------------+  +-----------------------------+
                    |                     :
                    v                     : optional encoder sidecar
   ggml-speech (qvac-ext-ggml@speech)     v
                    |                Apple Core ML
   +--------+-------+-------+---------+
   v        v       v       v         v
  CPU     Metal  Vulkan  OpenCL     CUDA
                        (Adreno)

Every component consumes one system ggml, so the whole stack shares a single ggml pin and file set. The ggml/ tree vendored inside the whisper subtree is never compiled.

Pipelines

whisper   wav  -> log-mel -> encoder -> decoder -> text            (+ Silero VAD, + Core ML encoder)
parakeet  wav  -> log-mel -> FastConformer encoder -> CTC | TDT | EOU | Sortformer
                                                   -> text | speaker segments | turn boundary
tts       text -> LM (T3 / Llama / Qwen2.5) -> acoustic tokens -> CFM or flow -> vocoder -> wav
                                                   (+ LavaSR denoise -> bandwidth extension)
audiogen  caption + lyrics -> ACE-Step LM -> FSQ detokenizer -> text encoder
                           -> condition encoder -> DiT flow matching
                           -> Oobleck VAE -> 48 kHz stereo

Repo layout

CMakeLists.txt              feature-gated umbrella superbuild
third_party/whisper.cpp/    upstream whisper.cpp, vendored as a git subtree,
                            pinned @ v1.9.1 (f049fff9); every QVAC delta is
                            declared in PATCHES.md and enforced by CI
engines/
  parakeet/                 ASR + diarization + end-of-utterance (NVIDIA Parakeet family)
  tts/                      text-to-speech, voice cloning, speech enhancement
  audiogen/                 music generation (ACE-Step)
docs/UPSTREAM-SYNC.md       how to sync the whisper subtree

Supported models

One row per model. Backends lists available engine paths; row notes and the engine-specific guides qualify model-level validation.

Speech-to-text and translation

Model	Engine	Languages	Params	Quantization	Backends	Notes
`whisper-tiny` / `tiny.en`	whisper	99 + translation	39 M	`f16`, `q5_1`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA, Core ML
`whisper-base` / `base.en`	whisper	99 + translation	74 M	`f16`, `q5_1`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA, Core ML
`whisper-small` / `small.en`	whisper	99 + translation	244 M	`f16`, `q5_1`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA, Core ML
`whisper-small.en-tdrz`	whisper	English	244 M	`f16`	CPU, Metal, Vulkan, OpenCL, CUDA	tinydiarize speaker turns
`whisper-medium` / `medium.en`	whisper	99 + translation	769 M	`f16`, `q5_0`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA, Core ML
`whisper-large-v1`	whisper	99 + translation	1.55 B	`f16`	CPU, Metal, Vulkan, OpenCL, CUDA, Core ML
`whisper-large-v2`	whisper	99 + translation	1.55 B	`f16`, `q5_0`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA, Core ML
`whisper-large-v3`	whisper	99 + translation	1.55 B	`f16`, `q5_0`	CPU, Metal, Vulkan, OpenCL, CUDA, Core ML
`whisper-large-v3-turbo`	whisper	99 + translation	809 M	`f16`, `q5_0`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA, Core ML	fastest large-class decode
`silero-v5.1.2`	whisper	language agnostic	2 M	`f16`	CPU	voice activity detection
`silero-v6.2.0`	whisper	language agnostic	2 M	`f16`	CPU	voice activity detection
`nvidia/parakeet-ctc-0.6b`	parakeet	English	600 M	`f32`, `f16`, `q8_0`, `q5_0`, `q4_0`	CPU, Metal, Vulkan, OpenCL, CUDA	offline + streaming + long-form
`nvidia/parakeet-ctc-1.1b`	parakeet	English	1.1 B	`f16`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA	offline + streaming + long-form
`ai4bharat/indic-conformer-600m-multilingual`	parakeet	22 Indic (CTC-only export)	600 M	`f16`, `q8_0`, `q4_0`	CPU	GPU backends share the CTC path but remain unvalidated; requires `--language` / `EngineOptions::language`
`nvidia/parakeet-tdt-0.6b-v3`	parakeet	~25 + punctuation and capitalization	600 M	`f32`, `f16`, `q8_0`, `q5_0`, `q4_0`	CPU, Metal, Vulkan, OpenCL, CUDA; Core ML offline encoder	graph decoder on Metal/Vulkan/CUDA; scalar on CPU/OpenCL
`nvidia/parakeet-tdt-1.1b`	parakeet	English	1.1 B	`f16`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA; Core ML offline encoder	no punctuation; graph decoder on Metal/Vulkan/CUDA

End-of-utterance and diarization

Model	Engine	Task	Params	Quantization	Backends	Notes
`nvidia/parakeet_realtime_eou_120m-v1`	parakeet	low-latency ASR + end-of-turn	120 M	`f16`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA	decoder graphs on Metal/Vulkan/CUDA, scalar on CPU/OpenCL; `is_eou_boundary`
`nvidia/diar_sortformer_4spk-v1`	parakeet	diarization, up to 4 speakers	123 M	`f16`, `q8_0`, `q4_0`	CPU, Metal, Vulkan, OpenCL, CUDA	offline + sliding-history live
`nvidia/diar_streaming_sortformer_4spk-v2`	parakeet	diarization, up to 4 speakers	117 M	`f16`, `q8_0`, `q4_0`	CPU, Metal, Vulkan, OpenCL, CUDA	streaming-trained encoder
`nvidia/diar_streaming_sortformer_4spk-v2.1`	parakeet	diarization, up to 4 speakers	117 M	`f16`, `q8_0`, `q4_0`	CPU, Metal, Vulkan, OpenCL, CUDA	Audio-Online Speaker Cache, stable slots across gaps

Parakeet's CUDA path is implemented but is not yet covered by hardware decoder parity CI. CUDA in these rows denotes availability, not completed validation.

Pair any CTC, TDT, or EOU GGUF with a Sortformer GGUF via --diarization-model for an attributed "who said what" transcript. See the Parakeet backend, Core ML, streaming, conversion, and package guide.

Text-to-speech and voice cloning

Model	Engine	Languages	Sample rate	Quantization	Backends	Notes
Chatterbox Turbo	tts	English	24 kHz	`f16`, `q8_0`, `q5_0`, `q4_0`	CPU, Metal, Vulkan, OpenCL, CUDA	zero-shot voice cloning, 2-step meanflow CFM, streaming
Chatterbox Multilingual	tts	23	24 kHz	`f16`, `q8_0`, `q5_0`, `q4_0`	CPU, Metal, Vulkan, OpenCL, CUDA	zero-shot voice cloning, CFG, `--cfm-steps` knob, streaming
Supertonic v1	tts	English	44.1 kHz	`f32`, `f16`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA	preset voices, streaming
Supertonic v2	tts	5 (`en`, `ko`, `es`, `pt`, `fr`)	44.1 kHz	`f32`, `f16`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA	preset voices, streaming
Supertonic v3	tts	31 + `na`	44.1 kHz	`f32`, `f16`, `q8_0`	CPU, Metal, Vulkan, OpenCL, CUDA	preset voices, streaming, `na` for unknown source language
Parler-TTS mini-v1	tts	English	44.1 kHz	`f32`, `f16`, `q8_0`, `q6_k`	CPU, Metal, Vulkan, OpenCL	description-conditioned voice, no cloning
Parler-TTS large-v1	tts	English	44.1 kHz	`f32`, `f16`, `q8_0`, `q6_k`	CPU, Metal, Vulkan, OpenCL	description-conditioned voice
Indic Parler-TTS	tts	21 Indic	44.1 kHz	`f32`, `f16`, `q8_0`, `q6_k`	CPU, Metal, Vulkan, OpenCL	Indic prompt BPE tokenizer
Fun-CosyVoice3-0.5B	tts	model-advertised multilingual text	24 kHz	`f32`	CPU, Metal, Vulkan, OpenCL	Qwen2.5 LM + DiT flow + CausalHiFT; zero-shot/cross-lingual cloning from a reference WAV (native speech_tokenizer_v3 + CAM++); Metal, desktop Vulkan, and OpenCL are the validated GPU paths
Audio8-TTS-Preview-0.6B	tts	multilingual	44.1 kHz	`f32`, `f16`, `q8_0`; LM also `q4_0`	CPU, Metal, Vulkan, OpenCL	DualAR + DAC codec, zero-shot cloning from reference audio and transcript

Speech enhancement

Model	Engine	Task	Rate	Quantization	Backends	Notes
LavaSR denoiser (UL-UNAS)	tts	speech denoising	rate preserving, 16 kHz internal STFT	`f32`, `f16`	CPU, Metal, Vulkan, OpenCL, CUDA	applied after synthesis or on captured audio
LavaSR enhancer (Vocos BWE)	tts	bandwidth extension	native in, 48 kHz out	`f32`, `f16`	CPU, Metal, Vulkan, OpenCL, CUDA	ConvNeXt + ISTFT head

Music generation

Model	Engine	Task	Rate	Quantization	Backends	Notes
ACE-Step v15 turbo	audiogen	text-to-music	48 kHz stereo	`f32`, `f16`, `bf16`, `q8_0`, `q4_k_m`	CPU, Vulkan, Metal, OpenCL (Adreno 700+)	8 diffusion steps by default
ACE-Step v15 sft	audiogen	text-to-music	48 kHz stereo	`f32`, `f16`, `bf16`, `q8_0`	CPU, Vulkan, Metal, OpenCL (Adreno 700+)	50 diffusion steps by default

Build

Prerequisites: CMake >= 3.20, a C++17 compiler, git.

# 1) system ggml (the branch the ggml-speech vcpkg port is cut from; the port
#    pins one commit, so check its portfile REF to match a port build exactly)
git clone --depth 1 --branch speech https://github.com/tetherto/qvac-ext-ggml ggml-src
cmake -S ggml-src -B ggml-src/build -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON \
      -DCMAKE_INSTALL_PREFIX=$PWD/ggml-install
cmake --build ggml-src/build -j && cmake --install ggml-src/build

# 2) the speech stack (whisper + parakeet + tts + audiogen, one shared ggml)
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH=$PWD/ggml-install
cmake --build build -j

CMake options

Option	Default	Effect
`SPEECH_BUILD_WHISPER`	`ON`	build `third_party/whisper.cpp`
`SPEECH_BUILD_PARAKEET`	`ON`	build `engines/parakeet`
`SPEECH_BUILD_TTS`	`ON`	build `engines/tts`
`SPEECH_BUILD_AUDIOGEN`	`ON`	build `engines/audiogen`
`SPEECH_BUILD_EXECUTABLES`	`ON`	build the CLIs; set `OFF` for library-only builds
`SPEECH_BUILD_TESTS`	`OFF`	build the engine test harnesses
`SPEECH_BUILD_WHISPER_TESTS`	`OFF`	also build whisper's tests (transcription tests need downloaded models)

GPU backends come from the ggml build: -DGGML_VULKAN=ON, -DGGML_OPENCL=ON, -DGGML_CUDA=ON; Metal is on by default on Apple. Core ML is gated per engine and defaults to off on both, so add -DWHISPER_COREML=ON -DPARAKEET_COREML=ON on Apple for the Whisper encoder and Parakeet offline TDT encoder sidecars. For tests, configure with -DSPEECH_BUILD_TESTS=ON, then run the non-GPU suite with ctest --test-dir build -LE 'gpu|perf'.

Each engine also configures standalone (cmake -S engines/parakeet, and so on), which is what the CI lanes use.

Consumable packages

One vcpkg port, speech-cpp, builds this repo through the umbrella CMakeLists.txt above: engine features select what gets built, and every enabled engine links the single ggml-speech ggml. Consumers depend on the engines they need, for example speech-cpp[whisper,parakeet,vulkan], and the backend features (metal, vulkan, opencl) fan out to the matching ggml-speech features so the whole stack resolves one ggml.

Feature	`find_package`	Imported target
(always)	`ggml`	`ggml::ggml`
`speech-cpp[whisper]`	`whisper`	`whisper::whisper`
`speech-cpp[parakeet]`	`qvac-parakeet`	`qvac::parakeet`
`speech-cpp[tts]`	`tts-cpp`	`tts-cpp::tts-cpp`
`speech-cpp[audiogen]`	`audiogen-cpp`	`audiogen-cpp::audiogen-cpp`

The per-engine whisper-cpp, parakeet-cpp, tts-cpp and audiogen-cpp ports that predate speech-cpp are superseded: they pinned this repo at four different commits, and speech-cpp replaces them with one pin for the whole stack.

Command line tools

Binary	Engine	Purpose
`whisper-cli`	whisper	transcribe and translate, with optional Silero VAD
`parakeet`	parakeet	transcribe, diarize, detect end-of-utterance, benchmark
`tts-cli`	tts	Chatterbox, Supertonic, and Parler synthesis, autodetected from GGUF metadata
`parler-cli`	tts	full Parler-TTS flag surface
`supertonic-cli`	tts	standalone Supertonic synthesis
`cosyvoice-cli`	tts	CosyVoice3 synthesis
`audio8-cli`	tts	Audio8 synthesis and zero-shot voice cloning
`music-cli`	audiogen	end-to-end text-to-music
`acestep-cli`	audiogen	Oobleck VAE decode and roundtrip harness
`lavasr-bench`	tts	denoiser and enhancer benchmark
`mel2wav`	tts	HiFT mel to wav

Whisper

./third_party/whisper.cpp/models/download-ggml-model.sh base.en
./build/bin/whisper-cli -m third_party/whisper.cpp/models/ggml-base.en.bin \
                        -f third_party/whisper.cpp/samples/jfk.wav

Parakeet

Models are converted from NeMo checkpoints with download-all-models.sh and convert-nemo-to-gguf.py; see engines/parakeet/README.md.

# transcribe (the GGUF metadata selects CTC / TDT / EOU)
./build/engines/parakeet/parakeet --model models/parakeet-tdt-0.6b-v3.q8_0.gguf \
                                  --wav engines/parakeet/test/samples/jfk.wav

# transcribe with speaker attribution
./build/engines/parakeet/parakeet --model models/parakeet-tdt-0.6b-v3.q8_0.gguf \
                                  --diarization-model models/diar_sortformer_4spk-v1.f16.gguf \
                                  --wav engines/parakeet/test/samples/diarization-sample-16k.wav

# streaming end-of-utterance, JSONL events
./build/engines/parakeet/parakeet --model models/parakeet_realtime_eou_120m-v1.q8_0.gguf \
                                  --wav engines/parakeet/test/samples/jfk.wav \
                                  --stream --stream-chunk-ms 1500 --emit jsonl

Text-to-speech

GGUF conversion steps and the umbrella/direct/vcpkg build-path matrix are in engines/tts/README.md. The umbrella build enables this package with SPEECH_BUILD_TTS=ON.

# Chatterbox Turbo, with voice cloning from a reference wav
./build/engines/tts/tts-cli --model      models/chatterbox-t3-turbo.gguf \
                            --s3gen-gguf models/chatterbox-s3gen.gguf \
                            --reference-audio me.wav \
                            --text "Hello from native C plus plus." --out out.wav

# Chatterbox Multilingual
./build/engines/tts/tts-cli --model      models/chatterbox-t3-mtl-q4_0.gguf \
                            --s3gen-gguf models/chatterbox-s3gen-mtl-q4_0.gguf \
                            --text "Hola, esto es una demostracion multilingue." \
                            --language es --cfm-steps 7 --out out.wav

# Supertonic, preset voice
./build/engines/tts/tts-cli --model models/supertonic2.gguf --voice M1 --language en \
                            --text "The quick brown fox jumps over the lazy dog." --out out.wav

# Parler-TTS, description-conditioned
./build/engines/tts/parler-cli --model models/parler-mini-v1-q8_0.gguf \
                               --description "A female speaker with a calm, clear voice, close up." \
                               --text "Hey, how are you doing today?" --out out.wav

# CosyVoice3
./build/engines/tts/cosyvoice-cli --model-dir models/cosyvoice3-0.5b \
                                  --text "Hello from a fully on-device pipeline." --out out.wav

# Audio8; drop --n-gpu-layers to stay on the CPU
./build/engines/tts/audio8-cli --lm models/audio8-lm-q8_0.gguf \
                               --codec-decoder models/audio8-codec-decoder-q8_0.gguf \
                               --text "Hello from Audio8." \
                               --n-gpu-layers 99 --out out.wav

--emotion and --pace work the same way on every engine that supports them; each CLI lists its own supported values via --list-emotions / --list-paces. See Voice conditioning.

./build/engines/tts/parler-cli --model models/parler-indic-q8_0.gguf \
                               --emotion happy --pace moderate \
                               --text "आज मौसम बहुत अच्छा है।" --out out.wav

./build/engines/tts/cosyvoice-cli --model-dir models/cosyvoice3-0.5b \
                                  --emotion happy \
                                  --text "Hello from a fully on-device pipeline." --out out.wav

Music generation

AudioGen uses four GGUF files for six runtime weight sets. The DiT file also contains the FSQ detokenizer and condition encoder; see the AudioGen model setup for validated file combinations and registry download instructions.

./build/engines/audiogen/music-cli --models models/acestep \
                                   --caption "driving synth pop, bright analog leads, 120 bpm" \
                                   --lyrics "[Instrumental]" --dur 8 --gpu --out song.wav

Performance

RTF = inference_time / audio_duration, lower is better. The parakeet and tts READMEs carry the full tables, methodology, and reproduction steps; audiogen has no benchmark suite yet and reports per-stage wall clock on stderr.

ASR, end-of-utterance, diarization

CI numbers, q8_0 GGUFs, 1 warmup plus 5 timed runs, host qvac-ubuntu2204-x64-gpu (CPU: Intel Core i5-13500, GPU: NVIDIA RTX 4000 SFF Ada, Vulkan). Full table: engines/parakeet/README.md.

Model	CPU RTF	CPU wall	Vulkan RTF	Vulkan wall
Parakeet CTC	0.078	1572 ms	0.0023	47 ms
Parakeet TDT	0.083	1670 ms	0.0035	71 ms
Parakeet EOU	0.030	607 ms	0.0052	105 ms
Sortformer	0.025	508 ms	0.0020	40 ms

Text-to-speech

CI numbers, q4_0 GGUFs, same host. Full table: engines/tts/README.md.

Model	CPU RTF	Vulkan RTF	Vulkan wall	Vulkan tok/s
Chatterbox Turbo	1.34	0.090	368 ms	186
Chatterbox Multilingual	4.31	0.189	1097 ms	73
Supertonic	0.079	n/a	n/a	n/a

That CI run has no Supertonic GPU lane, so its Vulkan columns are unrecorded rather than unsupported.

Apple silicon

Model	Host	Backend	Quantization	RTF	vs real-time
Parakeet TDT 0.6b v3	Apple silicon, host not recorded	Metal	`q8_0`	0.006	160x
Chatterbox Turbo	Mac Studio M3 Ultra	Metal	`q4_0`	0.16	6.4x
Chatterbox Turbo	Mac Studio M3 Ultra	CPU (NEON)	`q4_0`	1.05	0.96x
Chatterbox Multilingual (`--cfm-steps 7`)	Mac Studio M3 Ultra	Metal	`q4_0`	0.30	3.3x
Chatterbox Multilingual	Apple M4	Metal	`q4_0`	1.37	0.73x

Streaming latency

Chatterbox on Apple M4 Metal, 317 speech tokens (12.7 s of audio), --stream-first-chunk-tokens 10 --stream-chunk-tokens 25 --stream-cfm-steps 1. Full table: engines/tts/README.md.

Metric	Value
first audio out	279 ms
steady-state chunk RTF	0.30 to 0.63
overall RTF	0.90

On-device Android and iOS performance is tracked by the benchmark lanes in QVAC.

Use in QVAC

These engines ship inside QVAC as SDK addons, which consume the speech-cpp vcpkg port built from this repo. The CLIs here are development and validation entry points: for anything beyond them, such as the JavaScript and TypeScript APIs on the Bare runtime, model download and registry, and desktop plus mobile app integration, see QVAC.

QVAC addon	Wraps	`speech-cpp` features consumed
`@qvac/asr-ggml`	speech-to-text, diarization, end-of-utterance	`whisper`, `parakeet`
`@qvac/tts-ggml`	text-to-speech, voice cloning, speech enhancement	`tts`
`@qvac/audiogen-ggml`	music generation	`audiogen`
`@qvac/bci-whispercpp`	brain-computer interface transcription	`whisper`

Licenses

Component	Code license	Model weights
`third_party/whisper.cpp`	MIT	MIT (OpenAI Whisper), Silero VAD models under their own terms
`engines/parakeet`	Apache-2.0	CC-BY-4.0, except `parakeet_realtime_eou_120m-v1` under the NVIDIA Open Model License
`engines/tts`	MIT	Chatterbox MIT; Parler, CosyVoice3, Audio8, and LavaSR Apache-2.0; Supertonic OpenRAIL-M
`engines/audiogen`	MIT	ACE-Step 1.5 MIT, Qwen3-Embedding Apache-2.0

Per-engine NOTICE files list every third-party dependency and its license.

Documentation

Topic	Where
Product using these engines	QVAC
Speech-to-text engine	third_party/whisper.cpp/README.md
Whisper subtree deltas	third_party/whisper.cpp/PATCHES.md
Whisper subtree sync process	docs/UPSTREAM-SYNC.md
ASR, diarization, end-of-utterance	engines/parakeet/README.md
Text-to-speech and enhancement	engines/tts/README.md
Music generation	engines/audiogen/README.md
TTS memory behaviour	engines/tts/MEMORY.md
Development journals	engines/parakeet/PROGRESS.md, engines/tts/PROGRESS.md

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qvac-ext-lib-whisper.cpp

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qvac-ext-lib-whisper.cpp

Architecture

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Supported models

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End-of-utterance and diarization

Text-to-speech and voice cloning

Speech enhancement

Music generation

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Command line tools

Whisper

Parakeet

Text-to-speech

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