CrispASR

One C++ binary, 119 backends — 62 of them TTS engines — plus multilingual text translation, zero Python dependencies.

CrispASR started as a fork of whisper.cpp and extends that base into a unified speech engine called crispasr, backed by full ggml C++ runtimes for major open-weights ASR and TTS architectures. One build, one binary, one consistent CLI — pick the backend at the command line or let CrispASR auto-detect it from your GGUF file. See Text-to-Speech for the TTS side.

$ crispasr -m ggml-base.en.bin          -f samples/jfk.wav                    # OpenAI Whisper
$ crispasr -m parakeet-tdt-0.6b.gguf    -f samples/jfk.wav                    # NVIDIA Parakeet
$ crispasr -m canary-1b-v2.gguf         -f samples/jfk.wav                    # NVIDIA Canary
$ crispasr -m voxtral-mini-3b-2507.gguf -f samples/jfk.wav                    # Mistral Voxtral
$ crispasr --backend qwen3 -m auto      -f samples/jfk.wav                    # -m auto downloads
$ crispasr --backend kokoro -m auto --tts "Hello world" --tts-output out.wav  # TTS

No Python. No PyTorch. No separate per-model binary. No pip install. Just one C++ binary and a GGUF file.

Browser: All backends compile to WebAssembly (4.3 MB) via build-wasm.sh. Multithreaded, runs entirely client-side with COOP/COEP headers.

Demo: HuggingFace Space — live transcription + TTS + language detection, auto-deployed from hf-space/.

Ecosystem

Table of contents

• Start here — new to CrispASR? Two commands to your first working audio, no repo clone needed
• Supported backends — ASR + TTS + translation + post-processing + music & audio analysis
• Feature matrix
• Install & build — quick install (full guide in docs/install.md); which prebuilt Linux tarball to download — the -hip / -vulkan builds require the matching GPU driver and do not fall back to CPU (the -cuda tarballs do, since v0.8.30)
• Quick start — ASR
• Troubleshooting — it printed the banner and stopped, reading the exit code, --no-gpu bisect, which Windows zip
• Text-to-Speech (TTS) — 52 engines: Kokoro, Qwen3-TTS, VibeVoice, dots.tts, Orpheus, Chatterbox, IndexTTS, Irodori, VoxCPM2, CosyVoice3, CSM, Dia, Zonos, Bark, Piper, MeloTTS, and more
• Streaming & live transcription
• Server mode (HTTP API)
• Concurrency, parallelism & scaling — how one transcription uses multiple cores, concurrent server requests (--server-workers), bulk offline transcription, replicas behind a load balancer
• CLI reference — flags, VAD, CTC alignment, output formats, auto-download, audio formats
• Environment variables — the CRISPASR_<BACKEND>_<FEATURE> convention, global knobs, and every per-backend variable
• Language bindings — Python / Rust / Dart / Go / Java / JavaScript / Ruby / mobile
• Benchmarking CrispASR — how to measure transcribe time (not cold start): server/in-process reps, proof-of-work rules, phase-timing env vars
• Architecture — layered layout, src/core/ primitives, regression discipline
• Contributing — adding a new backend — 5-file recipe, ground-truth diff workflow
• Regression matrix — tools/test-all-backends.py capability tiers
• EU AI Act — synthetic-audio marking (watermark + C2PA + spoken disclaimer), what counts as a voice clone, the speaker-biometrics boundary, why there is no emotion recognition, and what stays your duty as deployer
• Quantize models — crispasr-quantize for all backends
• GPU backend selection
• Debugging & profiling
• Credits

Start here

Everything below this section is a catalogue — 100+ backends, browse it when you need one. If you just want CrispASR working, this is the whole path. No repo clone, no Python, no model hunting.

New to the project? docs/getting-started.md walks the same path step by step, with the expected output and the three usual first-run failures.

1. Get the binary

Download one file from Releases and unzip it:

Prefer to build it yourself? See Install & build. The -hip and -vulkan builds require the matching driver and do not fall back to CPU; the Linux -cuda tarballs do fall back.

Check it runs — this should print a version banner and exit:

crispasr --version          # Windows: .\crispasr.exe --version

CUDA builds also print cuda toolkit and cuda runtime ABI, so this command distinguishes the CUDA 12 and CUDA 13 packages without inspecting DLLs.

Windows CUDA packages bundle the runtime they were built against. The CUDA 12.6 legacy CLI and library assets use the -cuda126 suffix; their split packages require cuda126-runtime.zip from the same release. Use the included SHA-256 manifest when reusing runtime DLLs. Do not mix CUDA 12.8 binaries with CUDA 12.6 DLLs. The standard -cuda build retains CUDA 12.8 and native Blackwell support. CUDA 12.8 also supports Pascal and Volta in CrispASR; PyTorch's wheel architecture selection is a separate policy (NVIDIA architecture support).

2. Make it speak

-m auto downloads the model on first use (~135 MB here) and reuses it afterwards — nothing to find or install. It lands in ~/.cache/crispasr/ (%USERPROFILE%\.cache\crispasr on Windows).

crispasr --backend kokoro -m auto --tts "The quick brown fox jumps over the lazy dog." --tts-output hello.wav
# crispasr: TTS output written to 'hello.wav' (78000 samples @ 24000 Hz, 3.25 sec)

Play hello.wav. That is the TTS half working.

3. Transcribe it back

crispasr --backend parakeet -m auto -f hello.wav -l en
# crispasr: transcribed 3.2s audio in 0.32s (10.1x realtime)
# The quick brown fox jumps over the lazy dog.

(~467 MB on first run. -l en skips language auto-detection, which would otherwise fetch a small extra model.) Both halves now work — swap in your own .wav and you are running.

Where to go next

If nothing happened

Add -v to any command for verbose progress, and --dry-run-resolve to print which model files it would open (and whether they're on disk) without loading anything.

If a command printed its banner and then simply stopped — no error, no output file — that is a crash, not a refusal, and the exit code identifies it in one step. See docs/troubleshooting.md.

Supported backends

CrispASR sh