CrispASR: One C++ binary for multilingual ASR and TTS models

CrispASR, a C++ speech engine forked from whisper.cpp, now supports 54 ASR backends and 55 TTS engines in a single binary with no Python dependencies, according to the project's documentation. The tool, which also offers multilingual text translation, compiles to WebAssembly for browser use and includes a live demo on HuggingFace Space.

One C++ binary, 54 ASR backends + 55 TTS engines + multilingual text translation, zero Python dependencies. CrispASR started as a fork of whisper.cpp https://github.com/ggml-org/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 text-to-speech-models for the TTS side. bash $ 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 https://huggingface.co/spaces/cstr/CrispASR — live transcription + TTS + language detection, auto-deployed from hf-space/ . | Project | What it does | |---|---| | This repo — C++ speech engine. 54 ASR + 55 TTS backends, CLI + HTTP server + C-ABI + Python/Rust/Dart/Go/Ruby/Java bindings. | | Cross-platform Flutter transcription app built on CrispASR. Desktop + mobile, model browser with download queue, mic capture, SRT/VTT/JSON export, diarization, batch processing. Fully offline. | | Text-related engine via ggml — same philosophy as CrispASR but for embeddings, retrieval, OCR and OMR, Math and Music Notation. Numerous architectures XLM-R, Qwen3-Embed, Gemma3, ModernBERT, ... , dense + sparse + ColBERT + reranking. PP-OCR, Tesseract, EasyOCR, InternVL2, etc. Python/Rust/Dart bindings. | | Python ASR GUI with 9 backends faster-whisper, mlx-whisper, voxtral, insanely-fast-whisper, ... . The Python counterpart to CrispASR's C++ approach. | — new to CrispASR? Two commands to your first working audio, no repo clone needed Start here Supported backends supported-backends — ASR asr-backends + TTS text-to-speech-models + translation translation + post-processing post-processing-models + music & audio analysis music--audio-analysis Feature matrix feature-matrix Install & build install--build — quick install full guide in docs/install.md /CrispStrobe/CrispASR/blob/main/docs/install.md ;— the which prebuilt Linux tarball to download /CrispStrobe/CrispASR/blob/main/docs/install.md prebuilt-linux-tarballs--which-one-to-download-355 -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 quick-start Troubleshooting /CrispStrobe/CrispASR/blob/main/docs/troubleshooting.md — it printed the banner and stopped, reading the exit code, --no-gpu bisect, which Windows zip— 52 engines: Kokoro, Qwen3-TTS, VibeVoice, dots.tts, Orpheus, Chatterbox, IndexTTS, Irodori, VoxCPM2, CosyVoice3, CSM, Dia, Zonos, Bark, Piper, MeloTTS, and more Text-to-Speech TTS Streaming & live transcription /CrispStrobe/CrispASR/blob/main/docs/streaming.md Server mode HTTP API /CrispStrobe/CrispASR/blob/main/docs/server.md Concurrency, parallelism & scaling /CrispStrobe/CrispASR/blob/main/docs/concurrency.md — how one transcription uses multiple cores, concurrent server requests --server-workers , bulk offline transcription, replicas behind a load balancer CLI reference /CrispStrobe/CrispASR/blob/main/docs/cli.md — flags, VAD, CTC alignment, output formats, auto-download, audio formats Environment variables /CrispStrobe/CrispASR/blob/main/docs/environment-variables.md — the CRISPASR <BACKEND <FEATURE convention, global knobs, and every per-backend variable Language bindings /CrispStrobe/CrispASR/blob/main/docs/bindings.md — Python / Rust / Dart / Go / Java / JavaScript / Ruby / mobile Benchmarking CrispASR /CrispStrobe/CrispASR/blob/main/docs/benchmarking.md — how to measure transcribe time not cold start : server/in-process reps, proof-of-work rules, phase-timing env vars Architecture /CrispStrobe/CrispASR/blob/main/docs/architecture.md — layered layout, src/core/ primitives, regression discipline Contributing — adding a new backend /CrispStrobe/CrispASR/blob/main/docs/contributing.md — 5-file recipe, ground-truth diff workflow Regression matrix /CrispStrobe/CrispASR/blob/main/docs/regression-matrix.md — tools/test-all-backends.py capability tiers— 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 EU AI Act Quantize models /CrispStrobe/CrispASR/blob/main/docs/quantize.md — crispasr-quantize for all backends GPU backend selection gpu-backend-selection Debugging & profiling debugging--profiling Credits credits 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. Download one file from Releases https://github.com/CrispStrobe/CrispASR/releases/latest and unzip it: | Platform | Download | Notes | |---|---|---| Windows | crispasr-windows-x86 64-cpu.zip | Needs AVX2 2013+ Intel / 2015+ AMD . Older CPU → …-cpu-legacy.zip | Windows + NVIDIA | crispasr-windows-x86 64-cuda.zip | Self-contained; a CUDA Toolkit install is not required. CUDA-13-native build: …-cuda13.zip Turing+ | macOS | crispasr-macos.tar.gz | Metal GPU support built in | Linux | crispasr-linux-x86 64.tar.gz | …-cuda.tar.gz / …-vulkan.tar.gz for GPU | Prefer to build it yourself? See Install & build 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 -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. 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. | You want to… | Go to | |---|---| | Clone a voice from a recording | --i-have-rights | Which backend should I pick? which-backend-should-i-pick docs/cli.md /CrispStrobe/CrispASR/blob/main/docs/cli.md docs/streaming.md /CrispStrobe/CrispASR/blob/main/docs/streaming.md docs/server.md /CrispStrobe/CrispASR/blob/main/docs/server.md crispasr --list-backends 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 . CrispASR ships 54 ASR backends for transcription/translation and 55 TTS engines for synthesis. It also ships audio-to-audio S2S backends, including Sidon restoration and the VoxCPM2 AudioVAE speech upscaler; see the feature matrix /CrispStrobe/CrispASR/blob/main/docs/feature-matrix.md for the complete capability list. Pick at the CLI with --backend NAME , or omit it to let the binary auto-detect from the GGUF metadata. Jump to the TTS table text-to-speech-models for the synthesis side. | Backend | Model | Architecture | Languages | License | |---|---|---|---|---| whisper | ggml-base.en.bin | whisper distil-whisper/distil-large-v3 parakeet nvidia/parakeet-tdt-0.6b-v3 parakeet nvidia/parakeet-tdt-0.6b-v2 parakeet nvidia/parakeet-tdt-1.1b parakeet nvidia/parakeet-tdt ctc-110m parakeet nvidia/parakeet-tdt ctc-1.1b parakeet nvidia/parakeet-tdt ctc-0.6b-ja reazonspeech reazon-research/reazonspeech-nemo-v2 fastconformer-ctc nvidia/parakeet-ctc-0.6b fastconformer-ctc nvidia/parakeet-ctc-1.1b fastconformer-ctc grider-transwithai/parakeet-ctc-1.1b-ja canary nvidia/canary-1b-v2 -sl/-tl canary-qwen nvidia/canary-qwen-2.5b lfm2-audio LiquidAI/LFM2.5-Audio-1.5B lfm2-audio LiquidAI/LFM2.5-Audio-1.5B-JP mini-omni2 gpt-omni/mini-omni2 cohere CohereLabs/cohere-transcribe-03-2026 cohere efwkjn/cohere-asr-ja-v0.1 granite , https://huggingface.co/ibm-granite/granite-speech-3.3-2b ibm-granite/granite-speech-{3.2-8b,3.3-2b,3.3-8b} granite-4.0-1b-speech more /CrispStrobe/CrispASR/blob/main/docs/architecture.md granite--granite-41--granite-41-plus--granite-41-nar granite-4.1 ibm-granite/granite-speech-4.1-2b more /CrispStrobe/CrispASR/blob/main/docs/architecture.md granite--granite-41--granite-41-plus--granite-41-nar granite-4.1-plus ibm-granite/granite-speech-4.1-2b-plus more /CrispStrobe/CrispASR/blob/main/docs/architecture.md granite--granite-41--granite-41-plus--granite-41-nar granite-4.1-nar ibm-granite/granite-speech-4.1-2b-nar more /CrispStrobe/CrispASR/blob/main/docs/architecture.md granite--granite-41--granite-41-plus--granite-41-nar fastconformer-ctc nvidia/stt en fastconformer ctc large voxtral mistralai/Voxtral-Mini-3B-2507 voxtral4b mistralai/Voxtral-Mini-4B-Realtime-2602 qwen3 Qwen/Qwen3-ASR-0.6B qwen3-1.7b Qwen/Qwen3-ASR-1.7B qwen3-ja-anime jaykwok/Qwen3-ASR-1.7B-JA-Anime-Galgame-hf mega-asr zhifeixie/Mega-ASR higgs-stt bosonai/higgs-audio-v3-stt more /CrispStrobe/CrispASR/blob/main/docs/architecture.md higgs-stt wav2vec2 jonatasgrosman/wav2vec2-large-xlsr-53-english wav2vec2 facebook/data2vec-audio-base-960h wav2vec2 facebook/hubert-large-ls960-ft glm-asr zai-org/GLM-ASR-Nano-2512 kyutai-stt kyutai/stt-1b-en fr kyutai-stt kyutai/stt-2.6b-en firered-asr FireRedTeam/FireRedASR2-AED moonshine UsefulSensors/moonshine-{tiny,base} moonshine‑de fidoriel/moonshine-base-de moonshine‑tiny‑de fidoriel/moonshine-tiny-de moonshine-streaming UsefulSensors/moonshine-streaming-{tiny,small,medium} gemma4-e2b google/gemma-4-E2B-it gemma4-e4b google/gemma-4-E4B-it --backend gemma4-e2b omniasr omniASR-CTC-1B-v2 more /CrispStrobe/CrispASR/blob/main/docs/architecture.md omniasr-ctc--llm--unlimited 1600+ omniasr‑300m omniASR-CTC-300M-v2 more /CrispStrobe/CrispASR/blob/main/docs/architecture.md omniasr-ctc--llm--unlimited 1600+ omniasr-llm omniASR-LLM-300M-v2 more /CrispStrobe/CrispASR/blob/main/docs/architecture.md omniasr-ctc--llm--unlimited 1600+ omniasr-llm omniASR-LLM-Unlimited-300M-v2 more /CrispStrobe/CrispASR/blob/main/docs/architecture.md omniasr-ctc--llm--unlimited 1600+ vibevoice microsoft/VibeVoice-ASR more /CrispStrobe/CrispASR/blob/main/docs/architecture.md vibevoice vibevoice-bitnet VibeVoice-ASR-BitNet more /CrispStrobe/CrispASR/blob/main/docs/architecture.md vibevoice mimo-asr XiaomiMiMo/MiMo-V2.5-ASR more /CrispStrobe/CrispASR/blob/main/docs/architecture.md mimo-asr ark-asr experimental/WIP base https://huggingface.co/cstr/ark-asr-3b-GGUF cstr/ark-asr-3b-GGUF https://huggingface.co/AutoArk-AI/ARK-ASR-3B AutoArk-AI/ARK-ASR-3B more /CrispStrobe/CrispASR/blob/main/docs/architecture.md ark-asr moss-audio OpenMOSS-Team/MOSS-Audio-4B-Instruct more /CrispStrobe/CrispASR/blob/main/docs/architecture.md moss-audio moss-transcribe OpenMOSS-Team/MOSS-Transcribe-preview-2B more /CrispStrobe/CrispASR/blob/main/docs/architecture.md moss-transcribe moss-diarize OpenMOSS-Team/MOSS-Transcribe-Diarize-0.9B whisper tiron experimental base https://huggingface.co/cstr/tiron-GGML Trelis/tiron https://huggingface.co/Trelis/tiron Trelis/tiron funasr FunAudioLLM/Fun-ASR-Nano-2512 fun-asr-mlt-nano FunAudioLLM/Fun-ASR-MLT-Nano-2512 paraformer funasr/paraformer-zh foxnose speaker diarization Wespeaker/wespeaker-voxceleb-resnet34-LM --diarize-method foxnose : WeSpeaker ResNet34-LM 256-d embeddings + GMM/BIC speaker counting + spectral clustering + Viterbi temporal smoothing more /CrispStrobe/CrispASR/blob/main/docs/architecture.md foxnose-diarize . 3.18 % DER on VoxConverse dev vs the upstream reference implementation's 3.07 % gigaam base https://huggingface.co/cstr/gigaam-v3-GGUF ai-sage/GigaAM-v3 https://huggingface.co/ai-sage/GigaAM-v3 ai-sage/GigaAM-v3 e2e rnnt / e2e ctc emit punctuation + casing + ITN from a SentencePiece vocab, rnnt / ctc emit bare lowercase Cyrillic more /CrispStrobe/CrispASR/blob/main/docs/architecture.md gigaam sensevoice FunAudioLLM/SenseVoiceSmall -oj JSON expose the tags as separate fields. Upstream's emotion classifier is not exposed — see EU AI Act /CrispStrobe/CrispASR/blob/main/docs/eu-ai-act.md 41-emotion-recognition--removed-not-gated | Backend | Model | Architecture | Input / output | License | |---|---|---|---|---| sidon | KevinAHM/Sidon-GGUF | https://huggingface.co/sarulab-speech/sidon-v0.1 sarulab-speech/sidon-v0.1 more /CrispStrobe/CrispASR/blob/main/docs/architecture.md sidon voxcpm2-vae , converted with https://huggingface.co/openbmb/VoxCPM2 openbmb/VoxCPM2 --vae-only more /CrispStrobe/CrispASR/blob/main/docs/architecture.md voxcpm2-vae huggingface-cli download KevinAHM/Sidon-GGUF sidon-v0.1-f16.gguf --local-dir models crispasr -m models/sidon-v0.1-f16.gguf -f input.wav --s2s --s2s-output restored.wav python models/convert-voxcpm2-to-gguf.py --input openbmb/VoxCPM2 \ --output models/voxcpm2-vae-f32.gguf --vae-only crispasr -m models/voxcpm2-vae-f32.gguf -f input.wav --s2s \ --s2s-output upscaled.wav Synthesis backends, driven by the --tts flag and a --tts-output PATH.wav . See the dedicated Text-to-Speech text-to-speech-models section below for quick-start commands and engine selection guidance. | Backend | Models | Architecture | Languages | License | |---|---|---|---|---| miotts | MioTTS-0.6B | vibevoice-tts , https://huggingface.co/cstr/vibevoice-realtime-0.5b-GGUF VibeVoice-Realtime-0.5B VibeVoice-1.5B kugelaudio kugelaudio-0-open qwen3-tts , https://huggingface.co/cstr/qwen3-tts-0.6b-base-GGUF Qwen3-TTS-12Hz-0.6B-Base , https://huggingface.co/cstr/qwen3-tts-1.7b-base-GGUF 1.7B-Base 1.7B-VoiceDesign more /CrispStrobe/CrispASR/blob/main/docs/architecture.md qwen3-tts qwen3-tts-customvoice 1.7B-CustomVoice --voice <name ; optional style via --instruct e.g. "spoke very slowly" more /CrispStrobe/CrispASR/blob/main/docs/architecture.md qwen3-tts moss-tts OpenMOSS-Team/MOSS-TTS-v1.5 --voice ref.wav ; --backend moss-tts -m <backbone --codec-model <codec moss-tts-local OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 --backend moss-tts-local -m <backbone --codec-model <codec omnivoice k2-fsa/OmniVoice more /CrispStrobe/CrispASR/blob/main/docs/architecture.md omnivoice melotts EN V2 https://github.com/myshell-ai/MeloTTS myshell-ai/MeloTTS more /CrispStrobe/CrispASR/blob/main/docs/architecture.md melotts piper community voices https://github.com/rhasspy/piper rhasspy/piper --g2p-dict kokoro + German backbones https://huggingface.co/hexgrad/Kokoro-82M hexgrad/Kokoro-82M more /CrispStrobe/CrispASR/blob/main/docs/architecture.md kokoro orpheus + https://huggingface.co/cstr/orpheus-3b-0.1-ft-GGUF Orpheus-3B-FT SNAC 24 kHz more /CrispStrobe/CrispASR/blob/main/docs/architecture.md orpheus chatterbox + Nano/turbo/fine-tune variants https://huggingface.co/cstr/chatterbox-GGUF cstr/chatterbox-GGUF more /CrispStrobe/CrispASR/blob/main/docs/architecture.md chatterbox--chatterbox-turbo--chatterbox-nano--chatterbox-finnish-nano--kartoffelbox-turbo--lahgtna-chatterbox chatterbox-finnish-nano fine-tunes indextts cstr/indextts-1.5-GGUF voxcpm2-tts cstr/voxcpm2-GGUF --voice <wav voxtral-tts mistralai/Voxtral-4B-TTS-2603 cosyvoice3-tts cstr/cosyvoice3-0.5b-2512-GGUF --voice <name , or any WAV via --voice ref.wav --ref-text "<exact transcript " . --backend cosyvoice3-tts-rl selects upstream's RL-tuned talker same companions csm cstr/csm-1b-GGUF more /CrispStrobe/CrispASR/blob/main/docs/architecture.md csm lfm2-audio + https://huggingface.co/cstr/lfm2-audio-1.5b-GGUF cstr/lfm2-audio-1.5b-GGUF jp dia nari-labs/Dia-1.6B S1 / S2 tags use 100-char prompts zonos-tts + https://huggingface.co/cstr/zonos-v0.1-transformer-GGUF cstr/zonos-v0.1-transformer-GGUF cstr/dac-44khz-GGUF more /CrispStrobe/CrispASR/blob/main/docs/architecture.md zonos-tts bark cstr/bark-small-GGUF .npz prompts --voice <file.npz speecht5 cstr/speecht5-tts-GGUF --voice <xvector.bin fastpitch cstr/fastpitch-en-GGUF more /CrispStrobe/CrispASR/blob/main/docs/architecture.md fastpitch bananamind-tts Banaxi-Tech/BananaMind-TTS-V2.1-Preview more /CrispStrobe/CrispASR/blob/main/docs/architecture.md bananamind-tts parler-tts cstr/parler-tts-mini-v1.1-GGUF --instruct outetts cstr/outetts-0.3-1b-GGUF --voice <speaker.json pocket-tts cstr/pocket-tts-GGUF more /CrispStrobe/CrispASR/blob/main/docs/architecture.md pocket-tts tada + https://huggingface.co/cstr/tada-tts-1b-GGUF cstr/tada-tts-1b-GGUF HumeAI/tada-3b-ml tada-ref.gguf , custom voices via --voice <tada-ref.gguf built with models/convert-tada-ref-to-gguf.py more /CrispStrobe/CrispASR/blob/main/docs/architecture.md tada TTS feature matrix | Backend | Voice cloning | Sampling | kHz | Auto-download | Flash attn | |---|---|---|---|---|---| | vibevoice-tts | yes | temp | 24 | yes | yes | | qwen3-tts | yes | temp | 24 | yes | yes | | omnivoice | yes | temp | 24 | — | — | | kokoro | — | — | 24 | yes | — | | orpheus | — | temp | 24 | yes | yes | | chatterbox | yes | temp | 24 | yes | yes | | outetts | yes JSON | temp | 24 | yes | yes | | indextts | yes | temp | 24 | yes | yes | | voxcpm2-tts | yes | — | 48 | yes | — | | cosyvoice3-tts | yes | temp | 24 | yes | yes | | f5-tts | yes | — | 24 | yes | — | | irodori-tts | yes WAV | VoiceDesign: --instruct | 48 | yes | — | | csm | — | temp | 24 | yes | — | | dia | — | temp | 44 | yes | — | | bark | yes .npz | temp | 24 | yes | — | | speecht5 | yes xvec | — | 16 | yes | — | | parler-tts | — | temp | 44 | yes | — | | fastpitch | — | — | 22 | — | — | | piper | — | — | 22 | — | — | | pocket-tts | yes | temp | 24 | yes | — | | tada | yes | temp | 24 | yes | — | | dots-tts | yes --voice ref.wav | 16-step CFG Euler | 48 | yes | — | | confucius4-tts | yes --voice ref.wav | 25-step CFG Euler | 22.05 | yes | — | CustomVoice variant only; Base uses baked speakers via --voice <name . Output language. -tl <lang or -l selects the language to speak; cosyvoice3-tts , qwen3-tts and moss-tts act on it natively. For cross-lingual cloning — an English reference clip speaking German, the subtitle-dubbing case — also pass -sl <lang for the language the reference is spoken in, so cosyvoice3 drops the reference transcript instead of carrying its accent. Over HTTP: "language" + "source lang" on POST /v1/audio/speech . See docs/tts.md /CrispStrobe/CrispASR/blob/main/docs/tts.md output-language-and-cross-lingual-cloning--tl---sl . Text-to-text translation, distinct from the audio-side --translate flag which routes audio → English text on whisper / canary / etc. . Driven by --text "..." -sl <src -tl <tgt . | Backend | Models | Architecture | Languages | License | |---|---|---|---|---| m2m100 | facebook/m2m100 418M | more /CrispStrobe/CrispASR/blob/main/docs/architecture.md m2m100--wmt21 m2m100-wmt21 + https://huggingface.co/cstr/wmt21-dense-24-wide-en-x-GGUF facebook/wmt21-dense-24-wide-en-x facebook/wmt21-dense-24-wide-x-en more /CrispStrobe/CrispASR/blob/main/docs/architecture.md m2m100--wmt21 en-x / x-en checkpoints madlad google/madlad400-3b-mt more /CrispStrobe/CrispASR/blob/main/docs/architecture.md madlad m2m100 base production-ready ./build/bin/crispasr --backend m2m100 -m auto \ --text "Hello world, how are you today?" \ -sl en -tl de → Hallo Welt, wie bist du heute? WMT21 dense English ↔ X, 4.7B — auto-downloads ~2.5 GB . Two separate checkpoints: en-x for English-source, x-en for English-target. Pick the one matching your -sl / -tl direction or pass an explicit -m <path to load the other manually . ./build/bin/crispasr --backend m2m100-wmt21 -m auto \ --text "The president said he would not attend." \ -sl en -tl de uses wmt21-dense-24-wide-en-x ./build/bin/crispasr --backend m2m100-wmt21 \ -m models/wmt21-dense-24-wide-x-en-q4 k.gguf \ --text "Le président a dit qu'il ne serait pas présent." \ -sl fr -tl en uses wmt21-dense-24-wide-x-en MADLAD-400 3B 419 languages, bit-token-identical to Python SP ./build/bin/crispasr --backend madlad -m auto \ --text "Hello world." \ -sl en -tl ta For 2-stage pipelines e.g., ASR → m2m100 , use the dedicated --tr-sl / --tr-tl flags; they fall back to -sl / -tl when unset, so single-stage standalone usage is just -sl/-tl . Work with all backends. | Model | Task | Architecture | Languages | License | HuggingFace | |---|---|---|---|---|---| FireRedPunc | Punctuation restoration | BERT-base 12L, d=768 , 5 classes | Chinese + English | Apache-2.0 | cstr/fireredpunc-GGUF | fullstop-punc cstr/fullstop-punc-multilang-GGUF punctuate-all cstr/punctuate-all-GGUF PCS --punc-model pcs truecaser‑lstm --truecase-model lstm truecaser‑crf --truecase-model crf truecaser‑de --truecase-model auto CLD3 cstr/cld3-GGUF GlotLID-V3 cstr/glotlid-GGUF LID-176 cstr/fasttext-lid176-GGUF Shared codec modules used by TTS backends. Also available standalone for encode/decode. | Model | Architecture | Sample Rate | Token Rate | License | HuggingFace | |---|---|---|---|---|---| MioCodec v2 | WavLM encoder → FSQ 12800 → Transformer decoder + AdaLN-Zero + SnakeBeta upsampler + iSTFT | 44.1 kHz | 25 Hz 341 bps | MIT | cstr/miocodec-v2-44k-GGUF | SNAC 24 kHz cstr/snac-24khz-GGUF All runtimes share ggml-based inference. The speech-LLM backends qwen3 , voxtral , voxtral4b , granite , glm-asr , kyutai-stt inject audio encoder frames directly into an autoregressive language model's input embeddings, instead of using a dedicated CTC/transducer/seq2seq decoder. The fastconformer-ctc backend hosts the NeMo FastConformer-CTC standalone ASR family — stt en fastconformer ctc {large,xlarge,xxlarge} and the architecturally-identical parakeet-ctc-{0.6b,1.1b} different training data + tokenizer, same encoder + head shape — with greedy CTC decoding. Same C++ runtime as the canary-ctc aligner. Beyond speech, CrispASR runs several music/audio analysis tasks — each a small GGUF with the architecture auto-detected, no Python. See docs/cli.md /CrispStrobe/CrispASR/blob/main/docs/cli.md for the per-task flags and output formats. Source separation --separate — split a mix into stems <input <stem .wav via mel-band-roformer vocal/instrumental, MIT or htdemucs 4-stem . --stems vocals,drums selects a subset; --sep-output-dir sets the output location. Piano transcription --backend piano-transcription — piano audio → MIDI note events 88 keys @ 100 fps, ByteDance/Kong CRNN; F16 GGUF ≈ 77 MB . Guitar tablature --tab — per-frame fret-per-string grid via TabCNN Wiggins & Kim, ISMIR 2019; CC BY 4.0 weights . The backend emits per-string emission scores, not a decided tablature — run your own constrained Viterbi via crispasr session tab emissions for playable output. Beat / downbeat tracking --beats — beat grid via Beat This CPJKU, ISMIR 2024; MIT for code and weights, no patent-encumbered DBN . Chord recognition --chords — chord timeline .lab via BTC ISMIR 2019 . Weights are CC-BY-NC-SA, gated behind --accept-license cc-by-nc-sa-4.0 . Pitch / F0 estimation --pitch — monophonic pitch track via CREPE MIT . Run crispasr --list-backends to see it live. Each backend declares capabilities at runtime; if you ask for a feature the selected backend does not support, CrispASR prints a warning and silently ignores the flag. Sortable / filterable view: docs/feature-matrix.html /CrispStrobe/CrispASR/blob/main/docs/feature-matrix.html — click any column header to sort, type to filter rows, click cap pills to require a capability. Generated from crispasr --list-backends-json single source of truth — drift impossible . Regenerate via python tools/gen-feature-matrix.py . A Markdown twin lives at . /CrispStrobe/CrispASR/blob/main/docs/feature-matrix.md docs/feature-matrix.md The static table below is a curated subset focusing on the ASR backends and the cross-cutting features that matter for ASR pipelines. The full 109-backend × 27-cap surface is in the generated views. | Feature | whisper | parakeet | canary | cohere | granite | granite‑4.1 | voxtral | voxtral4b | qwen3 | fc‑ctc | wav2vec2 | glm‑asr | kyutai‑stt | firered | moonshine | moon‑stream | omniasr | omniasr‑llm | vibevoice | gemma4‑e2b | mimo‑asr | funasr | paraformer | sensevoice | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| | Native timestamps | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||||||||||||| | CTC timestamps | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||| | Word-level timing | ✔ | ✔ | ✔ | ✔ | -am | ✔† | -am | -am | -am | -am | -am | -am | ✔ | -am | -am | -am | -am | -am | -am | -am | -am | -am | || | Per-token confidence | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||| | Language auto-detect | ✔ | ✔ | LID | LID | LID | LID | LID | LID | ✔ | LID | LID | ✔ | LID | LID | LID | LID | LID | LID | LID | ✔ | LID | LID | LID | ✔ | | Speech translation | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||||||||||| | Speaker diarization | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | | Grammar GBNF | ✔ | ||||||||||||||||||||||| | Temperature sampling | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||| | Beam search | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||||||| | Flash attention | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | || | Punctuation toggle | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||| | Punc restoration | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | pp | | Source / target language | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||||||||||||| Audio Q&A --ask | | | ✔ | | | | | ||||||||||||||||| | Streaming | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | Auto-download -m auto | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | KV quant CRISPASR KV QUANT , plus per-half K / V | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||||||| mmap weights CRISPASR GGUF MMAP | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | | | TTS | ✔ | The matrix above covers 24 ASR backends. Additional ASR backends not shown: nemotron 39-lang streaming ASR with cache-aware FastConformer + RNN-T , lfm2-audio ASR + TTS + S2S in one model , moss-audio audio understanding + ASR , moss-transcribe Qwen3-Omni encoder + Qwen3-1.7B ASR , mini-omni2 ASR + TTS + S2S , kugelaudio 7B audio understanding . See docs/feature-matrix.md /CrispStrobe/CrispASR/blob/main/docs/feature-matrix.md for the full 109-backend matrix. TTS-only backends kokoro , qwen3-tts + variants, vibevoice-tts , orpheus + DE variants, chatterbox / chatterbox-turbo / chatterbox-nano / kartoffelbox-turbo / lahgtna-chatterbox , dia , bark , outetts , zonos , csm , f5-tts , irodori-tts , parler-tts , speecht5 , piper , fastpitch , pocket-tts , melotts , cosyvoice3 , voxcpm2 , tada-tts all carry the TTS, AUTO DOWNLOAD, TEMPERATURE, and FLASH ATTN caps; per-backend cloning + voice-pack support is documented in the Text-to-Speech models text-to-speech-models table above and . The vibevoice and lfm2-audio columns mark dual-mode ASR + TTS backends. /CrispStrobe/CrispASR/blob/main/docs/tts.md docs/tts.md Key: ✔ = native/built-in, -am = via CTC forced aligner -am canary-ctc-aligner.gguf or -am qwen3-forced-aligner.gguf , LID = via external language identification pre-step -l auto , pp = via --punc-model post-processor FireRedPunc or fullstop-punc , = experimental or partial support, † = PLUS variant only native T:N word timestamps with -owts ; base uses -am . granite-4.1 covers both the regular and -plus variants; granite-4.1-nar is a non-autoregressive variant with encoder+projector only no LLM decode features . The KV quant row marks backends that honor CRISPASR KV QUANT={f16,q8 0,q4 0} — CTC-style backends without a KV cache parakeet, fc-ctc, wav2vec2, kyutai-stt, firered, moonshine variants, omniasr-CTC don't apply. The same backends also honor the per-half CRISPASR KV QUANT K / CRISPASR KV QUANT V overrides llama.cpp --cache-type-k / --cache-type-v parity for asymmetric K-vs-V precision; common recipe K=q8 0 V=q4 0 saves ~40 % more KV memory than symmetric Q8 0. The mmap weights row marks backends consuming core gguf::load weights and therefore honoring CRISPASR GGUF MMAP=1 ; whisper itself uses upstream's loader and is unaffected. See docs/cli.md /CrispStrobe/CrispASR/blob/main/docs/cli.md Memory footprint for usage + recommended combos. Speaker diarization as a post-processing step via --diarize : energy / xcorr — stereo-only, no extra deps foxnose — best accuracy, no external deps : WeSpeaker ResNet34-LM embeddings + GMM/BIC speaker counting + spectral clustering + Viterbi smoothing. Estimates the speaker count rather than needing it up front; --diarize-embedder auto fetches the GGUF 24 MB, CC-BY-4.0 . 7.3 % DER on VoxConverse dev where pyannote + TitaNet scores 7.8 %, and 3.18 % vs the upstream reference's 3.07 % when scored on turns more /CrispStrobe/CrispASR/blob/main/docs/architecture.md foxnose-diarize pyannote — native GGUF no Python, no sherpa-onnx ; add --diarize-embedder auto TitaNet or --diarize-embedder indextts ECAPA-TDNN for globally stable speaker IDs across long files sherpa / ecapa — external sherpa-onnx https://github.com/k2-fsa/sherpa-onnx subprocess; runs once globally on full audio for consistent speaker IDs 110 vad-turns — mono-friendly gap-based proxy The server endpoint supports response format=diarized json for structured speaker-labelled output with normalised speaker letters A, B, C … — see docs/server.md /CrispStrobe/CrispASR/blob/main/docs/server.md diarized-json-format-206 . Full reference + tuning knobs cluster threshold, max speakers, pluggable embedder adapters : see docs/cli.md diarization /CrispStrobe/CrispASR/blob/main/docs/cli.md diarization . Language identification for backends without native LID: --lid-backend whisper default, 75 MB ggml-tiny.bin , --lid-backend silero native GGUF, 16 MB, 95 languages , or --lid-backend firered FireRedLID, 1.7 GB, 120 languages — Conformer encoder + Transformer decoder . Voice activity detection : --vad uses the default Silero VAD ~885 KB, auto-downloaded . Each VAD segment is transcribed independently, producing separate SRT/VTT entries with correct timestamps. Use --vad --split-on-punct for best subtitle output. Four VAD backends: Silero default , FireRedVAD -vm firered , recommended , MarbleNet -vm marblenet , 439 KB, 6 languages , Whisper-VAD-EncDec -vm whisper-vad , experimental . Punctuation restoration --punc-model : CTC-based backends output lowercase without punctuation. Named shortcuts: auto / firered Chinese+English , fullstop EN/DE/FR/IT, XLM-R-large , punctuate-all 12 languages, XLM-R-base , pcs 47 languages, punc + truecasing + sentence boundary detection in one model . Or pass a GGUF path directly. Also available via Python/Rust/Dart wrappers crispasr.PuncModel . Truecasing --truecase-model : Restore German noun/name capitalization in lowercase ASR output. Three options in ascending quality: auto statistical, 1.7 MB , crf CRF with context, 8.5 MB , lstm BiLSTM char-level, 3.2 MB, recommended — 97.9% F1, handles adjective/noun distinction and formal "Ihnen" . All auto-download from cstr/truecaser-de https://huggingface.co/cstr/truecaser-de . Or use --punc-model pcs for neural punc + truecasing in one pass 47 languages . Which backends produce punctuation natively? | Backend | Punctuation | Capitalization | Notes | |---|---|---|---| | whisper | ✔ | ✔ | Full punctuation and casing | | parakeet | ✔ | ✔ | | | canary | ✔ | ✔ | | | cohere | ✔ | ✔ | Toggleable via --no-punctuation | | granite | ✔ | ✔ | LLM output | | voxtral | ✔ | ✔ | LLM output | | voxtral4b | ✔ | ✔ | LLM output | | qwen3 | ✔ | ✔ | LLM output | | funasr | ✔ | ✔ | LLM output Qwen3-0.6B decoder . Chinese chars carry full-width period; mlt-nano variant adds Latin-script casing + punctuation. | | sensevoice | ✔ | ✔ | CTC output with native ITN — toggle off via --no-punctuation , which controls Arabic-digit vs spelled-out numerals + comma/period emission. | | paraformer | no | no | NAR character-level output — add --punc-model | | gigaam | ✔ e2e | ✔ e2e | The e2e revisions carry punctuation + casing + inverse text normalization in the SentencePiece vocabulary. The charwise ctc / rnnt revisions emit lowercase Cyrillic with no punctuation — but auto-restoration is still suppressed for them, because the auto-enabled FireRedPunc is a Chinese/English model and injects full-width CJK punctuation into Russian. Use an e2e revision for punctuated output, or pass an explicit --punc-model . | | glm-asr | ✔ | ✔ | LLM output | | kyutai-stt | ✔ | ✔ | LLM output | | moonshine | ✔ | ✔ | Encoder-decoder output | fastconformer-ctc | no | no | CTC — add --punc-model | wav2vec2 | no | no | CTC — add --punc-model | firered-asr | no | no | CTC — add --punc-model | omniasr CTC | no | no | CTC — add --punc-model | omniasr LLM | ✔ | ✔ | Autoregressive decoder | Other freely-licensed alternatives that could be added: felflare/bert-restore-punctuation https://huggingface.co/felflare/bert-restore-punctuation MIT, English, includes truecasing , xashru/punctuation-restoration https://github.com/xashru/punctuation-restoration Apache-2.0, 40+ languages, BiLSTM-CRF . Progressive subtitle output --flush-after : By default, non-whisper backends buffer all segments and print output at the end. For real-time subtitle consumption PotPlayer, custom media players , use --flush-after 1 to print each SRT entry to stdout immediately after its VAD segment is transcribed: crispasr --backend parakeet -m parakeet.gguf --vad --flush-after 1 -osrt -f long audio.wav SRT entries appear progressively as each segment finishes JSON output with language detection : When using -l auto -oj , the JSON output includes detected language info: { "crispasr": { "backend": "cohere", "language": "en", "language detected": "en", "language confidence": 0.977, "language source": "ecapa" }, "transcription": ... } | Need | Pick | |---|---| | Battle-tested, all features exposed | whisper | | Lowest English WER | cohere | Fastest 16x realtime on CPU | moonshine tiny , fc-ctc 10x | | Multilingual + word timestamps + fast | parakeet 2.9x RT | Multilingual with explicit language control | canary | Speech translation X→en or en→X | canary, voxtral, qwen3 | 30 languages + Chinese dialects | qwen3 | 1600+ languages | omniasr CTC or LLM | Realtime streaming ASR native incremental encoder, ~2× RT feed; sub-second-token target deferred to phase 2 | voxtral4b | | Highest-quality offline speech-LLM | voxtral | | Apache-licensed speech-LLM | granite, voxtral, qwen3, omniasr-llm | Lightweight CTC-only fast, no decoder | wav2vec2, fc-ctc, data2vec, omniasr | Russian | gigaam e2e rnnt — 8.4 % avg WER, punctuation + ITN , whisper, qwen3 | Mandarin + Chinese dialects | firered-asr, qwen3, glm-asr, funasr, paraformer, sensevoice | Multilingual 31 langs speech-LLM | fun-asr-mlt-nano, qwen3, omniasr-llm, gemma4-e2b | Multilingual 50+ langs + LID + audio-event in one pass | sensevoice encoder-only CTC, non-AR, 15× faster than Whisper-Large | Audio-LLM backends qwen3 , voxtral , granite , glm-asr , etc. run full transformer decoder stacks 28+ layers, 2048-dim and are dramatically slower on CPU than encoder-only backends. On older dual-core hardware they can drop below 0.01× realtime. If you're on CPU-only hardware: - Prefer moonshine 16× RT , fc-ctc 10× RT , parakeet 2.9× RT , or whisper for usable speeds. - Use --flush-after 1 to see results as each VAD slice completes instead of waiting for the entire file. - Use -pp / --print-progress for per-slice progress indicators on all backends unified backends show slice-level progress; whisper shows encoder-level progress . - Quantize models to Q4 K or Q5 K to reduce memory and compute. Cohere, canary, granite, voxtral and voxtral4b need an explicit language code up front. If you don't know the language, pass -l auto and crispasr runs an optional LID pre-step before the main transcribe call: Downloads ggml-tiny.bin 75 MB, 99 languages on first use crispasr --backend cohere -m $TC/cohere-transcribe-q5 0.gguf \ -f unknown.wav -l auto crispasr lid : detected 'en' p=0.977 via whisper-tiny crispasr: LID - language = 'en' whisper, p=0.977 These LID providers are available: - --lid-backend whisper default — uses a small multilingual ggml- .bin model via the crispasr C API. Auto-downloads ~75 MB on first use. 99 languages. - --lid-backend silero — native GGUF port of Silero's 95-language classifier. 16 MB F32. Runs as a ggml graph multi-threaded SIMD on CPU, GPU offload on Metal/CUDA; on Vulkan the graph is routed to CPU pending an upstream kernel fix . Analyzes the first 30 s of audio CRISPASR SILERO LID MAX S overrides ; CRISPASR SILERO LID LEGACY=1 restores the old scalar path. - --lid-backend ecapa — recommended : ECAPA-TDNN Apache-2.0 . Purpose-built for language ID. Very high accuracy on TTS benchmark. Two variants via --lid-model :— VoxLingua107, 43 MB F16, 107 languages, ISO codes en, de, ... . cstr/ecapa-lid-107-GGUF Default. — CommonLanguage, 40 MB F16, 45 languages, full names English, German, ... . cstr/ecapa-lid-commonlanguage-GGUF - --lid-backend firered — FireRedLID Conformer encoder + Transformer decoder . Q4 K 544 MB , 120 languages including Chinese dialects. Slower but covers more languages. - --lid-backend probe — no second model at all: ask the ASR model itself . Transcribes a 20 s clip once per language the model declares and keeps the best-scoring candidate length × text-LID agreement × distinct-token ratio², the last term catching the repetitive output a wrong-language prompt produces . Currently implemented by cohere . The reason to prefer it is correctness, not just the saved download: an external detector knows 99 languages while Cohere Transcribe accepts 14 — and its Arabic finetune only en / ar — so external LID regularly returns a language the model was never trained on, and Cohere answers a wrong language fluently rather than failing. The probe cannot. Cost is one encode + one short decode per candidate, so it runs automatically only for a model with ≤ 4 languages CRISPASR COHERE PROBE MAX LANGS ; CRISPASR COHERE PROBE TEXTLID=0 drops the text-LID agreement term. The ceiling is about cost, not accuracy. Measured on the real models: the two-language Arabic finetune picks ar for an Arabic clip p=0.675 and en for samples/jfk.wav p=0.647 ; the 14-language base model, probed across all 14, also gets both right en p=0.169, ar p=0.254 — it is simply slower than an external detector. The encoder output is language-independent, so the probe encodes once and decodes per candidate encode is ~87 % of a pass ; a 14-candidate probe measured 12 s → 4-5 s against one-encode-per-candidate, byte-identical output. CRISPASR COHERE PROBE REUSE ENC=0 restores the naive path.The one soft spot worth knowing: asking the model for a language it was not trained on can yield a clean translation rather than garbage, which a text LID then confirms — "fluent French out" is not evidence of French in. That is what makes a mismatched whitelist dangerous, and it is measurable: force a 14-language list onto the two-language Arabic finetune and its fr probe returns real French and wins. The real base model's fr probe instead code-switches "Et so, my fellow Americans…", agreement 0.00 and loses, as it should. These VAD providers are available: Silero VAD default — ~885 KB, auto-downloaded via --vad . Industry-standard, well-tested. FireRedVAD — DFSMN-based, 2.4 MB, F1=97.57%. Pass --vad -vm firered to auto-download. Recommended. MarbleNet — NVIDIA 1D separable CNN, 439 KB, 6 languages EN/DE/FR/ES/RU/ZH . Pass --vad -vm marblenet to auto-download. Smallest model. cstr/marblenet-vad-GGUF Whisper-VAD-EncDec experimental — Whisper-base encoder + TransformerDecoder head, 22 MB Q4 K. Trained on Japanese ASMR; may not generalise well to all domains. Pass --vad -vm whisper-vad . Slower than others ~1s vs ~50ms . cstr/whisper-vad-encdec-asmr-GGUF Pass --lid-backend off to skip LID entirely. Audio LID above tags what was spoken ; text LID tags what was written . Text LID runs on a transcript or any UTF-8 string and is useful for routing post-ASR pipelines translation, punctuation, sub selection without re-running an audio model. Three GGUF families, one binary — the dispatcher picks by general.architecture : | Backend | Labels | Size F16 | License | HF repo | |---|---|---|---|---| CLD3 Google compact language detector v3 | 109 ISO 639-1 | 440 KB | Apache-2.0 | cstr/cld3-GGUF | GlotLID-V3 cis-lmu fastText cstr/glotlid-GGUF LID-176 Facebook fastText cstr/fasttext-lid176-GGUF ¹ LID-176 is CC-BY-NC-4.0 — non-commercial use only. CLD3 + GlotLID-V3 are Apache-2.0 with no such constraint. Pick CLD3 for the smallest, fastest path; GlotLID for maximum coverage low-resource languages ; LID-176 only if you need its specific 176-label space and accept its non-commercial terms. Standalone CLI — auto-routes by GGUF arch, with auto-download: crispasr-lid -m auto --text "Bonjour le monde" → cstr/cld3-GGUF default, ~440 KB crispasr-lid -m auto:glotlid --text "Bonjour le monde" -k 5 crispasr-lid -m auto:lid-fasttext176 --text "Hallo Welt" Or pass an explicit path / canonical filename looked up in the registry : crispasr-lid -m cld3-f16.gguf --text "你好世界" zh 0.997816 echo "Привет мир" | crispasr-lid -m auto --quiet ru 0.907322 Post-ASR pipeline — --lid-on-transcript runs the same dispatcher on the assembled transcript also accepts auto :variant : crispasr -m ggml-tiny.bin -f speech.wav --lid-on-transcript auto transcript on stdout lang=de conf=0.997123 backend=lid-cld3 The dispatcher src/text lid dispatch.{h,cpp} is a thin C ABI façade — one integer compare per call; per-stage diff harness is green at cos≥0.999 across 8 multilingual smoke samples. Don't want to build? Prebuilt binaries for Windows, macOS and Linux are on the releases page https://github.com/CrispStrobe/CrispASR/releases/latest — see Start here start-here for which file to take. The rest of this section is for building from source. git clone --recursive https://github.com/CrispStrobe/CrispASR cd CrispASR already cloned without --recursive? initialize the bundled ggml submodule: git submodule update --init --recursive cmake -B build -DCMAKE BUILD TYPE=Release cmake --build build -j$ nproc The ggml/ submodule is required. If you cloned without --recursive , run git submodule update --init --recursive first — otherwise CMake stops with a message telling you to do exactly that. Produces build/bin/crispasr main CLI , build/bin/crispasr-quantize , and build/bin/crispasr-diff . No Python, PyTorch, or pip required at runtime — just a C++17 compiler and CMake 3.14+. For GPU acceleration, add the matching ggml flag at configure time: cmake -B build -DCMAKE BUILD TYPE=Release -DGGML CUDA=ON NVIDIA cmake -B build -DCMAKE BUILD TYPE=Release -DGGML METAL=ON Apple Silicon cmake -B build -DCMAKE BUILD TYPE=Release -DGGML VULKAN=ON cross-vendor See for the full guide: all GPU backends CUDA / Metal / Vulkan / MUSA / SYCL , Windows convenience scripts, ffmpeg ingestion, optional BLAS, glibc notes, and the docs/install.md scripts/dev-build.sh wrapper.If a build runs but the binary exits with no output, see docs/troubleshooting.md /CrispStrobe/CrispASR/blob/main/docs/troubleshooting.md . Deeper ASR examples below. If this is your first run, use Start here start-here instead. For TTS, the runnable guide is docs/tts.md /CrispStrobe/CrispASR/blob/main/docs/tts.md Text-to-Speech text-to-speech-models below is the model catalogue . Download a whisper model same as upstream whisper.cpp ./models/download-ggml-model.sh base.en ./build/bin/crispasr -m models/ggml-base.en.bin -f samples/jfk.wav 00:00:00.000 -- 00:00:07.940 And so my fellow Americans ask not what your country can do for you 00:00:07.940 -- 00:00:10.760 ask what you can do for your country. Grab the quantized model ~467 MB curl -L -o parakeet.gguf \ https://huggingface.co/cstr/parakeet-tdt-0.6b-v3-GGUF/resolve/main/parakeet-tdt-0.6b-v3-q4 k.gguf ./build/bin/crispasr -m parakeet.gguf -f samples/jfk.wav Auto-detected backend 'parakeet' from GGUF metadata. And so, my fellow Americans, ask not what your country can do for you, ask what you can do for your country. Word-level timestamps one line per word ./build/bin/crispasr -m parakeet.gguf -f samples/jfk.wav -ml 1 Transcription source == target ./build/bin/crispasr --backend canary -m canary-1b-v2-q5 0.gguf -f audio.de.wav -sl de -tl de Translation German speech → English text ./build/bin/crispasr --backend canary -m canary-1b-v2-q5 0.gguf -f audio.de.wav -sl de -tl en ...or use the familiar crispasr flag: ./build/bin/crispasr --backend canary -m canary-1b-v2-q5 0.gguf -f audio.de.wav -l de --translate First run downloads ~2.5 GB to ~/.cache/crispasr/ via curl, then runs ./build/bin/crispasr --backend voxtral -m auto -f samples/jfk.wav Subsequent runs use the cached file ./build/bin/crispasr --backend voxtral -m auto -f samples/jfk.wav -l en 0.6B default, ~500 MB ./build/bin/crispasr --backend qwen3 -m auto -f audio.zh.wav 1.7B higher quality, ~1.3 GB — supports both -hf and non-hf source models ./build/bin/crispasr --backend qwen3 -m qwen3-1.7b --auto-download -f audio.wav Japanese anime/galgame fine-tune ~1.3 GB ./build/bin/crispasr --backend qwen3 -m qwen3-ja-anime --auto-download -f anime.wav Long audio: the default is safe 30 s chunking. --chunk-seconds 0 decodes the whole file in ONE pass matches the reference model verbatim on multi-minute clips, 218 — but the encoder's full attention is O N² in audio length, so keep single-pass clips under ~10 minutes on 16 GB machines. For long-form use prefer the plain -q4 k / -q8 0 GGUFs over the -imatrix variants see the model card . ./build/bin/crispasr --backend glm-asr -m auto -f audio.wav Long audio in one pass up to 655 s — 30 s encoder windows, one LLM prompt, same layout as the HF/zai reference; matches it verbatim on the 218 clip : ./build/bin/crispasr --backend glm-asr -m auto --chunk-seconds 0 -f long.wav Note: in single-pass mode the model like the reference skips leading non-speech audio; the default 30 s-chunked mode transcribes more of such clips. Custom --ask / non-English --language instructions need a GGUF with baked BPE merges re-published 2026-07; older GGUFs fall back to the default transcription prompt with a warning . Download the LM + audio tokenizer the tokenizer is a separate model huggingface-cli download cstr/mimo-asr-GGUF mimo-asr-q4 k.gguf \ --local-dir ~/.cache/crispasr huggingface-cli download cstr/mimo-tokenizer-GGUF mimo-tokenizer-q4 k.gguf \ --local-dir ~/.cache/crispasr Transcribe auto-discovers tokenizer if it sits next to the LM ./build/bin/crispasr \ --backend mimo-asr \ -m ~/.cache/crispasr/mimo-asr-q4 k.gguf \ --codec-model ~/.cache/crispasr/mimo-tokenizer-q4 k.gguf \ -f samples/jfk.wav Output: And so, my fellow Americans, ask not what your country can do for you. Ask what you can do for your country. The 4.5 GB Q4 K is the recommended quant; F16 14.9 GB needs ~16 GB RAM during inference. JFK matches the upstream Python MimoAudio.asr sft reference verbatim; performance on M1+Metal is ~0.3× realtime Q4 K dequant per step is the bottleneck — F16 + KV-reuse follow-ups are queued under PLAN 51a/b/c . English Q4 K quantized, 212 MB — 6x smaller than F16 curl -L -o wav2vec2-en-q4k.gguf \ https://huggingface.co/cstr/wav2vec2-large-xlsr-53-english-GGUF/resolve/main/wav2vec2-xlsr-en-q4 k.gguf ./build/bin/crispasr -m wav2vec2-en-q4k.gguf -f samples/jfk.wav and so my fellow americans ask not what your country can do for you ask what you can do for your country German curl -L -o wav2vec2-de-q4k.gguf \ https://huggingface.co/cstr/wav2vec2-large-xlsr-53-german-GGUF/resolve/main/wav2vec2-xlsr-de-q4 k.gguf ./build/bin/crispasr -m wav2vec2-de-q4k.gguf -f audio.de.wav Convert any HuggingFace Wav2Vec2ForCTC model: python models/convert-wav2vec2-to-gguf.py \ --model-dir jonatasgrosman/wav2vec2-large-xlsr-53-german \ --output wav2vec2-de.gguf --dtype f32 Then optionally quantize: ./build/bin/crispasr-quantize wav2vec2-de.gguf wav2vec2-de-q4k.gguf q4 k CrispASR has three feature areas that warrant their own docs pages: — Streaming & live transcription /CrispStrobe/CrispASR/blob/main/docs/streaming.md --stream , --mic , --live , sliding-window chunking, per-token confidence.— Kokoro multilingual, smallest , Qwen3-TTS highest fidelity, voice cloning , VibeVoice lowest-latency streaming , Orpheus 3 B Llama + SNAC , Chatterbox flow-matching + HiFT vocoder, German via Kartoffelbox , IndexTTS, VoxCPM2, and CosyVoice3 9 langs + 18 zh dialects; baked-voice bank + arbitrary-WAV cloning . Voice packs, language routing, and qwen3-tts environment switches. All TTS output is watermarked; post-embed verification warns if confidence is low. Use Text-to-Speech TTS /CrispStrobe/CrispASR/blob/main/docs/tts.md --detect-watermark file.wav to check any WAV for AI watermarks.— persistent model, OpenAI-compatible Server mode HTTP API /CrispStrobe/CrispASR/blob/main/docs/server.md /v1/audio/transcriptions ASR and /v1/audio/speech + /v1/voices TTS, automatic on any loaded CAP TTS backend , per-request voice + speed + instructions, CORS, long-form sentence chunking, API keys, Docker Compose, prebuilt CUDA images.— one transcription already uses multiple cores; the server accepts requests concurrently but serializes inference on one model by default; Concurrency, parallelism & scaling /CrispStrobe/CrispASR/blob/main/docs/concurrency.md --server-workers N runs N model instances so pure-ASR requests run concurrently; and for bulk/throughput workloads, process-level fan-out xargs -P / GNU parallel or N replicas behind a load balancer. Also covers what is not supported batched multi-stream inference, PagedAttention and why. Quickest taste of each: Streaming from microphone crispasr --mic -m model.gguf TTS via auto-downloaded VibeVoice ~636 MB on first run crispasr --backend vibevoice-tts -m auto --tts "Hello world" --tts-output hello.wav CosyVoice3 on GPU; companions auto-download beside the LLM crispasr --backend cosyvoice3-tts -m auto --tts "Hello world" --tts-output cosy.wav CosyVoice3 fast mode: 5 flow steps instead of the quality-default 10 COSYVOICE3 FLOW STEPS=5 crispasr --backend cosyvoice3-tts -m auto \ --tts "Hello world" --tts-output cosy-fast.wav Persistent HTTP server, OpenAI-compatible crispasr --server -m model.gguf --port 8080 curl -F "file=@audio.wav" http://localhost:8080/v1/audio/transcriptions TTS over HTTP — load a TTS backend, hit /v1/audio/speech crispasr --server --backend qwen3-tts-customvoice -m auto --voice-dir ./voices --port 8080 curl http://localhost:8080/v1/audio/speech \ -H 'Content-Type: application/json' \ -d '{"input":"Hello world","voice":"vivian"}' -o out.wav CosyVoice3 uses batched classifier-free guidance and request-sized KV caching by default. Baked voices load only the LLM, flow, HiFT, and voice bank; the larger S3 tokenizer and CAMPPlus companions load lazily when a .wav cloning voice is first requested. Common flags: crispasr -m auto --backend parakeet -f audio.wav --vad -osrt --split-on-punct | Flag | Meaning | |---|---| -m FNAME / --backend NAME | Model path or auto and forced backend | -f FNAME | Input audio repeatable; positional accepted | --vad | Silero VAD chunking — strongly recommended for multi-minute audio | -osrt / -ovtt / -otxt / -oj / -ojf | Output formats also -ocsv , -olrc | -am FNAME | CTC aligner GGUF for word-level timestamps on LLM backends | --align-only | Standalone forced alignment: text/ .srt + audio → timestamped SRT/JSON no ASR needed ; .srt input keeps its cues and gets re-timed --align-granularity auto|word|segment | -tp F / -bs N | Sampling temperature / beam search width | -n N / --frequency-penalty F | Generated-token cap / opt-in repeated-token penalty for supported autoregressive ASR backends | -l auto / --detect-language | LID pre-step for backends without native lang detect | --hotwords "A,B,C" | Contextual biasing — boost named terms during CTC/TDT decode or LLM prompt | -ck N | Fallback chunk size when VAD is off default 30 s | --list-backends | Print the capability matrix and exit | See for the full reference: every flag, VAD details, CTC alignment workflow, output JSON layout, the auto-download registry, and supported audio formats. docs/cli.md See for Python / Rust / Dart / Go / Java / JavaScript / Ruby / mobile. docs/bindings.md CrispASR is structured as a stable C-ABI in src/ every algorithm: VAD, diarize, LID, alignment, cache, registry consumed by all language wrappers, with thin presentation layers in examples/cli/ . Per-model runtimes live in src/{whisper,parakeet,canary,...}.cpp , sharing primitives from src/core/ mel, ffn, attention, GGUF loader, FastConformer / Conformer / Granite-LLM blocks, etc. . — full layered layout, file-by-file tour of docs/architecture.md src/ and examples/cli/ , per-backend internals table, regression discipline.— adding a new backend in five files, clang-format-18 setup, the docs/contributing.md crispasr-diff PyTorch-ground-truth workflow, and the TTS audio-cosine-vs-reference regression target.— docs/regression-matrix.md tools/test-all-backends.py capability tiers, cache modes keep / ephemeral , --skip-missing for CI. Shared libraries cross-repo with CrispEmbed : crisp audio/ — Whisper-shape audio encoder Conv-stem + Transformer crisp punc/ — punctuation restoration FireRedPunc + PCS crisp lid/ — text-based language identification fastText + CLD3 crisp truecase/ — truecasing statistical + CRF + BiLSTM Both are self-contained static libraries with CMakeLists.txt. CrispEmbed links them via add subdirectory ../CrispASR/crisp / ; CrispASR uses them directly. If the shared dir is absent, both repos fall back to local copies of the source files. For benchmarks see PERFORMANCE.md /CrispStrobe/CrispASR/blob/main/PERFORMANCE.md ; for the session-by-session port log and the bug-class lessons, see . /CrispStrobe/CrispASR/blob/main/LEARNINGS.md LEARNINGS.md build/bin/crispasr-quantize is a single, model-agnostic GGUF re-quantization tool that works across all supported model families Whisper, Parakeet, Canary, Cohere, Voxtral, Qwen3, Granite, Wav2Vec2, MiMo-ASR, GLM-ASR, Moonshine, VibeVoice, Kokoro, Qwen3-TTS, … : ./build/bin/crispasr-quantize input.gguf output.gguf q4 k See for the full guide: supported quant types, K-quant alignment fallback, recommended quant per backend, and worked examples for each architecture. docs/quantize.md All backends use ggml backend init best which automatically picks the highest-priority compiled backend: CUDA Metal Vulkan CPU. To force a specific backend: Force Vulkan even when CUDA is available crispasr --gpu-backend vulkan -m model.gguf -f audio.wav Pin a specific GPU useful on Vulkan systems with iGPU + dGPU crispasr --gpu-backend vulkan -dev 1 -m model.gguf -f audio.wav Force CPU useful for benchmarking crispasr -ng -m model.gguf -f audio.wav CUDA unified memory swap to RAM when VRAM exhausted GGML CUDA ENABLE UNIFIED MEMORY=1 crispasr -m model.gguf -f audio.wav Build flags: -DGGML CUDA=ON , -DGGML METAL=ON , -DGGML VULKAN=ON . Notes: --gpu-backend vulkan selects the Vulkan backend, but it does not choose which physical GPU to use. Use -dev N to select the Vulkan device index.- On some Windows laptops, Vulkan device 0 is the Intel iGPU and the NVIDIA GPU is 1 . If Vulkan looks unexpectedly slow, rerun with -dev 1 . - The Windows convenience script build-vulkan.bat creates a separate Vulkan-capable binary at build-vulkan\bin\crispasr.exe . For most backends, -v / --verbose surfaces per-stage timings and device picks. For headless / library use where the CLI flag isn't plumbed through , set CRISPASR VERBOSE=1 instead. Per-stage timing breakdown mel / encoder / prefill / decode : crispasr -v --backend gemma4-e2b -m model.gguf -f audio.wav gemma4 e2b: mel 128x1099 17.2 ms gemma4 e2b: encoder done: 1536x275 719.0 ms gemma4 e2b: prefill done, first token=3133 1464.0 ms gemma4 e2b: decoded 25 tokens 7748.3 ms total crispasr: transcribed 11.0s audio in 7.75s 1.4x realtime Hugging Face access for gated models Voxtral, Gemma4-E2B, … : HF TOKEN=hf xxx crispasr -m auto --backend gemma4-e2b -f audio.wav The server has its own auth env: CRISPASR API KEYS see Server mode /CrispStrobe/CrispASR/blob/main/docs/server.md . Per-backend debug / bench / dump-dir env vars developer These are useful when porting a new backend or chasing a regression. The BENCH=1 toggles emit per-stage timings even without -v ; the DEBUG=1 toggles emit per-step diagnostic prints; the DUMP DIR= paths write per-stage F32 tensors for diff-testing against a PyTorch reference see Debug a new backend against PyTorch ground truth /CrispStrobe/CrispASR/blob/main/docs/contributing.md debug-a-new-backend-against-pytorch-ground-truth . | Env var | Purpose | |---|---| CRISPASR VERBOSE=1 | Forces verbose mode for any backend parallel to the -v flag . | CRISPASR DUMP DIR=path/ | Generic per-stage F32 tensor dump for the crispasr-diff harness. | GEMMA4 E2B BENCH=1 | Per-stage timings for the Gemma-4-E2B backend. | COHERE BENCH=1 / COHERE DEBUG=1 | Cohere transcribe per-stage timings / per-step diagnostics. | COHERE PROF=1 | Cohere graph-level profiling per-op timings . | COHERE THREADS=N | Override thread count for the Cohere backend. | COHERE DEVICE=cpu|cuda|metal|vulkan | Force the Cohere backend onto a specific device. | COHERE DUMP ATTN=path/ | Dump attention activations for Cohere used by the diff harness . | FIRERED BENCH=1 | Per-stage timings for the FireRedASR backend. | FIREREDPUNC DEBUG=1 | Per-step diagnostics for the FireRed punctuation post-step. | MOONSHINE STREAMING BENCH=1 | Per-stage timings for moonshine-streaming. | OMNIASR BENCH=1 / OMNIASR DEBUG=1 / OMNIASR DUMP DIR= | OmniASR per-stage timings, diagnostics, and stage dumps. | PARAKEET DEBUG=1 | Parakeet TDT per-step diagnostics joint network, blank-id sanity . | QWEN3 TTS BENCH=1 / QWEN3 TTS DEBUG=1 / QWEN3 TTS DUMP DIR= | Qwen3-TTS per-stage timings, diagnostics, and stage dumps. | VIBEVOICE BENCH=1 / VIBEVOICE DEBUG=1 / VIBEVOICE DUMP DIR= | VibeVoice ASR per-stage timings, diagnostics, and stage dumps. | VIBEVOICE REF FEATURES=path | Replace the live encoder with a saved feature tensor regression harness . | VIBEVOICE TTS DUMP=path/ | VibeVoice TTS per-stage dumps token IDs, base/TTS hidden, neg condition, frame-0 noise/v cfg/latent/acoustic embed for the diff harness. | VIBEVOICE TTS DUMP PERFRAME=1 | Per-frame VibeVoice TTS dumps written as perframe <stage f<NNN .bin . Pair with VIBEVOICE TTS DUMP=path/ and VIBEVOICE TTS NOISE=path for stage-by-stage AR diff against tools/run official vibevoice.py . | VIBEVOICE TTS TRACE=1 | Extra one-line traces negative-condition prefill rms, scaling/bias factors loaded . Same effect as library verbosity ≥ 2; there is no CLI flag for it -v caps verbosity at 1 . | VIBEVOICE VOICE AUDIO=path.wav | Reference voice WAV for 1.5B-base TTS without a .gguf voice cache. | VIBEVOICE TTS NOISE=path | Override the per-frame Gaussian init noise. Flat little-endian float32 N frames, vae dim — typically the noise.bin written by tools/run official vibevoice.py . | VIBEVOICE VAE BACKEND=cpu|metal|cuda|vulkan | Pin the VAE decoder onto a specific backend. | WAV2VEC2 BENCH=1 / WAV2VEC2 VERBOSE=1 / WAV2VEC2 DUMP DIR= | wav2vec2 per-stage timings, verbose graph traces, and stage dumps. | CRISPASR VOXTRAL4B STREAM TIMING=1 | Per-stage timings for the voxtral4b streaming path encoder drain / prefill / first-text-token / decode-step p50/p95 . | CRISPASR VOXTRAL4B STREAM CHUNK MS=N | Override the internal encoder chunk size default 240 ms . Must be a multiple of 80 ms. Larger = faster feed kernel-launch amortisation , longer live-caption latency floor. | CRISPASR VOXTRAL4B STREAM BATCH ENCODER=1 | Regression-debug: ignore the streaming encoder's audio embeds and re-run the whole batch encoder at flush. | CRISPASR VOXTRAL4B STREAM DEBUG=1 / CRISPASR VOXTRAL4B STREAM DIFF=1 | Per-step decode prints / side-by-side encoder cosine vs the batch encoder. | CRISPASR VOXTRAL4B STREAM LIVE=1 | Live-captions decode-during-feed PLAN 7 phase 3 . get text polled during feed returns progressive transcript. Default OFF PTT semantics . Wrappers: Python Session.stream open live=True , Rust stream open ex .., live: true . | CRISPASR VOXTRAL4B STREAM DECODER THREAD=1 | Decoder worker thread PLAN 7 phase 4, implies live mode . Lets feed return between encoder chunks without waiting for the decode loop — useful for mic-driven workloads. On M1 the Metal queue serializes encoder and decoder so total wall-clock is unchanged; faster GPUs with kernel-level parallelism see real overlap. | CRISPASR VOXTRAL4B FUSED QKV=0 | Opt out of the runtime fused-QKV LLM path default-on, ~7-8 % decode speedup on M1 Q4 K, ~500 MB extra memory . | CRISPASR QWEN3 ASR FUSED QKV=0 | Opt out of the runtime fused-QKV LLM path for qwen3-asr default-on; works on F16/F32/Q4 K/Q8 0/... . | CRISPASR VOXTRAL FUSED QKV=1 | Opt in to the runtime fused-QKV LLM path for voxtral 3B. Off by default no measurable speedup on JFK-shape decodes; useful for long-form workloads where decode dominates . | QWEN3 TTS FUSED QKV=1 | Opt in to the runtime fused-QKV talker path. | GRANITE DISABLE ENCODER GRAPH=1 | Force the granite-speech / -plus / -nar encoder back to the per-layer CPU loop slower but kept around for debugging . The single ggml-graph encoder with per-layer Shaw RPE is the default and is bit-near-identical to the CPU loop while being ~2× faster end-to-end across all three variants. | CRISPASR NO REL POS=1 | Ablate the relative-position bias in the Gemma-4 audio encoder development only . | ECAPA REF FBANK=path | Reference filterbank tensor for the ECAPA-TDNN LID model regression harness . | CRISPASR SHERPA LID BIN=path | Override the auto-detected sherpa-onnx LID binary. | CRISPASR ARG DEVICE=N | Default GPU device index when -dev isn't passed. | GGML CUDA ENABLE UNIFIED MEMORY=1 | Let CUDA swap to RAM when VRAM is exhausted. | GGML VK VISIBLE DEVICES / CUDA VISIBLE DEVICES | Standard ggml/CUDA device-visibility filters. | HF TOKEN and HUGGING FACE HUB TOKEN are both honoured for gated-model downloads in that order . — the original ggml inference engine and Whisper runtime this fork is built on whisper.cpp https://github.com/ggml-org/whisper.cpp — the tensor library everything runs on ggml https://github.com/ggml-org/ggml NVIDIA NeMo — parakeet-tdt-{0.6b-v2,0.6b-v3,1.1b}, parakeet-tdt ctc-{110m,1.1b,0.6b-ja}, parakeet-ctc-{0.6b,1.1b}, canary-1b-v2, canary-ctc aligner, and the FastConformer-CTC family stt en fastconformer ctc {large,xlarge,xxlarge} plus CTC branches of the stt fastconformer hybrid large pc fleet: en-pc, de, es, fr, it, nl, pl, ru, ua, hr, be, ar, fa, ka, hy, uz, kk-ru — all usable both as ASR backends and as compact ~82 MB -am forced aligners Cohere — cohere-transcribe-03-2026 Qwen team Alibaba — Qwen3-ASR-0.6B, Qwen3-ASR-1.7B, Qwen3-ForcedAligner-0.6B Mistral AI — Voxtral Mini 3B and 4B Realtime IBM Granite team — Granite Speech 3.2-8b, 3.3-2b, 3.3-8b, 4.0-1b Meta / wav2vec2 — wav2vec2 CTC models XLSR-53 English, German, multilingual via any Wav2Vec2ForCTC checkpoint — optional diarization via subprocess ONNX models sherpa-onnx https://github.com/k2-fsa/sherpa-onnx — VAD native GGUF and language identification native GGUF, 95 languages Silero https://github.com/snakers4/silero-vad — speaker diarization segmentation native GGUF port pyannote https://github.com/pyannote/pyannote-audio + miniaudio https://miniaud.io/ + stb vorbis https://github.com/nothings/stb — embedded/linked audio decoders WAV/MP3/FLAC/AIFF/OGG/Opus, no ffmpeg; AAC/M4A/ALAC via Apple AudioToolbox libopus/opusfile https://opus-codec.org/ MIT — in-tree clean-room codec suite: MP3/AAC-LC/Ogg-Opus output encoders TTS glint https://github.com/CrispStrobe/glint .mp3 / .aac / .opus and cross-platform ADTS AAC-LC + Ogg Opus input decoders no libopus needed Anthropic — significant portions of the crispasr integration layer, all model converters, and the FastConformer/attention/mel/FFN/BPE core helpers were co-authored with Claude Claude Code https://claude.ai/claude-code Same as upstream whisper.cpp: MIT . Per-model weights are covered by their respective HuggingFace model licenses see Supported backends supported-backends . The crispasr binary itself links model runtimes that are mostly permissively licensed MIT / Apache-2.0 / CC-BY-4.0 for weights .