CrispASR

CrispASR

One C++ binary, 145 backends — 69 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

Project	What it does
CrispASR	This repo — C++ speech engine. 145 backends (69 TTS), CLI + HTTP server + C-ABI + Python/Rust/Dart/Go/Ruby/Java bindings.
CrisperWeaver	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.
CrispEmbed	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.
Susurrus	Python ASR GUI with 9 backends (faster-whisper, mlx-whisper, voxtral, insanely-fast-whisper, ...). The Python counterpart to CrispASR's C++ approach.

---

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 -vulkan build requires the Vulkan driver and does not fall back to CPU (the -cuda tarballs do since v0.8.30, the -hip tarball since #586)
• 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:

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 12.6 legacy build: …-cuda126.zip (Pascal/Volta and newer, through Hopper); CUDA-13-native build: …-cuda13.zip (Turing+)
macOS, Apple Silicon	crispasr-macos-arm64.tar.gz	Metal GPU + Accelerate; crispasr-macos.tar.gz is the arm64 compatibility alias
macOS, Intel	crispasr-macos-x86_64.tar.gz	CPU + Accelerate; requires AVX2/FMA/F16C (Haswell or newer). Older Intel Mac → crispasr-macos-x86_64-cpu-legacy.tar.gz
Linux	crispasr-linux-x86_64.tar.gz	…-cuda.tar.gz / …-vulkan.tar.gz for GPU

Prefer to build it yourself? See Install & build. The -vulkan build requires the Vulkan driver and does not fall back to CPU; the Linux -cuda and -hip 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

You want to…	Go to
Clone a voice from a recording	docs/tts.md — and read the consent rules first; cloning requires --i-have-rights
Pick a better ASR model	Which backend should I pick?
Transcribe Japanese with Haas-ja	Haas-ja usage and models
Transcribe with word timestamps, SRT/VTT	docs/cli.md
Live mic / streaming	docs/streaming.md
Run it as an HTTP server	docs/server.md
See every backend this binary has	crispasr --list-backends

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 ships 145 backends (see the generated feature matrix for the authoritative, auto-counted list) — the majority for transcription/translation, and 69 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 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 for the synthesis side.

ASR backends

Backend	Model	Architecture	Languages	License
whisper	ggml-base.en.bin and all OpenAI Whisper variants	Encoder-decoder transformer	99	MIT
whisper	distil-whisper/distil-large-v3	Distilled Whisper: 32L encoder + 2L decoder (6.3x faster)	English	MIT
dolphin	DataoceanAI1/dolphin-cn-dialect-small-streaming (-m dolphin)	E-Branchformer + Transformer decoder + CTC; CTC prefix beam + attention rescoring	Mandarin + Chinese dialects (lang/region predicted)	Apache-2.0
xasr	GilgameshWind/X-ASR-zh-en (-m xasr)	Streaming Zipformer2 transducer (icefall); 160 / 480 / 960 / 1920 ms chunks, realtime WebSocket session	zh, en (punct + case)	Apache-2.0
parakeet	nvidia/parakeet-tdt-0.6b-v3	FastConformer + TDT	25 EU (auto-detect)	CC-BY-4.0
parakeet	oruk/orukeet (-m orukeet)	parakeet-tdt-0.6b-v3 fine-tune, half the encoder's depthwise kernels replaced by fitted Gabor functions	25 EU (auto-detect)	CC-BY-SA-4.0
parakeet	moondream/parakeet-ultra (-m parakeet-ultra)	parakeet-tdt-0.6b-v3 architecture shipped in transformers format; converted with --hf	25 EU (auto-detect)	CC-BY-4.0
parakeet	moondream/parakeet-redux (-m parakeet-redux)	parakeet-tdt-0.6b-v3 with a ternary (base-3 packed) encoder, dequantised exactly by the converter	25 EU (auto-detect)	CC-BY-4.0
phonon2	FermionResearch/Phonon-2 (-m phonon2)	Parakeet TDT v3 with retrained five-value encoder weights; conversion and validation, architecture	English	CC-BY-4.0
parakeet	nvidia/parakeet-tdt-0.6b-v2	FastConformer + TDT, original Open ASR Leaderboard topper	en (mixed-case + punct)	CC-BY-4.0
parakeet	nvidia/parakeet-tdt-1.1b	42L FastConformer + TDT, larger English variant	en (lowercase)	CC-BY-4.0
parakeet	nvidia/parakeet-tdt_ctc-110m	17L FastConformer + TDT+CTC hybrid; smallest variant, auto-CTC decode	en	CC-BY-4.0
parakeet	nvidia/parakeet-tdt_ctc-1.1b	42L FastConformer + TDT+CTC hybrid; largest, mixed-case + punct	en	CC-BY-4.0
parakeet	nvidia/parakeet-tdt_ctc-0.6b-ja	FastConformer-TDT-CTC, xscaling, 80 mels	Japanese	CC-BY-4.0
haas	third-intelligence/Haas-ja-v1.0 (--backend haas -m auto; usage and model variants)	FastConformer + Kimi Delta Attention predictor + TDT	Japanese	Apache-2.0
reazonspeech	reazon-research/reazonspeech-nemo-v2	FastConformer-RNNT, local attn (w=256), 80 mels, 619M params	Japanese	Apache-2.0
fastconformer-ctc	nvidia/parakeet-ctc-0.6b	24L FastConformer + CTC, 80 mels (same arch as fc-ctc-xlarge)	en	CC-BY-4.0
fastconformer-ctc	nvidia/parakeet-ctc-1.1b	42L FastConformer + CTC, 80 mels	en	CC-BY-4.0
fastconformer-ctc	grider-transwithai/parakeet-ctc-1.1b-ja	42L FastConformer + CTC, 80 mels, Japanese fine-tune	Japanese	Apache-2.0
canary	nvidia/canary-1b-v2	FastConformer + Transformer decoder	25 EU (explicit -sl/-tl)	CC-BY-4.0
canary	handy-computer/canary-180m-flash-gguf (base nvidia/canary-180m-flash)	17L FastConformer (d=512) + trained 512→1024 projection + 4L Transformer decoder; existing transcribe.cpp GGUFs load directly (more)	en, de, es, fr (explicit); EN↔DE/ES/FR translation	CC-BY-4.0
canary-qwen	nvidia/canary-qwen-2.5b	FastConformer + Qwen3-1.7B SALM	en	CC-BY-4.0
index-echo	2B, 9B	AuT + Qwen3.5; bilingual timestamped subtitles (details); 2B F16/Q8 and 9B F16 CPU/CUDA validated; pairs 2.589 GiB (2B Q8), 17.914 GiB (9B F16)	upstream recipe: zh → en/ja/es	Apache-2.0
lfm2-audio	LiquidAI/LFM2.5-Audio-1.5B	FastConformer + LFM2 hybrid conv+attention backbone (ASR+TTS)	en	LFM Open v1.0
lfm2-audio	LiquidAI/LFM2.5-Audio-1.5B-JP	FastConformer + LFM2 hybrid conv+attention backbone (ASR+TTS)	ja	LFM Open v1.0
mini-omni2	gpt-omni/mini-omni2	Whisper-small + Qwen2-0.5B (ASR+TTS+S2S)	en	MIT
cohere	CohereLabs/cohere-transcribe-03-2026	Conformer + Transformer	13	Apache-2.0
cohere	efwkjn/cohere-asr-ja-v0.1	Japanese fine-tune of cohere-transcribe-03-2026 (TedX/JSUT-tuned)	Japanese	Apache-2.0
granite	granite-speech-5.0-2b, 3.2/3.3, 4.0	Conformer + Q-Former + Granite LLM (μP); 5.0 F16/Q4_K validated on CPU/CUDA, native context4096 (more)	en fr de es pt ja	Apache-2.0
granite-4.1	ibm-granite/granite-speech-4.1-2b	16L Conformer + Q-Former + Granite LLM; single ggml graph (more)	en fr de es pt ja	Apache-2.0
granite-4.1-plus	ibm-granite/granite-speech-4.1-2b-plus	4.1 + hidden-state concat; punctuated output (more)	en fr de es pt	Apache-2.0
granite-4.1-nar	ibm-granite/granite-speech-4.1-2b-nar	Non-autoregressive: single LLM forward + slot argmax (more)	en fr de es pt	Apache-2.0
fastconformer-ctc	nvidia/stt_en_fastconformer_ctc_large	FastConformer + CTC (NeMo family, all sizes)	en	CC-BY-4.0
voxtral	mistralai/Voxtral-Mini-3B-2507	Whisper encoder + Mistral 3B LLM	8	Apache-2.0
voxtral4b	mistralai/Voxtral-Mini-4B-Realtime-2602	Causal encoder + 3.4B LLM, sliding window	13, realtime streaming	Apache-2.0
qwen3	Qwen/Qwen3-ASR-0.6B	Whisper-style audio encoder + Qwen3 0.6B LLM	30 + 22 Chinese dialects	Apache-2.0
qwen3-1.7b	Qwen/Qwen3-ASR-1.7B	Whisper-style audio encoder + Qwen3 1.7B LLM	30 + 22 Chinese dialects	Apache-2.0
confucius4-r2t2	netease-youdao/Confucius4-R2T2	Qwen3-ASR-1.7B streaming fine-tune; append-only realtime session (prefix rollback)	30 langs	NetEase Youdao Model Use License
raon-speech	KRAFTON/Raon-Speech-9B	Qwen3-Omni audio tower + adaptor + Qwen3 36L LLM (speech-to-text subset; 8 s chunks at 24 kHz)	en, ko	CC-BY-NC-4.0 (non-commercial)
qwen3-ja-anime	jaykwok/Qwen3-ASR-1.7B-JA-Anime-Galgame-hf	Qwen3-ASR-1.7B fine-tuned for Japanese anime/galgame speech	ja + 30 langs	Apache-2.0
mega-asr	zhifeixie/Mega-ASR	Qwen3-ASR-1.7B + merged robustness LoRA; always-on robust path	noisy / degraded speech	Apache-2.0
higgs-stt	bosonai/higgs-audio-v3-stt	Whisper-large-v3 encoder (4 s chunked) + Qwen3-1.7B LLM (more)	en	Apache-2.0
wav2vec2	jonatasgrosman/wav2vec2-large-xlsr-53-english	CNN + 24L transformer + CTC head (any Wav2Vec2ForCTC)	per-model	Apache-2.0
wav2vec2	facebook/data2vec-audio-base-960h	Data2Vec Audio (79 MB Q4_K)	English	Apache-2.0
wav2vec2	facebook/hubert-large-ls960-ft	HuBERT Large (212 MB Q4_K)	English	Apache-2.0
glm-asr	zai-org/GLM-ASR-Nano-2512	Whisper encoder + 4-frame projector + Llama 1.5B (GQA)	Mandarin (+ Chinese dialects), English, Cantonese	MIT
hikari	sbintuitions/hikari-medium — hosted as cstr/hikari-medium-GGUF (f16 + q8_0 + Silero; -m auto)	Whisper-medium enc-dec with a causal encoder; one token (or WAIT) per 80 ms — simultaneous translation, --stream emits while the speaker talks (more)	English speech → DE / JA / RU text, or English ASR (--tr-tl de)	MIT
kyutai-stt	kyutai/stt-1b-en_fr	Mimi codec (SEANet + RVQ) + 16L causal LM	en, fr	MIT
kyutai-stt	kyutai/stt-2.6b-en	Mimi codec + 48L causal LM (2.6B, English-only; 3.5 s lookahead)	en	MIT
firered-asr	FireRedTeam/FireRedASR2-AED	Conformer + CTC + beam search; also LID (120 langs)	Mandarin, English, 20+ Chinese dialects	Apache-2.0
moonshine	UsefulSensors/moonshine-{tiny,base}	Conv + 6L enc + 6L dec; multilingual variants	English + 6 langs	MIT
moonshine‑de	fidoriel/moonshine-base-de	German fine-tune of moonshine-base (6.9% WER CV22)	German	CC‑BY‑NC‑SA‑4.0
moonshine‑tiny‑de	fidoriel/moonshine-tiny-de	German fine-tune of moonshine-tiny (11.4% WER CV22)	German	CC‑BY‑NC‑SA‑4.0
moonshine-streaming	UsefulSensors/moonshine-streaming-{tiny,small,medium}	Streaming: sliding-window encoder + AR decoder (34–245M)	English	MIT
moonshine-streaming-de-onnx	Official Tiny/Small exports	Optional CPU ONNX Runtime; int8 or f32 five-graph streaming	German	MIT
moonshine-tiny-de-dattazigzag	dattazigzag/moonshine-tiny-de	Tiny German checkpoint; GGUF or legacy two-graph ONNX export	German	MIT
gemma4-e2b	google/gemma-4-E2B-it	USM Conformer 12L + Gemma4 LLM 35L (GQA, PLE)	140+ langs	Apache-2.0
gemma4-e4b	google/gemma-4-E4B-it	Same USM Conformer 12L + larger Gemma4 LLM 42L (GQA, PLE); runs on --backend gemma4-e2b	140+ langs	Apache-2.0
omniasr	omniASR-CTC-1B-v2	wav2vec2 CNN + 48L transformer + CTC (more)	1600+	Apache-2.0
omniasr‑300m	omniASR-CTC-300M-v2	Same arch, 24L, ~194 MB Q4_K; auto-chunks >7 s (more)	1600+	Apache-2.0
omniasr-llm	omniASR-LLM-300M-v2	Same encoder + 12L LLaMA decoder (more)	1600+	Apache-2.0
omniasr-llm	omniASR-LLM-Unlimited-300M-v2	Streaming: 15s segment protocol, unlimited audio (more)	1600+	Apache-2.0
vibevoice	microsoft/VibeVoice-ASR	σ-VAE ConvNeXt + Qwen2.5-7B (more)	50+	MIT
vibevoice-streaming	microsoft/VibeVoice-ASR-Streaming-1.5B	σ-VAE ConvNeXt + Qwen2.5-1.5B; persistent-KV 2.93 s chunks with 0.53 s lookahead (more)	multilingual	MIT
vibevoice-bitnet	VibeVoice-ASR-BitNet	Same arch, TQ2_0 ternary LM (1.6 GB) (more)	7+	MIT
mimo-asr	XiaomiMiMo/MiMo-V2.5-ASR	6L transformer + 36L Qwen2 LM + RVQ codec (more)	Mandarin + dialects + English	MIT
ark-asr ⚠️experimental/WIP	cstr/ark-asr-3b-GGUF (base AutoArk-AI/ARK-ASR-3B)	Whisper-large-v3 enc (partial RoPE) + Qwen2.5-3B LM (more)	19 (zh, en, de, ja, fr, ko, es, pl, it, ro, hu, cs, nl, fi, hr, sk, sl, et, lt)	see base
moss-audio	OpenMOSS-Team/MOSS-Audio-4B-Instruct	32L Whisper encoder + DeepStack 3-tap + 36L Qwen3 LM; audio understanding + ASR (more)	zh, en	Apache-2.0
hojo-asr	HojoAI/Hojo-ASR-Multi-V1	Qwen3-Omni audio tower (32L) + 2-block WeNet Conformer adapter + Qwen3-4B LM; multilingual ASR, beam 4 by default as upstream (more)	de, fr, it, pt, es, ja, ar, ko, ru	Apache-2.0
hojo-asr (V1)	HojoAI/Hojo-ASR-V1 (-m hojo-asr-v1)	Same architecture; the Chinese + English checkpoint (works better than Multi-V1 on English)	zh, en	Apache-2.0
moss-transcribe	OpenMOSS-Team/MOSS-Transcribe-preview-2B	Qwen3-Omni audio encoder (32L, windowed attn) + GatedMLP adapter + Qwen3-1.7B LM; ASR (more)	zh, en	Apache-2.0
moss-diarize	OpenMOSS-Team/MOSS-Transcribe-Diarize-0.9B	Stock Whisper encoder (24L, 80 mel) + 4x merge + VQAdaptor + Qwen3-0.6B LM; joint ASR + speaker diarization + timestamps	multi	Apache-2.0
whisper (tiron) ⚠️experimental	Trelis/tiron (base Trelis/tiron)	Whisper large-v3 with an extended vocab: emits inline `<	speakerN	>` markers + 20 ms timestamps for joint transcription + per-window speaker attribution, via a constrained-decoding grammar; cross-window linking to stable speakers (#295)
funasr	FunAudioLLM/Fun-ASR-Nano-2512	70-block SANM encoder + 2-block Transformer adaptor + Qwen3-0.6B LLM	zh, yue, en, ja, ko	FunASR Model License v1.1 (commercial OK w/ attribution)
fun-asr-mlt-nano	FunAudioLLM/Fun-ASR-MLT-Nano-2512	Same architecture, multilingual decoder	31 langs incl. de, fr, es, pt, ru, ar, hi, vi, th, ko	FunASR Model License v1.1
paraformer	funasr/paraformer-zh	50-block SANM encoder + CIF predictor + 16-block NAR decoder (single-pass, non-autoregressive); character-level vocab (8404); 220M params	zh, en	FunASR Model License (commercial OK w/ attribution)
foxnose (speaker diarization)	Wespeaker/wespeaker-voxceleb-resnet34-LM	Speaker diarization via --diarize-method foxnose: WeSpeaker ResNet34-LM 256-d embeddings + GMM/BIC speaker counting + spectral clustering + Viterbi temporal smoothing (more). 3.18 % DER on VoxConverse dev vs the upstream reference implementation's 3.07 %	any	weights CC-BY-4.0
gigaam	ai-sage/GigaAM-v3 (base ai-sage/GigaAM-v3)	16-layer rotary Conformer (220M) + CTC or RNN-T head; four revisions — e2e_rnnt / e2e_ctc emit punctuation + casing + ITN from a SentencePiece vocab, rnnt / ctc emit bare lowercase Cyrillic (more)	en ru	MIT
sensevoice	FunAudioLLM/SenseVoiceSmall	70-block SANM encoder + CTC head; emits transcript + language ID + audio-event in one forward pass (non-AR, 15× faster than Whisper-Large); structured C ABI + -oj JSON expose the tags as separate fields. Upstream's emotion classifier is not exposed — see EU AI Act	50+ langs; native LID + audio-event tags	FunASR Model License v1.1

Speech-to-speech audio upscaling and restoration

Backend	Model	Architecture	Input / output	License
sidon	KevinAHM/Sidon-GGUF (base sarulab-speech/sidon-v0.1)	w2v-BERT 2.0 predictor + continuous DAC decoder (more)	16 kHz mono → restored 48 kHz mono	MIT
voxcpm2-vae	AudioVAE V2 from openbmb/VoxCPM2, converted with --vae-only	Isolated causal AudioVAE encoder + decoder (more)	16 kHz mono → upscaled 48 kHz mono	Apache-2.0

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

Text-to-Speech models

Synthesis backends, driven by the --tts flag and a --tts-output PATH.wav. See the dedicated Text-to-Speech section below for quick-start commands and engine selection guidance.

Backend	Models	Architecture	Languages	License
bt2-tts	breeze-tts-2	T5Gemma2 text encoder + Qwen3 backbone + 12L depth decoder over 16 codebooks @ 12.5 Hz; voice cloning, plus voice design / direction via --tts-instruct (strengthen with --tts-cfg-scale 4). Codec companion is the shipped qwen3-tts tokenizer. NON-COMMERCIAL weights — needs --accept-license other. Derived from Breeze TTS 2 by BreezeBlue and licensed for research and non-commercial use only.	en, zh	other (BreezeBlue Research & Non-Commercial)
miotts	MioTTS-0.6B	Qwen3 LLM + MioCodec-v2 FSQ codec (25 Hz, 44.1 kHz output)	ja, en	Apache-2.0
arktts	Edge0/Audio8-TTS-Realistic-0.6b	24L AR backbone + 4L RAS fast code predictor + Snake 44.1 kHz vocoder	multilingual	Apache-2.0
vibevoice-tts	VibeVoice-Realtime-0.5B, VibeVoice-1.5B	DPM-Solver++ + σ-VAE decoder; voice presets or cloning	en, zh	MIT
kugelaudio	kugelaudio-0-open	Qwen2.5-7B LM + 4L DiT diffusion + acoustic VAE decoder; voice cloning	multilingual	Apache-2.0
qwen3-tts	Qwen3-TTS-12Hz-0.6B-Base, 1.7B-Base, 1.7B-VoiceDesign	Qwen3 talker LM + 12 Hz RVQ (more)	multilingual	Apache-2.0
qwen3-tts-customvoice	1.7B-CustomVoice	Same talker + 9 premium built-in speakers (--voice <name>); optional style via --instruct (e.g. "spoke very slowly") (more)	multilingual	Apache-2.0
moss-tts	OpenMOSS-Team/MOSS-TTS-v1.5	Qwen3-8B backbone emitting 32 RVQ audio codebooks under a delay pattern, decoded by a 1.6B pure-transformer codec companion; voice cloning via --voice ref.wav; --backend moss-tts -m <backbone> --codec-model <codec>	multilingual	Apache-2.0
moss-tts-local	OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5	Qwen3-4B backbone; a 1-layer local/depth transformer autoregressively emits 12 RVQ codebooks per frame (RQ-Transformer, no delay), decoded to 48 kHz by MOSS-Audio-Tokenizer-v2 (downmixed to mono); --backend moss-tts-local -m <backbone> --codec-model <codec>	multilingual	Apache-2.0
omnivoice	k2-fsa/OmniVoice	Qwen3-0.6B + masked iterative 8-codebook TTS (SoundStorm-style); voice cloning; 600+ languages (more)	600+ langs	CC-BY-NC weights + custom tokenizer terms; explicit bundle acceptance, review pending
arktts	Edge0/Audio8-TTS-Realistic-0.6b	Dual transformer (24L Slow LM + 4L Fast Subnetwork) + 44.1 kHz Snake causal vocoder; 10 acoustic codebooks; 1.2 GB Q4_K	en, zh	Apache-2.0
melotts	myshell-ai/MeloTTS EN_V2	VITS2 (6L transformer + SDP/DP + transformer coupling flow + HiFi-GAN); 44.1 kHz, 102 MB + 238 MB F16 BERT companion (340 MB total); neural G2P; 4 EN speakers (more)	en	MIT (Melo); Apache-2.0 (BERT)
piper	rhasspy/piper community voices	VITS (6L transformer + SDP + 4-block coupling flow + HiFi-GAN); 22 kHz mono, 30 MB F16 per voice; built-in G2P for EN/DE/FR/ES/RU (--g2p-dict)	30+ langs (built-in + espeak dlopen)	MIT
kokoro	hexgrad/Kokoro-82M + German backbones	StyleTTS2 / iSTFTNet (82M); per-voice GGUF (more)	en, es, fr, hi, it, ja, pt, zh, de	Apache-2.0
orpheus	Orpheus-3B-FT + SNAC 24 kHz	Llama-3.2-3B + SNAC RVQ codec; 8 speakers (more)	en, de	Llama 3.2 Community License / MIT
chatterbox	cstr/chatterbox-GGUF + Nano/turbo/fine-tune variants	T3 AR + S3Gen flow-matching (more)	23 multilingual; separate Arabic, German, and Finnish (chatterbox-finnish-nano) fine-tunes	MIT
indextts	cstr/indextts-1.5-GGUF	GPT-2 AR (24L/1280d) + Conformer conditioning + BigVGAN vocoder; voice cloning via reference audio	zh, en	Apache-2.0
voxcpm2-tts	cstr/voxcpm2-GGUF	Tokenizer-free CFM diffusion AR (TSLM + RALM + LocDiT) at 48 kHz native; zero-shot + voice cloning via --voice <wav>	30 languages	Apache-2.0
voxtral-tts	mistralai/Voxtral-4B-TTS-2603	Ministral-3B AR (26L GQA) + 3L FM acoustic transformer (7-step Euler ODE) + Voxtral codec decoder at 24 kHz; 20 preset voices; SOTA French technical text	en, fr, de, es, it, pt, nl, ar, hi	CC‑BY‑NC‑4.0
cosyvoice3-tts	cstr/cosyvoice3-0.5b-2512-GGUF	Qwen2-0.5B AR speech-token LM + DiT-CFM (10-step Euler) + HiFT (NSF + iSTFT) at 24 kHz; baked-voice zero-shot cloning via --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)	9 langs + 18 zh dialects	Apache-2.0
csm	cstr/csm-1b-GGUF	Sesame CSM-1B conversational TTS: Llama-3.2 1B backbone + 100M depth decoder (32-codebook RVQ) + Kyutai Mimi codec at 24 kHz (more)	en	Apache-2.0
lfm2-audio	cstr/lfm2-audio-1.5b-GGUF + jp	LFM2.5-Audio ASR+TTS+S2S: FastConformer enc + LFM2 hybrid backbone + depthformer (8-codebook Mimi) + ISTFT detokenizer at 24 kHz; interleaved text+audio generation	en, ja	LFM Open v1.0
dia	nari-labs/Dia-1.6B	Byte-level text encoder (12L) + AR audio decoder (18L GQA + CFG) → 9 delayed DAC codebooks + 44.1 kHz DAC codec; dialogue style with [S1]/[S2] tags (use >100-char prompts)	en	Apache-2.0
zonos-tts	cstr/zonos-v0.1-transformer-GGUF + cstr/dac-44khz-GGUF	Zyphra Zonos-v0.1: 26L GQA AR transformer (2B) + 9-codebook DAC @ 44.1 kHz; CFG-guided; voice cloning via reference WAV (more)	en	Apache-2.0
bark	cstr/bark-small-GGUF	Suno Bark 3-stage GPT-2 TTS: text→semantic (12L) → coarse EnCodec (12L, 2 codebooks) → fine (12L, 8 codebooks) → EnCodec 24 kHz decoder; speaker conditioning via .npz prompts (--voice <file.npz>)	multilingual	MIT
speecht5	cstr/speecht5-tts-GGUF	SpeechT5 80M: char-level encoder (12L) + AR mel decoder (6L) + 5-layer conv postnet + HiFi-GAN at 16 kHz; speaker via 512-d x-vector (--voice <xvector.bin>)	en	MIT
fastpitch	cstr/fastpitch-en-GGUF	NVIDIA FastPitch 60M: non-autoregressive parallel TTS — 6L encoder + duration/pitch predictors + 6L decoder + HiFi-GAN at 22 kHz; deterministic, single forward pass (more)	en	CC-BY-4.0
bananamind-tts	Banaxi-Tech/BananaMind-TTS-V2.1-Preview	BananaMind-TTS 13M: Tacotron-lite char-level encoder (Conv+BN+BiLSTM) + AR GRU decoder with location-sensitive attention + postnet + HiFi-GAN at 22 kHz; fixed voice per locale (more)	en, de	Apache-2.0
parler-tts	cstr/parler-tts-mini-v1.1-GGUF	Parler TTS Mini v1.1 (~900M): T5 encoder + MusicGen decoder + DAC 44.1 kHz; prompt-conditioned (describe voice in text via --instruct)	en	Apache-2.0
outetts	cstr/outetts-0.3-1b-GGUF	OLMo-1B talker + WavTokenizer single-codebook VQ-GAN at 24 kHz; voice cloning via speaker profile JSON (--voice <speaker.json>)	en	CC-BY-NC-SA-4.0
pocket-tts	cstr/pocket-tts-GGUF	Kyutai Pocket TTS 100M: continuous-latent AR at 12.5 Hz + one-step LSD flow + Mimi VAE 24 kHz; voice cloning via ref audio or official prepared .safetensors voices (more)	en, de, es, it, pt; fr (24L preview)	CC-BY-4.0 + gated-use conditions
tada	cstr/tada-tts-1b-GGUF + HumeAI/tada-3b-ml	Llama-3.2 1B/3B backbone + per-token FM diffusion head + TADA codec at 24 kHz; 1:1 text-to-acoustic alignment; default prompt via tada-ref.gguf, custom voices via --voice <tada-ref.gguf> built with models/convert-tada-ref-to-gguf.py (more)	en	Llama 3.2 Community License

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	—
supertonic	presets F1-F5/M1-M5	--tts-speed, --tts-steps	44.1	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	—
fireredtts3	yes (--voice ref.wav --ref-text "...")	10-step CFG Euler	24	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.

Translation

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	12L enc + 12L dec transformer, SentencePiece 128K (more)	100 langs, any-to-any	MIT
m2m100-wmt21	facebook/wmt21-dense-24-wide-en-x + facebook/wmt21-dense-24-wide-x-en	Same as m2m100, scaled to 4.7B (24L enc) (more)	English ↔ 7 langs (separate en-x / x-en checkpoints)	MIT
marian	Helsinki-NLP/opus-mt-de-en, opus-mt-en-de — hosted as cstr/opus-mt-<src>-<tgt>-GGUF (f16 + q8_0) for 24 directions: de↔en, fr↔de, fr↔en, ar↔de, ar↔en, it↔de, it↔en, es↔de, es↔en, he↔de, en→he, he→en, tr→en, en→tr (he→en and en→tr are the larger tc-big models); other pairs via models/convert-marian-to-gguf.py	MarianMT 6L enc + 6L dec transformer, d=512, ~75M parameters, SentencePiece unigram + 58K joint vocab (more)	one direction per checkpoint (de→en, en→de)	CC-BY-4.0 (OPUS-MT; attribution required)
madlad	google/madlad400-3b-mt	T5 enc-dec (12L+12L, d=2048, gated-GELU, RMSNorm) (more)	419 languages	Apache-2.0

# 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

# Opus-MT / MarianMT (one small model per direction: `-m auto` is de→en,
# `-m opus-mt-en-de` the other way; ~84 MB, CC-BY-4.0)
./build/bin/crispasr --backend marian -m auto \
    --text "Die Konferenz findet am 3. Oktober in Berlin statt." \
    -sl de -tl en --beam-size 1
# → The conference will take place on 3 October in Berlin.

# 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.

Post-processing models

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	Punctuation restoration	XLM-RoBERTa-large (24L, d=1024), 6 classes	EN, DE, FR, IT	MIT	cstr/fullstop-punc-multilang-GGUF
punctuate-all	Punctuation restoration	XLM-RoBERTa-base (12L, d=768), 6 classes	12 languages	MIT	cstr/punctuate-all-GGUF
PCS	Punc + truecase + SBD	XLM-RoBERTa-base (12L), 4 heads	47 languages	Apache-2.0	--punc-model pcs
truecaser‑lstm	German truecasing (best)	BiLSTM char-level (2×150, 3.2 MB, 97.9% F1)	German	Apache-2.0	--truecase-model lstm
truecaser‑crf	German truecasing	CRF + context features (8.5 MB)	German	MIT	--truecase-model crf
truecaser‑de	German truecasing (simple)	Statistical word-frequency (71K entries, 1.7 MB)	German	MIT	--truecase-model auto
CLD3	Text language ID	Embedding-bag → FC + ReLU → softmax (~1.5 MB F32)	109 ISO 639-1	Apache-2.0	cstr/cld3-GGUF
GlotLID-V3	Text language ID	fastText supervised, flat softmax	2102 ISO 639-3 + script	Apache-2.0	cstr/glotlid-GGUF
LID-176	Text language ID	fastText supervised, hierarchical softmax	176 ISO 639-1	CC-BY-NC-4.0	cstr/fasttext-lid176-GGUF

Audio codecs

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	3-codebook RVQ + decoder blocks (stride 8/8/4/2)	24 kHz	3×12.5 Hz	MIT	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.

Music & audio analysis

Beyond speech, CrispASR runs several music/audio analysis tasks — each a small GGUF with the architecture auto-detected, no Python. See 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), htdemucs (4-stem), or mini-bs-roformer (HiDolen V2.5, 46.2M parameters, stereo bass/drums/other/vocals at 44.1 kHz). --stems vocals,drums selects a subset; --sep-output-dir sets the output location. Mini BS RoFormer uses an explicitly converted local GGUF. The SCNet / SCNet-large candidate backend adds native four-stem separation with local F16/Q8_0/Q4_K GGUFs; original-checkpoint fidelity and paired performance remain qualification gates.
• Piano transcription (--backend piano-transcription) — piano audio → note events (88 keys @ 100 fps, ByteDance/Kong CRNN; F16 GGUF ≈ 77 MB).
• Polyphonic note events (--backend basic-pitch) — Spotify Basic Pitch, any instrument → note events (~110 KB model).
• Multi-instrument transcription (--backend mt3, alias music-transcription) — MT3's T5 encoder/decoder emits note events with per-instrument programs (F16 GGUF ≈ 96 MB).
• Onsets & Frames (--backend onsets-and-frames) — Hawthorne et al.'s piano transcriber (MIT), the best solo-piano model here per megabyte: 69.0% note F1 on MusicNet's piano pieces against Basic Pitch's 57.5%, and F1-identical to its ONNX export at f32 and Q8_0. Q4_0 GGUF 18.6 MiB, Q8_0 30.8 MiB, F32 101.9 MiB. See docs/music-transcription/ONSETS_AND_FRAMES.md.
• hFT-Transformer (--backend hft-transformer) — Toyama et al.'s hierarchical frequency-time transformer (MIT), the most accurate solo-piano model here and by far the smallest: 70.5% note F1 on MusicNet's piano pieces from 5.5 M parameters, Q8_0 GGUF 7.0 MiB, Q4_0 4.5 MiB, F32 21.8 MiB. It is also much the most expensive to run — 249 GFLOP of matrix multiply per 2 s of audio, and quantisation makes it slower, not faster, on a CPU without int8 dot-product instructions. Read docs/music-transcription/HFT_TRANSFORMER.md before choosing it over onsets-and-frames.
• All five take --piano-format text|json|midi; midi writes a Standard MIDI File.
• 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).

Feature matrix

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 — 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 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 119-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	✔
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), hojo-asr (Qwen3-Omni encoder + Conformer adapter + Qwen3-4B multilingual ASR), mini-omni2 (ASR + TTS + S2S), kugelaudio (7B audio understanding). See 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, supertonic, 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 table above and docs/tts.md. The vibevoice and lfm2-audio columns mark dual-mode (ASR + TTS) backends.

Nemotron managed downloads use pinned F16 (~1.3 GB); the former Q4 default fails the fixed CPU/T4 original-reference controls. See the default qualification and existing-release usage.

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 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)
• sortformer — NVIDIA Nemotron-3-Diarization (streaming Sortformer v3, #466): one end-to-end network, up to 8 speakers in order of appearance, overlap-aware, no embedder or clustering. --diarize-model auto fetches NVIDIA's 107 MB q8_0 GGUF (OpenMDW-1.1, commercial use allowed). Matches transformers exactly on every p > 0.5 decision (more)
• 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-