One C++ binary, 119 backends — 62 of them TTS engines — plus multilingual text translation, zero Python dependencies.
CrispASR started as a fork of whisper.cpp and extends that base into a unified speech engine called crispasr, backed by full ggml C++ runtimes for major open-weights ASR and TTS architectures. One build, one binary, one consistent CLI — pick the backend at the command line or let CrispASR auto-detect it from your GGUF file. See Text-to-Speech for the TTS side.
$ crispasr -m ggml-base.en.bin -f samples/jfk.wav # OpenAI Whisper
$ crispasr -m parakeet-tdt-0.6b.gguf -f samples/jfk.wav # NVIDIA Parakeet
$ crispasr -m canary-1b-v2.gguf -f samples/jfk.wav # NVIDIA Canary
$ crispasr -m voxtral-mini-3b-2507.gguf -f samples/jfk.wav # Mistral Voxtral
$ crispasr --backend qwen3 -m auto -f samples/jfk.wav # -m auto downloads
$ crispasr --backend kokoro -m auto --tts "Hello world" --tts-output out.wav # TTS
No Python. No PyTorch. No separate per-model binary. No pip install. Just one C++ binary and a GGUF file.
Browser: All backends compile to WebAssembly (4.3 MB) via build-wasm.sh.
Multithreaded, runs entirely client-side with COOP/COEP headers.
Demo: HuggingFace Space —
live transcription + TTS + language detection, auto-deployed from hf-space/.
| Project | What it does |
|---|---|
| CrispASR | This repo — C++ speech engine. 119 backends (62 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. |
-hip / -vulkan builds require the matching GPU driver and do not fall back to CPU (the -cuda tarballs do, since v0.8.30)--no-gpu bisect, which Windows zip--server-workers), bulk offline transcription, replicas behind a load balancerCRISPASR_<BACKEND>_<FEATURE> convention, global knobs, and every per-backend variablesrc/core/ primitives, regression disciplinetools/test-all-backends.py capability tierscrispasr-quantize for all backendsEverything 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.
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-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. The -hip
and -vulkan builds require the matching driver and do not fall back to CPU;
the Linux -cuda tarballs do fall back.
Check it runs — this should print a version banner and exit:
crispasr --version # Windows: .\crispasr.exe --version
CUDA builds also print cuda toolkit and cuda runtime ABI, so this command
distinguishes the CUDA 12 and CUDA 13 packages without inspecting DLLs.
-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 | 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 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 |
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 119 backends (see the generated feature matrix for the authoritative, auto-counted list) — the majority for transcription/translation, and
62 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.
| 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 |
| parakeet | nvidia/parakeet-tdt-0.6b-v3 |
FastConformer + TDT | 25 EU (auto-detect) | 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 |
| 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-qwen | nvidia/canary-qwen-2.5b |
FastConformer + Qwen3-1.7B SALM | en | CC-BY-4.0 |
| 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 | ibm-granite/granite-speech-{3.2-8b,3.3-2b,3.3-8b}, granite-4.0-1b-speech |
Conformer + Q-Former + Granite LLM (μP) (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 |
| 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 |
| 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 |
| 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 |
| 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) |
multi (en focus) | Apache-2.0 |
| 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 |
| 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
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 |
|---|---|---|---|---|
| miotts | MioTTS-0.6B |
Qwen3 LLM + MioCodec-v2 FSQ codec (25 Hz, 44.1 kHz output) | ja, en | 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 | Apache-2.0 |
| melotts | myshell-ai/MeloTTS EN_V2 |
VITS2 (6L transformer + SDP/DP + transformer coupling flow + HiFi-GAN); 44.1 kHz, 102 MB + 52 MB BERT Q4_K companion (154 MB total); neural G2P; 4 EN speakers (more) | en | MIT |
| 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 |