---

license: other

license_name: mixed-upstream-licenses

license_link: https://huggingface.co/cswk/sayit-asr-gguf#licenses

pipeline_tag: automatic-speech-recognition

library_name: transcribe.cpp

base_model_relation: quantized

base_model:

- nvidia/parakeet-unified-en-0.6b

- nvidia/nemotron-3.5-asr-streaming-0.6b

- FunAudioLLM/SenseVoiceSmall

- FunAudioLLM/Fun-ASR-Nano-2512

- Qwen/Qwen3-ASR-0.6B

- Qwen/Qwen3-ASR-1.7B

language:

- zh

- en

- ja

- ko

- yue

tags:

- gguf

- asr

- speech-to-text

- voice-typing

- dictation

- transcribe.cpp

- sensevoice

- parakeet

- qwen3-asr

- fun-asr-nano

- windows

---

SayIt ASR — GGUF weights

The local speech-recognition models shipped with

SayIt, an open-source voice typing app for

Windows: press a shortcut, speak, and AI-cleaned text lands at your cursor.

These seven files are byte-identical re-uploads of the

handy-computer GGUF conversions, mirrored here

so SayIt's model downloader does not depend on a third-party repository staying online.

No requantization, no retraining, no modification of any kind.

Acknowledgements

**All the hard work here belongs to Handy and

@cjpais.**

SayIt's entire local speech-recognition stack rests on two things they built and gave

away: transcribe.cpp, the ggml-based

C/C++ inference library (MIT), and the

handy-computer model org, which converted these

ASR models to GGUF and keeps a much larger catalog than the seven files here. Without

those conversions there would be no local mode in SayIt at all — running SenseVoice,

Fun-ASR, Qwen3-ASR, Parakeet and Nemotron on consumer hardware through one runtime is

the difficult part, and it was already solved.

If you are looking for the canonical source of any file in this repo, go upstream:

| This repo | Upstream conversion |

| --- | --- |

| parakeet-unified-en-0.6b-Q4_K_M.gguf | handy-computer/parakeet-unified-en-0.6b-gguf |

| SenseVoiceSmall-Q8_0.gguf | handy-computer/SenseVoiceSmall-gguf |

| nemotron-3.5-asr-streaming-0.6b-Q4_K_M.gguf | handy-computer/nemotron-3.5-asr-streaming-0.6b-gguf |

| Fun-ASR-Nano-2512-Q8_0.gguf | handy-computer/Fun-ASR-Nano-2512-gguf |

| Qwen3-ASR-0.6B-Q8_0.gguf | handy-computer/Qwen3-ASR-0.6B-gguf |

| Qwen3-ASR-1.7B-Q4_K_M.gguf | handy-computer/Qwen3-ASR-1.7B-gguf |

| Qwen3-ASR-1.7B-Q5_K_M.gguf | handy-computer/Qwen3-ASR-1.7B-gguf |

Also with thanks to the labs that trained and released the original models: NVIDIA

(Parakeet, Nemotron), Alibaba FunAudioLLM (SenseVoice, Fun-ASR Nano) and

Alibaba Qwen (Qwen3-ASR).

> ### Compatibility

>

> These are transcribe.cpp GGUF files.

> They are not loadable by llama.cpp or whisper.cpp. The general.architecture

> tags (sensevoice, funasr_nano, parakeet, qwen3_asr) are specific to that

> runtime, so a same-named GGUF from another uploader will fail to load even though the

> download and checksum succeed.

Models

Ordered fast to slow.

| File | Base model | Quant | Size | Languages | RAM in use |

| --- | --- | --- | --- | --- | --- |

| parakeet-unified-en-0.6b-Q4_K_M.gguf | nvidia/parakeet-unified-en-0.6b | Q4_K_M | 477 MB | English only | ~0.9 GB |

| SenseVoiceSmall-Q8_0.gguf | FunAudioLLM/SenseVoiceSmall | Q8_0 | 253 MB | zh, en, ja, ko, yue | ~0.35 GB |

| nemotron-3.5-asr-streaming-0.6b-Q4_K_M.gguf | nvidia/nemotron-3.5-asr-streaming-0.6b | Q4_K_M | 496 MB | 32 locales | ~1.05 GB |

| Fun-ASR-Nano-2512-Q8_0.gguf | FunAudioLLM/Fun-ASR-Nano-2512 | Q8_0 | 891 MB | zh, en, ja | ~1.4 GB |

| Qwen3-ASR-0.6B-Q8_0.gguf | Qwen/Qwen3-ASR-0.6B | Q8_0 | 850 MB | 30+ | ~1.5 GB |

| Qwen3-ASR-1.7B-Q4_K_M.gguf | Qwen/Qwen3-ASR-1.7B | Q4_K_M | 1.32 GB | 30+ | ~2.3 GB |

| Qwen3-ASR-1.7B-Q5_K_M.gguf | Qwen/Qwen3-ASR-1.7B | Q5_K_M | 1.52 GB | 30+ | ~2.6 GB |

RAM in use is a measured resident working set while the model is loaded, not the

file size. Autoregressive models such as Qwen3-ASR add 700 MB to 1 GB on top of the

weights for KV cache and compute buffers.

Which one to pick

• English only → parakeet-unified-en-0.6b. In English it is simultaneously the

fastest and the most accurate file here (upstream LibriSpeech test-clean WER 1.62% at

Q4_K_M), so it dominates every other entry. It returns an empty string on Chinese

audio, which is why it cannot be a general-purpose default.

• Smallest footprint → SenseVoiceSmall. 253 MB on disk, ~350 MB resident.
• Chinese dictation, best value → Fun-ASR-Nano-2512. Beats Qwen3-ASR 0.6B on both

speed and accuracy (upstream WER 1.79% vs 2.11%) at roughly half the decode cost of

the 1.7B, but only covers zh / en / ja.

• Widest language coverage → nemotron-3.5-asr-streaming-0.6b, 32 locales including

Spanish, French, German, Italian, Portuguese, Dutch, Russian, Arabic, Hindi, Turkish,

Vietnamese and Ukrainian. Noticeably less accurate in English than parakeet

(WER 3.28% vs 1.62%), so pick it for coverage, not for English.

• Highest accuracy across many languages → Qwen3-ASR-1.7B-Q5_K_M (upstream WER

1.65%). The Q4_K_M variant trades WER 1.65% → 1.81% for 188 MB less download and

lower memory pressure.

All WER figures above are the values reported upstream for these quantizations, not our

own measurements.

Usage notes

Three behaviours that are easy to misdiagnose as bugs:

1. Punctuation on SenseVoice and Fun-ASR Nano is behind the ITN flag. With

itn=off the model takes the <|woitn|> branch and returns unpunctuated text.

1. Nemotron 3.5 only accepts region-qualified language codes (en-US, zh-CN).

Passing a bare en returns unsupported language (status 10). Every other model

here takes bare codes. SayIt resolves this in gguf_asr.rs::resolve_language

(exact match → first locale of the same language → fall back to auto-detect).

1. Parakeet and Nemotron ignore the ITN/PnC options — both report unsupported.

Casing and punctuation are inherent model behaviour. Parakeet normalizes numbers

(2026, not spelled out); Nemotron keeps them in spoken form, which is a training

choice rather than a defect.

With SayIt

Nothing to do. Open SayIt → Settings → Voice engine → Local mode, and pick a model. It

downloads, verifies, and loads it, with Vulkan GPU acceleration when a GPU is available

(AMD and Intel included, not just NVIDIA).

Direct download

huggingface-cli download cswk/sayit-asr-gguf SenseVoiceSmall-Q8_0.gguf --local-dir .

Checksums

SHA-256, for verifying a manual download:

| File | SHA-256 |

| --- | --- |

| parakeet-unified-en-0.6b-Q4_K_M.gguf | a8bf3de2b393bd14ead5a858c3748d5e3b07a20fdeabdd3b498fba4f463fa929 |

| SenseVoiceSmall-Q8_0.gguf | 6c759ee4c9748c9b3f7a5a60ca74f0f7e685fb9d45d1378fce7cfd62f59adf29 |

| nemotron-3.5-asr-streaming-0.6b-Q4_K_M.gguf | 41c99fa5fb6f3d35f68e79adc3e755eca2232a8d921178bd647b71194792b8fd |

| Fun-ASR-Nano-2512-Q8_0.gguf | 681caef6df15a2c0e153b40ca7fe4087fdf65751fa5e6fe605d8a75dff969e61 |

| Qwen3-ASR-0.6B-Q8_0.gguf | f081b2d5e23bd669d92cc331d722a8a0681943b8e6f34b48996fd5c319b5acd8 |

| Qwen3-ASR-1.7B-Q4_K_M.gguf | b7afe3674f653fa84f712ed2440353c6e7cf7f93697fef76b05a26538b24844e |

| Qwen3-ASR-1.7B-Q5_K_M.gguf | 034c557fe92ff8fcd9a9c041cbdaad347be0a86a58d3a348f63cf3f0180879d0 |

Licenses

<a id="licenses"></a>

This repository carries a mix of upstream licenses. **Each file stays under the license

of its original model** — re-hosting does not change it. Check the one you plan to use:

| File | License | Holder |

| --- | --- | --- |

| parakeet-unified-en-0.6b-Q4_K_M.gguf | CC-BY-4.0 (attribution required) | NVIDIA |

| nemotron-3.5-asr-streaming-0.6b-Q4_K_M.gguf | NVIDIA model license (other) | NVIDIA |

| SenseVoiceSmall-Q8_0.gguf | see upstream model card (other) | Alibaba FunAudioLLM |

| Fun-ASR-Nano-2512-Q8_0.gguf | see upstream model card (other) | Alibaba FunAudioLLM |

| Qwen3-ASR-0.6B-Q8_0.gguf | Apache-2.0 | Alibaba Qwen |

| Qwen3-ASR-1.7B-*.gguf | Apache-2.0 | Alibaba Qwen |

SayIt itself is AGPL-3.0. That license applies to the application, not to these weights.