Coco Voice speech models (mirror)
This repository mirrors the speech-to-text model files used by the Coco Voice app that, until now, were hosted only by the Handy project (blob.handy.computer and the handy-computer Hugging Face org).
- Every file is a byte-for-byte copy of Handy's file. Nothing was re-converted, re-quantized or repacked. The SHA-256 of each file is listed below and in its
ATTRIBUTION.md, and it equals the Git LFS object id on this repo. - Layout:
<model-id>/<file>, with<model-id>/LICENSE(upstream model license plus Handy's MIT notice) and<model-id>/ATTRIBUTION.md(original publisher, Handy's role, upstream URL, SHA-256) beside each file. - Licenses differ per model. The per-folder
LICENSEandATTRIBUTION.mdgovern each model; the repo-levellicense: othertag just means "see each folder". - Credits: every model belongs to its original publisher (column below). Handy (MIT, Copyright (c) 2025 CJ Pais) converted, quantized or packaged these files. NVIDIA models are CC-BY-4.0: they are credited to NVIDIA Corporation, and the conversion is indicated in their folders.
- Pin a commit when downloading (
/resolve/<commit>/<model-id>/<file>) and verify the SHA-256.
Mirrored models (52 files, 59,191,683,971 bytes, about 59.2 GB)
| model id | file | size (bytes) | sha256 | license | original publisher | converted by Handy? |
|---|---|---|---|---|---|---|
medium | whisper-medium-q4_1.bin | 491,852,915 (491.9 MB) | 79283fc1f9fe12ca3248543fbd54b73292164d8df5a16e095e2bceeaaabddf57 | MIT | OpenAI | Yes: ggml q4_1 quant |
breeze-asr | breeze-asr-q5_k.bin | 1,080,732,108 (1.08 GB) | 8efbf0ce8a3f50fe332b7617da787fb81354b358c288b008d3bdef8359df64c6 | Apache-2.0 | MediaTek Research | Yes: ggml conversion + q5_K quant |
moonshine-tiny-streaming-en | moonshine-tiny-streaming-en.tar.gz | 33,006,044 (33.0 MB) | 465addcfca9e86117415677dfdc98b21edc53537210333a3ecdb58509a80abaf | MIT | Useful Sensors, Inc. (dba Moonshine AI) | Repackaged (tar.gz of ORT export) |
moonshine-small-streaming-en | moonshine-small-streaming-en.tar.gz | 104,842,676 (104.8 MB) | dbb3e1c1832bd88a4ac712f7449a136cc2c9a18c5fe33a12ed1b7cb1cfe9cdd5 | MIT | Useful Sensors, Inc. (dba Moonshine AI) | Repackaged (tar.gz of ORT export) |
moonshine-medium-streaming-en | moonshine-medium-streaming-en.tar.gz | 201,780,020 (201.8 MB) | 07a66f3bff1c77e75a2f637e5a263928a08baae3c29c4c053fc968a9a9373d13 | MIT | Useful Sensors, Inc. (dba Moonshine AI) | Repackaged (tar.gz of ORT export) |
canary-180m-flash | canary-180m-flash-Q8_0.gguf | 218,447,552 (218.4 MB) | e13c7f5d0952b056a027cfffec13e3a3a134d1608babed24f983568f141e297c | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q8_0 quant |
cohere-transcribe-03-2026 | cohere-transcribe-03-2026-Q5_K_M.gguf | 1,770,270,208 (1.77 GB) | 14d02f1ad6dd77b3a60f82639879012c3adb4fe25c50a5a47a2c4c661daf1558 | Apache-2.0 | Cohere Labs (Cohere) | Yes: GGUF conversion + Q5_K_M quant |
whisper-medium | whisper-medium-Q8_0.gguf | 831,538,144 (831.5 MB) | 09e6a65e7de377aa5b10bae24608bc6f8ca2ed04b3993ef10d4a02bcd9a82adf | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
Voxtral-Mini-4B-Realtime-2602 | Voxtral-Mini-4B-Realtime-2602-Q5_K_M.gguf | 3,281,439,008 (3.28 GB) | e20a7582c5cf8159454c909b7f57b184b287cb3d7c3ec85744727a2cec51b08f | Apache-2.0 | Mistral AI | Yes: GGUF conversion + Q5_K_M quant |
parakeet-tdt-0.6b-v3 | parakeet-tdt-0.6b-v3-Q8_0.gguf | 739,508,576 (739.5 MB) | 5859f77944efcd8eafa23a6350731960b2b55b2203df51f319665c807d802cc7 | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q8_0 quant |
parakeet-tdt-0.6b-v2 | parakeet-tdt-0.6b-v2-Q8_0.gguf | 729,574,912 (729.6 MB) | f0d0e99cebb6d3b83f1f7069b82b5d3c2e39a54545b0da039cb4bafd9c4e5caa | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q8_0 quant |
Qwen3-ASR-0.6B | Qwen3-ASR-0.6B-Q8_0.gguf | 850,423,456 (850.4 MB) | f081b2d5e23bd669d92cc331d722a8a0681943b8e6f34b48996fd5c319b5acd8 | Apache-2.0 | Qwen team, Alibaba Cloud | Yes: GGUF conversion + Q8_0 quant |
Fun-ASR-MLT-Nano-2512 | Fun-ASR-MLT-Nano-2512-Q8_0.gguf | 891,271,232 (891.3 MB) | d12476d8d9f2baa0ebf738fa955fa05ed33a654f1567289033a810c45d9d9002 | Apache-2.0 | FunAudioLLM (Tongyi Lab, Alibaba Group) | Yes: GGUF conversion + Q8_0 quant |
canary-1b-flash | canary-1b-flash-Q5_K_M.gguf | 769,563,424 (769.6 MB) | 7eed3cac92f255a4adbd518c58663d3fbf65984d2619189e593f2d374b05c601 | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q5_K_M quant |
canary-1b-v2 | canary-1b-v2-Q5_K_M.gguf | 836,664,032 (836.7 MB) | 9c3a893c93795438baf9b4b1c853c39b60316c3a0d259a3ba6e284712f5ddb71 | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q5_K_M quant |
canary-qwen-2.5b | canary-qwen-2.5b-Q5_K_M.gguf | 1,983,729,024 (1.98 GB) | d71b1d9ae442dc538665ed4df63a69520eb9be769ec0bc78c7d2f40040928306 | CC-BY-4.0 (+ Apache-2.0 for the Qwen3-1.7B component) | NVIDIA Corporation | Yes: GGUF conversion + Q5_K_M quant |
Fun-ASR-Nano-2512 | Fun-ASR-Nano-2512-Q8_0.gguf | 891,270,912 (891.3 MB) | 681caef6df15a2c0e153b40ca7fe4087fdf65751fa5e6fe605d8a75dff969e61 | Apache-2.0 | FunAudioLLM (Tongyi Lab, Alibaba Group) | Yes: GGUF conversion + Q8_0 quant |
gigaam-v3-ctc | gigaam-v3-ctc-Q8_0.gguf | 271,803,328 (271.8 MB) | 71e5c82890e9e243a6bd7575f129f5d1bd2c3ca3ae79aab75cfb1a6934c6a62b | MIT | ai-sage / GigaChat Team (Sber) | Yes: GGUF conversion + Q8_0 quant |
gigaam-v3-e2e-ctc | gigaam-v3-e2e-ctc-Q8_0.gguf | 272,151,136 (272.2 MB) | 9ccce4750dc813a493d96ca15ee251712bedec15ac9a02fa3d2bd732f08ae5eb | MIT | ai-sage / GigaChat Team (Sber) | Yes: GGUF conversion + Q8_0 quant |
gigaam-v3-rnnt | gigaam-v3-rnnt-Q8_0.gguf | 273,022,880 (273.0 MB) | 130d54450be2922144b265d90d453b781fc2b5adaee52a16954b00f3686201f7 | MIT | ai-sage / GigaChat Team (Sber) | Yes: GGUF conversion + Q8_0 quant |
gigaam-v3-e2e-rnnt | gigaam-v3-e2e-rnnt-Q8_0.gguf | 273,724,832 (273.7 MB) | 78d63b47723b7f8d78c6113a6ef983b5a86e2a86f6c273e1f5cb6967b1c4467a | MIT | ai-sage / GigaChat Team (Sber) | Yes: GGUF conversion + Q8_0 quant |
granite-speech-4.1-2b-nar | granite-speech-4.1-2b-nar-Q5_K_M.gguf | 1,782,089,344 (1.78 GB) | 88d7c7b5b8b59c95bb6580a1e7d5d81cae63943cef71405ff477527b0bb69fca | Apache-2.0 | IBM (IBM Granite team) | Yes: GGUF conversion + Q5_K_M quant |
granite-4.0-1b-speech | granite-4.0-1b-speech-Q5_K_M.gguf | 1,829,704,640 (1.83 GB) | 4f47208116b3d4dcf73b8562a739bd675f80eeefb4f64e7481b08aee223c9b86 | Apache-2.0 | IBM (IBM Granite team) | Yes: GGUF conversion + Q5_K_M quant |
granite-speech-4.1-2b | granite-speech-4.1-2b-Q5_K_M.gguf | 1,829,704,640 (1.83 GB) | 1ea15c2174b485b8de81337a1da3ae6703a7892a5330345b9a2212e9a6a0c365 | Apache-2.0 | IBM (IBM Granite team) | Yes: GGUF conversion + Q5_K_M quant |
granite-speech-4.1-2b-plus | granite-speech-4.1-2b-plus-Q5_K_M.gguf | 1,691,297,216 (1.69 GB) | 4b762cb221169db53863aa2ace3489310d619263b3a5382b39f1abac60f87301 | Apache-2.0 | IBM (IBM Granite team) | Yes: GGUF conversion + Q5_K_M quant |
moonshine-tiny | moonshine-tiny-Q8_0.gguf | 35,466,912 (35.5 MB) | 2fd348d7b38f97d309cc3ec6848f3f57f537b80244950f07d2637e463f95a3a1 | MIT | Useful Sensors, Inc. (dba Moonshine AI) | Yes: GGUF conversion + Q8_0 quant |
moonshine-streaming-tiny | moonshine-streaming-tiny-Q8_0.gguf | 50,462,816 (50.5 MB) | 930e4622ad3a24158b91406c30c977fa6a26b34cb32d6ac3e57cfb23383a869e | MIT | Useful Sensors, Inc. (dba Moonshine AI) | Yes: GGUF conversion + Q8_0 quant |
moonshine-base | moonshine-base-Q8_0.gguf | 77,476,480 (77.5 MB) | 7f0027dfd857d310b63a85ef57cadf183da712cc374f85a648f8bc18aaa2efc8 | MIT | Useful Sensors, Inc. (dba Moonshine AI) | Yes: GGUF conversion + Q8_0 quant |
moonshine-streaming-small | moonshine-streaming-small-Q8_0.gguf | 198,506,848 (198.5 MB) | d03670f69629b649085d0f44a63d97668b4119117cc9611a4e4ad94341713dfc | MIT | Useful Sensors, Inc. (dba Moonshine AI) | Yes: GGUF conversion + Q8_0 quant |
moonshine-streaming-medium | moonshine-streaming-medium-Q8_0.gguf | 295,793,568 (295.8 MB) | f7c9564249b508f6012927ec4f9e536087da53a7047f858ca9975bea5f75299e | MIT | Useful Sensors, Inc. (dba Moonshine AI) | Yes: GGUF conversion + Q8_0 quant |
parakeet-tdt_ctc-110m | parakeet-tdt_ctc-110m-Q8_0.gguf | 135,373,280 (135.4 MB) | 7dd44c74a331d788a4e5f8b16913b3feb29ced22cf5613aad0e0f6cd30516296 | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q8_0 quant |
parakeet-ctc-0.6b | parakeet-ctc-0.6b-Q8_0.gguf | 722,271,424 (722.3 MB) | e47ae86c1b34dbaf054334592575c29aee7de3546e75d1f0ccee05904c5c49ae | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q8_0 quant |
parakeet-rnnt-0.6b | parakeet-rnnt-0.6b-Q8_0.gguf | 729,687,456 (729.7 MB) | 06da5ba773736984e0513037b27ff9300e95c55eaa2b79dbc688efc34b25df51 | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q8_0 quant |
parakeet-ctc-1.1b | parakeet-ctc-1.1b-Q5_K_M.gguf | 928,584,736 (928.6 MB) | 8b38f60bb1c56b518953a39703068adc09991993b5aab52782e038a86a6c04cc | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q5_K_M quant |
parakeet-tdt-1.1b | parakeet-tdt-1.1b-Q5_K_M.gguf | 935,758,496 (935.8 MB) | 18c22888fed10676fde72fdd5b833fab9175a6abf4e0cb390eb0a59b0b107cd3 | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q5_K_M quant |
parakeet-rnnt-1.1b | parakeet-rnnt-1.1b-Q5_K_M.gguf | 935,755,008 (935.8 MB) | 8ee2ead0c30773440743591cd8a9f8ac180c27f450c9b053805b2bab6bf352cb | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q5_K_M quant |
parakeet-tdt_ctc-1.1b | parakeet-tdt_ctc-1.1b-Q5_K_M.gguf | 935,758,080 (935.8 MB) | da23eb8c78fe3813b88f616db85e8191cfbfffdbc2b8bb8bd74a94eb8987caee | CC-BY-4.0 | NVIDIA Corporation | Yes: GGUF conversion + Q5_K_M quant |
Qwen3-ASR-1.7B | Qwen3-ASR-1.7B-Q5_K_M.gguf | 1,517,290,464 (1.52 GB) | 034c557fe92ff8fcd9a9c041cbdaad347be0a86a58d3a348f63cf3f0180879d0 | Apache-2.0 | Qwen team, Alibaba Cloud | Yes: GGUF conversion + Q5_K_M quant |
Voxtral-Mini-3B-2507 | Voxtral-Mini-3B-2507-Q5_K_M.gguf | 3,464,182,432 (3.46 GB) | 9ea2eb85359f7771270715849dc2b0e80381a59dab09d9c5d85352ab9c53f675 | Apache-2.0 | Mistral AI | Yes: GGUF conversion + Q5_K_M quant |
Voxtral-Small-24B-2507 | Voxtral-Small-24B-2507-Q5_K_M.gguf | 17,138,659,808 (17.14 GB) | a53f73a5f63b7663fe155977616a636acac567dac898f6ef82ca519017e8b6e7 | Apache-2.0 | Mistral AI | Yes: GGUF conversion + Q5_K_M quant |
whisper-tiny | whisper-tiny-Q8_0.gguf | 45,981,088 (46.0 MB) | 325b9c7997cd1eff81ef709d55766565e71be696130cc3a3d444713798706834 | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
whisper-tiny.en | whisper-tiny.en-Q8_0.gguf | 45,904,544 (45.9 MB) | e8c9b73c06344307d8b346e07fbe93dd88d894627854bcff31523f1ce44394fa | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
whisper-base | whisper-base-Q8_0.gguf | 84,962,880 (85.0 MB) | 81c069428bc8a24551a8169cf31cf09bcfd9d4cf50389ae281323c9aa9648c81 | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
whisper-base.en | whisper-base.en-Q8_0.gguf | 84,886,208 (84.9 MB) | 3b46ca40bccbf7609c68d88a36d96077a04ca7c87f2060ede06f129fac3e7652 | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
whisper-small.en | whisper-small.en-Q8_0.gguf | 269,674,144 (269.7 MB) | 9614e6b7fda2d26018e4f268aece8ca25a83296ea0b534169a585b740bfd71ef | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
whisper-small | whisper-small-Q8_0.gguf | 269,751,136 (269.8 MB) | 9b9c8811bbcc82a7766f0fb0925614bdacb0923b2cc630daeac17108b655b860 | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
whisper-medium.en | whisper-medium.en-Q8_0.gguf | 831,460,928 (831.5 MB) | 03d7257fef498750ce272631bc6a34de322fc2b438aab5c268ff49dfd1b64c49 | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
whisper-large-v3-turbo | whisper-large-v3-turbo-Q8_0.gguf | 886,381,760 (886.4 MB) | b2e30cc286bc9f3aba4db9099fc7403543497c05ce7100d0d83091ddfd25a183 | MIT | OpenAI | Yes: GGUF conversion + Q8_0 quant |
whisper-large-v3 | whisper-large-v3-Q5_K_M.gguf | 1,161,143,008 (1.16 GB) | 6053d0fd69a0fd48b8fea5ea7a52b9e0cde389343566fa30e453a1b2b258dc38 | MIT | OpenAI | Yes: GGUF conversion + Q5_K_M quant |
whisper-large | whisper-large-Q5_K_M.gguf | 1,160,366,048 (1.16 GB) | b1dfb9fa0e9b0574d8c822f51662b8e59915831e1a3ca0d191f18ff8ba742639 | MIT | OpenAI | Yes: GGUF conversion + Q5_K_M quant |
whisper-large-v2 | whisper-large-v2-Q5_K_M.gguf | 1,160,366,080 (1.16 GB) | 94fab1289a98d26bf375174a8daa00f44c7b16a9944726dde05361e5a8bd07ff | MIT | OpenAI | Yes: GGUF conversion + Q5_K_M quant |
Breeze-ASR-25 | Breeze-ASR-25-Q5_K_M.gguf | 1,160,366,080 (1.16 GB) | c871fc811b33a16a5607c4d4166cfa2c0a1d359f7796e3da255e3e922f59139b | Apache-2.0 | MediaTek Research | Yes: GGUF conversion + Q5_K_M quant |
Model ids are the Coco Voice ids: catalog.json slugs for the GGUF entries, and model.rs ids for medium, breeze-asr and the three moonshine-*-streaming-en tarballs. Some catalog slugs, such as parakeet-tdt-0.6b-v3, are also used by older model.rs entries. Those older entries are not hosted here (see the next section); here the folder holds the catalog GGUF.
Not hosted here: publisher-direct models
The following Coco Voice models are not hosted in this repository. The app downloads them directly from the original publisher's (or the original converter's) Hugging Face repository, pinned to a commit, because those files are byte-identical (same SHA-256) to what Handy shipped:
| Coco Voice model id (model.rs) | downloaded from (repo @ pinned commit) |
|---|---|
small | ggerganov/whisper.cpp@5359861 |
turbo | ggerganov/whisper.cpp@5359861 |
large | ggerganov/whisper.cpp@5359861 |
parakeet-tdt-0.6b-v2 | istupakov/parakeet-tdt-0.6b-v2-onnx@0bbb45a |
parakeet-tdt-0.6b-v3 | istupakov/parakeet-tdt-0.6b-v3-onnx@8f23f0c |
moonshine-base | moonshine-ai/moonshine@48b4e42 |
sense-voice-int8 | csukuangfj/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17@2365bae |
gigaam-v3-e2e-ctc | istupakov/gigaam-v3-onnx@322c3b2 |
canary-180m-flash | istupakov/canary-180m-flash-onnx@92c2231, istupakov/parakeet-tdt-0.6b-v3-onnx@8f23f0c |
canary-1b-v2 | istupakov/canary-1b-v2-onnx@5ebc152, istupakov/parakeet-tdt-0.6b-v3-onnx@8f23f0c |
cohere-int8 | csukuangfj2/sherpa-onnx-cohere-transcribe-14-lang-int8-2026-04-01@156a470, tristanripke/cohere-transcribe-onnx-int8@9ecc3a5 |
Some other models that earlier Handy builds offered are also intentionally not mirrored here, for licensing reasons.
Disclaimer
The models are provided as-is, under their respective licenses, without warranty of any kind. Coco Research is not affiliated with, or endorsed by, the original publishers or the Handy project.