--- title: "Haas-ja-v1.0-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6b81478c-e1c4-4cd0-85f7-356e7044f4ed" updated: "2026-11-13" --- # Haas-ja-v1.0-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF files of a Japanese speech recognition model that run only in speech.cpp, on macOS, Windows, Linux or the CPU alone. For people who transcribe Japanese audio on their own computer. Haas-ja-v1.0-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by sakasegawa ## About ### What it is This repository holds GGUF copies of Haas-ja-v1.0, a Japanese speech recognition model from Third Intelligence with 616M parameters. It is a FastConformer recognizer whose decoder uses Kimi Delta Attention layers. The files use speech.cpp's own layout, so they run only in speech.cpp 0.9.4 or later and not in llama.cpp, LM Studio or Ollama. ### Who it suits People who want to turn Japanese speech into text on their own machine, and developers who want a local recognizer with a command line and an HTTP server. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Meetings and notes | Teams with Japanese recordings | Transcribe audio locally, with voice detection for long files | A recorded meeting saved as a WAV file | | Software development | Developers adding speech to text | Run the local server and call its HTTP API | A transcription endpoint on their own machine | | Language research | People studying Japanese speech recognition | Try several file sizes on a CPU | Compare the smaller and larger files on one clip | You can skip it if you need another language or want to load the files in a tool other than speech.cpp. ### Where to use it - speech.cpp 0.9.4 or later on macOS with Metal - speech.cpp on Windows and Linux with Vulkan - speech.cpp on the CPU alone, with no NVIDIA GPU needed - A local server started with the speech serve command | Use case | Who | What they get back | | --- | --- | --- | | Transcribe a short clip | Anyone with a WAV file | The recognized Japanese text | | Transcribe a long recording | Note takers | Text for the regions where someone speaks | | Recognize special words | People with names or jargon | Better odds that a custom word appears in your spelling | ### How to set it up You need speech.cpp first, since nothing else reads these files. After that, the speech command fetches the model files from this repository the first time you run it. - speech.cpp 0.9.4 or later, installed by its own instructions - Enough disk space for the file type you choose 1. Install speech.cpp. 2. Run any command from the next section with the model name. 3. Let it fetch the Q8\_0 file on that first run, or name another type. If you want the repository files yourself, Hugging Face gives these steps: ```sh pip install -U huggingface_hub hf download sakasegawa/Haas-ja-v1.0-GGUF ``` ### How to use it You run the speech command with the model name and an audio file. You can also give the path of a file you already downloaded. A type other than Q8\_0 is picked by adding a colon and the type to the name, such as q4\_k. ```sh speech asr haas-ja-v1.0 utterance.wav speech asr haas-ja-v1.0 --timestamps utterance.wav speech asr haas-ja-v1.0 --vad silero-vad meeting.wav speech asr haas-ja-v1.0 --keywords $'Haas=ハース\nAIモデル' utterance.wav speech serve haas-ja-v1.0 --open ``` | Tool | What it does | | --- | --- | | speech asr | Transcribes an audio file with the model | | --timestamps | Adds times to the output | | --vad silero-vad | Recognizes only the spoken regions, best for recordings over about 30 seconds | | --keywords | Raises the odds of custom words, one per line, with optional readings after an equals sign | | speech serve | Starts a local server with an HTTP API | ### Example You run the recognizer on meeting.wav with voice detection switched on. The model returns the Japanese text for the stretches where someone speaks. Adding keywords makes it favor names you list, and that run returns the text without times. ### For newcomers and experts If you are new, install speech.cpp and run the first command on a short WAV file. Check the text that comes back before you try keywords or the server. For experts, the model is a FastConformer encoder with a TDT decoder whose prediction network uses Kimi Delta Attention layers. It decodes greedily, as NeMo does by default. Keywords are favored while decoding with a weight of 2 unless you change it, and a keyword run decodes twice. Smaller file types save space and are generally less exact, though the card gives no accuracy numbers. Audio file in → speech.cpp fetches the GGUF file → FastConformer encoder, TDT decoder → Japanese text out ### Good to know File size by type in GB | Label | Value | | --- | --- | | F16 | 1.23 GB | | Q8\_0 | 0.66 GB | | Q6\_K | 0.51 GB | | Q5\_K | 0.43 GB | | Q4\_K | 0.35 GB | - Runtime: only speech.cpp 0.9.4 or later reads these files - Older version: speech.cpp 0.9.3 fetches an earlier revision without the custom vocabulary settings - Language: Japanese only - License: Apache 2.0 - Cost to run: none named - Data it reaches: the audio files you give it, and a download of the model files on first use - Not for: llama.cpp, LM Studio, Ollama or other programs that read GGUF ## README ````markdown # Haas-ja-v1.0-GGUF GGUF files of a Japanese speech recognition model that run only in speech.cpp, on macOS, Windows, Linux or the CPU alone. For people who transcribe Japanese audio on their own computer. Made by sakasegawa on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository holds GGUF copies of Haas-ja-v1.0, a Japanese speech recognition model from Third Intelligence with 616M parameters. It is a FastConformer recognizer whose decoder uses Kimi Delta Attention layers. The files use speech.cpp's own layout, so they run only in speech.cpp 0.9.4 or later and not in llama.cpp, LM Studio or Ollama. Who it suits: People who want to turn Japanese speech into text on their own machine, and developers who want a local recognizer with a command line and an HTTP server. ```table Industry | Who uses it | How they use it | Example Meetings and notes | Teams with Japanese recordings | Transcribe audio locally, with voice detection for long files | A recorded meeting saved as a WAV file Software development | Developers adding speech to text | Run the local server and call its HTTP API | A transcription endpoint on their own machine Language research | People studying Japanese speech recognition | Try several file sizes on a CPU | Compare the smaller and larger files on one clip ``` You can skip it if you need another language or want to load the files in a tool other than speech.cpp. Where to use it: - speech.cpp 0.9.4 or later on macOS with Metal - speech.cpp on Windows and Linux with Vulkan - speech.cpp on the CPU alone, with no NVIDIA GPU needed - A local server started with the speech serve command ```table Use case | Who | What they get back Transcribe a short clip | Anyone with a WAV file | The recognized Japanese text Transcribe a long recording | Note takers | Text for the regions where someone speaks Recognize special words | People with names or jargon | Better odds that a custom word appears in your spelling ``` How to set it up: You need speech.cpp first, since nothing else reads these files. After that, the speech command fetches the model files from this repository the first time you run it. - speech.cpp 0.9.4 or later, installed by its own instructions - Enough disk space for the file type you choose 1. Install speech.cpp. 2. Run any command from the next section with the model name. 3. Let it fetch the Q8_0 file on that first run, or name another type. If you want the repository files yourself, Hugging Face gives these steps: ```sh pip install -U huggingface_hub hf download sakasegawa/Haas-ja-v1.0-GGUF ``` How to use it: You run the speech command with the model name and an audio file. You can also give the path of a file you already downloaded. A type other than Q8_0 is picked by adding a colon and the type to the name, such as q4_k. ```sh speech asr haas-ja-v1.0 utterance.wav speech asr haas-ja-v1.0 --timestamps utterance.wav speech asr haas-ja-v1.0 --vad silero-vad meeting.wav speech asr haas-ja-v1.0 --keywords $'Haas=ハース\nAIモデル' utterance.wav speech serve haas-ja-v1.0 --open ``` ```table Tool | What it does speech asr | Transcribes an audio file with the model --timestamps | Adds times to the output --vad silero-vad | Recognizes only the spoken regions, best for recordings over about 30 seconds --keywords | Raises the odds of custom words, one per line, with optional readings after an equals sign speech serve | Starts a local server with an HTTP API ``` Example: You run the recognizer on meeting.wav with voice detection switched on. The model returns the Japanese text for the stretches where someone speaks. Adding keywords makes it favor names you list, and that run returns the text without times. For newcomers and experts: If you are new, install speech.cpp and run the first command on a short WAV file. Check the text that comes back before you try keywords or the server. For experts, the model is a FastConformer encoder with a TDT decoder whose prediction network uses Kimi Delta Attention layers. It decodes greedily, as NeMo does by default. Keywords are favored while decoding with a weight of 2 unless you change it, and a keyword run decodes twice. Smaller file types save space and are generally less exact, though the card gives no accuracy numbers. ```flow Audio file in speech.cpp fetches the GGUF file FastConformer encoder, TDT decoder Japanese text out ``` Good to know: ```chart Title: File size by type in GB F16: 1.23 GB Q8_0: 0.66 GB Q6_K: 0.51 GB Q5_K: 0.43 GB Q4_K: 0.35 GB ``` - Runtime: only speech.cpp 0.9.4 or later reads these files - Older version: speech.cpp 0.9.3 fetches an earlier revision without the custom vocabulary settings - Language: Japanese only - License: Apache 2.0 - Cost to run: none named - Data it reaches: the audio files you give it, and a download of the model files on first use - Not for: llama.cpp, LM Studio, Ollama or other programs that read GGUF ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Parameters: 616M - Weights: about 308 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 5,149 - Likes: 7 - License: apache-2.0 - Languages: ja - Based on: third-intelligence/Haas-ja-v1.0 - Tags: gguf, ggml, speech.cpp, fastconformer, automatic-speech-recognition ## Links - Model: https://huggingface.co/sakasegawa/Haas-ja-v1.0-GGUF - Files: https://huggingface.co/sakasegawa/Haas-ja-v1.0-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download sakasegawa/Haas-ja-v1.0-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [sakasegawa/Haas-ja-v1.0-GGUF on Hugging Face](https://huggingface.co/sakasegawa/Haas-ja-v1.0-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Haas-ja-v1.0-GGUF (this page) | 5,149 | 7 | 616M | apache-2.0 | 2026-10-31 | | [parakeet-tdt-0.6b-v3](https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120) | 529,594 | 1,272 | 627M | cc-by-4.0 | 2026-08-05 | | [Parakeet RNNT 1.1B](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [balbal-v2e-zellige](https://mindket.com/p/dfb9137a-26b4-47b3-9265-b841ad06be36) | 64 | 3 | — | cc-by-nc-sa-4.0 | 2026-11-08 | | [YuE2-GGUF](https://mindket.com/p/5c031f13-7ab7-4b31-9032-8dd7c7e51b9e) | 8,466,401 | 31 | 632M | cc-by-nc-4.0 | 2026-10-08 | | [Qwen3-TTS GGUF](https://mindket.com/p/ab5107b1-bdce-42f1-b3e2-abd1bae80d7c) | 1,347,066 | 67 | 915M | apache-2.0 | 2026-09-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-31 - First published: 2026-10-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sakasegawa/Haas-ja-v1.0-GGUF on Hugging Face](https://huggingface.co/sakasegawa/Haas-ja-v1.0-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6b81478c-e1c4-4cd0-85f7-356e7044f4ed) - [Its page on Mindket](https://mindket.com/p/6b81478c-e1c4-4cd0-85f7-356e7044f4ed)