---
title: "Haas-ja-v1.0"
kind: "Kit"
category: "Productivity"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/1ce7beb7-a71b-4ade-ae19-a31087c59e45"
updated: "2026-11-12"
---

# Haas-ja-v1.0

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Turns Japanese speech into text with a 615.8M speech recognition model built for speed and accuracy. For developers and teams who transcribe Japanese audio on a Linux machine with an NVIDIA GPU.

Haas-ja-v1.0 is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by third-intelligence

## About

### What it is

Haas-ja-v1.0 turns spoken Japanese into text, with speed and accuracy as its two goals. It builds on NVIDIA's Parakeet-TDT design and swaps in Kimi Delta Attention in its prediction network. The card reports a strict CER of 6.12% on a benchmark made from five public Japanese datasets.

### Who it suits

It suits developers and teams who need Japanese audio turned into text at speed.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Media | Video teams | Transcribe Japanese interviews and clips | A script from a recorded interview |
| Business | Operations teams | Turn Japanese meetings into text | Notes from a recorded call |
| Research | Speech researchers | Compare Japanese recognizers | A test on the card's five-dataset benchmark |

People who work in other languages, or on a Mac or a machine without an NVIDIA GPU, can skip it.

### Where to use it

- An NVIDIA GPU on a Linux box, with Python 3.11 or newer and NeMo 2.7, as the card requires
- A Hugging Face Space demo, which the card links
- Windows, macOS or Linux for downloading the files, though the card says CPU and macOS runs are not supported

| Use case | Who | What they get back |
| --- | --- | --- |
| Short clips | Developers | The Japanese text of a recording |
| Long recordings | Operations teams | Text from audio over 30 seconds, split at quiet points |
| Names and terms | Editors | A custom vocabulary so rare names are written as you want |

### How to set it up

You need these first:

- Linux with an NVIDIA GPU
- Python 3.11 or newer and NeMo 2.7
- Triton and CUDA, which the attention kernels depend on
- The haas-asr inference package, which the card installs from its code repository

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Install the inference package into your environment.

```sh
pip install -U huggingface_hub
hf download third-intelligence/Haas-ja-v1.0
```

### How to use it

Load the model, load an audio file, and call transcribe. For recordings longer than 30 seconds use the long-form call, which splits the audio at quiet points and works through the pieces in batches.

```python
import haas_asr

model = haas_asr.load_model("third-intelligence/Haas-ja-v1.0")
audio = haas_asr.load_audio("example.wav")
output = model.transcribe([audio])
print(output[0].text)
```

```python
audio = haas_asr.load_audio("example.flac")
text = haas_asr.transcribe_longform(model, audio, batch_size=64)
```

| Tool | What it does |
| --- | --- |
| haas\_asr.load\_model | Returns the model as a standard NeMo object, set to bfloat16 on the GPU |
| haas\_asr.load\_audio | Reads WAV, FLAC, MP3 or OGG, makes it mono and resamples it to 16 kHz |
| model.transcribe | Transcribes a list of audio clips |
| haas\_asr.transcribe\_longform | Splits long audio at quiet points and transcribes it in batches |

### Example

You load a recording of a meeting in Japanese and call transcribe on it. The model returns the spoken Japanese as text, and printing the first result shows it. A recording over 30 seconds goes through the long-form call instead.

### For newcomers and experts

Speech recognition turns spoken words into written text. A first small thing to try is one short Japanese WAV file and the four lines of the first snippet.

For experts, the stack pairs a FastConformer encoder with a TDT decoder, as in Parakeet-TDT. The decoder also predicts how long each token lasts, so it can skip several encoder frames at a time. Kimi Delta Attention replaces the LSTM in the prediction network, which the card says raises accuracy at almost the same speed. The cost is a hard dependency on Triton and CUDA.

Load a Japanese audio file → It is made mono and resampled to 16 kHz → The encoder reads the audio frames → The decoder predicts tokens and durations → You get the Japanese text

### Good to know

Parameters by part, in millions

| Label | Value |
| --- | --- |
| Encoder | 608.9 M |
| Prediction network | 3.9 M |
| Joint network | 3.0 M |

- Size: 615.8M parameters, with a tokenizer of 3,072 tokens
- Language: Japanese
- Variants: Hugging Face lists 14 quantized and 2 fine-tuned versions made by other people
- License: apache-2.0
- Cost to run: none named, no API key or account. The card says it costs a small fraction of commercial speech-to-text APIs
- Data it reaches: it downloads model files from Hugging Face and works on the audio files you give it
- Not for: CPU or macOS runs, which the card says are not supported, or languages other than Japanese

## README

````markdown
# Haas-ja-v1.0

Turns Japanese speech into text with a 615.8M speech recognition model built for speed and accuracy. For developers and teams who transcribe Japanese audio on a Linux machine with an NVIDIA GPU.

Made by third-intelligence 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: Haas-ja-v1.0 turns spoken Japanese into text, with speed and accuracy as its two goals. It builds on NVIDIA's Parakeet-TDT design and swaps in Kimi Delta Attention in its prediction network. The card reports a strict CER of 6.12% on a benchmark made from five public Japanese datasets.

Who it suits: It suits developers and teams who need Japanese audio turned into text at speed.

```table
Industry | Who uses it | How they use it | Example
Media | Video teams | Transcribe Japanese interviews and clips | A script from a recorded interview
Business | Operations teams | Turn Japanese meetings into text | Notes from a recorded call
Research | Speech researchers | Compare Japanese recognizers | A test on the card's five-dataset benchmark
```

People who work in other languages, or on a Mac or a machine without an NVIDIA GPU, can skip it.

Where to use it:

- An NVIDIA GPU on a Linux box, with Python 3.11 or newer and NeMo 2.7, as the card requires
- A Hugging Face Space demo, which the card links
- Windows, macOS or Linux for downloading the files, though the card says CPU and macOS runs are not supported

```table
Use case | Who | What they get back
Short clips | Developers | The Japanese text of a recording
Long recordings | Operations teams | Text from audio over 30 seconds, split at quiet points
Names and terms | Editors | A custom vocabulary so rare names are written as you want
```

How to set it up: You need these first:

- Linux with an NVIDIA GPU
- Python 3.11 or newer and NeMo 2.7
- Triton and CUDA, which the attention kernels depend on
- The haas-asr inference package, which the card installs from its code repository

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Install the inference package into your environment.

```sh
pip install -U huggingface_hub
hf download third-intelligence/Haas-ja-v1.0
```

How to use it: Load the model, load an audio file, and call transcribe. For recordings longer than 30 seconds use the long-form call, which splits the audio at quiet points and works through the pieces in batches.

```python
import haas_asr

model = haas_asr.load_model("third-intelligence/Haas-ja-v1.0")
audio = haas_asr.load_audio("example.wav")
output = model.transcribe([audio])
print(output[0].text)
```

```python
audio = haas_asr.load_audio("example.flac")
text = haas_asr.transcribe_longform(model, audio, batch_size=64)
```

```table
Tool | What it does
haas_asr.load_model | Returns the model as a standard NeMo object, set to bfloat16 on the GPU
haas_asr.load_audio | Reads WAV, FLAC, MP3 or OGG, makes it mono and resamples it to 16 kHz
model.transcribe | Transcribes a list of audio clips
haas_asr.transcribe_longform | Splits long audio at quiet points and transcribes it in batches
```

Example: You load a recording of a meeting in Japanese and call transcribe on it. The model returns the spoken Japanese as text, and printing the first result shows it. A recording over 30 seconds goes through the long-form call instead.

For newcomers and experts: Speech recognition turns spoken words into written text. A first small thing to try is one short Japanese WAV file and the four lines of the first snippet.

For experts, the stack pairs a FastConformer encoder with a TDT decoder, as in Parakeet-TDT. The decoder also predicts how long each token lasts, so it can skip several encoder frames at a time. Kimi Delta Attention replaces the LSTM in the prediction network, which the card says raises accuracy at almost the same speed. The cost is a hard dependency on Triton and CUDA.

```flow
Load a Japanese audio file
It is made mono and resampled to 16 kHz
The encoder reads the audio frames
The decoder predicts tokens and durations
You get the Japanese text
```

Good to know:

```chart
Title: Parameters by part, in millions
Encoder: 608.9 M
Prediction network: 3.9 M
Joint network: 3.0 M
```

- Size: 615.8M parameters, with a tokenizer of 3,072 tokens
- Language: Japanese
- Variants: Hugging Face lists 14 quantized and 2 fine-tuned versions made by other people
- License: apache-2.0
- Cost to run: none named, no API key or account. The card says it costs a small fraction of commercial speech-to-text APIs
- Data it reaches: it downloads model files from Hugging Face and works on the audio files you give it
- Not for: CPU or macOS runs, which the card says are not supported, or languages other than Japanese

## On Hugging Face, 13 November 2026

- Task: automatic speech recognition
- Library: nemo
- Downloads in the last 30 days: 3,665
- Likes: 77
- License: apache-2.0
- Languages: ja
- Quantized versions: 14
- Fine-tunes: 2
- Tags: nemo, speech, audio, automatic-speech-recognition, japanese, fastconformer, conformer

## Links

- Model: https://huggingface.co/third-intelligence/Haas-ja-v1.0
- Files: https://huggingface.co/third-intelligence/Haas-ja-v1.0/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download third-intelligence/Haas-ja-v1.0
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 5 KB, downloaded as a zip.

```text
README.md  5 KB
```

## Source

[third-intelligence/Haas-ja-v1.0 on Hugging Face](https://huggingface.co/third-intelligence/Haas-ja-v1.0)

## 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 (this page) | 3,665 | 77 | — | apache-2.0 | 2026-10-30 |
| [Orukeet](https://mindket.com/p/01ec445a-061b-4b1f-a0e9-91cef756f183) | 35,262 | 110 | 627M | cc-by-sa-4.0 | 2026-11-11 |
| [parakeet-ultra-gguf](https://mindket.com/p/0f756bf9-7825-4294-af8b-c884a8d3877d) | 1,375,499 | 8 | 627M | cc-by-4.0 | 2026-10-16 |
| [Ming-3.0-audio-tiny](https://mindket.com/p/0a09263a-2803-4edd-86b0-747ca737db25) | 5,202 | 62 | 8.9B | mit | 2026-10-29 |
| [orukqwen-asr-1.7b](https://mindket.com/p/0e586967-b382-45bd-8cf6-969ca6f6477e) | 222 | 6 | 2B | apache-2.0 | 2026-11-11 |
| [MOSS-Transcribe-Diarize](https://mindket.com/p/54596977-df9c-49a1-98ed-0a671e987045) | 121,031 | 520 | 909M | apache-2.0 | 2026-10-31 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions.

- [Bluesky · GIGAZINE · 2026-11-02](https://bsky.app/profile/gigazine.net/post/3mzcouu6upt2h): 日本語特化で高速かつ高精度な文字起こしAI「Haas-ja-v1.0」が登場、オープンモデルの中で最高性能を記録 https://gigazine.net/news/20261102-haas-ja-v1-asr/
- [Bluesky · @yomimonoid.bsky.social · 2026-11-02](https://bsky.app/profile/yomimonoid.bsky.social/post/3mzcrktpupt22): Third Intelligence Releases Haas-ja-v1.0 Japanese Speech Recognition Model Haas-ja-v1.0 runs under Apache License 2.0 on Hugging Face, so anyone can fine-tune it. The demo's manual corrections suggest the accuracy claim holds on clean audio, not on messy real-world recordings.
- [Bluesky · Tech Trending · 2026-11-02](https://bsky.app/profile/tech-trending.bsky.social/post/3mzddjgs2qt2k): 日本語特化で高速かつ高精度な文字起こしAI「Haas-ja-v1.0」が登場、オープンモデルの中で最高性能を記録 https://gigazine.net/news/20261102-haas-ja-v1-asr/
- [Bluesky · Tech Trending · 2026-10-31](https://bsky.app/profile/tech-trending.bsky.social/post/3mz6oekltqh2s): third-intelligence/Haas-ja-v1.0 · Hugging Face https://huggingface.co/third-intelligence/Haas-ja-v1.0
- [Bluesky · fetuin · 2026-11-03](https://bsky.app/profile/fetuin.bsky.social/post/3mzerxzpbtr24): この会社は利用者各々の元で成長する「遍在型AGI」の開発を目指すらしい 日本のAIスタートアップThird Intelligenceが日本語特化で高速かつ高精度な文字起こしAI「Haas-ja-v1.0」 https://usepocket.com/c/UP13/UP13737.html \#AIが止まらない
- [Bluesky · fetuin · 2026-11-03](https://bsky.app/profile/fetuin.bsky.social/post/3mzelbjd7wh2h): 日本のAIスタートアップThird Intelligenceが日本語特化で高速かつ高精度な文字起こしAI「Haas-ja-v1.0」https://usepocket.com/c/UP13/UP13737.html \#AIが止まらない
- [Bluesky · knoike · 2026-11-02](https://bsky.app/profile/knoike.bsky.social/post/3mzebf4cfmc27): \| 日本語特化で高速かつ高精度な文字起こしAI「Haas-ja-v1.0」が登場、オープンモデルの中で最高性能を記録 - GIGAZINE gigazine.net/news/2026110...
- [Bluesky · huzioka · 2026-11-02](https://bsky.app/profile/huzioka.bsky.social/post/3mzcpf7baxk2s): 日本語特化で高速かつ高精度な文字起こしAI「Haas-ja-v1.0」が登場、オープンモデルの中で最高性能を記録 gigazine.net/news/2026110...

## 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: 2026-10-30
- First published: 2026-10-08
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open third-intelligence/Haas-ja-v1.0 on Hugging Face](https://huggingface.co/third-intelligence/Haas-ja-v1.0)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1ce7beb7-a71b-4ade-ae19-a31087c59e45)
- [Its page on Mindket](https://mindket.com/p/1ce7beb7-a71b-4ade-ae19-a31087c59e45)