---
license: apache-2.0
language:
  - ja
  - en
library_name: transformers
base_model: Qwen/Qwen3-ASR-1.7B
pipeline_tag: automatic-speech-recognition
tags:
  - automatic-speech-recognition
  - asr
  - japanese
  - qwen3-asr
  - speech
  - audio
---

# Japanese Proper-Noun Fine-tuned Qwen3-ASR

A fine-tuned checkpoint of [`Qwen/Qwen3-ASR-1.7B`](https://huggingface.co/Qwen/Qwen3-ASR-1.7B) optimized for Japanese automatic speech recognition.

This variant is specifically trained to improve recognition of Japanese proper nouns, organization names, product names, service names, and kanji-heavy expressions that general-purpose ASR models tend to mistranscribe.

## Intended Use

Designed for Japanese ASR tasks requiring accurate transcription of:

- Proper nouns (people, places, organizations)
- Difficult or uncommon kanji spellings
- Company and product names
- Mixed Japanese/English technical terminology

### Example Normalizations

#### Product / Company Names

| Spoken | Expected output |
| ------ | --------------- |
| ジェミニ | `Gemini` |
| アンスロピック | `Anthropic` |
| ノートブックLM | `NotebookLM` |

#### Japanese Numerals

| Spoken | Expected output |
| ------ | --------------- |
| 一万二千三百四十五 | `1万2345` |
| 十人 | `10人` |

#### Math / Code

| Spoken | Expected output |
| ------ | --------------- |
| AイコールAプラス1 | `A=A+1` |

## Model Details

|                       |                                              |
| --------------------- | -------------------------------------------- |
| **Base model**        | `Qwen/Qwen3-ASR-1.7B`                        |
| **Task**              | Automatic speech recognition                 |
| **Language**          | Japanese (ja), English (en)                  |
| **Fine-tuning focus** | Japanese proper nouns & technical vocabulary |

## Quickstart

Install the runtime package:

```bash
pip install -U qwen-asr
```

Transcribe a local audio file:

```python
import torch
from qwen_asr import Qwen3ASRModel

model = Qwen3ASRModel.from_pretrained(
    "neosophie/Qwen3-ASR-1.7B-JA",
    dtype=torch.bfloat16,
    device_map="cuda:0",
)

results = model.transcribe(audio="/path/to/audio.wav")

print(results[0].language)
print(results[0].text)
```

## Acknowledgments

- [Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B) — base model by Alibaba Cloud

---

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