---
source: 'https://howaiworks.ai/blog/qwen3-asr-announcement'
section: blog
title: 'Qwen3-ASR: SOTA Multilingual Speech Recognition and Forced Alignment'
description: >-
  Alibaba's Qwen team releases Qwen3-ASR and Qwen3-ForcedAligner, setting new
  benchmarks in multilingual speech-to-text and precise timestamping.
date: '2026-01-30'
author: HowAIWorks Team
tags:
  - Qwen3-ASR
  - Speech Recognition
  - Open Source AI
  - Multilingual AI
  - Forced Alignment
  - Alibaba Qwen
  - ASR Benchmarks
  - Audio Intelligence
readingTime: 6 minutes
isReviewed: false
---

# Qwen3-ASR: SOTA Multilingual Speech Recognition and Forced Alignment

> Alibaba's Qwen team releases Qwen3-ASR and Qwen3-ForcedAligner, setting new benchmarks in multilingual speech-to-text and precise timestamping.

## Introduction

Alibaba’s Qwen team has officially unveiled **Qwen3-ASR**, a significant leap forward in open-source speech-to-text technology. This release includes the Qwen3-ASR-1.7B and 0.6B models, alongside the **Qwen3-ForcedAligner**, providing a comprehensive toolkit for everything from real-time transcription to hyper-precise audio alignment.

Built on the foundations of the Qwen3-Omni architecture, these models aren't just incremental updates. They represent a shift toward "all-in-one" speech intelligence, capable of handling 52 languages and dialects, identifying speakers, and maintaining high accuracy even in the presence of noise or background music.

## Key Features of the Qwen3-ASR Family

Qwen3-ASR is designed to be a versatile powerhouse for modern AI applications. Its architecture focuses on several core pillars:

- **All-in-One Multilingual Support**: A single model handles 30 major languages and 22 regional Chinese dialects, eliminating the need for separate models for language identification.
- **Robustness in Chaos**: Whether it's heavy background noise, low speech quality, or complex linguistic patterns like heavy accents and singing, Qwen3-ASR maintains a low Word Error Rate (WER).
- **Extreme Efficiency**: The 0.6B variant is built for scale, capable of transcribing 2,000 seconds of speech in just one second at high concurrency, with sub-100ms time-to-first-token.
- **Flexible Inference Options**: Native support for vLLM-based batch inference, asynchronous serving, and streaming modes makes it ready for both production backends and edge devices.

## Performance Benchmarks

In technical evaluations, the 1.7B model has established itself as the new state-of-the-art (SOTA) among open-source speech models.

### Multilingual Recognition (WER %)

When compared against industry heavyweights like Whisper-large-v3, Qwen3-ASR-1.7B shows a clear advantage across various datasets:

| Benchmark | Qwen3-ASR-1.7B | Whisper-large-v3 | GLM-ASR-Nano |
| :--- | :---: | :---: | :---: |
| **MLS** (Average) | **8.55** | 8.62 | 13.32 |
| **CommonVoice** | **9.18** | 10.77 | 19.40 |
| **Fleurs-12** | **4.90** | 5.27 | 16.08 |

Beyond raw numbers, Qwen3-ASR excels in **English accented speech** (covering accents from 16 countries) and **Chinese regional dialects**, where it significantly narrows the gap between standard Mandarin and local variants like Cantonese or Fujianese.

## Precision Timing with Qwen3-ForcedAligner

For developers working on subtitles, karaoke apps, or video editing tools, the **Qwen3-ForcedAligner-0.6B** is perhaps the most exciting part of this release. Unlike autoregressive models that can struggle with long-form audio, this non-autoregressive (NAR) model provides rock-solid timestamp accuracy.

Evaluation results show that it outperforms established tools like **WhisperX** and **Nemo-Forced-Aligner (NFA)**, particularly on long, concatenated audio files up to 5 minutes in length. It ensures that every word is mapped to the exact millisecond it was spoken.

## Conclusion

The release of Qwen3-ASR and the Forced Aligner marks a new era for open-weights speech technology. By combining state-of-the-art accuracy with production-ready efficiency and a massive multilingual reach, Alibaba is providing the building blocks for the next generation of voice-enabled agents and accessibility tools.

Whether you are building a real-time translation service or a high-precision subtitle generator, the Qwen3-ASR family offers a level of performance that was previously only available via expensive, commercial cloud APIs.

## Sources

- [Official Qwen3-ASR GitHub Repo](https://github.com/QwenLM/Qwen3-ASR)
- [Qwen3-ASR Model Collection on HuggingFace](https://huggingface.co/collections/Qwen/qwen3-asr)
- [Qwen3-ASR Technical Paper](https://github.com/QwenLM/Qwen3-ASR/blob/main/assets/Qwen3_ASR.pdf)
- [Interactive Demo](https://huggingface.co/spaces/Qwen/Qwen3-ASR)

## Frequently Asked Questions

### What is Qwen3-ASR?

Qwen3-ASR is a family of all-in-one multilingual speech recognition models (1.7B and 0.6B) developed by Alibaba's Qwen team, supporting 52 languages and dialects.

### How does Qwen3-ASR compare to OpenAI's Whisper?

The 1.7B model achieves state-of-the-art performance, consistently outperforming Whisper-large-v3 and even commercial APIs like GPT-4o in multilingual and accented benchmarks.

### What is Qwen3-ForcedAligner?

It is a non-autoregressive (NAR) model designed for high-precision speech-to-text timestamping, outperforming existing tools like WhisperX.

### Does Qwen3-ASR support real-time transcription?

Yes, both the 1.7B and 0.6B models support streaming mode for real-time applications, with the 0.6B model optimized for ultra-low latency.

### Can it recognize singing voices?

Yes, Qwen3-ASR features robust performance in transcribing songs even with heavy background music, achieving low Word Error Rates (WER).

---

Source: https://howaiworks.ai/blog/qwen3-asr-announcement — HowAIWorks.ai