Qwen-Audio-3.1-ASR Technical Report
Summary
Qwen-Audio-3.1-ASR 技术报告介绍了一系列音频语言模型,在语音识别、说话人分离转录和通用音频理解方面较 Qwen-Audio-3.0-ASR 有显著提升,支持 30 种语言和 16 种汉语方言,并提供流式与非流式等多种部署变体。
View Cached Full Text
Cached at: 10/22/26, 03:41 PM
Paper page - Qwen-Audio-3.1-ASR Technical Report
Source: https://huggingface.co/papers/2610.28711 Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
Abstract
WepresentQwen-Audio-3.1-ASR,afamilyofaudio-languagemodelsthatbuildsuponitspredecesorQwen-Audio-3.0-ASRwithimprovedspeechrecognitionandexpandedcapabilitiesinspeaker-diarizedtranscriptionandgeneralaudiounderstanding.Thefamilyretainssupportfor30languagesand16Chinesedialects,togetherwithinstruction-controlledcapabilitiesincludinghistorical-contextconditioning,hierarchicalhotwordcustomization,andnativetranscriptionpolishing.Speaker-diarizedASRproducesstructuredoutputscontainingspeakerlabels,timestamps,andrecognizedtext,maintainingconsistentspeakerlabelsacrossturnsandtherepresentationofoverlappingspeech.Thegeneralaudiounderstandingcapabilitiescoveraudio-,speech-,andmusic-captioning,temporalgroundingandsoundeventdetection,aswellasaudioquestionansweringandmultiple-choicereasoning.Thesetasksbroadentheapplicationscopefromrecognizingspokenwordstointerpretingspeakers,acousticevents,andtheirtemporalrelations.Toaccommodatedifferentdeploymentrequirements,thefamilyincludesvariantsforasynchronousfiletranscription,low-latencystreaming,interactivevoiceinput,andnon-streamingrecognition,withspeaker-diarizedtranscriptionandgeneralaudiounderstandingavailableincertainvariants.EvaluationsspanconventionalASR,multilingualanddialectalspeech,speaker-diarizedtranscription,andgeneralaudiounderstanding,demonstratingthecapabilitiesoftheQwen-Audio-3.1-ASRfamilyacrosstranscriptionandbroaderaudioanalysistasks.
View arXiv pageView PDFAdd to collection
Get this paper in your agent:
hf papers read 2610\.28711
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2610.28711 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2610.28711 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2610.28711 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
@MaxForAI: If you are working on voice agents, you should try this project. A team from NTU, NUS, and Shanghai AI Lab released: Mega-ASR. This fully open-source ASR is built on Qwen3-ASR, aiming to break the long-standing bottleneck of ASR performance in noisy, reverberant, or other impaired real-world environments...
NTU, NUS, and Shanghai AI Lab jointly released Mega-ASR, a fully open-source ASR model built on Qwen3-ASR. Using the Voices-in-the-Wild-2M dataset and progressive acoustic-to-semantic optimization, it achieves up to 30% relative Word Error Rate (WER) reduction in real-world noisy environments. With only 1.7B parameters, it enables efficient inference on consumer-grade hardware.
@rohanpaul_ai: An open multilingual ASR model just posted 3.54 average WER across 5 languages Hojo-ASR-Multi-V1 combines an acoustic e…
Hojo-ASR-Multi-V1 开源多语言语音识别模型以 3.54 的平均 WER 横跨 5 种语言取得出色成绩,其采用声学编码器搭配 Qwen3-4B 大语言模型解码器的架构,已在 Hugging Face 上以 Apache 2.0 许可发布。
@FeitengLi: Actually, these problems can be well solved: 1. Ditch whisper, switch to an ASR model. Qwen3-ASR is great with few hallucinations, and there are other ASR options. Whisper has many hallucinations and requires 30s segments. Qwen3-ASR gets more accurate with longer audio, supporting up to 20…
Recommends using Qwen3-ASR instead of Whisper to reduce hallucinations, using LattifAI tools for precise audio-text alignment and subtitle generation, and introducing their own OmniVAD-Kit project for voice activity detection.
@aigclink: Alibaba Tongyi Lab just dropped Fun-ASR 1.5—one industrial-grade model handles 30 languages, all 7 major Chinese dialect families + 20+ regional accents, even classical-poetry recitation. Dialect CER down 56.2 % vs last gen; 5 dialects top 90 % accuracy…
Alibaba Tongyi Lab releases Fun-ASR 1.5: a single model covering 30 languages, seven Chinese dialect groups and 20+ local accents; character-error rate in key dialect scenarios falls 56.2 %, with five dialects exceeding 90 % accuracy.
Qwen3-TTS Technical Report
The Qwen3-TTS technical report introduces a series of advanced multilingual text-to-speech models with voice cloning and controllable generation, featuring a dual-track LM architecture and specialized tokenizers for low-latency streaming.