Qwen ASR is a multilingual speech recognition service designed for precise transcription, language identification, robust performance in noise, and context-aware results. It is built on Qwen3-Omni intelligence and trained on large-scale audio-text data.
The service focuses on accuracy, coverage, and reliability. It supports 11 languages, handles complex audio, and accepts plain text context to bias recognition.
Automatic detection and transcription for Chinese, English, French, German, Russian, Italian, Spanish, Portuguese, Japanese, Korean, and Arabic, with support for regional accents.
Paste any background text to nudge recognition toward expected terms, names, and domain phrases. Works with keywords, paragraphs, and mixed text of any length.
Maintains quality in noisy rooms, far-field microphones, compressed recordings, and even singing with background music.
Identifies all supported languages in auto mode and rejects non-speech segments like silence and background noise.
One service covers multilingual and domain-specific scenarios, reducing the need for model switching.
Optionally convert spoken forms to clean text, such as numbers and dates, for ready-to-use transcripts.
Audio is first scanned to determine the likely language. The system then performs transcription tuned to that language. This improves usability for mixed-language environments and removes manual setup.
Users can provide arbitrary text that biases decoding. It is useful for domain terms, names, and product vocabulary. The input can be short keyword lists or full paragraphs.
Recognition holds up in background noise and multimedia vocals. The model is trained on diverse conditions and keeps errors low across challenging inputs.
Processing overview
Running one recognition service reduces operational overhead. There is no need to switch models per language or audio condition, which simplifies integration and helps with consistent output quality across use cases.
This approach benefits teams building edtech tools, media workflows, and support systems where inputs vary by speaker and environment. It keeps setup predictable while covering a wide range of needs.
Upload an audio file, add optional context, and see the transcription with language identification and inverse text normalization options.
Lecture capture, course subtitles, and language learning support. Automatic language detection helps with mixed-language classrooms and guest speakers.
Subtitling for interviews, documentaries, and podcasts. Context input keeps names, technical terms, and brand phrases consistent across episodes.
Transcribe customer calls and internal recordings. Non-speech rejection reduces noise-only segments and focuses on spoken content.
Clean transcripts for research groups and cross-border teams. Share background text to bias recognition toward project vocabulary.
Live captions and transcripts for events and public talks. Works across accents and noisy venues.
Handle songs and speech mixed with music. Produce text that is readable and consistent across tracks.
Context is optional text you provide to tailor results. It can be as simple as a list of hotwords or as rich as full background paragraphs. The service reads the text and gently nudges recognition toward the terms you care about.
Chinese, English, French, German, Russian, Italian, Spanish, Portuguese, Japanese, Korean, and Arabic. Many regional accents are covered.
Yes. Provide context text. It can include names, product terms, and any domain phrases. The system will bias results toward them.
The model is trained with varied audio conditions. It rejects non-speech segments and keeps transcripts focused on spoken content.
It recognizes singing voices and can transcribe lyrics even with background music, within reasonable audio quality limits.
It converts spoken forms into clean text outputs, such as numbers and dates, making transcripts easier to read and analyze.
No. Auto mode detects the language before transcribing. You can still specify a language if you want to lock it.