What Qwen-Audio-3.1-ASR-Flash-Filetrans does
Qwen-Audio-3.1-ASR-Flash-Filetrans is Alibaba Cloud Model Studio's offline, end-to-end automatic speech recognition model. Automatic speech recognition, or ASR, converts spoken audio into written text. This particular model is aimed at long recordings rather than short conversational turns or real-time voice interaction.
The model's canonical API identifier is qwen-audio-3.1-asr-flash-filetrans. A request submits one publicly accessible HTTP or HTTPS URL for an audio or video file. Alibaba Cloud then processes the file asynchronously: the initial request returns a task identifier, and the application retrieves the completed transcription or receives it through the supported result workflow.
The model is available through Alibaba Cloud Model Studio in the Singapore international region and the China (Beijing) region. Its role in the Qwen-Audio 3.1 family is specialized: it is the file-based, non-real-time transcription option, while Alibaba Cloud documents a separate Qwen-Audio-3.1-ASR-Flash-Streaming model for real-time recognition.
Recognition capabilities
Its main advantage is the collection of controls designed for difficult or information-dense recordings. Documented capabilities include multilingual speech recognition, recognition of multiple regional Chinese dialects, punctuation prediction, text normalization, hot-word enhancement, context enhancement, and speaker separation.
- Multilingual and dialect recognition: useful for recordings containing supported languages or regional Chinese speech varieties.
- Speaker separation: distinguishes speakers in a multi-person recording, making meeting minutes, interviews, and call analysis easier to organize.
- Timestamps: associates transcript content with points in the recording, which is useful for subtitles, searching, editing, and reviewing evidence.
- Hot words: improves recognition of important names, brands, technical terms, or organization-specific vocabulary supplied as contextual terms.
- Context enhancement: gives the recognizer additional information that can help resolve words that are difficult to identify from audio alone.
- Punctuation and normalization: converts raw recognized speech into more readable text and applies text-formatting or normalization behavior supported by the service.
- Noise robustness: Alibaba Cloud describes the model as suitable for complex acoustic environments, although the supplied documentation does not provide a benchmark score or guaranteed noise threshold.
These features are provider-documented capabilities, not independent benchmark results. Recognition quality will still depend on factors such as recording quality, overlapping speech, accents, vocabulary, and background noise.
File types, size limits, and processing behavior
The non-real-time API accepts one publicly accessible HTTP or HTTPS audio or video file URL per request. Supported formats listed by Alibaba Cloud include AAC, AMR, AVI, FLAC, FLV, M4A, MKV, MOV, MP3, MP4, MPEG, OGG, OPUS, WAV, WEBM, WMA, and WMV.
The maximum documented file size is 2 GB, and the maximum duration is 12 hours. Those limits make the model appropriate for long meetings, lectures, interviews, broadcasts, and archived media that would be inconvenient to divide into short clips. Alibaba Cloud recommends keeping recordings to roughly two hours when speaker diarization is enabled because longer diarized jobs have a greater risk of recognition failures or timeouts.
The URL requirement is an important practical constraint. The model does not provide a native local-file upload workflow in the documented request format. A file must first be placed at an address that the service can access over HTTP or HTTPS. Access permissions, URL expiration, and the availability of the media during processing therefore become part of the integration design.
Input, output, and API profile
The model accepts recorded audio and video files as input, but its result is text transcription. It does not generate audio, music, images, or video. A video file is used as a media container for its audio track; this is not documented as visual-video understanding.
| Specification | Documented value |
|---|---|
| Model ID | qwen-audio-3.1-asr-flash-filetrans |
| Primary task | Asynchronous, offline speech-to-text transcription |
| Input | One publicly accessible audio or video file URL |
| Output | Text transcription, with supported timestamps and speaker information |
| Maximum file size | 2 GB |
| Maximum duration | 12 hours |
| Context length | 8,192 |
| Maximum output tokens | 1,024 |
| Processing | Asynchronous; not real-time streaming |
| Documented regions | Singapore international and China (Beijing) |
The listed 8,192 context length and 1,024 maximum output-token values are catalog specifications supplied for this model. They should not be interpreted as a promise that a 12-hour recording will be returned as one short response. Long-file transcription is handled by the service's asynchronous workflow and may involve result structures appropriate to the recording and enabled options.
The model is not documented as supporting function calling, structured outputs, web search, prefix completion, context caching, batch inference, or fine-tuning. It is therefore best treated as a focused transcription endpoint rather than a general-purpose assistant or agent.
Pricing and throughput
Alibaba Cloud lists token-based pricing for this model rather than a simple per-minute recording fee. In the Singapore international region, the listed price is USD 0.15 per 1 million input tokens and USD 0.47 per 1 million output tokens. In China (Beijing), the listed prices are USD 0.113 per 1 million input tokens and USD 0.382 per 1 million output tokens.
Because billing is token-based, the final cost depends on the service's token accounting for the submitted audio and generated transcription rather than only on the file's duration. The two regional price schedules should not be mixed: the applicable rate depends on where the request is processed.
Alibaba Cloud lists a limit of 600 requests per minute in both documented regions. That rate limit concerns request throughput, not a guarantee that every recording will finish within a particular time. Long files, speaker separation, file accessibility, and service conditions can affect completion time.
Main strengths and limitations
Where it is strong
- It is designed specifically for long, asynchronous recordings rather than requiring applications to manage many short real-time chunks.
- Speaker separation and timestamps support practical downstream tasks such as meeting minutes, subtitle preparation, interview review, and searchable archives.
- Multilingual recognition and regional Chinese dialect support broaden its use beyond standard single-language recordings.
- Hot words and context enhancement can help organizations handle names, specialist vocabulary, and recurring terminology.
- The 2 GB and 12-hour limits accommodate many full-length recordings.
Important limitations
- It is not a real-time transcription model. Applications needing live captions or immediate voice interaction should use a streaming ASR option instead.
- Media must be available through a publicly accessible HTTP or HTTPS URL, which may require a separate storage and access-control step.
- Speaker diarization is more demanding, and Alibaba Cloud recommends shorter recordings when that option is enabled.
- There is no documented support for text-to-speech, audio generation, general conversation, image or video generation, tool use, or web search.
- The supplied research does not provide an independent accuracy benchmark, a guaranteed word-error rate, or a documented emotion-recognition feature. Emotion recognition should not be assumed.
- Token-based regional pricing may be less straightforward to forecast than a clearly stated per-minute transcription price.
When to choose this model
Choose Qwen-Audio-3.1-ASR-Flash-Filetrans when the central requirement is reliable batch processing of recorded speech and the application can tolerate asynchronous completion. Good examples include converting a two-hour meeting into a speaker-labeled transcript, indexing a collection of interviews, generating subtitle drafts from media files, transcribing multilingual customer calls, or making long audio archives searchable.
It is especially suitable when timestamps, dialect recognition, hot words, or speaker separation matter more than conversational reasoning. The model can provide the transcript, but a separate application or model may still be needed to summarize meetings, extract action items, classify calls, or answer questions about the resulting text.
Choose a streaming speech-recognition model instead when users need live captions, immediate partial transcripts, or interactive voice input. The related Qwen-Audio-3.1-ASR-Flash-Streaming model is the relevant sibling mentioned in Alibaba Cloud's documentation for real-time recognition. Choose a general-purpose language model after transcription when the primary task is analysis, writing, coding, or question answering rather than speech recognition itself.
Overall assessment
Qwen-Audio-3.1-ASR-Flash-Filetrans is a specialized long-file transcription service, not a broad multimodal assistant. Its value comes from combining long-duration asynchronous processing with practical ASR controls such as speaker separation, timestamps, hot words, context enhancement, punctuation, and text normalization. Its limitations are equally clear: it is not designed for live audio, it requires an accessible media URL, and its documented API does not include general tools or generation features.
For organizations processing recorded meetings, interviews, calls, subtitles, or media archives, the model offers a focused balance of scale and transcription features. The main decisions are whether asynchronous processing is acceptable, whether the file can be exposed through an accessible URL, and whether regional token pricing fits the workload.