Aliyun Qwen Asr logo

Aliyun Qwen Asr

Community
cinience
aliyun-qwen-asr

Use when transcribing non-realtime speech with Alibaba Cloud Model Studio Qwen ASR models (`qwen3-asr-flash`, `qwen-audio-asr`, `qwen3-asr-flash-filetrans`). Use when converting recorded audio files to text, generating transcripts with timestamps, or documenting DashScope/OpenAI-compatible ASR request and response fields.

Overview

Publishercinience
Repositoryalicloud-skills
Skill namealiyun-qwen-asr
Stars
397
Forks
34
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cinience on GitHub. Read the source before you install it.

Installation

Install the Aliyun Qwen Asr AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cinience/alicloud-skills/tree/main/skills/ai/audio/aliyun-qwen-asr
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cinience/alicloud-skills.git /tmp/alicloud-skills
mkdir -p .claude/skills
cp -r /tmp/alicloud-skills/skills/ai/audio/aliyun-qwen-asr .claude/skills/aliyun-qwen-asr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aliyun Qwen Asr in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aliyun Qwen Asr on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aliyun Qwen Asr is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Category: provider

Model Studio Qwen ASR (Non-Realtime)

Validation

bash
mkdir -p output/aliyun-qwen-asr
python -m py_compile skills/ai/audio/aliyun-qwen-asr/scripts/transcribe_audio.py && echo "py_compile_ok" > output/aliyun-qwen-asr/validate.txt

Pass criteria: command exits 0 and output/aliyun-qwen-asr/validate.txt is generated.

Output And Evidence

  • Store transcripts and API responses under output/aliyun-qwen-asr/.
  • Keep one command log or sample response per run.

Use Qwen ASR for recorded audio transcription (non-realtime), including short audio sync calls and long audio async jobs.

Critical model names

Use one of these exact model strings:

  • qwen3-asr-flash
  • qwen3-asr-flash-2026-02-10
  • qwen-audio-asr
  • qwen3-asr-flash-filetrans
  • qwen3-asr-flash-filetrans-2025-11-17

Selection guidance:

  • Use qwen3-asr-flash, qwen3-asr-flash-2026-02-10, or qwen-audio-asr for short/normal recordings (sync).
  • Use qwen3-asr-flash-filetrans or qwen3-asr-flash-filetrans-2025-11-17 for long-file transcription (async task workflow).

Prerequisites

  • Install SDK dependencies (script uses Python stdlib only):
bash
python3 -m venv .venv
. .venv/bin/activate
  • Set DASHSCOPE_API_KEY in environment, or add dashscope_api_key to ~/.alibabacloud/credentials.

Normalized interface (asr.transcribe)

Request

  • audio (string, required): public URL or local file path.
  • model (string, optional): default qwen3-asr-flash.
  • language_hints (array, optional): e.g. zh, en.
  • sample_rate (number, optional)
  • vocabulary_id (string, optional)
  • disfluency_removal_enabled (bool, optional)
  • timestamp_granularities (array, optional): e.g. sentence.
  • async (bool, optional): default false for sync models, true for qwen3-asr-flash-filetrans.

Response

  • text (string): normalized transcript text.
  • task_id (string, optional): present for async submission.
  • status (string): SUCCEEDED or submission status.
  • raw (object): original API response.

Quick start (official HTTP API)

Sync transcription (OpenAI-compatible protocol):

bash
curl -sS --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions' \
  --header "Authorization: Bearer $DASHSCOPE_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "qwen3-asr-flash",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "input_audio",
            "input_audio": {
              "data": "https://dashscope.oss-cn-beijing.aliyuncs.com/audios/welcome.mp3"
            }
          }
        ]
      }
    ],
    "stream": false,
    "asr_options": {
      "enable_itn": false
    }
  }'

Async long-file transcription (DashScope protocol):

bash
curl -sS --location 'https://dashscope.aliyuncs.com/api/v1/services/audio/asr/transcription' \
  --header "Authorization: Bearer $DASHSCOPE_API_KEY" \
  --header 'X-DashScope-Async: enable' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "qwen3-asr-flash-filetrans",
    "input": {
      "file_url": "https://dashscope.oss-cn-beijing.aliyuncs.com/audios/welcome.mp3"
    }
  }'

Poll task result:

bash
curl -sS --location "https://dashscope.aliyuncs.com/api/v1/tasks/<task_id>" \
  --header "Authorization: Bearer $DASHSCOPE_API_KEY"

Local helper script

Use the bundled script for URL/local-file input and optional async polling:

bash
python skills/ai/audio/aliyun-qwen-asr/scripts/transcribe_audio.py \
  --audio "https://dashscope.oss-cn-beijing.aliyuncs.com/audios/welcome.mp3" \
  --model qwen3-asr-flash \
  --language-hints zh,en \
  --print-response

Long-file mode:

bash
python skills/ai/audio/aliyun-qwen-asr/scripts/transcribe_audio.py \
  --audio "https://dashscope.oss-cn-beijing.aliyuncs.com/audios/welcome.mp3" \
  --model qwen3-asr-flash-filetrans \
  --async \
  --wait

Operational guidance

  • For local files, use input_audio.data (data URI) when direct URL is unavailable.
  • Keep language_hints minimal to reduce recognition ambiguity.
  • For async tasks, use 5-20s polling interval with max retry guard.
  • Save normalized outputs under output/aliyun-qwen-asr/transcripts/.

Output location

  • Default output: output/aliyun-qwen-asr/transcripts/
  • Override base dir with OUTPUT_DIR.

Workflow

  1. Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.
  2. Run one minimal read-only query first to verify connectivity and permissions.
  3. Execute the target operation with explicit parameters and bounded scope.
  4. Verify results and save output/evidence files.

References

  • references/api_reference.md
  • references/sources.md
  • Realtime synthesis is provided by skills/ai/audio/aliyun-qwen-tts-realtime/.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aliyun Qwen Asr AI skill do?

Use when transcribing non-realtime speech with Alibaba Cloud Model Studio Qwen ASR models (`qwen3-asr-flash`, `qwen-audio-asr`, `qwen3-asr-flash-filetrans`). Use when converting recorded audio files to text, generating transcripts with timestamps, or documenting DashScope/OpenAI-compatible ASR request and response fields.

Why use Aliyun Qwen Asr on TypingMind?

Because you install it once and use it with any model. Aliyun Qwen Asr is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aliyun Qwen Asr in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cinience/alicloud-skills/tree/main/skills/ai/audio/aliyun-qwen-asr. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aliyun Qwen Asr?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aliyun Qwen Asr?

As many as you like. As long as a model supports skills, you can use Aliyun Qwen Asr with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aliyun Qwen Asr AI skill free?

Yes. It is published on GitHub by cinience under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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