qwen3-asr-toolkit 1.0.1

Python toolkit for the Qwen3-ASR API—parallel high‑throughput calls, robust long‑audio transcription, multi‑sample‑rate support.

Qwen3-ASR-Toolkit

An advanced, high-performance Python command-line toolkit for using the Qwen-ASR API (formerly Qwen3-ASR-Flash). This implementation overcomes the API's 3-minute audio length limitation by intelligently splitting long audio/video files and processing them in parallel, enabling rapid transcription of hours-long content.

🚀 Key Features

• Break the 3-Minute Limit: Seamlessly transcribe audio and video files of any length by bypassing the official API's duration constraint.
• Smart Audio Splitting: Utilizes Voice Activity Detection (VAD) to split audio into meaningful chunks at natural silent pauses. This ensures that words and sentences are not awkwardly cut off.
• High-Speed Parallel Processing: Leverages multi-threading to send audio chunks to the Qwen-ASR API concurrently, dramatically reducing the total transcription time for long files.
• Automatic Audio Resampling: Automatically converts audio from any sample rate and channel count to the 16kHz mono format required by the Qwen-ASR API. You can use any audio file without worrying about pre-processing.
• Universal Media Support: Supports virtually any audio and video format (e.g., .mp4, .mov, .mkv, .mp3, .wav, .m4a) thanks to its reliance on FFmpeg.
• Simple & Easy to Use: A straightforward command-line interface allows you to get started with just a single command.

⚙️ How It Works

This tool follows a robust pipeline to deliver fast and accurate transcriptions for long-form media:

1. Media Loading: The script first loads your local audio or video file.
2. VAD-based Chunking: It analyzes the audio stream using Voice Activity Detection (VAD) to identify silent segments.
3. Intelligent Splitting: The audio is then split into smaller chunks based on the detected silences. Each chunk is kept under the 3-minute API limit, preventing mid-sentence cuts.
4. Parallel API Calls: A thread pool is initiated to upload and process these chunks concurrently using the DashScope Qwen-ASR API.
5. Result Aggregation: The transcribed text segments from all chunks are collected, re-ordered, and saved.

🏁 Getting Started

Follow these steps to set up and run the project on your local machine.

Prerequisites

• Python 3.8 or higher.
• FFmpeg: The script requires FFmpeg to be installed on your system to handle media files.
  • Ubuntu/Debian: sudo apt update && sudo apt install ffmpeg
  • macOS: brew install ffmpeg
  • Windows: Download from the official FFmpeg website and add it to your system's PATH.
• DashScope API Key: You need an API key from Alibaba Cloud's DashScope.

  • You can obtain one from the DashScope Console. If you are calling the API services of Tongyi Qwen for the first time, you can follow the tutorial on this website to create your own API Key.

  • For better security and convenience, it is highly recommended to set your API key as an environment variable named DASHSCOPE_API_KEY. The script will automatically use it, and you won't need to pass the --api-key argument in the command.

    On Linux/macOS:

    export DASHSCOPE_API_KEY="your_api_key_here"
    

    (To make this permanent, add the line to your ~/.bashrc, ~/.zshrc, or ~/.profile file.)

    On Windows (Command Prompt):

    set DASHSCOPE_API_KEY="your_api_key_here"
    

    On Windows (PowerShell):

    $env:DASHSCOPE_API_KEY="your_api_key_here"
    

    (For a permanent setting on Windows, search for "Edit the system environment variables" in the Start Menu and add DASHSCOPE_API_KEY to your user variables.)

Installation

We recommend installing the tool directly from PyPI for the simplest setup.

Option 1: Install from PyPI (Recommended)

Simply run the following command in your terminal. This will install the package and make the qwen3-asr command available system-wide.

pip install qwen3-asr-toolkit

Option 2: Install from Source

If you want to install the latest development version or contribute to the project, you can install from the source code.

1. Clone the repository:

   git clone https://github.com/QwenLM/Qwen3-ASR-Toolkit.git
   cd Qwen3-ASR-Toolkit
   

2. Install the package:

   pip install .
   

📖 Usage

Once installed, you can use the qwen3-asr command directly from your terminal.

Command

qwen3-asr -i <input_file> [-key <api_key>] [-j <num_threads>] [-v]

Arguments

Argument	Short	Description	Required/Optional
--input	-i	Path to the local audio or video file you want to transcribe.	Required
--dashscope-api-key	-key	Your DashScope API Key.	Optional (if DASHSCOPE_API_KEY env var is set)
--num-threads	-j	The number of concurrent threads to use for API calls.	Optional, Default: 4
--verbose	-v	Verbose mode, print detailed information like chunking and progress.	Optional

Output

The full transcription result will be printed to the terminal and also saved in a .txt file in the same directory as the input file. For example, if you process my_video.mp4, the output will be saved to my_video.txt.

---

✨ Examples

Here are a few examples of how to use the tool.

1. Basic Transcription

Transcribe a video file using the default 4 threads. Assuming you have set the DASHSCOPE_API_KEY environment variable.

qwen3-asr -i "/path/to/my/long_lecture.mp4"

2. Transcribe an Audio File with Increased Concurrency

Transcribe a long podcast audio file using 8 parallel threads to speed up the process. This example also shows how to pass the API key directly.

qwen3-asr -i "/path/to/my/podcast_episode_01.wav" -j 8 -key "your_api_key_here"

3. Transcription with Verbose Output

Use the -v or --verbose flag to see detailed logs during the transcription process, such as audio chunking details and API call status. This is useful for debugging or monitoring progress.

qwen3-asr -i "/path/to/my/meeting_recording.m4a" -v

🤝 Contributing

Contributions are welcome! If you have suggestions for improvements, please feel free to fork the repo, create a feature branch, and open a pull request. You can also open an issue with the "enhancement" tag.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.