QWEN3-ASR-TOOLKIT: An advanced open source Python command line toolkit for using QWEN-ASR API for over 3 minutes/10 MB limit

Qwen has been published qwen3-asr-toolkita Python CLI programmatically bypassing the MIT license of the QWEN3-ASR-FLASH API 3 minutes/10 MB per person Limited by performing VAD-AWARE blocks through FFMPEG, parallel API calls and automatic resampling/format normalization. The result is a stable hour-scale transcription pipeline with configurable concurrency, context injection and clean text post-processing. Python ≥3.8 Prerequisites, install the following installation:

pip install qwen3-asr-toolkit

What the toolkit adds at the top of the API

• Long-term operation. use Voice Activity Detection (VAD) At natural pauses, keep each block under the hard duration/size cover of the API and then merge the outputs in sequence.
• Parallel throughput. A single line pool sends multiple blocks at the same time Dashscope Endpoint, improves the wall delay of one hour input. You can -j/--num-threads.
• Format and rate normalization. Any common Audio/Video Convert containers (MP4/MOV/MKV/MP3/WAV/M4A, etc.) to the required API Mono 16 kHz Before submission. FFMPEG needs to be installed on the path.
• Text cleanup and context. This tool includes post-processing to reduce repetition/illusion and support Context Injection Positive recognition domain terminology; basic APIs also exposed Language detection and Inverse Text Normalization (ITN) Switch.

Officials qwen3-asr-flash The API is executed in a single way ≤3 minutes Duration and ≤10mb Payload per call. This is reasonable for interactive requests, but is awkward for long media. The toolkit runs best practices (VAD-AWARE segmentation + concurrent calls), so teams can batch large archives or capture dumps in real time without writing choreography from scratch.

Start quickly

1. Installation prerequisites

# System: FFmpeg must be available
# macOS
brew install ffmpeg
# Ubuntu/Debian
sudo apt update && sudo apt install -y ffmpeg

2. Install the CLI

pip install qwen3-asr-toolkit

3. Configure credentials

# International endpoint key
export DASHSCOPE_API_KEY="sk-..."

4. running

# Basic: local video, default 4 threads
qwen3-asr -i "/path/to/lecture.mp4"

# Faster: raise parallelism and pass key explicitly (optional if env var set)
qwen3-asr -i "/path/to/podcast.wav" -j 8 -key "sk-..."

# Improve domain accuracy with context
qwen3-asr -i "/path/to/earnings_call.m4a" 
  -c "tickers, CFO name, product names, Q3 revenue guidance"

The argument you actually use:
-i/--input-file (file path or http/https url), -j/--num-threads,,,,, -c/--context,,,,, -key/--dashscope-api-key,,,,, -t/--tmp-dir,,,,, -s/--silence. The output is printed and saved as .txt.

Minimum pipeline architecture

1. load Local file or URL → 2) vad Find the boundary of silence → 3) Big chunk Under API hat → 4) Resampling To 16 kHz mono→5) Parallel submission Go to Dashscope→6) Total Market segments in order → 7 Post-process Text (dedupe, repeat) → 8) emission .txt Transcript.

Summary

QWEN3-ASR-ToolKit turns QWEN3-ASR-FLASH into a practical long-channel pipeline by converting VAD-based segmentation, FFMPEG normalization (Mono/16 KHz) and VAD-based segmentation, FFMPEG normalization (Mono/16 KHz) and parallel API scheduling under 3-minute/10 MB covers. The team gained deterministic blocks, configurable throughput, and optional context/cover/ITN controls without custom orchestration. For production, fix the package version, verify the region endpoint/key, and then calculate the thread count into your network and QPS – then pip install qwen3-asr-toolkit and the boat.

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