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Qwen3-ASR-Toolkit: An Advanced Open Source Python Command-Line Toolkit for Using the Qwen-ASR API Beyond the 3 Minutes/10 MB Limit

ByRicardo

Qwen has launched Qwen3-ASR-Toolkit, an MIT-licensed Python CLI that programmatically bypasses the Qwen3-ASR-Flash API’s 3-minute/10 MB per-request restrict by performing VAD-aware chunking, parallel API calls, and computerized resampling/format normalization by way of FFmpeg. The result’s steady, hour-scale transcription pipelines with configurable concurrency, context injection, and clear textual content post-processing. Python ≥3.8 prerequisite, Install with:

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pip set up qwen3-asr-toolkit

What the toolkit provides on prime of the API

  • Long-audio dealing with. The toolkit slices enter utilizing voice exercise detection (VAD) at pure pauses, retaining every chunk beneath the API’s laborious period/dimension caps, then merges outputs so as.
  • Parallel throughput. A thread pool dispatches a number of chunks concurrently to DashScope endpoints, enhancing wall-clock latency for hour-long inputs. You management concurrency by way of -j/--num-threads.
  • Format & fee normalization. Any frequent audio/video container (MP4/MOV/MKV/MP3/WAV/M4A, and so on.) is transformed to the API’s required mono 16 kHz earlier than submission. Requires FFmpeg put in on PATH.
  • Text cleanup & context. The device contains post-processing to scale back repetitions/hallucinations and helps context injection to bias recognition towards area phrases; the underlying API additionally exposes language detection and inverse textual content normalization (ITN) toggles.

The official Qwen3-ASR-Flash API is single-turn and enforces ≤3 min period and ≤10 MB payloads per name. That is affordable for interactive requests however awkward for lengthy media. The toolkit operationalizes greatest practices—VAD-aware segmentation + concurrent calls—so groups can batch massive archives or stay seize dumps with out writing orchestration from scratch.

Quick begin

  1. Install stipulations
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# System: FFmpeg should be out there
# macOS
brew set up ffmpeg
# Ubuntu/Debian
sudo apt replace && sudo apt set up -y ffmpeg
  1. Install the CLI
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pip set up qwen3-asr-toolkit
  1. Configure credentials
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# International endpoint key
export DASHSCOPE_API_KEY="sk-..."
  1. Run
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# Basic: native video, default 4 threads
qwen3-asr -i "/path/to/lecture.mp4"

# Faster: elevate parallelism and move key explicitly (non-obligatory if env var set)
qwen3-asr -i "/path/to/podcast.wav" -j 8 -key "sk-..."

# Improve area accuracy with context
qwen3-asr -i "/path/to/earnings_call.m4a" 
  -c "tickers, CFO identify, product names, Q3 income steering"

Arguments you’ll truly use:
-i/--input-file (file path or http/https URL), -j/--num-threads, -c/--context, -key/--dashscope-api-key, -t/--tmp-dir, -s/--silence. Output is printed and saved as <input_basename>.txt.

Minimal pipeline structure

  1. Load native file or URL → 2) VAD to search out silence boundaries → 3) Chunk beneath API caps → 4) Resample to 16 kHz mono → 5) Parallel submit to DashScope → 6) Aggregate segments so as → 7) Post-process textual content (dedupe, repetitions) → 8) Emit .txt transcript.

Summary

Qwen3-ASR-Toolkit turns Qwen3-ASR-Flash right into a sensible long-audio pipeline by combining VAD-based segmentation, FFmpeg normalization (mono/16 kHz), and parallel API dispatch beneath the 3-minute/10 MB caps. Teams get deterministic chunking, configurable throughput, and non-obligatory context/LID/ITN controls with out customized orchestration. For manufacturing, pin the bundle model, confirm area endpoints/keys, and tune thread rely to your community and QPS—then pip set up qwen3-asr-toolkit and ship.

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