🎤 **智能分块处理长音频**：该工具的核心功能在于其能够将超过API限制的长音频文件智能分割成多个小片段。它利用语音活动检测（VAD）技术，在自然的停顿处进行切分，确保每个片段都符合API的3分钟/10MB的限制要求，然后按顺序合并转录结果，实现对小时级音频的处理。
🚀 **高效并行处理提升吞吐量**：为了缩短长音频的转录时间，Qwen3-ASR-Toolkit采用了多线程池机制，能够并行地将音频片段发送至DashScope API。用户可以通过`-j`或`--num-threads`参数灵活配置并发线程数，从而显著提高处理速度，尤其适用于批量处理大量音频文件。
🎛️ **音频格式与采样率标准化**：该工具集成了FFmpeg，能够自动将各种常见的音频/视频格式（如MP4, MOV, MP3, WAV等）转换为API所要求的单声道16kHz格式。这确保了音频数据在提交给API前满足兼容性要求，简化了用户的预处理工作。
📝 **文本优化与上下文注入**：为了提高转录结果的准确性和可用性，该工具提供了文本后处理功能，可以减少重复和幻觉。此外，它还支持上下文注入，允许用户提供领域特定术语，以指导ASR模型进行更精确的识别，同时还暴露了语言检测和逆文本归一化（ITN）等API选项。
Qwen has released Qwen3-ASR-Toolkit, an MIT-licensed Python CLI that programmatically bypasses the Qwen3-ASR-Flash API’s 3-minute/10 MB per-request limit by performing VAD-aware chunking, parallel API calls, and automatic resampling/format normalization via FFmpeg. The result is stable, hour-scale transcription pipelines with configurable concurrency, context injection, and clean text post-processing. Python ≥3.8 prerequisite, Install with:
pip install qwen3-asr-toolkit-j/--num-threads.Format & rate normalization. Any common audio/video container (MP4/MOV/MKV/MP3/WAV/M4A, etc.) is converted to the API’s required mono 16 kHz before submission. Requires FFmpeg installed on PATH.Text cleanup & context. The tool includes post-processing to reduce repetitions/hallucinations and supports context injection to bias recognition toward domain terms; the underlying API also exposes language detection and inverse text normalization (ITN) toggles. The official Qwen3-ASR-Flash API is single-turn and enforces ≤3 min duration and ≤10 MB payloads per call. That is reasonable for interactive requests but awkward for long media. The toolkit operationalizes best practices—VAD-aware segmentation + concurrent calls—so teams can batch large archives or live capture dumps without writing orchestration from scratch.
# System: FFmpeg must be available# macOSbrew install ffmpeg# Ubuntu/Debiansudo apt update && sudo apt install -y ffmpegpip install qwen3-asr-toolkit# International endpoint keyexport DASHSCOPE_API_KEY="sk-..."# Basic: local video, default 4 threadsqwen3-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 contextqwen3-asr -i "/path/to/earnings_call.m4a" \ -c "tickers, CFO name, product names, Q3 revenue guidance"Arguments you’ll 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. Output is printed and saved as <input_basename>.txt.
.txt transcript. Qwen3-ASR-Toolkit turns Qwen3-ASR-Flash into a practical long-audio pipeline by combining VAD-based segmentation, FFmpeg normalization (mono/16 kHz), and parallel API dispatch under the 3-minute/10 MB caps. Teams get deterministic chunking, configurable throughput, and optional context/LID/ITN controls without custom orchestration. For production, pin the package version, verify region endpoints/keys, and tune thread count to your network and QPS—then pip install qwen3-asr-toolkit and ship.
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The post Qwen3-ASR-Toolkit: An Advanced Open Source Python Command-Line Toolkit for Using the Qwen-ASR API Beyond the 3 Minutes/10 MB Limit appeared first on MarkTechPost.
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