Transcribe Chinese-heavy classroom recordings locally using SenseVoice wrapper scripts.
npx skills add https://github.com/zhuchenyu2008/skills --skill sensevoice-local
Please help me install this Agent Skill. Skill: sensevoice-local Source: https://github.com/zhuchenyu2008/skills/tree/main/sensevoice-local Command: npx skills add https://github.com/zhuchenyu2008/skills --skill sensevoice-local
Local classroom audio transcripts are often private, large, or restricted by cloud services; this skill provides a local, CPU-based transcription workflow to convert class recordings into text without exporting data.
Run the transcription by calling the transcribe_file.sh script with your audio path.
High-intent search queries and answers about installing and using this skill.
You can transcribe Chinese-heavy classroom recordings locally by running the provided transcribe_file.sh wrapper script with your audio path to invoke SenseVoice on your own CPU-based server without exporting data.
To upload large audio files for transcription when messaging platforms restrict size, you can start the on-demand one-shot upload page, which provides a temporary web interface to securely receive your large payloads.
Yes, you can run offline speech recognition on a local server using SenseVoice. The skill provides deterministic wrappers and runtime isolation to execute the transcription process entirely offline without relying on external cloud services.
You do not need to manually run Docker commands for local ASR transcription. The skill provides wrapper scripts that handle the invocation of SenseVoice automatically, ensuring a safe and reliable setup without ad-hoc manual configurations.
If your local audio transcription script is not working in an OpenClaw environment, consult the included troubleshooting references designed to ensure reliable operation and resolve common runtime or configuration issues.
Yes, SenseVoice local transcription supports CPU-based processing for classroom audio. This allows you to convert class recordings into text entirely on your own machine, ensuring maximum privacy and control over sensitive educational data.
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