turbo-whisper-local-stt

当用户想要**音频转文字**、**语音转文本**、**转录录音**、**生成字幕**、**会议录音转文字**、**语音笔记转文本**、**本地转录音频**时自动触发。 使用本地 Faster-Whisper（large-v3-ct2 等模型）进行高性能、中文优先的音频转文字，完全离线、隐私安全，支持 wav/mp...

Before installing/running: - Expect initial network activity: the scripts will pip install packages (including torch) and download models from Hugging Face and PyPI. If you need fully offline operation, pre-download the model and pass --model_path. - Check Python version: the code enforces Python 3.10–3.12 and will exit otherwise. - Disk usage: model files and a virtualenv can be multi-GB; ensure you have space and choose an appropriate output/model path. - Sandbox if possible: because the skill runs pip installs and executes subprocesses, run it in an isolated environment (VM/container) or review/approve the code before giving it access to important systems. - Paths and defaults: note the scripts create a venv in a parent-level venv directory and a default model_path set to a Windows D:/ path — adjust paths for your environment. - No credentials requested: the skill does not ask for API keys or tokens; it uses public Hugging Face downloads. If you intend to use private models, do not supply credentials unless you trust the code. Overall this package appears coherent and appropriate for local STT, but it performs network downloads and installs software automatically — treat those side effects as part of the installation risk and proceed accordingly.

Type: OpenClaw Skill Name: turbo-whisper-local-stt Version: 1.0.6 The skill bundle is a legitimate tool for local audio-to-text transcription using the Faster-Whisper library. It contains robust environment management scripts (env_manager.py, ensure_package.py) that automate the setup of a Python virtual environment, detect NVIDIA GPUs via nvidia-smi, and install heavy dependencies like PyTorch and audio-processing libraries. The main logic in transcribe.py handles model downloads from Hugging Face (huggingface.co/wangminrui2022) and performs batch or single-file transcription. While the scripts use high-privilege subprocess calls for package management and environment bootstrapping, these actions are transparently documented and strictly aligned with the stated purpose of providing a high-performance, offline STT service.