STT repo skill

Use this skill when the task is about the jianchang512/stt local speech-to-text app: installing it, launching the local server, transcribing audio/video through the browser UI, calling its HTTP APIs, or debugging ffmpeg/model/CUDA failures.

The app is a Flask/gevent service around faster-whisper. It exposes a browser UI plus a legacy /api endpoint and an OpenAI-compatible /v1/audio/transcriptions endpoint. It can run on CPU by default and can use NVIDIA CUDA when the runtime and model stack are prepared.

First route the task

• Setup, launch, config, and backend checks: read sub-skills/setup/SKILL.md when the user asks how to install dependencies, choose Python, place models, configure set.ini, verify ffmpeg/ffprobe, launch the local server, or enable/diagnose CUDA.
• Transcription usage and clients: read sub-skills/transcription/SKILL.md when the user wants browser upload steps, batch exports, request fields for /api, OpenAI SDK/client integration, or response-format parsing.
• Cross-cutting diagnosis: read references/troubleshooting.md when symptoms span launch and request handling, such as model downloads, empty output, update-check noise, or backend warnings.
• Repo context and staleness: read references/overview.md for the component map and references/repo-provenance.md before assuming the generated skill matches a newer checkout.

Quick operating context

• Default local service address is 127.0.0.1:9977 unless the runtime config changes it.
• Supported result formats are text, json, and srt.
• The browser flow uploads files, converts them to mono 16 kHz WAV through ffmpeg, queues transcription, polls progress, and renders/export results.
• The legacy API wraps results in {code, msg, data}; the OpenAI-compatible endpoint uses the /v1/audio/transcriptions route and OpenAI-style multipart fields.
• Model choices are controlled by the runtime model list; larger models improve quality but raise CPU/GPU memory pressure.
• Once model files and ffmpeg are present, transcription can run locally; first-run model download and update checks may touch the network.

Install and minimal verification

For a fresh source checkout, create an isolated Python environment, install the runtime dependencies, and run the lightweight runtime probe before launching:

python -m pip install -r requirements.txt
python scripts/check-runtime.py --repo-root <checkout>

If CUDA needs to be checked, use sub-skills/setup/scripts/check-cuda.py after the runtime probe. Then route to sub-skills/setup/SKILL.md or sub-skills/transcription/SKILL.md for the detailed workflow.

Bundled helpers

Use these helpers instead of relying on source examples directly:

• scripts/check-runtime.py checks core imports and ffmpeg/ffprobe availability from a candidate checkout.
• sub-skills/setup/scripts/check-cuda.py reports optional CUDA/CuDNN/CTranslate2 visibility without blocking when CUDA is absent.
• sub-skills/setup/scripts/launch-server.py wraps the repo launcher with sanity checks.
• sub-skills/transcription/scripts/api-smoke.py adapts the repo's API example into an argument-driven client smoke test.

What not to use this skill for

• Generic speech AI tasks that do not involve this STT app.
• TTS, voice cloning, diarization, forced alignment, audio enhancement, or model training unrelated to the app.
• Maintaining vendored static assets or release packaging unless the user explicitly asks for repository maintenance rather than app operation.

Evidence-backed constraints

• The source checkout has no standard Python packaging metadata; prepare dependencies from the documented runtime requirement set and then run the app from a checkout.
• The code currently exposes cuda_com_type in config but does not pass that key into WhisperModel; do not promise it changes compute type unless the code changes.
• The browser worker path and API helper path do not use every config key identically; check sub-skills/setup/references/configuration.md before diagnosing a config setting that appears ignored.

Router metadata

references/repo-routing-metadata.json places this skill in the speech-ai-modeling-and-audio-workflows scenario for managed repo-skill import. This run was requested as not import, so treat that metadata as ready for later verification/import tooling, not as evidence that import already happened.