nlp-research-repo-package-installment
Align Python version and repo-declared dependencies (requirements.txt / environment.yml) before installing packages for NLP research code reproduction.
- License 许可
- Apache-2.0
- 最近更新
- 2026-04-21
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01 前言 Front Matter
- name
- nlp-research-repo-package-installment
- version
- 1.0
- description
- Align Python version and repo-declared dependencies (requirements.txt / environment.yml) before installing packages for NLP research code reproduction.
03 SKILL.md 正文
NLP Research Repo Package Installment
When reproducing an NLP research repo, always align the environment to the repo’s declared dependencies first. Most failures come from Python version mismatch or installing packages without following requirements.txt / environment.yml.
What to do (must run before any install)
- Read the repo dependency files
- Prefer
environment.yml/environment.yaml(often pins Python + channels + non-pip deps) - Otherwise use
requirements.txt(pip deps) - If both exist, treat
environment.ymlas the base,requirements.txtas supplemental unless README says otherwise
- Log the current environment (Python version is critical)
Write/root/python_int.txtcontaining:
python -VV(required; Python version is often the root cause)python -m pip --versionpython -m pip freeze
- Compare & decide
-
If the repo expects a specific Python major/minor and the current Python does not match, it’s usually best to set up a matching environment before installing dependencies. Example: set up a fresh Python 3.11 environment (Docker / Ubuntu) with uv # Install uv apt-get update apt-get install -y --no-install-recommends curl ca-certificates rm -rf /var/lib/apt/lists/* curl -LsSf https://astral.sh/uv/0.9.7/install.sh | sh export PATH="/root/.local/bin:$PATH"
# Install Python + create a venv uv python install 3.11.8 uv venv --python 3.11.8 /opt/py311 # Use the new Python for installs/runs /opt/py311/bin/python -VV /opt/py311/bin/python -m pip install -U pip setuptools wheel -
Prefer installing from the repo’s dependency files (avoid random upgrades), then run a quick import/smoke test.
04 同仓库更多 Skills
data-reconciliation
暂无简介
speech-to-text
Transcribe video to timestamped text using Whisper tiny model (pre-installed).
video-processor
Process videos by removing segments and concatenating remaining parts. Use when you need to remove detected pauses/openings from videos, create highlight reels, or batch process segment removals using ffmpeg filter_complex.
silence-detector
Detect initial silence segments in audio/video using energy-based analysis. Use when you need to find low-energy periods at the start of recordings (title slides, setup time, pre-roll silence).
segment-combiner
Combine multiple segment detection results into a unified list. Use when you need to merge segments from different detectors, prepare removal lists for video processing, or consolidate detection outputs.
report-generator
Generate compression reports for video processing. Use when you need to create structured JSON reports with duration statistics, compression ratios, and segment details after video processing.