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Audio Stem Separator mit Demucs

Trennt Audiospuren in einzelne Stems (Gesang, Schlagzeug, Bass, Sonstiges) mithilfe von Metas neuronalem Netzwerkmodell Demucs.

Was ist Audio Stem Separator mit Demucs?

Diese Fähigkeit ermöglicht die Trennung von Audiodateien in einzelne Stems (Gesang, Schlagzeug, Bass und Sonstiges) unter Verwendung von Metas Demucs-Modell und dem Python-Paket demucs. Sie unterstützt die Stapelverarbeitung von WAV- und MP3-Dateien und gibt isolierte Stems in den Formaten FLAC oder WAV aus. Zudem erfolgt eine Integration mit FFmpeg für Formatkonvertierungen und Lautstärkeanpassungen nach der Trennung. Die Installation erfordert entweder eine CPU-Umgebung (über environment-cpu.yml) oder eine GPU-Umgebung (über environment-cuda.yml) mit CUDA-Unterstützung, sowie den Befehl `pip install -e .`. Die Fähigkeit ist plattformunabhängig und nutzt die offizielle Demucs-Quelle auf GitHub. Sie ist besonders nützlich für Musikproduktion, Remixing oder Audioanalyse, wo sauber getrennte Spuren erforderlich sind. Benutzer sollten mindestens Python 3.8 und bei Windows die Anaconda-Konsole verwenden.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/audio-stem-separator-demucs
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Dokumentation

Was macht Audio Stem Separator mit Demucs?

Trennt Audiospuren in einzelne Stems (Gesang, Schlagzeug, Bass, Sonstiges) mithilfe von Metas Demucs-Neuronalnetzwerkmodell über das Python-Paket demucs. Unterstützt die Stapelverarbeitung von WAV- und MP3-Dateien, gibt isolierte Stems im FLAC- oder WAV-Format aus und integriert sich mit FFmpeg für Formatkonvertierung und Lautstärkeanpassung nach der Trennung.

Installation

Verwenden Sie den Upstream-Installations- oder Einrichtungspfad, der Ihrer Umgebung entspricht:

  • conda env update -f environment-cpu.yml # wenn Sie keine GPUs haben
  • conda env update -f environment-cuda.yml # wenn Sie GPUs haben
  • conda activate demucs
  • pip install -e .

Anforderungen und Hinweise aus dem Upstream:

  • erfordert benutzerdefinierten CUDA-Code, der noch nicht zur Veröffentlichung bereit ist.
  • Sie benötigen mindestens Python 3.8. Siehe requirements_minimal.txt für Anforderungen nur zur Trennung.
  • Ersetzen Sie jedes Mal, wenn Sie python3 sehen, durch python.exe. Sie sollten Befehle immer von der Konsole aus ausführen.

Grundlegende Verwendung oder erste Schritte:

Quelle

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

Academic Paper Citation Network Mapper

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ACF Extended for Advanced Custom Fields Workflows

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ACF to Gutenberg Block Migrator

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Activepieces Open Source Workflow Automation

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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act Local GitHub Actions Runner

act is an open-source CLI tool that runs GitHub Actions workflows locally using Docker, enabling fast feedback on workflow changes without pushing to GitHub. It is a standard tool for local Actions development and testing.

Add a browser review checkpoint before risky agent actions with

Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.

Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

Add IDE-style semantic code navigation and symbol-safe refactors

Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

Add managed long-term memory to LangGraph agents with LangMem

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

Add multimodal lifelong memory to MCP and Python agents with Sim

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

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