"semantic-release" matching MCP servers:

Voice input for Claude Code — speak Vietnamese or English; hold a hotkey, speak, release, and the transcribed text is cleaned and pasted into the input box for editing before sending.

Style-aware auto-mixing and mastering for Ableton Live, providing tools for analysis, preview rendering, and release checks.

Enables AI agents and MCP clients to drive Ableton Live 12 end to end, from setting tempo and building instruments through writing MIDI, arranging, mixing, bouncing and mastering. Adds real-time rendering plus loudness and spectral analysis and multi-format encoding with tags and artwork, for turning an empty set into a release-ready track.

MCP server that turns any audio or video source into structured, searchable intelligence for agents, enabling download, transcription, semantic search, speaker identification, and more.

Enables a coding agent to operate an Elektron Analog Rytm MKII through semantic MCP tools, with validation-first safety, snapshots, and rollback for persistent changes.

Local podcast transcription, embedding, and semantic search with an MCP server for AI assistant integration. All processing runs on your machine.

Enables semantic search of local audio samples by describing sounds, MIDI generation, stem separation, and other music production tools from Claude Desktop.

An MCP server that gives AI agents the ability to listen to and understand music/audio files, enabling semantic analysis, stem separation, lyrics transcription, and signal processing via tool calls.

Enables headless offline audio processing through MCP tools for rendering stems, analyzing audio and masking, auto-mixing, applying EQ/compression, mastering, comparing references, and running release checks. It works on local audio files without a DAW window, audio interface, GPU, model download, or cloud account.

Enables AI assistants to programmatically edit, analyze, and export audio projects through MCP tools, including multi-track editing, effects, transcription, and semantic search.

Enables real-time voice agents and streaming audio pipelines to detect conversational turn completion, distinguishing finished speech turns from mid-sentence hesitation pauses using acoustic VAD and semantic scoring. It provides low-latency decision and telemetry output through a native MCP interface for integration with MCP-compliant clients.

Enables agents to conjure individual sounds by semantic text search over a CLAP-embedded audio database and render them into stereo, 5.1, or 7.1.4 spatial mixes, with tools for summing mixes, analyzing a mix's sounds and spatial balance, and mimicking existing mixes. Documentation resources are exposed so the client can read usage guides on demand.

Enables decoding of acoustic signals such as fundamental frequency, speaking rate, jitter, and pause ratio together with semantic sentiment to infer high-dimensional emotional states and emit real-time expressive TTS prosody directives. It runs as a zero-dependency JSON-RPC 2.0 MCP server over stdio, exposing these capabilities as callable tools.

Exposes an MCP tool that fuses acoustic prosody measurements (F0, speaking rate, jitter, pause ratio) with semantic sentiment to infer high-dimensional emotional states from speech. Returns tone, speaking-rate multiplier, and pitch-shift directives so agents can drive real-time expressive TTS modulation through JSON-RPC over stdio.