🚀 Deep Cuts v0.1.0 — First Binary Release

Welcome to the first official binary release of Deep Cuts! This release marks a major milestone: a 100% offline, private, and zero-dependency studio audio analysis and music intelligence desktop application designed to run locally and sandboxed on macOS.

Download it from GitHub

https://youtu.be/bev7k1K8cmY

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✨ Core Features

🎛️ 100% Offline Digital Signal Processing (DSP)

• Precision BPM Detection: Spectral-flux onset envelope tracking with autocorrelation and parabolic sub-sample refinement (40–210 BPM range).

• BPM Double/Half-Time Correction: Automatic dual-pass sweeps using coarse metadata genres and high-fidelity machine learning style classifiers to resolve tempo scale errors.

• Key & Scale Detection: Harmonic Pitch Class Profiles (HPCP) suppress irrelevant overtones, correlating with Krumhansl-Schmuckler profiles for accurate key detection.

• EBU R128 Loudness: Calculates precise integrated loudness (LUFS) and Loudness Range (LRA) to match studio standards.

• Waveform Profiles: Extracts 128-point RMS energy profiles for rapid visual rendering.

🧠 Local AI & Machine Learning Indexing

• Essentia Classifier (Discogs-Effnet): Offline neural network style analysis predicting 400 hierarchical genre classes, vocal/instrumental likelihood, and 7 mood axes (happy, sad, aggressive, relaxed, party, acoustic, electronic).

• CLAP Acoustic Embeddings: LAION CLAP ONNX model extracts deep semantic embeddings from 10-second windows, enabling instantaneous acoustic similarity (KNN) search via a local sqlite-vec virtual table.

• Description Embeddings: Pinned all-MiniLM-L6-v2 ONNX model encodes natural language text and generated descriptions to facilitate semantic search.

💬 Local Multimodal AI Chat

• Conversational Track Q&A: A dedicated Chat tab in the Track Detail Pane allowing producers to converse directly with their audio files using Qwen2-Audio-7B-Instruct running completely locally.

• Sonic Arrangements & Troubleshooting: Ask questions like: "Why does this mix sound muddy?", "What is the song structure?", or "Give me EQ tips for this master."

• Context-Smart Payload: Waveform metadata is attached only on the first turn; subsequent turns default to lightning-fast, text-only conversation to preserve context length.

• WaveSurfer Region Selector: Interactive waveform drag-and-drop region selection to target analysis on specific song sections (intro, chorus, drop) without blowing context windows.

• Real-time SSE Token Streaming: Full Server-Sent Events integration for instantaneous token streaming and highly responsive, snappy chat feedback.

🗺️ Interactive Music Map (UMAP)

• 2D Audio Projection: High-performance Rust-native UMAP dimensionality reduction projects your entire CLAP-embedded collection onto an interactive 2D canvas.

• Filter-Aware Canvas: Smooth D3-driven zooming, panning, and legend rendering instantly reflect active sidebar filters (BPM, Key, Genre, Monitored folders, Vocals).

• Neighborhood KNN Inspector: Click any coordinate to reveal and scrub through the nearest 10 acoustically similar reference tracks in your library.

📁 Zero-Config Staging & Bundling

• Fully Self-Contained Sidecar: The app bundles the local LLM runtime (llama-server) alongside all its modular dynamic dependencies (libggml, libllama, libllama-server-impl, etc.) out-of-the-box, ensuring relocatability and preventing system linker mismatches.

• In-App Model Downloader & Verification: Dynamic folder configuration allows users to store models on custom external drives or local sandboxed application data directories. Features dynamic verify-and-resume downloads for heavy model checkpoints directly inside the settings drawer.

• Sidecar JSON Persistence (.dc.json): Auto-writes lightweight sidecars next to your files. If you move your library or reinstall the app, re-indexing takes fractions of a second by directly restoring computed features from sidecar files.

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🔧 Installation

1. Download Deep Cuts_0.1.0_aarch64.dmg for Apple Silicon Macs.

2. Drag and drop Deep Cuts.app into your Applications folder.

3. Open Settings (gear icon), select your Model Folder (or use the default sandboxed folder), and click Manage Models to fetch your local weights with a single click!