Muzic

Muzic is a deep learning platform and framework for AI-driven music analysis, composition, and synthesis. It functions as a music generation framework and analysis tool, utilizing large language models and autonomous agents to orchestrate the creation and interpretation of symbolic and audio music.

The project is distinguished by its cross-modal capabilities, mapping natural language and symbolic music into a shared joint embedding space for zero-shot classification and information retrieval. It employs a variety of specialized architectures, including diffusion frameworks for audio synthesis, dual-grain attention mechanisms for long-sequence structural consistency, and a hybrid system that combines music theory rules with neural networks.

The platform covers a broad range of capabilities, including the generation of MIDI sequences from text and lyrics, neural singing voice synthesis, and automated lyrics transcription. It also provides tools for music structure modeling, attribute-based symbolic generation, and the orchestration of external music tools via autonomous agents.

Supporting utilities include data engineering pipelines for large-scale MIDI binarization, dataset encoding, and audio signal processing for melody note extraction and speech-to-phoneme alignment.

Features

AI Music Composition - Creating musical audio or MIDI sequences from natural language descriptions, emotional targets, or existing lyrics.
Generative Music Agents - Uses large language models and autonomous agents to orchestrate the creation of symbolic and audio music.
Agentic Music Orchestration - Using autonomous agents and deep learning to orchestrate the creation of melodies, lyrics, and instrumental accompaniments.
Text-to-Music Engines - Synthesizes melodies from text by applying music theory constraints on tone, rhythm, and structure.
Autonomous Agent Orchestration - Coordinates autonomous agents to select and combine deep learning models and external tools for music processing.
Cross-Modal Representations - Maps symbolic music and natural language into a shared joint embedding space via contrastive learning.
Deep Learning Audio Libraries - Synthesizes musical audio and MIDI sequences using neural networks and deep learning models.
Agent-Based Music Analysis - Employs autonomous agents and LLMs to perform versatile music processing and interpretation tasks.
Long-Form Composition Models - Generates long musical sequences with structural consistency using dual-grain attention mechanisms.
Cross-Modal Retrieval - Retrieves symbolic music information by aligning different modalities through a shared embedding space.
Audio Language Model Training - Trains language models to learn melodic patterns by treating MIDI notes as discrete string tokens.
Music Embeddings - Maps symbolic music and natural language into a shared joint embedding space using contrastive learning.
Music Structure Modeling - Analyzes and predicts the structural organization of musical compositions using deep learning architectures.
Diffusion-Based - Synthesizes diverse audio music tracks using a denoising diffusion framework on universal representations.
Music Retrieval - Implements a joint embedding space for retrieving symbolic music and scores via natural language queries.
Music Information Retrieval - Retrieves symbolic music information by aligning language and music representations.
Audio and Music Processing - Processes raw audio files and MIDI data to extract musical features using signal processing.
Structured Melody Generation - Produces musical melodies by combining rule-based expert systems with neural networks to ensure structured musical form.
Agentic Task Orchestration - Coordinates autonomous agents to select and combine deep learning models for music processing.
AI Lyric Transcribers - Converts audio recordings into text lyrics using machine learning and data augmentation.
Attribute-Based Sequence Generation - Produces MIDI music sequences based on a set of defined musical attributes using deep learning.
Lyrics-to-Melody Synthesis - Transforms written lyrics into musical melodies using template-based or relationship-aware neural methods.
Singing Voice Synthesis - Produces high-fidelity neural singing voices to simulate human vocal performances.
MIDI-Conditioned Track Generation - Produces new instrumental tracks by conditioning output on existing MIDI lead or chord tracks.
Sequence Infilling - Implements the ability to fill missing sections of a musical composition using partial track conditions.
Symbolic Music Copilots - Provides a copilot interface to create symbolic musical compositions based on natural language descriptions.
Attribute Mapping - Translates natural language descriptions into structured music attributes to guide the generation process.
Dual-Grain Attention - Balances fine-grained and coarse-grained attention to maintain structural consistency across long musical sequences.
Music Model Hyperparameter Tuning - Provides a framework for configuring vocabularies and hyperparameters to build custom music generation models.
Audio Dataset Preprocessing - Provides tools for cleaning and converting raw MIDI and audio files into formats suitable for ML training.
Musical Melody Refinement - Improves generated melodies iteratively using a neural network that processes the music phrase by phrase.
Quality Evaluators - Evaluates the aesthetic and musical quality of generated melodies based on their relationship to lyrics.
Generative Music Evaluation - Measures the accuracy of generated music by comparing objective attributes against gold label standards.
Model Training Optimizers - Optimizes deep learning weights and attention parameters using multi-GPU setups to improve music pattern learning.
Music Genre Classifiers - Uses a large-scale pre-trained model to classify music genres and styles.
Music Structure Analysis - Models musical form and structure using fine- and coarse-grained attention mechanisms.
Rule-Based Neural Hybrids - Combines expert systems based on music theory with neural networks to ensure structured musical form.
Retrieval Augmented Generation Pipelines - Produces MIDI files by combining generative neural networks with a retrieval system for chord progressions.
Melodic Similarity Scoring - Quantifies the quality of musical sequences by calculating pitch and duration similarity.
Sequence-to-Sequence Mappings - Generates lyrics from melodies or melodies from lyrics using a masked sequence-to-sequence framework.
Emotional Music Generation - Creates musical compositions based on target emotional states by mapping emotions to music attributes.
Zero-Shot Classification Models - Assigns labels to symbolic music by comparing features against text-based prompt templates without specific training.
MIDI Music Composition Tools - Provides a workflow that transforms text lyrics and musical attributes into MIDI files for AI composition.
Melody-to-Lyric Generation - Creates corresponding lyrics for a given melody sequence using a masked sequence-to-sequence framework.
Rap Lyric Generation - Produces lyrics with rhyme and rhythm by generating text in reverse order and inserting beat symbols.
Lyrics-to-MIDI Pipelines - Produces MIDI files from text lyrics using a generation-retrieval pipeline with chord progressions.
Natural Language Search - Retrieves symbolic musical scores by calculating similarity between text-based queries and music-encoded features.
Generative Vocal Accompaniment - Generates instrumental accompaniment tracks specifically for pop music styles.
Music Recommendation Engines - Identifies musical pieces with similar characteristics by comparing textual descriptions of symbolic music files.
Audio Tools and Editors - Ai-driven music understanding and generation research.

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microsoftmuzic

Features