AI Music and Audio Generation

ACE Step 1.5 is a local text-to-music generation and audio editing system that runs on consumer hardware. It transforms plain-language descriptions into full-length songs with lyrics, and can edit existing audio through cover generation, vocal removal, track separation, and selective repainting. The system supports multilingual prompts and lyrics in over 50 languages, and provides precise control over musical structure including duration, BPM, key, and time signature. The project distinguishes itself through a dual-stream diffusion architecture that processes separate latent streams for vocals and instruments, synchronized through cross-attention layers during denoising. It enables style personalization through lightweight LoRA adapters that can be trained from a few songs in about one hour, and supports batch generation of up to eight songs simultaneously. The system can generate complete songs in under ten seconds on a standard consumer GPU while using less than four gigabytes of video memory. The software is accessible through multiple interfaces including a Gradio web UI, a REST API, a CLI wizard, and a VST3 plugin for direct integration into digital audio workstations. It also includes a pre-trained source separation pipeline for isolating vocal and instrumental stems from mixed audio.

ACE-Step is a high-fidelity audio synthesis system and diffusion model designed to generate music and vocals from text descriptions. It functions as a music generator and vocal synthesizer, using a diffusion transformer decoder to produce audio across various languages and genres. The project provides tools for text-guided audio editing, including the ability to extend the duration of tracks, regenerate specific song segments, and perform latent-space audio inpainting to modify lyrics or styles. It also includes a framework for audio style fine-tuning using low-rank adaptation to adapt vocal characteristics and musical styles. The system covers broad capabilities in music production, such as synthesizing instrumental samples and loops, generating vocal accompaniments from recordings, and producing complementary instrument stems based on reference audio. It supports variable-length sequence generation to synthesize audio of custom durations.

AudioLDM is a latent diffusion framework for generating high-fidelity audio, music, and sound effects. It functions as a text-to-audio generator that converts natural language descriptions into synthetic audio signals with control over pitch and environment. The system provides specialized tools for audio-to-audio synthesis and generative repair. This includes the ability to perform audio style transfer and replicate specific acoustic events based on existing files. The project covers a broad range of audio transformation tasks, including audio super-resolution for increasing signal fidelity and audio inpainting for filling missing segments of a recording. These capabilities allow for the restoration and modification of audio signals using text guidance to maintain sonic consistency.

Heartlib is an audio processing library for large language models that provides tools for audio tokenization, compression, and cross-modal alignment. It implements core models for audio-text embedding, automatic speech recognition, neural codecs, and text-driven audio synthesis. The project features a text-to-audio synthesis engine capable of generating high-fidelity music and speech from text descriptions or reference files. It also includes a neural audio codec designed for low-bitrate compression that preserves acoustic structure and sound quality. Additional capabilities cover audio-text alignment via a shared latent space for retrieval, as well as transcription tools specifically designed to convert vocal lyrics and singing into written text.

Bark is a generative audio engine and machine learning inference library designed to convert written text into high-fidelity speech and sound effects. It functions as a text-to-audio transformer, utilizing multi-stage neural network architectures to map semantic input tokens into detailed audio codebooks for synthesis. The system distinguishes itself through a hierarchical transformer stacking approach that separates semantic understanding from acoustic realization. By employing autoregressive token prediction and vector quantized codebook mapping, the engine bridges linguistic and sonic domains within a shared mathematical space. This architecture ensures that audio generation remains consistent and reproducible through deterministic seeded generation. The library supports integration into broader machine learning pipelines, allowing developers to embed audio synthesis capabilities into automated content creation workflows. Users can execute generation tasks directly via command-line interfaces or through standard model loading and inference protocols.

Magenta is a comprehensive toolkit for training, synthesizing, and performing music through neural models and hardware-integrated engines. It functions as a machine learning framework that enables the generation, manipulation, and real-time performance of audio, providing the structural foundations for musical intelligence through hierarchical sequence modeling and symbolic processing. The project distinguishes itself by enabling real-time, low-latency neural audio synthesis that can be integrated directly into professional digital audio workstations. It supports interactive musical jamming and live performance by allowing users to trigger and modulate generative models using standard MIDI controllers and hardware interfaces. Users can navigate complex latent spaces to interpolate between musical styles, morph instrument timbres, or evolve soundscapes dynamically during live sessions. Beyond core synthesis, the framework covers a broad spectrum of intelligent music production capabilities, including automated composition, rhythmic humanization, and audio feature analysis. It provides tools for training custom models on local hardware, allowing for the creation of personalized virtual instruments and the generation of long-form musical sequences that maintain structural coherence. The system also facilitates the development of custom interfaces for parameter mapping, enabling users to visualize and control high-dimensional musical data.

AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation. The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while preserving meaning. The project covers a broad range of audio capabilities, including restoration, source separation, and automatic speech transcription. Additional functional areas include sound analysis for event detection, spatial audio conversion from mono to binaural formats, and speech style transfer.

Audiocraft is a deep learning audio library and machine learning framework designed for training, fine-tuning, and evaluating generative models for music and sound effects. It functions as a text-to-music generative model and a neural audio codec, providing the tools necessary to compress audio signals into discrete representations and synthesize high-fidelity waveforms from textual descriptions. The framework is distinguished by its ability to combine multiple conditioning signals, allowing for the generation of audio based on text prompts, melodic excerpts, or style-based audio clips. It also includes a specialized audio watermarking tool for embedding and detecting invisible markers within signals to protect ownership and track content origins. The project covers a broad range of capabilities, including neural audio compression, audio data augmentation, and the execution of complex training pipelines for diffusion and masked audio models. It provides utilities for model lifecycle management, such as checkpoint exporting and experiment tracking, alongside evaluation metrics for measuring signal fidelity and perceptual quality.

This project is a comprehensive software suite for voice synthesis and model management, providing a framework for training custom acoustic models and performing voice conversion. It utilizes deep-learning-based acoustic modeling to map source audio characteristics to target voice identities, enabling the transformation of input audio into specific vocal profiles. The system distinguishes itself through a feature-retrieval-based inference mechanism, which employs vector index files to perform nearest-neighbor searches on acoustic features for high-fidelity timbre matching. Users can manage these processes through a browser-based orchestration layer or via command-line interface scripts, allowing for both graphical interaction and automated workflow execution. The platform also supports voice model hybridization, enabling the merging of distinct model checkpoints to create blended vocal identities. The software includes a modular audio processing pipeline that integrates pitch extraction, vocal track isolation, and timbre fidelity adjustment. These tools facilitate the preparation of high-quality training data and the refinement of conversion results. The project supports both offline and real-time voice conversion, with persistent checkpoint management to allow for incremental model training and the resumption of interrupted sessions.

ace-step-ui is an AI music production workspace and interface for generating, editing, and organizing synthetic audio tracks and vocals. It provides a technical control panel for managing prompts, seeds, and style parameters to produce high-quality audio. The project includes a digital audio workstation interface for trimming and fading files, alongside an audio stem separation tool that splits mixed tracks into individual components such as drums, bass, and vocals. It also features a music video creator for generating visual content and procedural album art to accompany generated music. The software covers the full production lifecycle, including lyric composition tools and prompt optimization to transform genre tags into technical specifications. Workflow management is supported through batch track generation and a searchable audio library for organizing assets into playlists and favorites.

This project is an automated audio production system that converts document content, such as PDFs, into spoken dialogue and audio files. It functions as a pipeline that transforms static text into natural two-person scripts for podcast generation. The system synthesizes realistic multilingual speech that includes regional accents and nonverbal cues like laughing or sighing. These voice tracks are combined with generated ambient background music and atmospheric noise to create layered audio compositions. The project also includes capabilities for conversational AI agents, utilizing generation pipelines and tool-augmented prompting to handle multi-turn interactions. To support execution on limited hardware, it incorporates local model optimization through low-precision quantized model loading.

AudioKit is an audio framework for iOS, macOS, and tvOS that provides tools for digital audio synthesis, signal processing, and audio analysis. It functions as a synthesis engine for generating audio waveforms and textures, a processing library for modifying tonal characteristics, and a toolkit for extracting frequency and amplitude data from sonic signals. The framework utilizes a modular node architecture and graph-based signal routing to connect audio generators, processors, and outputs. It wraps low-level audio primitives in high-level classes to facilitate sound generation and modification. The system supports real-time audio processing and analysis, enabling the application of filters and effects to live audio streams.

TTS-WebUI is a web interface and speech synthesis manager designed to convert written text into spoken audio files. It serves as a self-hosted audio AI suite that allows users to configure speech synthesis models, manage speaker profiles, and generate audio through a graphical dashboard. The system functions as both a visual manager and a generative audio API, providing standardized endpoints and OpenAI-compatible request formats for external applications to trigger synthesis programmatically. It includes a plugin-based extension system that allows new tools and models to be added via external packages and configuration files. The platform covers a broad range of audio capabilities, including emotional text-to-speech generation, generative music and sound effect production, and audio processing tasks such as source separation and conversion. It supports long-form speech synthesis by segmenting extensive text into chunks and joining the resulting audio files. The application is available as a containerized deployment for consistent hosting and includes a credential-based authentication layer to secure the user interface.

This repository provides a collection of reference implementations and code examples for training and deploying machine learning models using the MLX framework. It serves as a practical guide for executing distributed training, fine-tuning large language models, converting model weights, and implementing multimodal generative workflows. The project distinguishes itself through specialized examples for local hardware execution, featuring weight quantization to reduce memory usage and low-rank adaptation for parameter-efficient fine-tuning. It also includes scripts for transforming external model formats into MLX-compatible versions and merging adapter weights for standalone deployment. The examples cover a broad range of capabilities, including natural language processing with decoder-only and mixture-of-experts architectures, computer vision for image classification and segmentation, and audio processing for speech-to-text and music generation. Additionally, it demonstrates generative AI workflows for text-to-image and text-to-video synthesis, alongside graph-based neural networks and multimodal systems that utilize shared embedding spaces.

This project is a neural text-to-speech engine and voice cloning toolkit designed to generate synthetic speech that mimics the vocal characteristics of a target speaker. It functions as a real-time audio synthesizer, utilizing a deep learning pipeline to convert written text into high-fidelity speech output with minimal latency. The system employs a transfer learning framework that leverages pre-trained speaker verification models to adapt synthesis to new, unseen vocal identities. By using an encoder-based speaker embedding process, the toolkit maps variable-length audio samples into a latent space to preserve unique speaker characteristics. The architecture is organized into a modular pipeline that separates the encoding, synthesis, and vocoder stages, allowing for independent optimization of each component. The synthesis process relies on autoregressive sequence generation to transform text into acoustic representations, which are then converted into time-domain waveforms by a neural vocoder. Users can interact with the system through both command-line and graphical interfaces to process custom recordings or pre-trained models for speech generation.

GPT-SoVITS is a text-to-speech synthesis engine and voice cloning toolkit designed for generating natural-sounding human speech. It functions as a neural audio processing pipeline that maps input text to high-fidelity audio waveforms, utilizing conditional variational autoencoders and flow-based decoders to ensure expressive output. The platform distinguishes itself through its ability to perform few-shot voice cloning and cross-lingual speech generation, allowing users to maintain a specific speaker's vocal identity and emotional delivery across multiple languages. By employing cross-modal latent alignment, the system effectively bridges text-based linguistic features with speaker-specific embeddings, while a generative adversarial network-based vocoder ensures the final audio maintains high time-domain quality. The software provides a modular pipeline that supports the entire lifecycle of custom voice model development, including data preprocessing, fine-tuning on small datasets, and inference. It incorporates self-supervised speech representation models to extract discrete linguistic units, facilitating robust voice conversion and automated audio content creation. The project includes documentation for model training, inference procedures, and command-line execution.

CSM is a conversational speech generation model and text-to-speech engine that converts text and audio inputs into synthetic speech. It utilizes a large language model architecture to predict and decode audio tokens for voice synthesis. The system functions as a zero-shot voice cloner, replicating specific speaker identities using short audio samples without requiring additional training. This enables precise control over speaker identity and the creation of synthetic speech that mimics a specific person. The model covers conversational speech synthesis and text-to-speech generation, transforming written text into spoken audio while maintaining natural flow and cadence.

This project is a generative speech synthesis engine that converts text into high-fidelity human speech. It utilizes a two-stage autoregressive transformer architecture that separates semantic token prediction from acoustic detail reconstruction to balance linguistic accuracy with audio quality. The system is designed to support multilingual output and conversational AI development, enabling the generation of context-aware speech that maintains flow across multiple dialogue turns. The platform distinguishes itself through a production-ready inference server that employs continuous batching to maximize hardware utilization and reduce latency. It includes a comprehensive voice cloning toolkit that replicates unique vocal characteristics from short reference audio samples without requiring additional model training. Users can further customize output through low-rank adaptation fine-tuning, which allows for efficient style adjustments, and speaker-specific token embeddings that manage distinct voice characteristics during multi-speaker generation. Beyond core synthesis, the project provides a full suite of utilities for training and alignment, including reinforcement learning techniques to optimize for semantic accuracy and instruction adherence. It supports a variety of operational interfaces, including a command-line tool, a web-based dashboard, and an authenticated HTTP server for remote generation workloads. The system also includes data preparation and serialization tools to streamline the process of organizing and normalizing audio datasets for model training.

Magenta is an AI creative suite and TensorFlow generative art framework used to train and deploy models for the production of artistic media. It functions as a generative music library and a deep learning art generator, providing tools to automate the creation of original musical compositions and visual artwork. The project covers AI music composition and generative visual art through neural art generation and machine learning creativity. It enables the training of generative models to produce original songs, images, and drawings based on learned patterns.