facebookresearch/audiocraft

Features

• Text-to-Audio Synthesis - Creates high-fidelity musical audio samples based on natural language descriptions and optional constraints.
• Audio Machine Learning Frameworks - Provides a complete framework for training, fine-tuning, and evaluating generative models for music and sound effects.
• Audio Watermarking - Embeds invisible markers into audio signals to identify origin and protect content ownership.
• Watermark Detection - Identifies watermarked sections within audio files and maintains detection accuracy after signal edits.
• Audio Tokenization - Converts raw audio waveforms into discrete codes using encoder-decoder bottlenecks for efficient language modeling.
• Neural Audio Compression - Encodes audio into a high-fidelity compressed format using a neural tokenizer for efficient storage and processing.
• Waveform Decoders - Implements multi-band diffusion models to decode discrete audio tokens into high-fidelity waveforms.
• Deep Learning Audio Libraries - Ships a library for processing and generating high-fidelity audio using neural networks and transformer models.
• Deep Learning Training Pipelines - Provides specialized training pipelines for developing and fine-tuning generative audio models.
• Audio Multi-Conditioning - Combines textual prompts and melodic excerpts into a shared embedding space to guide the generative process.
• Multi-Band Decoding - Reconstructs audio waveforms from discrete tokens by predicting multiple frequency bands simultaneously for higher fidelity.
• Flow-Matching Audio Diffusion - Generates high-fidelity waveforms by learning a vector field that transforms noise into continuous audio latents.
• Model Training Pipelines - Provides comprehensive workflows to train neural networks for audio generation, compression, and watermarking.
• Audio Language Model Training - Implements a training pipeline for language modeling over discrete audio tokens extracted via a neural codec.
• Audio - Implements an auto-regressive transformer that predicts subsequent audio tokens to synthesize coherent music.
• Text-to-Sound Effect Generation - Produces high-fidelity sound effects and environmental audio using textual prompts.
• Audio Prompt Continuation - Generates subsequent audio content based on a provided starting audio clip to extend a sound sequence.
• Melodic Conditioning - Produces new musical content by using an existing audio melody as a signal to guide the output.
• Training Data Augmentation - Provides utilities to modify audio signals with noise and frequency filtering to diversify training datasets.
• Audio Flow Matching - Implements a flow matching objective to train models on continuous latents extracted from audio compressors.
• Quantized Audio Encoder-Decoders - Trains encoder-decoder models with a quantization bottleneck to reconstruct audio using objective and perceptual losses.
• Experiment Tracking - Manages hyper-parameter sets via unique signatures to ensure reproducibility and prevent configuration drift.
• Quality Evaluators - Measures the perceptual quality of synthesized speech signals using standardized acoustic metrics.
• Generative Model Training Tools - Implements a modular system to apply textual and melodic constraints to generative audio models.
• Text and Melody Joint Conditioning - Produces music by conditioning the output on both a text prompt and an existing melodic sequence.
• Style-Based Music Generation - Extracts features from a short audio excerpt to generate new music that mimics the input style.
• Perplexity Calculators - Provides utilities to calculate model perplexity based on cross-entropy loss for evaluating audio generation performance.
• Training Lifecycle Management - Provides a system for managing the end-to-end training process by combining datasets, models, and optimizers into recipes.
• Model Fine-Tuning - Initializes training processes from pre-trained model checkpoints to adapt them to specific audio data or styles.
• Masked Audio Modeling - Implements a generative pipeline that learns to predict masked discrete audio tokens across multiple streams.
• Watermarking Model Training - Executes a joint training pipeline for the watermark generator and detector using custom datasets and robustness recipes.
• Audio Diffusion Training - Executes training pipelines to generate waveform audio conditioned on pre-trained tokenizer embeddings.
• Training Execution Loops - Implements a training loop that integrates datasets and optimization across training, validation, and evaluation stages.
• Performance Metrics - Calculates cross-entropy and perplexity to measure the objective performance of audio generation models.
• Training Recipes - Uses modular configuration files to define datasets, optimizers, and loss functions for reproducible training pipelines.
• Text and Style Joint Conditioning - Combines textual descriptions and audio style excerpts to generate music that follows both constraints.
• Audio Signal Fidelity Metrics - Measures signal fidelity and speech quality using signal-to-noise ratios and objective listener metrics.
• AI & Machine Learning - Generative modeling library for high-fidelity audio and music.
• Audio Generation - Library for audio processing and generation using deep learning.
• Generative Media Tools - Library for audio processing and generation.
• Music And Audio Generation - Meta's library for generating music and sound effects from text.

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