MoonInTheRiver/DiffSinger
DiffSinger
DiffSinger is an AI vocal synthesizer and neural audio generator designed to produce high-fidelity singing and speech. It functions as a text-to-speech system and a diffusion-based singing voice synthesis tool that transforms text and pitch into audible audio.
The system utilizes a shallow diffusion mechanism and iterative noise refinement to generate realistic vocal performances. It incorporates specialized sampling plugins and numerical solvers to accelerate inference and reduce the time required to generate synthetic voices.
The project covers acoustic modeling, mel-spectrogram synthesis, and neural vocoder reconstruction to convert text into time-domain audio waveforms. It also includes capabilities for synthetic vocal enhancement to improve the sonic quality of recordings.
Features
- Singing Voice Synthesis - Generates synthetic singing audio and realistic vocal performances based on text and timing prompts.
- Audio Generation Models - Produces high-fidelity audio waveforms and spectrograms for both singing and spoken language.
- Mel-Spectrogram Processing - Produces mel-spectrograms as the intermediate time-frequency representation between text input and audio waveforms.
- Neural Vocoders - Utilizes a deep learning-based neural vocoder to reconstruct time-domain audio waveforms from mel-spectrograms.
- Shallow Diffusion Sampling - Generates high-fidelity audio by iteratively refining noise into mel-spectrograms using a limited number of sampling steps.
- Text-to-Speech Conversions - Transforms written text into audible speech by predicting pitch and mel-spectrograms.
- Text-to-Speech - Synthesizes natural human speech from text input by predicting pitch and mel-spectrograms.
- Diffusion-Based Singing Synthesis - Employs a generative diffusion model to create realistic singing vocals from text and pitch.
- Vocal Synthesizers - Provides a complete system for generating and enhancing synthetic singing performances using deep learning.
- Inference Acceleration - Optimizes inference speed by employing specialized numerical solvers to reduce the number of diffusion iterations.
- Inference Accelerators - Accelerates audio generation through optimized sampling plugins and numerical solvers.
- Acoustic Parameter Predictions - Predicts fundamental frequency and spectral envelopes from text and notation to drive the synthesis engine.
- Iterative Prediction Refiners - Implements a reverse diffusion process that iteratively refines random Gaussian noise into a structured voice signal.
- Inference Optimization - Reduces the time required to generate synthetic voices using optimized sampling plugins.
