MoonInTheRiver/DiffSinger
DiffSinger
DiffSinger ist ein KI-Gesangssynthesizer und neuronaler Audiogenerator, der darauf ausgelegt ist, hochqualitativen Gesang und Sprache zu produzieren. Er fungiert als Text-to-Speech-System und als diffusionsbasiertes Tool zur Synthese von Gesangsstimmen, das Text und Tonhöhe in hörbares Audio transformiert.
Das System nutzt einen flachen Diffusionsmechanismus und iterative Rauschverfeinerung, um realistische Gesangsdarbietungen zu generieren. Es integriert spezialisierte Sampling-Plugins und numerische Löser, um die Inferenz zu beschleunigen und die Zeit zu reduzieren, die zur Generierung synthetischer Stimmen erforderlich ist.
Das Projekt deckt akustische Modellierung, Mel-Spektrogramm-Synthese und neuronale Vocoder-Rekonstruktion ab, um Text in Zeitbereichs-Audio-Wellenformen zu konvertieren. Es enthält zudem Funktionen zur synthetischen Stimmverbesserung, um die klangliche Qualität von Aufnahmen zu steigern.
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.
