FlowSE: Flow-Matching Model for Speech Enhancement

FlowSE is the first flow-matching model for Speech Enhancement (SE), designed to address the key challenges faced by existing generative models in SE tasks. Traditional approaches like language model-based SE often degrade timbre and intelligibility due to quantization loss, while diffusion models suffer from complex training and high inference latency. FlowSE provides an efficient and innovative solution to these issues.

🔑 Key Features

• Flow Matching for Speech Enhancement: FlowSE is trained on noisy mel spectrograms and optional text sequences, optimizing a condition flow matching loss with ground-truth mel spectrograms as labels.
• Implicit Learning of Temporal-Spectral Structure and Text Alignment: FlowSE learns the speech’s temporal-spectral structure and text-to-speech alignment implicitly without explicit alignment procedures.
• Flexible Inference Modes:
  • Inference with noisy mel spectrograms only
  • Inference with noisy mel spectrograms and additional transcripts, providing enhanced performance

📊 Experimental Results

Extensive experiments demonstrate that FlowSE significantly outperforms state-of-the-art generative SE methods, establishing a new standard for generative-based SE and highlighting the potential of flow matching in advancing the field.

🗃️ Project Structure

FlowSE/
│
├── data/                  # Data preprocessing and loading utilities
├── models/                # FlowSE model code
├── checkpoints/           # Pre-trained model weights
├── utils/                 # Utility functions
├── inference.py           # Inference script
├── train.py               # Training script
└── README.md              # This documentation