BigVGAN v2 44kHz Neural Vocoder
| Property | Value |
|---|---|
| Model Size | 122M parameters |
| License | MIT |
| Paper | Research Paper |
| Sampling Rate | 44 kHz |
| Mel Bands | 128 |
| Upsampling Ratio | 512x |
What is bigvgan_v2_44khz_128band_512x?
BigVGAN v2 is NVIDIA's state-of-the-art neural vocoder designed for high-fidelity audio generation. This particular model represents the highest quality configuration, supporting 44kHz sampling rate with 128 mel frequency bands and an impressive 512x upsampling ratio. It's trained on a large-scale compilation of diverse audio types, making it highly versatile for various audio synthesis tasks.
Implementation Details
The model leverages advanced architectural features including a custom CUDA kernel for accelerated inference, achieving 1.5-3x faster processing on A100 GPUs. It implements a multi-scale sub-band CQT discriminator and multi-scale mel spectrogram loss for improved audio quality.
- Custom CUDA kernel for optimized inference speed
- Multi-scale sub-band CQT discriminator architecture
- Comprehensive mel spectrogram loss function
- PyTorch-based implementation with Hugging Face integration
Core Capabilities
- High-fidelity audio generation at 44kHz
- Support for multiple languages and audio types
- Environmental sound and instrument synthesis
- Efficient real-time processing with CUDA optimization
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its high sampling rate (44kHz), large upsampling ratio (512x), and custom CUDA kernel implementation for faster inference. It's trained on a diverse dataset making it truly universal in its application.
Q: What are the recommended use cases?
The model is ideal for high-quality text-to-speech systems, audio content generation, voice conversion, and any application requiring high-fidelity audio synthesis. It's particularly effective for multi-lingual applications and diverse audio types including speech, environmental sounds, and musical instruments.
