Name: bigvgan v2 44khz 128band 512x
Author: nvidia

Overview

BigVGAN v2 is a high-fidelity neural vocoder designed for professional-grade speech and audio synthesis. Operating at a 44.1kHz sampling rate with 128-band resolution, it excels at transforming mel-spectrograms back into time-domain waveforms with minimal artifacts and high perceptual clarity. Unlike many legacy vocoders that struggle with high-frequency reconstruction or exhibit metallic buzzing, this model provides a stable, high-resolution output suitable for production-ready TTS and voice conversion pipelines. For developers, it offers a computationally efficient alternative to autoregressive models while maintaining competitive audio quality, making it an ideal choice for low-latency deployment in audio-to-audio workflows.

Highlights

High-fidelity 44.1kHz audio output for professional synthesis
Efficient mel-spectrogram to waveform reconstruction
Low-latency performance ideal for real-time applications
MIT licensed for flexible commercial integration
Reduced artifacts compared to standard GAN vocoders

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("nvidia/bigvgan_v2_44khz_128band_512x")
tokenizer = AutoTokenizer.from_pretrained("nvidia/bigvgan_v2_44khz_128band_512x")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download nvidia/bigvgan_v2_44khz_128band_512x

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download nvidia/bigvgan_v2_44khz_128band_512x config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('nvidia/bigvgan_v2_44khz_128band_512x')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x')
tokenizer = AutoTokenizer.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x')

Full Documentation

来源: HuggingFace

---
license: mit
license_link: https://huggingface.co/nvidia/BigVGAN/blob/main/LICENSE
tags:

neural-vocoder

audio-generation

library_name: PyTorch
pipeline_tag: audio-to-audio
---

BigVGAN: A Universal Neural Vocoder with Large-Scale Training

#### Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon

[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN)

![PWC](https://paperswithcode.com/sota/speech-synthesis-on-libritts?p=bigvgan-a-universal-neural-vocoder-with-large)

News

Jul 2024 (v2.3):

- General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark.

Jul 2024 (v2.2): The repository now includes an interactive local demo using gradio.

Jul 2024 (v2.1): BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.

Jul 2024 (v2): We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:

- Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU. - Improved discriminator and loss: BigVGAN-v2 is trained using a multi-scale sub-band CQT discriminator and a multi-scale mel spectrogram loss. - Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments. - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.

Installation

This repository contains pretrained BigVGAN checkpoints with easy access to inference and additional huggingface_hub support.

If you are interested in training the model and additional functionalities, please visit the official GitHub repository for more information: https://github.com/NVIDIA/BigVGAN

shell

git lfs install
git clone https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x

Usage

Below example describes how you can use BigVGAN: load the pretrained BigVGAN generator from Hugging Face Hub, compute mel spectrogram from input waveform, and generate synthesized waveform using the mel spectrogram as the model's input.

python

device = 'cuda'
import torch
import bigvgan
import librosa
from meldataset import get_mel_spectrogram
instantiate the model. You can optionally set use_cuda_kernel=True for faster inference.
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=False)
remove weight norm in the model and set to eval mode
model.remove_weight_norm()
model = model.eval().to(device)
load wav file and compute mel spectrogram
wav_path = '/path/to/your/audio.wav'
wav, sr = librosa.load(wav_path, sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
wav = torch.FloatTensor(wav).unsqueeze(0) # wav is FloatTensor with shape [B(1), T_time]
compute mel spectrogram from the ground truth audio
mel = get_mel_spectrogram(wav, model.h).to(device) # mel is FloatTensor with shape [B(1), C_mel, T_frame]
generate waveform from mel
with torch.inference_mode():
    wav_gen = model(mel) # wav_gen is FloatTensor with shape [B(1), 1, T_time] and values in [-1, 1]
wav_gen_float = wav_gen.squeeze(0).cpu() # wav_gen is FloatTensor with shape [1, T_time]
you can convert the generated waveform to 16 bit linear PCM
wav_gen_int16 = (wav_gen_float * 32767.0).numpy().astype('int16') # wav_gen is now np.ndarray with shape [1, T_time] and int16 dtype

Using Custom CUDA Kernel for Synthesis

You can apply the fast CUDA inference kernel by using a parameter use_cuda_kernel when instantiating BigVGAN:

python

import bigvgan
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=True)

When applied for the first time, it builds the kernel using nvcc and ninja. If the build succeeds, the kernel is saved to alias_free_activation/cuda/build and the model automatically loads the kernel. The codebase has been tested using CUDA 12.1.

Please make sure that both are installed in your system and nvcc installed in your system matches the version your PyTorch build is using.

For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis

Pretrained Models

We provide the pretrained models on Hugging Face Collections.
One can download the checkpoints of the generator weight (named bigvgan_generator.pt) and its discriminator/optimizer states (named bigvgan_discriminator_optimizer.pt) within the listed model repositories.

| Model Name | Sampling Rate | Mel band | fmax | Upsampling Ratio | Params | Dataset | Steps | Fine-Tuned |
|:--------------------------------------------------------------------------------------------------------:|:-------------:|:--------:|:-----:|:----------------:|:------:|:--------------------------:|:-----:|:----------:|
| bigvgan_v2_44khz_128band_512x | 44 kHz | 128 | 22050 | 512 | 122M | Large-scale Compilation | 5M | No |
| bigvgan_v2_44khz_128band_256x | 44 kHz | 128 | 22050 | 256 | 112M | Large-scale Compilation | 5M | No |
| bigvgan_v2_24khz_100band_256x | 24 kHz | 100 | 12000 | 256 | 112M | Large-scale Compilation | 5M | No |
| bigvgan_v2_22khz_80band_256x | 22 kHz | 80 | 11025 | 256 | 112M | Large-scale Compilation | 5M | No |
| bigvgan_v2_22khz_80band_fmax8k_256x | 22 kHz | 80 | 8000 | 256 | 112M | Large-scale Compilation | 5M | No |
| bigvgan_24khz_100band | 24 kHz | 100 | 12000 | 256 | 112M | LibriTTS | 5M | No |
| bigvgan_base_24khz_100band | 24 kHz | 100 | 12000 | 256 | 14M | LibriTTS | 5M | No |
| bigvgan_22khz_80band | 22 kHz | 80 | 8000 | 256 | 112M | LibriTTS + VCTK + LJSpeech | 5M | No |
| bigvgan_base_22khz_80band | 22 kHz | 80 | 8000 | 256 | 14M | LibriTTS +