--- 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
**Paper**: https://arxiv.org/abs/2206.04658 **Code**: https://github.com/NVIDIA/BigVGAN **Project page**: https://research.nvidia.com/labs/adlr/projects/bigvgan/ **🤗 Spaces Demo**: https://huggingface.co/spaces/nvidia/BigVGAN ## News [Jul 2024] 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_base_22khz_80band ``` ## Usage Below example describes how you can use: load the pretrained BigVGAN generator, 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 # instantiate the model model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_base_22khz_80band') # remove weight norm in the model and set to eval mode model.remove_weight_norm() model = model.eval().to(device) import librosa from meldataset import get_mel_spectrogram # load wav file and compute mel spectrogram wav, sr = librosa.load('/path/to/your/audio.wav', 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 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_base_22khz_80band', 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_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. One can download the checkpoints of the pretrained generator weight, named as `bigvgan_generator.pt` within the listed HuggingFace repositories. |Model Name|Sampling Rate|Mel band|fmax|Upsampling Ratio|Params|Dataset|Fine-Tuned| |------|---|---|---|---|---|------|---| |[bigvgan_v2_44khz_128band_512x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x)|44 kHz|128|22050|512|122M|Large-scale Compilation|No| |[bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x)|44 kHz|128|22050|256|112M|Large-scale Compilation|No| |[bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x)|24 kHz|100|12000|256|112M|Large-scale Compilation|No| |[bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x)|22 kHz|80|11025|256|112M|Large-scale Compilation|No| |[bigvgan_v2_22khz_80band_fmax8k_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x)|22 kHz|80|8000|256|112M|Large-scale Compilation|No| |[bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band)|24 kHz|100|12000|256|112M|LibriTTS|No| |[bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band)|24 kHz|100|12000|256|14M|LibriTTS|No| |[bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band)|22 kHz|80|8000|256|112M|LibriTTS + VCTK + LJSpeech|No| |[bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band)|22 kHz|80|8000|256|14M|LibriTTS + VCTK + LJSpeech|No|