# BigVGAN-L
The 24kHz model was pretrained using LibriTTS dataset with a full 100-band mel spectrogram as input (see ```config.json``` for the exact hyperparameter setup) with the [BigVGAN](https://github.com/NVIDIA/BigVGAN)
repository. The pretraining was performed over 1300k steps with a 100 batch size with 8 A100 40GB GPUs.

# Inference
The run the inference with the example command for generating audio from the model. It computes mel spectrograms using wav files from --input_wavs_dir and saves the generated audio to --output_dir.
```
python NEMO_PATH/inference.py \
--checkpoint_file MODEL_PATH/BigVGAN-L/g_01300000.pt \
--input_wavs_dir AUDIO_PATH/input_wav \
--output_dir AUDIO_PATH/output_wav
```

# Continual finetuning
The vocoder can be finetuned further on using the NEMO_PATH/train.py script as the checkpoints save all the optimizer information.
{"license": "mit"}
transformers license:mit endpoints_compatible region:us
TAGS
#transformers #license-mit #endpoints_compatible #region-us 

# BigVGAN-L
The 24kHz model was pretrained using LibriTTS dataset with a full 100-band mel spectrogram as input (see  for the exact hyperparameter setup) with the BigVGAN
repository. The pretraining was performed over 1300k steps with a 100 batch size with 8 A100 40GB GPUs.

# Inference
The run the inference with the example command for generating audio from the model. It computes mel spectrograms using wav files from --input_wavs_dir and saves the generated audio to --output_dir.


# Continual finetuning
The vocoder can be finetuned further on using the NEMO_PATH/URL script as the checkpoints save all the optimizer information.
# BigVGAN-L
The 24kHz model was pretrained using LibriTTS dataset with a full 100-band mel spectrogram as input (see  for the exact hyperparameter setup) with the BigVGAN
repository. The pretraining was performed over 1300k steps with a 100 batch size with 8 A100 40GB GPUs. # Inference
The run the inference with the example command for generating audio from the model. It computes mel spectrograms using wav files from --input_wavs_dir and saves the generated audio to --output_dir. # Continual finetuning
The vocoder can be finetuned further on using the NEMO_PATH/URL script as the checkpoints save all the optimizer information.
TAGS
#transformers #license-mit #endpoints_compatible #region-us 
 # BigVGAN-L
The 24kHz model was pretrained using LibriTTS dataset with a full 100-band mel spectrogram as input (see  for the exact hyperparameter setup) with the BigVGAN
repository. The pretraining was performed over 1300k steps with a 100 batch size with 8 A100 40GB GPUs. # Inference
The run the inference with the example command for generating audio from the model. It computes mel spectrograms using wav files from --input_wavs_dir and saves the generated audio to --output_dir. # Continual finetuning
The vocoder can be finetuned further on using the NEMO_PATH/URL script as the checkpoints save all the optimizer information.
TAGS
#transformers #license-mit #endpoints_compatible #region-us 
# BigVGAN-L
The 24kHz model was pretrained using LibriTTS dataset with a full 100-band mel spectrogram as input (see  for the exact hyperparameter setup) with the BigVGAN
repository. The pretraining was performed over 1300k steps with a 100 batch size with 8 A100 40GB GPUs.# Inference
The run the inference with the example command for generating audio from the model. It computes mel spectrograms using wav files from --input_wavs_dir and saves the generated audio to --output_dir.# Continual finetuning
The vocoder can be finetuned further on using the NEMO_PATH/URL script as the checkpoints save all the optimizer information.