ZhangRC/vocos-audioset-32khz

This model is trained on Google's AudioSet (28GB data) for 1 million steps. (Originally planned 2 million steps, but I'm exploring better training schedule)

You can regard it as a pretrained base model, which is common in language models but not for vocoders.

How to load and use this model:

import torch
import torchaudio
from scipy.io.wavfile import write
with torch.no_grad():
  from vocos import Vocos
  A = torch.load("./vocos_checkpoint_epoch=464_step=1001610_val_loss=7.1732.ckpt", map_location="cpu")
  V = Vocos.from_hparams("./config.yaml")
  V.load_state_dict(A['state_dict'], strict=False)
  V.eval()
  def safe_log(x: torch.Tensor, clip_val: float = 1e-7):
    return torch.log(torch.clip(x, min=clip_val))
  voice, sr = torchaudio.load('example.wav') # must be sample_rate=32000
  if sr != 32000:
    raise ValueError
  mel = torchaudio.transforms.MelSpectrogram(
              sample_rate=32000, n_fft=2048, hop_length=1024, n_mels=128, center=True, power=1,
          )(voice)
  mel = safe_log(mel)
  audio = V.decode(mel)
write('out.wav', 32000, audio.flatten().numpy())