File size: 1,417 Bytes
90f7c1e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
# from nsf_hifigan.models import load_model
from modules.BigVGAN.inference import load_model
import librosa
import torch
import torch.nn.functional as F
import torchaudio
import torchaudio.transforms as transforms
import numpy as np
import soundfile as sf
class LogMelSpectrogram(torch.nn.Module):
def __init__(self):
super().__init__()
self.melspctrogram = transforms.MelSpectrogram(
sample_rate=22050,
n_fft=1024,
win_length=1024,
hop_length=256,
center=False,
power=1.0,
norm="slaney",
n_mels=80,
mel_scale="slaney",
f_max=8000,
f_min=0,
)
def forward(self, wav):
wav = F.pad(wav, ((1024 - 256) // 2, (1024 - 256) // 2), "reflect")
mel = self.melspctrogram(wav)
logmel = torch.log(torch.clamp(mel, min=1e-5))
return logmel
hifigan, cfg = load_model('modules/BigVGAN/ckpt/bigvgan_22khz_80band/g_05000000', device='cuda')
M = LogMelSpectrogram()
source, sr = torchaudio.load("music.mp3")
source = torchaudio.functional.resample(source, sr, 22050)
source = source.unsqueeze(0)
mel = M(source).squeeze(0)
# f0, f0_bin = get_pitch("116_1_pred.wav")
# f0 = torch.tensor(f0).unsqueeze(0)
with torch.no_grad():
y_hat = hifigan(mel.cuda()).cpu().numpy().squeeze(1)
sf.write('test.wav', y_hat[0], samplerate=22050) |