DL-Art-School/codes/scripts/audio/use_vocoder.py
2021-11-22 16:40:19 -07:00

46 lines
1.5 KiB
Python

import pathlib
import numpy
import torch
from scipy.io import wavfile
from tqdm import tqdm
import matplotlib.pyplot as plt
import librosa
from models.waveglow.waveglow import WaveGlow
class Vocoder:
def __init__(self):
self.model = WaveGlow(n_mel_channels=80, n_flows=12, n_group=8, n_early_size=2, n_early_every=4, WN_config={'n_layers': 8, 'n_channels': 256, 'kernel_size': 3})
sd = torch.load('../experiments/waveglow_256channels_universal_v5.pth')
self.model.load_state_dict(sd)
self.model = self.model.to('cuda')
self.model.eval()
def transform_mel_to_audio(self, mel):
if len(mel.shape) == 2: # Assume it's missing the batch dimension and fix that.
mel = mel.unsqueeze(0)
with torch.no_grad():
return self.model.infer(mel)
def plot_spectrogram(spec, title=None, ylabel="freq_bin", aspect="auto", xmax=None):
fig, axs = plt.subplots(1, 1)
axs.set_title(title or "Spectrogram (db)")
axs.set_ylabel(ylabel)
axs.set_xlabel("frame")
im = axs.imshow(librosa.power_to_db(spec), origin="lower", aspect=aspect)
if xmax:
axs.set_xlim((0, xmax))
fig.colorbar(im, ax=axs)
plt.show(block=False)
if __name__ == '__main__':
vocoder = Vocoder()
m = torch.load('test_mels.pth')
for i, b in enumerate(m):
plot_spectrogram(b.cpu())
wav = vocoder.transform_mel_to_audio(b)
wavfile.write(f'{i}.wav', 22050, wav[0].cpu().numpy())