Spaces:
Building
Building
File size: 2,347 Bytes
f6b56a2 |
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 52 53 54 55 56 |
import torch
import matplotlib.pyplot as plt
import numpy as np
def get_silence_mask(
sig: torch.Tensor, morph_kernel_size: int = 499, k_smooth=21, thresh=0.0001,
debug: bool = False) -> torch.Tensor:
with torch.no_grad():
smooth = torch.nn.Conv1d(1, 1, k_smooth, padding=k_smooth//2, bias=False).to(sig.device)
smooth.weight.data.fill_(1./k_smooth)
smoothed = smooth(torch.abs(sig))
st = 1.*(torch.abs(smoothed) < thresh*torch.ones_like(smoothed, device=sig.device))
sig_dil = torch.nn.MaxPool1d(morph_kernel_size, stride=1, padding=morph_kernel_size//2)(st)
sig_ero = -torch.nn.MaxPool1d(morph_kernel_size, stride=1, padding=morph_kernel_size//2)(-sig_dil)
if debug:
return sig_ero.squeeze(0), smoothed.squeeze(0), st.squeeze(0)
else:
return sig_ero
def visualize_silence_mask(sig: torch.Tensor, silence_thresh: float = 0.0001):
silence_thresh = 0.0001
silence_mask, smoothed_amplitude, _ = get_silence_mask(
sig, k_smooth=21, morph_kernel_size=499, thresh=silence_thresh, debug=True
)
plt.figure(figsize=(12, 4))
plt.subplot(121)
plt.plot(sig.squeeze(0).cpu().numpy(), "k-", label="voice", alpha=0.5)
plt.plot(0.01*silence_mask.cpu().numpy(), "r-", alpha=1., label="silence mask")
plt.grid()
plt.legend()
plt.title("Voice and silence mask")
plt.ylim(-0.04, 0.04)
plt.subplot(122)
plt.plot(smoothed_amplitude.cpu().numpy(), "g--", alpha=0.5, label="smoothed amplitude")
plt.plot(np.ones(silence_mask.shape[-1])*silence_thresh, "c--", alpha=1., label="threshold")
plt.plot(-silence_thresh+silence_thresh*silence_mask.cpu().numpy(), "r-", alpha=1, label="silence mask")
plt.grid()
plt.legend()
plt.title("Thresholding mechanism")
plt.ylim(-silence_thresh, silence_thresh*10)
plt.show()
if __name__ == "__main__":
from gyraudio.default_locations import SAMPLE_ROOT
from gyraudio.audio_separation.visualization.pre_load_audio import audio_loading
from gyraudio.audio_separation.properties import CLEAN, BUFFERS
sample_folder = SAMPLE_ROOT/"0009"
signals = audio_loading(sample_folder, preload=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
sig_in = signals[BUFFERS][CLEAN].to(device)
visualize_silence_mask(sig_in)
|