def embed(self, aud):
mel = mel_spectogram(aud) #preprocessed audio
part_frames = []
for ix in range(
0, mel.shape[1] - self.config_yml['SLIDING_WIN_SIZE'],
int(self.config_yml['SLIDING_WIN_SIZE'] *
self.config_yml['SLIDING_WIN_OVERLAP'])):
part_frame = mel[:, ix:ix + self.config_yml['SLIDING_WIN_SIZE']]
part_frames.append(part_frame)
# changed
frames = np.stack(part_frames)
frames = torch.Tensor(frames).view(
-1,
self.config_yml['MEL_CHANNELS'],
self.config_yml['SLIDING_WIN_SIZE'],
).to(device=self.device)
model_pickle = torch.load(self.model_save_string.format(self.epoch),
map_location=self.device)
self.load_state_dict(model_pickle['model_state_dict'])
with torch.no_grad():
self.eval()
embeds = self.forward(frames) #.cpu().data.numpy()
embeds = embeds * torch.reciprocal(
torch.norm(embeds, dim=1, keepdim=True))
embeds = torch.mean(embeds, dim=0)
embeds = embeds.cpu().data.numpy()
return embeds
def embed(self, aud, group=True):
aud_splits, mel_splits = split_audio_ixs(len(aud))
max_aud_length = aud_splits[-1].stop
if max_aud_length >= len(aud):
aud = np.pad(aud, (0, max_aud_length - len(aud)), "constant")
mel = mel_spectogram(aud).astype(np.float32).T
mels = np.array([mel[s] for s in mel_splits])
mels = torch.from_numpy(mels).to(self.device)
embeds_all = []
with torch.no_grad():
for i in range(0, mels.shape[0], 64):
embeds = self.forward(
mels[i:min(i+64, mels.shape[0]), :, :].to(self.device))
embeds = embeds / torch.norm(embeds, dim=1, keepdim=True)
embeds_all.append(embeds)
embeds = torch.cat(embeds_all)
if group:
embeds = torch.mean(embeds, dim=0)
embeds = embeds / torch.norm(embeds)
embeds = embeds.cpu().data.numpy()
# self.train()
return embeds, (aud_splits, mel_splits)