from monotonic_align import maximum_path from monotonic_align import mask_from_lens from monotonic_align.core import maximum_path_c import numpy as np import torch import copy from torch import nn import torch.nn.functional as F import torchaudio import librosa import matplotlib.pyplot as plt from munch import Munch def maximum_path(neg_cent, mask): """Cython optimized version. neg_cent: [b, t_t, t_s] mask: [b, t_t, t_s] """ device = neg_cent.device dtype = neg_cent.dtype neg_cent = np.ascontiguousarray(neg_cent.data.cpu().numpy().astype(np.float32)) path = np.ascontiguousarray(np.zeros(neg_cent.shape, dtype=np.int32)) t_t_max = np.ascontiguousarray( mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32) ) t_s_max = np.ascontiguousarray( mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32) ) maximum_path_c(path, neg_cent, t_t_max, t_s_max) return torch.from_numpy(path).to(device=device, dtype=dtype) def get_data_path_list(train_path=None, val_path=None): if train_path is None: train_path = "Data/train_list.txt" if val_path is None: val_path = "Data/val_list.txt" with open(train_path, "r", encoding="utf-8", errors="ignore") as f: train_list = f.readlines() with open(val_path, "r", encoding="utf-8", errors="ignore") as f: val_list = f.readlines() return train_list, val_list def length_to_mask(lengths): mask = ( torch.arange(lengths.max()) .unsqueeze(0) .expand(lengths.shape[0], -1) .type_as(lengths) ) mask = torch.gt(mask + 1, lengths.unsqueeze(1)) return mask # for norm consistency loss def log_norm(x, mean=-4, std=4, dim=2): """ normalized log mel -> mel -> norm -> log(norm) """ x = torch.log(torch.exp(x * std + mean).norm(dim=dim)) return x def get_image(arrs): plt.switch_backend("agg") fig = plt.figure() ax = plt.gca() ax.imshow(arrs) return fig def recursive_munch(d): if isinstance(d, dict): return Munch((k, recursive_munch(v)) for k, v in d.items()) elif isinstance(d, list): return [recursive_munch(v) for v in d] else: return d def log_print(message, logger): logger.info(message) print(message)