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| 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) | |