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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)
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