utils.py

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 = "./AuxiliaryASR/Data/train_list_subsection.csv"
    if val_path is None:
        val_path = "./AuxiliaryASR/Data/val_list_subsect.txt"

    with open(train_path, 'r') as f:
        train_list = f.readlines()
    with open(val_path, 'r') as f:
        val_list = f.readlines()

    # train_list = train_list[-1000:]
    # val_list = train_list[:1000]
    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)
    
    
def init_weights(m, mean=0.0, std=0.01):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        m.weight.data.normal_(mean, std)


def apply_weight_norm(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        weight_norm(m)


def get_padding(kernel_size, dilation=1):
    return int((kernel_size*dilation - dilation)/2)