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import os
import os.path as osp
import sys
import time
from collections import defaultdict
import matplotlib
import numpy as np
import soundfile as sf
import torch
from torch import nn
import jiwer
import matplotlib.pylab as plt
def calc_wer(target, pred, ignore_indexes=[0]):
target_chars = drop_duplicated(list(filter(lambda x: x not in ignore_indexes, map(str, list(target)))))
pred_chars = drop_duplicated(list(filter(lambda x: x not in ignore_indexes, map(str, list(pred)))))
target_str = ' '.join(target_chars)
pred_str = ' '.join(pred_chars)
error = jiwer.wer(target_str, pred_str)
return error
def drop_duplicated(chars):
ret_chars = [chars[0]]
for prev, curr in zip(chars[:-1], chars[1:]):
if prev != curr:
ret_chars.append(curr)
return ret_chars
def build_criterion(critic_params={}):
criterion = {
"ce": nn.CrossEntropyLoss(ignore_index=-1),
"ctc": torch.nn.CTCLoss(**critic_params.get('ctc', {})),
}
return criterion
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') as f:
train_list = f.readlines()
with open(val_path, 'r') as f:
val_list = f.readlines()
return train_list, val_list
def plot_image(image):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(image, aspect="auto", origin="lower",
interpolation='none')
fig.canvas.draw()
plt.close()
return fig |