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import time
import numpy as np
import sys
def progbar(i, n, size=16):
done = (i * size) // n
bar = ''
for i in range(size):
bar += '█' if i <= done else '░'
return bar
def stream(message) :
try:
sys.stdout.write("\r{%s}" % message)
except:
#Remove non-ASCII characters from message
message = ''.join(i for i in message if ord(i)<128)
sys.stdout.write("\r{%s}" % message)
def simple_table(item_tuples) :
border_pattern = '+---------------------------------------'
whitespace = ' '
headings, cells, = [], []
for item in item_tuples :
heading, cell = str(item[0]), str(item[1])
pad_head = True if len(heading) < len(cell) else False
pad = abs(len(heading) - len(cell))
pad = whitespace[:pad]
pad_left = pad[:len(pad)//2]
pad_right = pad[len(pad)//2:]
if pad_head :
heading = pad_left + heading + pad_right
else :
cell = pad_left + cell + pad_right
headings += [heading]
cells += [cell]
border, head, body = '', '', ''
for i in range(len(item_tuples)) :
temp_head = f'| {headings[i]} '
temp_body = f'| {cells[i]} '
border += border_pattern[:len(temp_head)]
head += temp_head
body += temp_body
if i == len(item_tuples) - 1 :
head += '|'
body += '|'
border += '+'
print(border)
print(head)
print(border)
print(body)
print(border)
print(' ')
def time_since(started) :
elapsed = time.time() - started
m = int(elapsed // 60)
s = int(elapsed % 60)
if m >= 60 :
h = int(m // 60)
m = m % 60
return f'{h}h {m}m {s}s'
else :
return f'{m}m {s}s'
def save_attention(attn, path):
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 6))
plt.imshow(attn.T, interpolation='nearest', aspect='auto')
fig.savefig(f'{path}.png', bbox_inches='tight')
plt.close(fig)
def save_attention_multiple(attn, path):
import matplotlib.pyplot as plt
num_plots = len(attn)
fig = plt.figure(figsize=(12, 6 * num_plots))
for i, a in enumerate(attn):
plt.subplot(num_plots, 1, i+1)
plt.imshow(a.T, interpolation='nearest', aspect='auto')
plt.xlabel("Decoder Step")
plt.ylabel("Encoder Step")
plt.title(f"Encoder-Decoder Alignment of No.{i} Sequence")
fig.savefig(f'{path}.png', bbox_inches='tight')
plt.close(fig)
def save_stop_tokens(stop, path):
import matplotlib.pyplot as plt
num_plots = len(stop)
fig = plt.figure(figsize=(12, 6 * num_plots))
for i, s in enumerate(stop):
plt.subplot(num_plots, 1, i+1)
plt.plot(s)
plt.xlabel("Timestep")
plt.ylabel("Stop Value")
plt.title(f"Stop Tokens of No.{i} Sequence")
fig.savefig(f'{path}.png', bbox_inches='tight')
plt.close(fig)
def save_spectrogram(M, path, length=None):
import matplotlib.pyplot as plt
M = np.flip(M, axis=0)
if length : M = M[:, :length]
fig = plt.figure(figsize=(12, 6))
plt.imshow(M, interpolation='nearest', aspect='auto')
plt.xlabel("Time")
plt.ylabel("Frequency")
plt.title("Generated Mel Spectrogram")
fig.savefig(f'{path}.png', bbox_inches='tight')
plt.close(fig)
def plot(array):
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(30, 5))
ax = fig.add_subplot(111)
ax.xaxis.label.set_color('grey')
ax.yaxis.label.set_color('grey')
ax.xaxis.label.set_fontsize(23)
ax.yaxis.label.set_fontsize(23)
ax.tick_params(axis='x', colors='grey', labelsize=23)
ax.tick_params(axis='y', colors='grey', labelsize=23)
plt.plot(array)
def plot_spec(M):
import matplotlib.pyplot as plt
M = np.flip(M, axis=0)
plt.figure(figsize=(18,4))
plt.imshow(M, interpolation='nearest', aspect='auto')
plt.show()
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