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()