Upload vocoder/display.py with huggingface_hub

vocoder/display.py

@@ -0,0 +1,120 @@
+import matplotlib.pyplot as plt
+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) :
+    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_spectrogram(M, path, length=None) :
+    M = np.flip(M, axis=0)
+    if length : M = M[:, :length]
+    fig = plt.figure(figsize=(12, 6))
+    plt.imshow(M, interpolation='nearest', aspect='auto')
+    fig.savefig(f'{path}.png', bbox_inches='tight')
+    plt.close(fig)
+
+
+def plot(array) : 
+    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) :
+    M = np.flip(M, axis=0)
+    plt.figure(figsize=(18,4))
+    plt.imshow(M, interpolation='nearest', aspect='auto')
+    plt.show()
+