mirror of
https://github.com/wassname/TTS.git
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Perform testing on example sentences
This commit is contained in:
Eren G
+11
-139
@@ -10,6 +10,7 @@ import subprocess
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import numpy as np
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from collections import OrderedDict
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from torch.autograd import Variable
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from utils.text import text_to_sequence
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class AttrDict(dict):
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@@ -159,142 +160,13 @@ def sequence_mask(sequence_length, max_len=None):
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return seq_range_expand < seq_length_expand
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class Progbar(object):
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"""Displays a progress bar.
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Args:
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target: Total number of steps expected, None if unknown.
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interval: Minimum visual progress update interval (in seconds).
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"""
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def __init__(self, target, width=30, verbose=1, interval=0.05):
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self.width = width
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self.target = target
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self.sum_values = {}
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self.unique_values = []
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self.start = time.time()
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self.last_update = 0
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self.interval = interval
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self.total_width = 0
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self.seen_so_far = 0
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self.verbose = verbose
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self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
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sys.stdout.isatty()) or
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'ipykernel' in sys.modules)
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def update(self, current, values=None, force=False):
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"""Updates the progress bar.
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# Arguments
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current: Index of current step.
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values: List of tuples (name, value_for_last_step).
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The progress bar will display averages for these values.
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force: Whether to force visual progress update.
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"""
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values = values or []
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for k, v in values:
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if k not in self.sum_values:
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self.sum_values[k] = [v * (current - self.seen_so_far),
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current - self.seen_so_far]
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self.unique_values.append(k)
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else:
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self.sum_values[k][0] += v * (current - self.seen_so_far)
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self.sum_values[k][1] += (current - self.seen_so_far)
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self.seen_so_far = current
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now = time.time()
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info = ' - %.0fs' % (now - self.start)
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if self.verbose == 1:
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if (not force and (now - self.last_update) < self.interval and
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self.target is not None and current < self.target):
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return
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prev_total_width = self.total_width
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if self._dynamic_display:
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sys.stdout.write('\b' * prev_total_width)
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sys.stdout.write('\r')
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else:
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sys.stdout.write('\n')
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if self.target is not None:
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numdigits = int(np.floor(np.log10(self.target))) + 1
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barstr = '%%%dd/%d [' % (numdigits, self.target)
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bar = barstr % current
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prog = float(current) / self.target
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prog_width = int(self.width * prog)
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if prog_width > 0:
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bar += ('=' * (prog_width - 1))
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if current < self.target:
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bar += '>'
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else:
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bar += '='
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bar += ('.' * (self.width - prog_width))
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bar += ']'
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else:
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bar = '%7d/Unknown' % current
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self.total_width = len(bar)
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sys.stdout.write(bar)
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if current:
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time_per_unit = (now - self.start) / current
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else:
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time_per_unit = 0
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if self.target is not None and current < self.target:
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eta = time_per_unit * (self.target - current)
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if eta > 3600:
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eta_format = '%d:%02d:%02d' % (
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eta // 3600, (eta % 3600) // 60, eta % 60)
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elif eta > 60:
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eta_format = '%d:%02d' % (eta // 60, eta % 60)
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else:
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eta_format = '%ds' % eta
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info = ' - ETA: %s' % eta_format
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if time_per_unit >= 1:
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info += ' %.0fs/step' % time_per_unit
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elif time_per_unit >= 1e-3:
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info += ' %.0fms/step' % (time_per_unit * 1e3)
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else:
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info += ' %.0fus/step' % (time_per_unit * 1e6)
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for k in self.unique_values:
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info += ' - %s:' % k
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if isinstance(self.sum_values[k], list):
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avg = np.mean(
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self.sum_values[k][0] / max(1, self.sum_values[k][1]))
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if abs(avg) > 1e-3:
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info += ' %.4f' % avg
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else:
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info += ' %.4e' % avg
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else:
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info += ' %s' % self.sum_values[k]
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self.total_width += len(info)
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if prev_total_width > self.total_width:
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info += (' ' * (prev_total_width - self.total_width))
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if self.target is not None and current >= self.target:
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info += '\n'
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sys.stdout.write(info)
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sys.stdout.flush()
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elif self.verbose == 2:
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if self.target is None or current >= self.target:
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for k in self.unique_values:
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info += ' - %s:' % k
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avg = np.mean(
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self.sum_values[k][0] / max(1, self.sum_values[k][1]))
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if avg > 1e-3:
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info += ' %.4f' % avg
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else:
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info += ' %.4e' % avg
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info += '\n'
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sys.stdout.write(info)
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sys.stdout.flush()
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self.last_update = now
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def add(self, n, values=None):
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self.update(self.seen_so_far + n, values)
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def synthesis(model, ap, text, use_cuda, text_cleaner):
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text_cleaner = [text_cleaner]
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seq = np.array(text_to_sequence(text, text_cleaner))
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chars_var = torch.from_numpy(seq).unsqueeze(0)
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if use_cuda:
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chars_var = chars_var.cuda().long()
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_, linear_out, _, _ = model.forward(chars_var)
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linear_out = linear_out[0].data.cpu().numpy()
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wav = ap.inv_spectrogram(linear_out.T)
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return wav
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