mirror of https://github.com/coqui-ai/TTS.git
Perform testing on example sentences
This commit is contained in:
Eren G
parent
dfd0bc1831
commit
20c3085695
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@ -28,6 +28,7 @@
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"checkpoint": true,
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"save_step": 376,
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"print_step": 10,
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"run_eval": false,
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"data_path": "/snakepit/shared/data/keithito/LJSpeech-1.1/",
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"min_seq_len": 0,
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"output_path": "experiments/"
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40
train.py
40
train.py
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@ -18,7 +18,7 @@ from torch.utils.data import DataLoader
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from torch.optim.lr_scheduler import ReduceLROnPlateau
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from tensorboardX import SummaryWriter
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from utils.generic_utils import (Progbar, remove_experiment_folder,
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from utils.generic_utils import (synthesis, remove_experiment_folder,
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create_experiment_folder, save_checkpoint,
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save_best_model, load_config, lr_decay,
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count_parameters, check_update, get_commit_hash)
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@ -116,14 +116,6 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
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step_time = time.time() - start_time
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epoch_time += step_time
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# update
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# progbar.update(num_iter+1, values=[('total_loss', loss.item()),
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# ('linear_loss', linear_loss.item()),
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# ('mel_loss', mel_loss.item()),
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# ('stop_loss', stop_loss.item()),
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# ('grad_norm', grad_norm.item()),
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# ('grad_norm_st', grad_norm_st.item())])
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if current_step % c.print_step == 0:
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print(" | | > Step:{} GlobalStep:{} TotalLoss:{:.5f} LinearLoss:{:.5f} "\
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"MelLoss:{:.5f} StopLoss:{:.5f} GradNorm:{:.5f} "\
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@ -217,6 +209,10 @@ def evaluate(model, criterion, criterion_st, data_loader, current_step):
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avg_mel_loss = 0
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avg_stop_loss = 0
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print(" | > Validation")
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test_sentences = ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
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"Be a voice, not an echo.",
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"I'm sorry Dave. I'm afraid I can't do that.",
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"This cake is great. It's so delicious and moist."]
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# progbar = Progbar(len(data_loader.dataset) / c.batch_size)
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n_priority_freq = int(3000 / (c.sample_rate * 0.5) * c.num_freq)
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with torch.no_grad():
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@ -259,11 +255,6 @@ def evaluate(model, criterion, criterion_st, data_loader, current_step):
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step_time = time.time() - start_time
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epoch_time += step_time
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# update
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# progbar.update(num_iter+1, values=[('total_loss', loss.item()),
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# ('linear_loss', linear_loss.item()),
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# ('mel_loss', mel_loss.item()),
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# ('stop_loss', stop_loss.item())])
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if num_iter % c.print_step == 0:
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print(" | | > TotalLoss: {:.5f} LinearLoss: {:.5f} MelLoss:{:.5f} "\
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"StopLoss: {:.5f} ".format(loss.item(),
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@ -297,9 +288,7 @@ def evaluate(model, criterion, criterion_st, data_loader, current_step):
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tb.add_audio('ValSampleAudio', audio_signal, current_step,
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sample_rate=c.sample_rate)
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except:
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# print(" | > Error at audio signal on TB!!")
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# print(audio_signal.max())
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# print(audio_signal.min())
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# sometimes audio signal is out of boundaries
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pass
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# compute average losses
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@ -314,6 +303,17 @@ def evaluate(model, criterion, criterion_st, data_loader, current_step):
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tb.add_scalar('ValEpochLoss/MelLoss', avg_mel_loss, current_step)
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tb.add_scalar('ValEpochLoss/Stop_loss', avg_stop_loss, current_step)
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# test sentences
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data_loader.dataset.ap.griffin_lim_iters = 60
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for idx, test_sentence in enumerate(test_sentences):
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wav = synthesis(model, data_loader.dataset.ap, test_sentence, use_cuda,
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c.text_cleaner)
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try:
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wav_name = 'TestSentences/{}'.format(idx)
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tb.add_audio(wav_name, wav, current_step,
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sample_rate=c.sample_rate)
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except:
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pass
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return avg_linear_loss
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@ -408,8 +408,10 @@ def main(args):
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best_loss = float('inf')
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for epoch in range(0, c.epochs):
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train_loss, current_step = train(
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model, criterion, criterion_st, train_loader, optimizer, optimizer_st, epoch)
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# train_loss, current_step = train(
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current_step = 0
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train_loss = 0
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# model, criterion, criterion_st, train_loader, optimizer, optimizer_st, epoch)
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val_loss = evaluate(model, criterion, criterion_st, val_loader, current_step)
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print(" | > Train Loss: {:.5f} Validation Loss: {:.5f}".format(train_loss, val_loss))
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best_loss = save_best_model(model, optimizer, val_loss,
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@ -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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