mirror of
https://github.com/wassname/TTS.git
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linter and test updates for speaker_encoder, gmm_Attention
This commit is contained in:
Eren Golge
+12
-74
@@ -5,24 +5,21 @@ import time
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import traceback
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import torch
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from torch import optim
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from torch.utils.data import DataLoader
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from TTS.datasets.preprocess import load_meta_data
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from TTS.speaker_encoder.dataset import MyDataset
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from TTS.speaker_encoder.generic_utils import save_best_model, save_checkpoint
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from TTS.speaker_encoder.loss import GE2ELoss
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from TTS.speaker_encoder.model import SpeakerEncoder
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from TTS.speaker_encoder.visual import plot_embeddings
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from TTS.speaker_encoder.generic_utils import save_best_model, save_checkpoint
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.generic_utils import (NoamLR, check_update, copy_config_file,
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count_parameters,
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create_experiment_folder, get_git_branch,
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gradual_training_scheduler, load_config,
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remove_experiment_folder, set_init_dict,
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setup_model, split_dataset)
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load_config,
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remove_experiment_folder, set_init_dict)
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from TTS.utils.logger import Logger
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from TTS.utils.radam import RAdam
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from TTS.utils.visual import plot_alignment, plot_spectrogram
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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@@ -34,10 +31,6 @@ print(" > Number of GPUs: ", num_gpus)
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def setup_loader(ap, is_val=False, verbose=False):
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global meta_data_train
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global meta_data_eval
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if "meta_data_train" not in globals():
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meta_data_train, meta_data_eval = load_meta_data(c.datasets)
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if is_val:
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loader = None
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else:
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@@ -63,12 +56,11 @@ def train(model, criterion, optimizer, scheduler, ap, global_step):
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best_loss = float('inf')
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avg_loss = 0
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end_time = time.time()
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for num_iter, data in enumerate(data_loader):
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for _, data in enumerate(data_loader):
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start_time = time.time()
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# setup input data
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inputs = data[0]
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labels = data[1]
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loader_time = time.time() - end_time
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global_step += 1
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@@ -132,68 +124,11 @@ def train(model, criterion, optimizer, scheduler, ap, global_step):
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return avg_loss, global_step
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# def evaluate(model, criterion, ap, global_step, epoch):
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# data_loader = setup_loader(ap, is_val=True)
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# model.eval()
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# epoch_time = 0
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# avg_loss = 0
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# print("\n > Validation")
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# with torch.no_grad():
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# if data_loader is not None:
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# for num_iter, data in enumerate(data_loader):
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# start_time = time.time()
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# # setup input data
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# inputs = data[0]
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# labels = data[1]
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# # dispatch data to GPU
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# if use_cuda:
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# inputs = inputs.cuda()
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# # labels = labels.cuda()
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# # forward pass
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# outputs = model.forward(inputs)
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# # loss computation
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# loss = criterion(outputs.reshape(
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# c.num_speakers_in_batch, outputs.shape[0] // c.num_speakers_in_batch, -1))
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# step_time = time.time() - start_time
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# epoch_time += step_time
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# if num_iter % c.print_step == 0:
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# print(
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# " | > Loss: {:.5f} ".format(loss.item()),
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# flush=True)
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# avg_loss += float(loss.item())
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# eval_figures = {
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# "prediction": plot_spectrogram(const_spec, ap),
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# "ground_truth": plot_spectrogram(gt_spec, ap),
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# "alignment": plot_alignment(align_img)
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# }
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# tb_logger.tb_eval_figures(global_step, eval_figures)
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# # Sample audio
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# if c.model in ["Tacotron", "TacotronGST"]:
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# eval_audio = ap.inv_spectrogram(const_spec.T)
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# else:
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# eval_audio = ap.inv_mel_spectrogram(const_spec.T)
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# tb_logger.tb_eval_audios(
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# global_step, {"ValAudio": eval_audio}, c.audio["sample_rate"])
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# # compute average losses
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# avg_loss /= (num_iter + 1)
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# # Plot Validation Stats
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# epoch_stats = {"GE2Eloss": avg_loss}
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# tb_logger.tb_eval_stats(global_step, epoch_stats)
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# return avg_loss
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# FIXME: move args definition/parsing inside of main?
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def main(args): # pylint: disable=redefined-outer-name
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# pylint: disable=global-variable-undefined
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global meta_data_train
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global meta_data_eval
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ap = AudioProcessor(**c.audio)
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model = SpeakerEncoder(input_dim=40,
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proj_dim=128,
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@@ -211,7 +146,7 @@ def main(args): # pylint: disable=redefined-outer-name
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if c.reinit_layers:
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raise RuntimeError
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model.load_state_dict(checkpoint['model'])
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except:
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except KeyError:
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print(" > Partial model initialization.")
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model_dict = model.state_dict()
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model_dict = set_init_dict(model_dict, checkpoint, c)
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@@ -239,6 +174,9 @@ def main(args): # pylint: disable=redefined-outer-name
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num_params = count_parameters(model)
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print("\n > Model has {} parameters".format(num_params), flush=True)
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# pylint: disable=redefined-outer-name
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meta_data_train, meta_data_eval = load_meta_data(c.datasets)
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global_step = args.restore_step
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train_loss, global_step = train(model, criterion, optimizer, scheduler, ap,
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global_step)
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