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train_vae.py
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train_vae.py
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import numpy as np
from util.arg_extractor import get_args
args, device = get_args() # get arguments from command line
rng = np.random.RandomState(seed=args.seed) # set the seeds for the experiment
import torch
import math
import operator
import util.torchaudio_transforms as transforms
from experiment_builders.vae_builder import VAEWORLDExperimentBuilder
from experiment_builders.vae_builder import VAERawExperimentBuilder
from models.vae import VAE
from models.common_networks import QuantisedInputModuleWrapper
from datasets.vcc_world_dataset import VCCWORLDDataset
from datasets.vcc_raw_dataset import VCCRawDataset
from datasets.vctk_dataset import VCTKDataset
from util.samplers import ChunkEfficientRandomSampler
torch.manual_seed(seed=args.seed)
vae_model = VAE(
input_shape=(1, 1, args.input_len),
encoder_arch=args.encoder,
generator_arch=args.generator,
latent_dim=args.latent_dim,
num_speakers=args.num_speakers,
speaker_dim=args.speaker_dim,
use_gated_convolutions=args.use_gated_convolutions)
if args.dataset == 'VCCWORLD2016':
print('VCC2016 dataset WORLD features.')
dataset_path = args.dataset_root_path
train_dataset = VCCWORLDDataset(root=dataset_path, scale=True)
val_dataset = VCCWORLDDataset(root=dataset_path, scale=True, eval=True)
# Create data loaders
train_data = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
num_workers=1,
sampler=ChunkEfficientRandomSampler(train_dataset, train_dataset.chunk_indices))
val_data = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size,
num_workers=1,
sampler=ChunkEfficientRandomSampler(val_dataset, val_dataset.chunk_indices))
vae_experiment = VAEWORLDExperimentBuilder(network_model=vae_model,
experiment_name=args.experiment_name,
num_epochs=args.num_epochs,
weight_decay_coefficient=args.weight_decay_coefficient,
learning_rate=args.learning_rate,
device=device,
continue_from_epoch=args.continue_from_epoch,
print_timings=args.print_timings,
train_data=train_data,
val_data=val_data)
elif args.dataset == 'VCCRaw2016':
print('VCC2016 dataset raw features.')
dataset_path = args.dataset_root_path
train_dataset = VCCRawDataset(root=dataset_path, transform=transforms.Compose([
transforms.MuLawEncoding(quantization_channels=args.num_input_quantization_channels)
]))
val_dataset = VCCRawDataset(root=dataset_path, eval=True, transform=transforms.Compose([
transforms.MuLawEncoding(quantization_channels=args.num_input_quantization_channels)
]))
# Create data loaders
train_data = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
val_data = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
quantised_input_vae_model = QuantisedInputModuleWrapper(args.num_input_quantization_channels, vae_model)
vae_experiment = VAERawExperimentBuilder(network_model=quantised_input_vae_model,
experiment_name=args.experiment_name,
num_epochs=args.num_epochs,
weight_decay_coefficient=args.weight_decay_coefficient,
learning_rate=args.learning_rate,
device=device,
continue_from_epoch=args.continue_from_epoch,
print_timings=args.print_timings,
train_data=train_data,
val_data=val_data)
elif args.dataset == 'VCTKRaw':
print('VCTK dataset raw features.')
dataset_path = args.dataset_root_path
chunk_size = 10000 # Number of files in the chunkfile
dataset = VCTKDataset(root=dataset_path, chunk_size=chunk_size, transform=transforms.Compose([
transforms.MuLawEncoding(quantization_channels=args.num_input_quantization_channels)
]))
num_chunks = math.ceil(len(dataset) / chunk_size)
chunk_indices = {i:(i*chunk_size, (i+1)*chunk_size-1) \
if i < num_chunks-1 \
else (i*chunk_size, len(dataset)-1) \
for i in range(num_chunks) }
# Last two chunks are for validation.
train_chunk_indices = {key:value for key, value in chunk_indices.items() if key < num_chunks-2}
val_chunk_indices = {key:value for key, value in chunk_indices.items() if key >= num_chunks-2}
train_dataset = torch.utils.data.Subset(dataset, range(min(train_chunk_indices.items(), key=operator.itemgetter(0))[1][0],
max(train_chunk_indices.items(), key=operator.itemgetter(0))[1][1]+1))
val_dataset = torch.utils.data.Subset(dataset, range(min(val_chunk_indices.items(), key=operator.itemgetter(0))[1][0],
max(val_chunk_indices.items(), key=operator.itemgetter(0))[1][1]+1))
# Re-compute chunk indices for the validation subset
val_num_chunks = math.ceil(len(val_dataset) / chunk_size)
val_chunk_indices = {i:(i*chunk_size, (i+1)*chunk_size-1) \
if i < val_num_chunks-1 \
else (i*chunk_size, len(val_dataset)-1) \
for i in range(val_num_chunks) }
# Create data loaders
train_data = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
num_workers=1,
sampler=ChunkEfficientRandomSampler(train_dataset, train_chunk_indices))
val_data = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size,
num_workers=1,
sampler=ChunkEfficientRandomSampler(val_dataset, val_chunk_indices))
quantised_input_vae_model = QuantisedInputModuleWrapper(args.num_input_quantization_channels, vae_model)
vae_experiment = VAERawExperimentBuilder(network_model=quantised_input_vae_model,
experiment_name=args.experiment_name,
num_epochs=args.num_epochs,
weight_decay_coefficient=args.weight_decay_coefficient,
learning_rate=args.learning_rate,
device=device,
continue_from_epoch=args.continue_from_epoch,
print_timings=args.print_timings,
train_data=train_data,
val_data=val_data)
else:
raise Exception('No such dataset!')
experiment_metrics, test_metrics = vae_experiment.run_experiment()