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train_wavernn.py
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train_wavernn.py
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import time
import numpy as np
import torch
from torch import optim
import torch.nn.functional as F
from utils.display import stream, simple_table
from utils.dataset import get_vocoder_datasets
from utils.distribution import discretized_mix_logistic_loss
from utils import hparams as hp
from models.fatchord_version import WaveRNN
from gen_wavernn import gen_testset
from utils.paths import Paths
import argparse
from utils import data_parallel_workaround
from utils.checkpoints import save_checkpoint, restore_checkpoint
def main():
# Parse Arguments
parser = argparse.ArgumentParser(description='Train WaveRNN Vocoder')
parser.add_argument('--lr', '-l', type=float, help='[float] override hparams.py learning rate')
parser.add_argument('--batch_size', '-b', type=int, help='[int] override hparams.py batch size')
parser.add_argument('--force_train', '-f', action='store_true', help='Forces the model to train past total steps')
parser.add_argument('--gta', '-g', action='store_true', help='train wavernn on GTA features')
parser.add_argument('--force_cpu', '-c', action='store_true', help='Forces CPU-only training, even when in CUDA capable environment')
parser.add_argument('--hp_file', metavar='FILE', default='hparams.py', help='The file to use for the hyperparameters')
args = parser.parse_args()
hp.configure(args.hp_file) # load hparams from file
if args.lr is None:
args.lr = hp.voc_lr
if args.batch_size is None:
args.batch_size = hp.voc_batch_size
paths = Paths(hp.data_path, hp.voc_model_id, hp.tts_model_id)
batch_size = args.batch_size
force_train = args.force_train
train_gta = args.gta
lr = args.lr
if not args.force_cpu and torch.cuda.is_available():
device = torch.device('cuda')
if batch_size % torch.cuda.device_count() != 0:
raise ValueError('`batch_size` must be evenly divisible by n_gpus!')
else:
device = torch.device('cpu')
print('Using device:', device)
print('\nInitialising Model...\n')
# Instantiate WaveRNN Model
voc_model = WaveRNN(rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode=hp.voc_mode).to(device)
# Check to make sure the hop length is correctly factorised
assert np.cumprod(hp.voc_upsample_factors)[-1] == hp.hop_length
optimizer = optim.Adam(voc_model.parameters())
restore_checkpoint('voc', paths, voc_model, optimizer, create_if_missing=True)
train_set, test_set = get_vocoder_datasets(paths.data, batch_size, train_gta)
total_steps = 10_000_000 if force_train else hp.voc_total_steps
simple_table([('Remaining', str((total_steps - voc_model.get_step())//1000) + 'k Steps'),
('Batch Size', batch_size),
('LR', lr),
('Sequence Len', hp.voc_seq_len),
('GTA Train', train_gta)])
loss_func = F.cross_entropy if voc_model.mode == 'RAW' else discretized_mix_logistic_loss
voc_train_loop(paths, voc_model, loss_func, optimizer, train_set, test_set, lr, total_steps)
print('Training Complete.')
print('To continue training increase voc_total_steps in hparams.py or use --force_train')
def voc_train_loop(paths: Paths, model: WaveRNN, loss_func, optimizer, train_set, test_set, lr, total_steps):
# Use same device as model parameters
device = next(model.parameters()).device
for g in optimizer.param_groups: g['lr'] = lr
total_iters = len(train_set)
epochs = (total_steps - model.get_step()) // total_iters + 1
for e in range(1, epochs + 1):
start = time.time()
running_loss = 0.
for i, (x, y, m) in enumerate(train_set, 1):
x, m, y = x.to(device), m.to(device), y.to(device)
# Parallelize model onto GPUS using workaround due to python bug
if device.type == 'cuda' and torch.cuda.device_count() > 1:
y_hat = data_parallel_workaround(model, x, m)
else:
y_hat = model(x, m)
if model.mode == 'RAW':
y_hat = y_hat.transpose(1, 2).unsqueeze(-1)
elif model.mode == 'MOL':
y = y.float()
y = y.unsqueeze(-1)
loss = loss_func(y_hat, y)
optimizer.zero_grad()
loss.backward()
if hp.voc_clip_grad_norm is not None:
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), hp.voc_clip_grad_norm)
if np.isnan(grad_norm):
print('grad_norm was NaN!')
optimizer.step()
running_loss += loss.item()
avg_loss = running_loss / i
speed = i / (time.time() - start)
step = model.get_step()
k = step // 1000
if step % hp.voc_checkpoint_every == 0:
gen_testset(model, test_set, hp.voc_gen_at_checkpoint, hp.voc_gen_batched,
hp.voc_target, hp.voc_overlap, paths.voc_output)
ckpt_name = f'wave_step{k}K'
save_checkpoint('voc', paths, model, optimizer,
name=ckpt_name, is_silent=True)
msg = f'| Epoch: {e}/{epochs} ({i}/{total_iters}) | Loss: {avg_loss:.4f} | {speed:.1f} steps/s | Step: {k}k | '
stream(msg)
# Must save latest optimizer state to ensure that resuming training
# doesn't produce artifacts
save_checkpoint('voc', paths, model, optimizer, is_silent=True)
model.log(paths.voc_log, msg)
print(' ')
if __name__ == "__main__":
main()