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import torch
from tqdm import tqdm
import librosa
from hparams import hparams
from wavenet_vocoder import builder
torch.set_num_threads(4)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
def build_model():
model = getattr(builder, hparams.builder)(
out_channels=hparams.out_channels,
layers=hparams.layers,
stacks=hparams.stacks,
residual_channels=hparams.residual_channels,
gate_channels=hparams.gate_channels,
skip_out_channels=hparams.skip_out_channels,
cin_channels=hparams.cin_channels,
gin_channels=hparams.gin_channels,
weight_normalization=hparams.weight_normalization,
n_speakers=hparams.n_speakers,
dropout=hparams.dropout,
kernel_size=hparams.kernel_size,
upsample_conditional_features=hparams.upsample_conditional_features,
upsample_scales=hparams.upsample_scales,
freq_axis_kernel_size=hparams.freq_axis_kernel_size,
scalar_input=True,
legacy=hparams.legacy,
)
return model
def wavegen(model, c=None, tqdm=tqdm):
"""Generate waveform samples by WaveNet.
"""
model.eval()
model.make_generation_fast_()
Tc = c.shape[0]
upsample_factor = hparams.hop_size
# Overwrite length according to feature size
length = Tc * upsample_factor
# B x C x T
c = torch.FloatTensor(c.T).unsqueeze(0)
initial_input = torch.zeros(1, 1, 1).fill_(0.0)
# Transform data to GPU
initial_input = initial_input.to(device)
c = None if c is None else c.to(device)
with torch.no_grad():
y_hat = model.incremental_forward(
initial_input, c=c, g=None, T=length, tqdm=tqdm, softmax=True, quantize=True,
log_scale_min=hparams.log_scale_min)
y_hat = y_hat.view(-1).cpu().data.numpy()
return y_hat |