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import numpy as np
import os
import torch
import commons
import models
import utils
from argparse import ArgumentParser
from tqdm import tqdm
from text import text_to_sequence
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("-m", "--model_dir", required=True, type=str)
parser.add_argument("-s", "--mels_dir", required=True, type=str)
args = parser.parse_args()
MODEL_DIR = args.model_dir # path to model dir
SAVE_MELS_DIR = args.mels_dir # path to save generated mels
if not os.path.exists(SAVE_MELS_DIR):
os.makedirs(SAVE_MELS_DIR)
hps = utils.get_hparams_from_dir(MODEL_DIR)
symbols = list(hps.data.punc) + list(hps.data.chars)
checkpoint_path = utils.latest_checkpoint_path(MODEL_DIR)
cleaner = hps.data.text_cleaners
model = models.FlowGenerator(
len(symbols) + getattr(hps.data, "add_blank", False),
out_channels=hps.data.n_mel_channels,
**hps.model
).to("cuda")
utils.load_checkpoint(checkpoint_path, model)
model.decoder.store_inverse() # do not calcuate jacobians for fast decoding
_ = model.eval()
def get_mel(text, fpath):
if getattr(hps.data, "add_blank", False):
text_norm = text_to_sequence(text, symbols, cleaner)
text_norm = commons.intersperse(text_norm, len(symbols))
else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality
text = " " + text.strip() + " "
text_norm = text_to_sequence(text, symbols, cleaner)
sequence = np.array(text_norm)[None, :]
x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda()
with torch.no_grad():
noise_scale = 0.667
length_scale = 1.0
(y_gen_tst, *_), *_, (attn_gen, *_) = model(
x_tst,
x_tst_lengths,
gen=True,
noise_scale=noise_scale,
length_scale=length_scale,
)
np.save(os.path.join(SAVE_MELS_DIR, fpath), y_gen_tst.cpu().detach().numpy())
for f in [hps.data.training_files, hps.data.validation_files]:
file_lines = open(f).read().splitlines()
for line in tqdm(file_lines):
fname, text = line.split("|")
fname = os.path.basename(fname).replace(".wav", ".npy")
get_mel(text, fname)
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