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from __future__ import absolute_import, division, print_function, unicode_literals
from typing import Tuple
import sys
from argparse import ArgumentParser
import torch
import numpy as np
import os
import json
import torch
sys.path.append(os.path.join(os.path.dirname(__file__), "../src/glow_tts"))
from scipy.io.wavfile import write
from hifi.env import AttrDict
from hifi.models import Generator
from text import text_to_sequence
import commons
import models
import utils
def check_directory(dir):
if not os.path.exists(dir):
sys.exit("Error: {} directory does not exist".format(dir))
class TextToMel:
def __init__(self, glow_model_dir, device="cuda"):
self.glow_model_dir = glow_model_dir
check_directory(self.glow_model_dir)
self.device = device
self.hps, self.glow_tts_model = self.load_glow_tts()
pass
def load_glow_tts(self):
hps = utils.get_hparams_from_dir(self.glow_model_dir)
checkpoint_path = utils.latest_checkpoint_path(self.glow_model_dir)
symbols = list(hps.data.punc) + list(hps.data.chars)
glow_tts_model = models.FlowGenerator(
len(symbols) + getattr(hps.data, "add_blank", False),
out_channels=hps.data.n_mel_channels,
**hps.model
) # .to(self.device)
if self.device == "cuda":
glow_tts_model.to("cuda")
utils.load_checkpoint(checkpoint_path, glow_tts_model)
glow_tts_model.decoder.store_inverse()
_ = glow_tts_model.eval()
return hps, glow_tts_model
def generate_mel(self, text, noise_scale=0.667, length_scale=1.0):
symbols = list(self.hps.data.punc) + list(self.hps.data.chars)
cleaner = self.hps.data.text_cleaners
if getattr(self.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, :]
del symbols
del cleaner
del text
del text_norm
if self.device == "cuda":
x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda()
else:
x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).long()
x_tst_lengths = torch.tensor([x_tst.shape[1]])
with torch.no_grad():
(y_gen_tst, *_), *_, (attn_gen, *_) = self.glow_tts_model(
x_tst,
x_tst_lengths,
gen=True,
noise_scale=noise_scale,
length_scale=length_scale,
)
del x_tst
del x_tst_lengths
torch.cuda.empty_cache()
return y_gen_tst
#return y_gen_tst.cpu().detach().numpy()
class MelToWav:
def __init__(self, hifi_model_dir, device="cuda"):
self.hifi_model_dir = hifi_model_dir
check_directory(self.hifi_model_dir)
self.device = device
self.h, self.hifi_gan_generator = self.load_hifi_gan()
pass
def load_hifi_gan(self):
checkpoint_path = utils.latest_checkpoint_path(self.hifi_model_dir, regex="g_*")
config_file = os.path.join(self.hifi_model_dir, "config.json")
data = open(config_file).read()
json_config = json.loads(data)
h = AttrDict(json_config)
torch.manual_seed(h.seed)
generator = Generator(h).to(self.device)
assert os.path.isfile(checkpoint_path)
print("Loading '{}'".format(checkpoint_path))
state_dict_g = torch.load(checkpoint_path, map_location=self.device)
print("Complete.")
generator.load_state_dict(state_dict_g["generator"])
generator.eval()
generator.remove_weight_norm()
return h, generator
def generate_wav(self, mel):
#mel = torch.FloatTensor(mel).to(self.device)
y_g_hat = self.hifi_gan_generator(mel.to(self.device)) # passing through vocoder
audio = y_g_hat.squeeze()
audio = audio * 32768.0
audio = audio.cpu().detach().numpy().astype("int16")
del y_g_hat
del mel
torch.cuda.empty_cache()
return audio, self.h.sampling_rate
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("-m", "--model", required=True, type=str)
parser.add_argument("-g", "--gan", required=True, type=str)
parser.add_argument("-d", "--device", type=str, default="cpu")
parser.add_argument("-t", "--text", type=str, required=True)
parser.add_argument("-w", "--wav", type=str, required=True)
args = parser.parse_args()
text_to_mel = TextToMel(glow_model_dir=args.model, device=args.device)
mel_to_wav = MelToWav(hifi_model_dir=args.gan, device=args.device)
mel = text_to_mel.generate_mel(args.text)
audio, sr = mel_to_wav.generate_wav(mel)
write(filename=args.wav, rate=sr, data=audio)
pass
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