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from __future__ import absolute_import, division, print_function, unicode_literals
import glob
import os
import numpy as np
import argparse
import json
import torch
from scipy.io.wavfile import write
from env import AttrDict
from meldataset import MAX_WAV_VALUE
from models import Generator
h = None
device = None
def load_checkpoint(filepath, device):
assert os.path.isfile(filepath)
print("Loading '{}'".format(filepath))
checkpoint_dict = torch.load(filepath, map_location=device)
print("Complete.")
return checkpoint_dict
def scan_checkpoint(cp_dir, prefix):
pattern = os.path.join(cp_dir, prefix + "*")
cp_list = glob.glob(pattern)
if len(cp_list) == 0:
return ""
return sorted(cp_list)[-1]
def inference(a):
generator = Generator(h).to(device)
state_dict_g = load_checkpoint(a.checkpoint_file, device)
generator.load_state_dict(state_dict_g["generator"])
filelist = os.listdir(a.input_mels_dir)
os.makedirs(a.output_dir, exist_ok=True)
generator.eval()
generator.remove_weight_norm()
with torch.no_grad():
for i, filname in enumerate(filelist):
x = np.load(os.path.join(a.input_mels_dir, filname))
x = torch.FloatTensor(x).to(device)
y_g_hat = generator(x)
audio = y_g_hat.squeeze()
audio = audio * MAX_WAV_VALUE
audio = audio.cpu().numpy().astype("int16")
output_file = os.path.join(
a.output_dir, os.path.splitext(filname)[0] + "_generated_e2e.wav"
)
write(output_file, h.sampling_rate, audio)
print(output_file)
def main():
print("Initializing Inference Process..")
parser = argparse.ArgumentParser()
parser.add_argument("--input_mels_dir", default="test_mel_files")
parser.add_argument("--output_dir", default="generated_files_from_mel")
parser.add_argument("--checkpoint_file", required=True)
a = parser.parse_args()
config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json")
with open(config_file) as f:
data = f.read()
global h
json_config = json.loads(data)
h = AttrDict(json_config)
torch.manual_seed(h.seed)
global device
if torch.cuda.is_available():
torch.cuda.manual_seed(h.seed)
device = torch.device("cuda")
else:
device = torch.device("cpu")
inference(a)
if __name__ == "__main__":
main()
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