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"""
/* Copyright (c) 2023 Amazon
Written by Jan Buethe */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""
import argparse
import torch
from scipy.io import wavfile
from time import time
from models import model_dict
from utils.lpcnet_features import load_lpcnet_features
from utils import endoscopy
debug = False
if debug:
args = type('dummy', (object,),
{
'input' : 'testitems/all_0_orig.se',
'checkpoint' : 'testout/checkpoints/checkpoint_epoch_5.pth',
'output' : 'out.wav',
})()
else:
parser = argparse.ArgumentParser()
parser.add_argument('input', type=str, help='path to input features')
parser.add_argument('checkpoint', type=str, help='checkpoint file')
parser.add_argument('output', type=str, help='output file')
parser.add_argument('--debug', action='store_true', help='enables debug output')
args = parser.parse_args()
torch.set_num_threads(2)
input_folder = args.input
checkpoint_file = args.checkpoint
output_file = args.output
if not output_file.endswith('.wav'):
output_file += '.wav'
checkpoint = torch.load(checkpoint_file, map_location="cpu")
# check model
if not 'name' in checkpoint['setup']['model']:
print(f'warning: did not find model name entry in setup, using pitchpostfilter per default')
model_name = 'pitchpostfilter'
else:
model_name = checkpoint['setup']['model']['name']
model = model_dict[model_name](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs'])
model.load_state_dict(checkpoint['state_dict'])
# generate model input
setup = checkpoint['setup']
testdata = load_lpcnet_features(input_folder)
features = testdata['features']
periods = testdata['periods']
if args.debug:
endoscopy.init()
start = time()
output = model.process(features, periods, debug=args.debug)
elapsed = time() - start
print(f"[timing] inference took {elapsed * 1000} ms")
wavfile.write(output_file, 16000, output.cpu().numpy())
if args.debug:
endoscopy.close()
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