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