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303 lines
13 KiB
Python
303 lines
13 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 torch
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from torch import nn
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import numpy as np
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from utils.ulaw import lin2ulawq, ulaw2lin
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from utils.sample import sample_excitation
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from utils.pcm import clip_to_int16
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from utils.sparsification import GRUSparsifier, calculate_gru_flops_per_step
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from utils.layers import DualFC
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from utils.misc import get_pdf_from_tree
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class LPCNet(nn.Module):
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def __init__(self, config):
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super(LPCNet, self).__init__()
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#
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self.input_layout = config['input_layout']
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self.feature_history = config['feature_history']
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self.feature_lookahead = config['feature_lookahead']
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# frame rate network parameters
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self.feature_dimension = config['feature_dimension']
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self.period_embedding_dim = config['period_embedding_dim']
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self.period_levels = config['period_levels']
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self.feature_channels = self.feature_dimension + self.period_embedding_dim
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self.feature_conditioning_dim = config['feature_conditioning_dim']
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self.feature_conv_kernel_size = config['feature_conv_kernel_size']
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# frame rate network layers
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self.period_embedding = nn.Embedding(self.period_levels, self.period_embedding_dim)
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self.feature_conv1 = nn.Conv1d(self.feature_channels, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid')
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self.feature_conv2 = nn.Conv1d(self.feature_conditioning_dim, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid')
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self.feature_dense1 = nn.Linear(self.feature_conditioning_dim, self.feature_conditioning_dim)
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self.feature_dense2 = nn.Linear(*(2*[self.feature_conditioning_dim]))
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# sample rate network parameters
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self.frame_size = config['frame_size']
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self.signal_levels = config['signal_levels']
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self.signal_embedding_dim = config['signal_embedding_dim']
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self.gru_a_units = config['gru_a_units']
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self.gru_b_units = config['gru_b_units']
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self.output_levels = config['output_levels']
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self.hsampling = config.get('hsampling', False)
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self.gru_a_input_dim = len(self.input_layout['signals']) * self.signal_embedding_dim + self.feature_conditioning_dim
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self.gru_b_input_dim = self.gru_a_units + self.feature_conditioning_dim
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# sample rate network layers
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self.signal_embedding = nn.Embedding(self.signal_levels, self.signal_embedding_dim)
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self.gru_a = nn.GRU(self.gru_a_input_dim, self.gru_a_units, batch_first=True)
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self.gru_b = nn.GRU(self.gru_b_input_dim, self.gru_b_units, batch_first=True)
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self.dual_fc = DualFC(self.gru_b_units, self.output_levels)
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# sparsification
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self.sparsifier = []
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# GRU A
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if 'gru_a' in config['sparsification']:
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gru_config = config['sparsification']['gru_a']
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task_list = [(self.gru_a, gru_config['params'])]
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self.sparsifier.append(GRUSparsifier(task_list,
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gru_config['start'],
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gru_config['stop'],
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gru_config['interval'],
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gru_config['exponent'])
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)
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self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a,
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gru_config['params'], drop_input=True)
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else:
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self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, drop_input=True)
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# GRU B
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if 'gru_b' in config['sparsification']:
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gru_config = config['sparsification']['gru_b']
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task_list = [(self.gru_b, gru_config['params'])]
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self.sparsifier.append(GRUSparsifier(task_list,
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gru_config['start'],
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gru_config['stop'],
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gru_config['interval'],
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gru_config['exponent'])
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)
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self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b,
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gru_config['params'])
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else:
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self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b)
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# inference parameters
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self.lpc_gamma = config.get('lpc_gamma', 1)
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def sparsify(self):
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for sparsifier in self.sparsifier:
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sparsifier.step()
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def get_gflops(self, fs, verbose=False):
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gflops = 0
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# frame rate network
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conditioning_dim = self.feature_conditioning_dim
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feature_channels = self.feature_channels
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frame_rate = fs / self.frame_size
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frame_rate_network_complexity = 1e-9 * 2 * (5 * conditioning_dim + 3 * feature_channels) * conditioning_dim * frame_rate
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if verbose:
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print(f"frame rate network: {frame_rate_network_complexity} GFLOPS")
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gflops += frame_rate_network_complexity
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# gru a
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gru_a_rate = fs
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gru_a_complexity = 1e-9 * gru_a_rate * self.gru_a_flops_per_step
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if verbose:
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print(f"gru A: {gru_a_complexity} GFLOPS")
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gflops += gru_a_complexity
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# gru b
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gru_b_rate = fs
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gru_b_complexity = 1e-9 * gru_b_rate * self.gru_b_flops_per_step
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if verbose:
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print(f"gru B: {gru_b_complexity} GFLOPS")
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gflops += gru_b_complexity
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# dual fcs
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fc = self.dual_fc
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rate = fs
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input_size = fc.dense1.in_features
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output_size = fc.dense1.out_features
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dual_fc_complexity = 1e-9 * (4 * input_size * output_size + 22 * output_size) * rate
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if self.hsampling:
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dual_fc_complexity /= 8
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if verbose:
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print(f"dual_fc: {dual_fc_complexity} GFLOPS")
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gflops += dual_fc_complexity
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if verbose:
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print(f'total: {gflops} GFLOPS')
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return gflops
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def frame_rate_network(self, features, periods):
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embedded_periods = torch.flatten(self.period_embedding(periods), 2, 3)
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features = torch.concat((features, embedded_periods), dim=-1)
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# convert to channels first and calculate conditioning vector
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c = torch.permute(features, [0, 2, 1])
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c = torch.tanh(self.feature_conv1(c))
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c = torch.tanh(self.feature_conv2(c))
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# back to channels last
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c = torch.permute(c, [0, 2, 1])
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c = torch.tanh(self.feature_dense1(c))
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c = torch.tanh(self.feature_dense2(c))
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return c
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def sample_rate_network(self, signals, c, gru_states):
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embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3)
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c_upsampled = torch.repeat_interleave(c, self.frame_size, dim=1)
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y = torch.concat((embedded_signals, c_upsampled), dim=-1)
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y, gru_a_state = self.gru_a(y, gru_states[0])
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y = torch.concat((y, c_upsampled), dim=-1)
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y, gru_b_state = self.gru_b(y, gru_states[1])
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y = self.dual_fc(y)
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if self.hsampling:
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y = torch.sigmoid(y)
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log_probs = torch.log(get_pdf_from_tree(y) + 1e-6)
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else:
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log_probs = torch.log_softmax(y, dim=-1)
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return log_probs, (gru_a_state, gru_b_state)
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def decoder(self, signals, c, gru_states):
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embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3)
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y = torch.concat((embedded_signals, c), dim=-1)
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y, gru_a_state = self.gru_a(y, gru_states[0])
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y = torch.concat((y, c), dim=-1)
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y, gru_b_state = self.gru_b(y, gru_states[1])
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y = self.dual_fc(y)
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if self.hsampling:
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y = torch.sigmoid(y)
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probs = get_pdf_from_tree(y)
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else:
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probs = torch.softmax(y, dim=-1)
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return probs, (gru_a_state, gru_b_state)
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def forward(self, features, periods, signals, gru_states):
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c = self.frame_rate_network(features, periods)
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log_probs, _ = self.sample_rate_network(signals, c, gru_states)
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return log_probs
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def generate(self, features, periods, lpcs):
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with torch.no_grad():
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device = self.parameters().__next__().device
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num_frames = features.shape[0] - self.feature_history - self.feature_lookahead
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lpc_order = lpcs.shape[-1]
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num_input_signals = len(self.input_layout['signals'])
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pitch_corr_position = self.input_layout['features']['pitch_corr'][0]
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# signal buffers
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pcm = torch.zeros((num_frames * self.frame_size + lpc_order))
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output = torch.zeros((num_frames * self.frame_size), dtype=torch.int16)
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mem = 0
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# state buffers
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gru_a_state = torch.zeros((1, 1, self.gru_a_units))
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gru_b_state = torch.zeros((1, 1, self.gru_b_units))
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gru_states = [gru_a_state, gru_b_state]
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input_signals = torch.zeros((1, 1, num_input_signals), dtype=torch.long) + 128
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# push data to device
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features = features.to(device)
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periods = periods.to(device)
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lpcs = lpcs.to(device)
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# lpc weighting
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weights = torch.FloatTensor([self.lpc_gamma ** (i + 1) for i in range(lpc_order)]).to(device)
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lpcs = lpcs * weights
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# run feature encoding
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c = self.frame_rate_network(features.unsqueeze(0), periods.unsqueeze(0))
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for frame_index in range(num_frames):
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frame_start = frame_index * self.frame_size
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pitch_corr = features[frame_index + self.feature_history, pitch_corr_position]
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a = - torch.flip(lpcs[frame_index + self.feature_history], [0])
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current_c = c[:, frame_index : frame_index + 1, :]
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for i in range(self.frame_size):
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pcm_position = frame_start + i + lpc_order
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output_position = frame_start + i
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# prepare input
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pred = torch.sum(pcm[pcm_position - lpc_order : pcm_position] * a)
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if 'prediction' in self.input_layout['signals']:
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input_signals[0, 0, self.input_layout['signals']['prediction']] = lin2ulawq(pred)
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# run single step of sample rate network
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probs, gru_states = self.decoder(
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input_signals,
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current_c,
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gru_states
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)
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# sample from output
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exc_ulaw = sample_excitation(probs, pitch_corr)
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# signal generation
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exc = ulaw2lin(exc_ulaw)
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sig = exc + pred
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pcm[pcm_position] = sig
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mem = 0.85 * mem + float(sig)
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output[output_position] = clip_to_int16(round(mem))
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# buffer update
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if 'last_signal' in self.input_layout['signals']:
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input_signals[0, 0, self.input_layout['signals']['last_signal']] = lin2ulawq(sig)
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if 'last_error' in self.input_layout['signals']:
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input_signals[0, 0, self.input_layout['signals']['last_error']] = lin2ulawq(exc)
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return output
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