""" /* 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 numbers import torch from torch import nn import torch.nn.functional as F from torch.nn.utils import weight_norm import numpy as np from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d from utils.layers.td_shaper import TDShaper from utils.complexity import _conv1d_flop_count from models.nns_base import NNSBase from models.silk_feature_net_pl import SilkFeatureNetPL from models.silk_feature_net import SilkFeatureNet from .scale_embedding import ScaleEmbedding import sys sys.path.append('../dnntools') from dnntools.quantization import soft_quant from dnntools.sparsification import create_sparsifier, mark_for_sparsification class NoLACE(NNSBase): """ Non-Linear Adaptive Coding Enhancer """ FRAME_SIZE=80 def __init__(self, num_features=47, pitch_embedding_dim=64, cond_dim=256, pitch_max=257, kernel_size=15, preemph=0.85, skip=91, comb_gain_limit_db=-6, global_gain_limits_db=[-6, 6], conv_gain_limits_db=[-6, 6], numbits_range=[50, 650], numbits_embedding_dim=8, hidden_feature_dim=64, partial_lookahead=True, norm_p=2, avg_pool_k=4, pool_after=False, softquant=False, sparsify=False, sparsification_schedule=[100, 1000, 100], sparsification_density=0.5, apply_weight_norm=False): super().__init__(skip=skip, preemph=preemph) self.num_features = num_features self.cond_dim = cond_dim self.pitch_max = pitch_max self.pitch_embedding_dim = pitch_embedding_dim self.kernel_size = kernel_size self.preemph = preemph self.skip = skip self.numbits_range = numbits_range self.numbits_embedding_dim = numbits_embedding_dim self.hidden_feature_dim = hidden_feature_dim self.partial_lookahead = partial_lookahead if isinstance(sparsification_density, numbers.Number): sparsification_density = 10 * [sparsification_density] norm = weight_norm if apply_weight_norm else lambda x, name=None: x # pitch embedding self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) # numbits embedding self.numbits_embedding = ScaleEmbedding(numbits_embedding_dim, *numbits_range, logscale=True) # feature net if partial_lookahead: self.feature_net = SilkFeatureNetPL(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim, hidden_feature_dim, softquant=softquant, sparsify=sparsify, sparsification_density=sparsification_density, apply_weight_norm=apply_weight_norm) else: self.feature_net = SilkFeatureNet(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim) # comb filters left_pad = self.kernel_size // 2 right_pad = self.kernel_size - 1 - left_pad self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) # spectral shaping self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) # non-linear transforms self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) # combinators self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) self.af3 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) self.af4 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) # feature transforms self.post_cf1 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) self.post_cf2 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) self.post_af1 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) self.post_af2 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) self.post_af3 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) if softquant: self.post_cf1 = soft_quant(self.post_cf1) self.post_cf2 = soft_quant(self.post_cf2) self.post_af1 = soft_quant(self.post_af1) self.post_af2 = soft_quant(self.post_af2) self.post_af3 = soft_quant(self.post_af3) if sparsify: mark_for_sparsification(self.post_cf1, (sparsification_density[4], [8, 4])) mark_for_sparsification(self.post_cf2, (sparsification_density[5], [8, 4])) mark_for_sparsification(self.post_af1, (sparsification_density[6], [8, 4])) mark_for_sparsification(self.post_af2, (sparsification_density[7], [8, 4])) mark_for_sparsification(self.post_af3, (sparsification_density[8], [8, 4])) self.sparsifier = create_sparsifier(self, *sparsification_schedule) def flop_count(self, rate=16000, verbose=False): frame_rate = rate / self.FRAME_SIZE # feature net feature_net_flops = self.feature_net.flop_count(frame_rate) comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate) shape_flops = self.tdshape1.flop_count(rate) + self.tdshape2.flop_count(rate) + self.tdshape3.flop_count(rate) feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate) + _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate)) if verbose: print(f"feature net: {feature_net_flops / 1e6} MFLOPS") print(f"comb filters: {comb_flops / 1e6} MFLOPS") print(f"adaptive conv: {af_flops / 1e6} MFLOPS") print(f"feature transforms: {feature_flops / 1e6} MFLOPS") return feature_net_flops + comb_flops + af_flops + feature_flops + shape_flops def feature_transform(self, f, layer): f0 = f.permute(0, 2, 1) f = F.pad(f0, [1, 0]) f = torch.tanh(layer(f)) return f.permute(0, 2, 1) def forward(self, x, features, periods, numbits, debug=False): periods = periods.squeeze(-1) pitch_embedding = self.pitch_embedding(periods) numbits_embedding = self.numbits_embedding(numbits).flatten(2) full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) cf = self.feature_net(full_features) y = self.cf1(x, cf, periods, debug=debug) cf = self.feature_transform(cf, self.post_cf1) y = self.cf2(y, cf, periods, debug=debug) cf = self.feature_transform(cf, self.post_cf2) y = self.af1(y, cf, debug=debug) cf = self.feature_transform(cf, self.post_af1) y1 = y[:, 0:1, :] y2 = self.tdshape1(y[:, 1:2, :], cf) y = torch.cat((y1, y2), dim=1) y = self.af2(y, cf, debug=debug) cf = self.feature_transform(cf, self.post_af2) y1 = y[:, 0:1, :] y2 = self.tdshape2(y[:, 1:2, :], cf) y = torch.cat((y1, y2), dim=1) y = self.af3(y, cf, debug=debug) cf = self.feature_transform(cf, self.post_af3) y1 = y[:, 0:1, :] y2 = self.tdshape3(y[:, 1:2, :], cf) y = torch.cat((y1, y2), dim=1) y = self.af4(y, cf, debug=debug) return y