""" /* 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 torch from torch import nn import torch.nn.functional as F 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.layers.noise_shaper import NoiseShaper from utils.complexity import _conv1d_flop_count from utils.endoscopy import write_data from models.nns_base import NNSBase from models.lpcnet_feature_net import LPCNetFeatureNet from .scale_embedding import ScaleEmbedding def print_channels(y, prefix="", name="", rate=16000): num_channels = y.size(1) for i in range(num_channels): channel_name = f"{prefix}_c{i:02d}" if len(name) > 0: channel_name += "_" + name ch = y[0,i,:].detach().cpu().numpy() ch = ((2**14) * ch / np.max(ch)).astype(np.int16) write_data(channel_name, ch, rate) class LaVoce(nn.Module): """ Linear-Adaptive VOCodEr """ FEATURE_FRAME_SIZE=160 FRAME_SIZE=80 def __init__(self, num_features=20, pitch_embedding_dim=64, cond_dim=256, pitch_max=300, kernel_size=15, preemph=0.85, comb_gain_limit_db=-6, global_gain_limits_db=[-6, 6], conv_gain_limits_db=[-6, 6], norm_p=2, avg_pool_k=4, pulses=False, innovate1=True, innovate2=False, innovate3=False, ftrans_k=2): super().__init__() 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.pulses = pulses self.ftrans_k = ftrans_k assert self.FEATURE_FRAME_SIZE % self.FRAME_SIZE == 0 self.upsamp_factor = self.FEATURE_FRAME_SIZE // self.FRAME_SIZE # pitch embedding self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) # feature net self.feature_net = LPCNetFeatureNet(num_features + pitch_embedding_dim, cond_dim, self.upsamp_factor) # noise shaper self.noise_shaper = NoiseShaper(cond_dim, self.FRAME_SIZE) # 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, use_bias=False, 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) self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, 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) self.af_prescale = 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) self.af_mix = 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) # 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) # non-linear transforms self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate1) self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate2) self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate3) # 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) self.af3 = 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) 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) # feature transforms self.post_cf1 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) self.post_cf2 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) self.post_af1 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) self.post_af2 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) self.post_af3 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) def create_phase_signals(self, periods): batch_size = periods.size(0) progression = torch.arange(1, self.FRAME_SIZE + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) progression = torch.repeat_interleave(progression, batch_size, 0) phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) chunks = [] for sframe in range(periods.size(1)): f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) if self.pulses: alpha = torch.cos(f).view(batch_size, 1, 1) chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) pulse_a = torch.relu(chunk_sin - alpha) / (1 - alpha) pulse_b = torch.relu(-chunk_sin - alpha) / (1 - alpha) chunk = torch.cat((pulse_a, pulse_b), dim = 1) else: chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) chunk_cos = torch.cos(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) chunk = torch.cat((chunk_sin, chunk_cos), dim = 1) phase0 = phase0 + self.FRAME_SIZE * f chunks.append(chunk) phase_signals = torch.cat(chunks, dim=-1) return phase_signals 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) + self.af_prescale.flop_count(rate) + self.af_mix.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 def feature_transform(self, f, layer): f = f.permute(0, 2, 1) f = F.pad(f, [self.ftrans_k - 1, 0]) f = torch.tanh(layer(f)) return f.permute(0, 2, 1) def forward(self, features, periods, debug=False): periods = periods.squeeze(-1) pitch_embedding = self.pitch_embedding(periods) full_features = torch.cat((features, pitch_embedding), dim=-1) cf = self.feature_net(full_features) # upsample periods periods = torch.repeat_interleave(periods, self.upsamp_factor, 1) # pre-net ref_phase = torch.tanh(self.create_phase_signals(periods)) if debug: print_channels(ref_phase, prefix="lavoce_01", name="pulse") x = self.af_prescale(ref_phase, cf) noise = self.noise_shaper(cf) if debug: print_channels(torch.cat((x, noise), dim=1), prefix="lavoce_02", name="inputs") y = self.af_mix(torch.cat((x, noise), dim=1), cf) if debug: print_channels(y, prefix="lavoce_03", name="postselect1") # temporal shaping + innovating y1 = y[:, 0:1, :] y2 = self.tdshape1(y[:, 1:2, :], cf) if debug: print_channels(y2, prefix="lavoce_04", name="postshape1") y = torch.cat((y1, y2), dim=1) y = self.af2(y, cf, debug=debug) if debug: print_channels(y, prefix="lavoce_05", name="postselect2") cf = self.feature_transform(cf, self.post_af2) y1 = y[:, 0:1, :] y2 = self.tdshape2(y[:, 1:2, :], cf) if debug: print_channels(y2, prefix="lavoce_06", name="postshape2") y = torch.cat((y1, y2), dim=1) y = self.af3(y, cf, debug=debug) if debug: print_channels(y, prefix="lavoce_07", name="postmix1") cf = self.feature_transform(cf, self.post_af3) # spectral shaping y = self.cf1(y, cf, periods, debug=debug) if debug: print_channels(y, prefix="lavoce_08", name="postcomb1") cf = self.feature_transform(cf, self.post_cf1) y = self.cf2(y, cf, periods, debug=debug) if debug: print_channels(y, prefix="lavoce_09", name="postcomb2") cf = self.feature_transform(cf, self.post_cf2) y = self.af1(y, cf, debug=debug) if debug: print_channels(y, prefix="lavoce_10", name="postselect3") cf = self.feature_transform(cf, self.post_af1) # final temporal env adjustment y1 = y[:, 0:1, :] y2 = self.tdshape3(y[:, 1:2, :], cf) if debug: print_channels(y2, prefix="lavoce_11", name="postshape3") y = torch.cat((y1, y2), dim=1) y = self.af4(y, cf, debug=debug) if debug: print_channels(y, prefix="lavoce_12", name="postmix2") return y def process(self, features, periods, debug=False): self.eval() device = next(iter(self.parameters())).device with torch.no_grad(): # run model f = features.unsqueeze(0).to(device) p = periods.unsqueeze(0).to(device) y = self.forward(f, p, debug=debug).squeeze() # deemphasis if self.preemph > 0: for i in range(len(y) - 1): y[i + 1] += self.preemph * y[i] # clip to valid range out = torch.clip((2**15) * y, -2**15, 2**15 - 1).short() return out