""" /* 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 from utils.layers.subconditioner import get_subconditioner from utils.layers import DualFC from utils.ulaw import lin2ulawq, ulaw2lin from utils.sample import sample_excitation from utils.pcm import clip_to_int16 from utils.sparsification import GRUSparsifier, calculate_gru_flops_per_step from utils.misc import interleave_tensors # MultiRateLPCNet class MultiRateLPCNet(nn.Module): def __init__(self, config): super(MultiRateLPCNet, self).__init__() # general parameters self.input_layout = config['input_layout'] self.feature_history = config['feature_history'] self.feature_lookahead = config['feature_lookahead'] self.signals = config['signals'] # frame rate network parameters self.feature_dimension = config['feature_dimension'] self.period_embedding_dim = config['period_embedding_dim'] self.period_levels = config['period_levels'] self.feature_channels = self.feature_dimension + self.period_embedding_dim self.feature_conditioning_dim = config['feature_conditioning_dim'] self.feature_conv_kernel_size = config['feature_conv_kernel_size'] # frame rate network layers self.period_embedding = nn.Embedding(self.period_levels, self.period_embedding_dim) self.feature_conv1 = nn.Conv1d(self.feature_channels, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') self.feature_conv2 = nn.Conv1d(self.feature_conditioning_dim, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') self.feature_dense1 = nn.Linear(self.feature_conditioning_dim, self.feature_conditioning_dim) self.feature_dense2 = nn.Linear(*(2*[self.feature_conditioning_dim])) # sample rate network parameters self.frame_size = config['frame_size'] self.signal_levels = config['signal_levels'] self.signal_embedding_dim = config['signal_embedding_dim'] self.gru_a_units = config['gru_a_units'] self.gru_b_units = config['gru_b_units'] self.output_levels = config['output_levels'] # subconditioning B sub_config = config['subconditioning']['subconditioning_b'] self.substeps_b = sub_config['number_of_subsamples'] self.subcondition_signals_b = sub_config['signals'] self.signals_idx_b = [self.input_layout['signals'][key] for key in sub_config['signals']] method = sub_config['method'] kwargs = sub_config['kwargs'] if type(kwargs) == type(None): kwargs = dict() state_size = self.gru_b_units self.subconditioner_b = get_subconditioner(method, sub_config['number_of_subsamples'], sub_config['pcm_embedding_size'], state_size, self.signal_levels, len(sub_config['signals']), **sub_config['kwargs']) # subconditioning A sub_config = config['subconditioning']['subconditioning_a'] self.substeps_a = sub_config['number_of_subsamples'] self.subcondition_signals_a = sub_config['signals'] self.signals_idx_a = [self.input_layout['signals'][key] for key in sub_config['signals']] method = sub_config['method'] kwargs = sub_config['kwargs'] if type(kwargs) == type(None): kwargs = dict() state_size = self.gru_a_units self.subconditioner_a = get_subconditioner(method, sub_config['number_of_subsamples'], sub_config['pcm_embedding_size'], state_size, self.signal_levels, self.substeps_b * len(sub_config['signals']), **sub_config['kwargs']) # wrap up subconditioning, group_size_gru_a holds the number # of timesteps that are grouped as sample input for GRU A # input and group_size_subcondition_a holds the number of samples that are # grouped as input to pre-GRU B subconditioning self.group_size_gru_a = self.substeps_a * self.substeps_b self.group_size_subcondition_a = self.substeps_b self.gru_a_rate_divider = self.group_size_gru_a self.gru_b_rate_divider = self.substeps_b # gru sizes self.gru_a_input_dim = self.group_size_gru_a * len(self.signals) * self.signal_embedding_dim + self.feature_conditioning_dim self.gru_b_input_dim = self.subconditioner_a.get_output_dim(0) + self.feature_conditioning_dim self.signals_idx = [self.input_layout['signals'][key] for key in self.signals] # sample rate network layers self.signal_embedding = nn.Embedding(self.signal_levels, self.signal_embedding_dim) self.gru_a = nn.GRU(self.gru_a_input_dim, self.gru_a_units, batch_first=True) self.gru_b = nn.GRU(self.gru_b_input_dim, self.gru_b_units, batch_first=True) # sparsification self.sparsifier = [] # GRU A if 'gru_a' in config['sparsification']: gru_config = config['sparsification']['gru_a'] task_list = [(self.gru_a, gru_config['params'])] self.sparsifier.append(GRUSparsifier(task_list, gru_config['start'], gru_config['stop'], gru_config['interval'], gru_config['exponent']) ) self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, gru_config['params'], drop_input=True) else: self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, drop_input=True) # GRU B if 'gru_b' in config['sparsification']: gru_config = config['sparsification']['gru_b'] task_list = [(self.gru_b, gru_config['params'])] self.sparsifier.append(GRUSparsifier(task_list, gru_config['start'], gru_config['stop'], gru_config['interval'], gru_config['exponent']) ) self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b, gru_config['params']) else: self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b) # dual FCs self.dual_fc = [] for i in range(self.substeps_b): dim = self.subconditioner_b.get_output_dim(i) self.dual_fc.append(DualFC(dim, self.output_levels)) self.add_module(f"dual_fc_{i}", self.dual_fc[-1]) def get_gflops(self, fs, verbose=False, hierarchical_sampling=False): gflops = 0 # frame rate network conditioning_dim = self.feature_conditioning_dim feature_channels = self.feature_channels frame_rate = fs / self.frame_size frame_rate_network_complexity = 1e-9 * 2 * (5 * conditioning_dim + 3 * feature_channels) * conditioning_dim * frame_rate if verbose: print(f"frame rate network: {frame_rate_network_complexity} GFLOPS") gflops += frame_rate_network_complexity # gru a gru_a_rate = fs / self.group_size_gru_a gru_a_complexity = 1e-9 * gru_a_rate * self.gru_a_flops_per_step if verbose: print(f"gru A: {gru_a_complexity} GFLOPS") gflops += gru_a_complexity # subconditioning a subcond_a_rate = fs / self.substeps_b subconditioning_a_complexity = 1e-9 * self.subconditioner_a.get_average_flops_per_step() * subcond_a_rate if verbose: print(f"subconditioning A: {subconditioning_a_complexity} GFLOPS") gflops += subconditioning_a_complexity # gru b gru_b_rate = fs / self.substeps_b gru_b_complexity = 1e-9 * gru_b_rate * self.gru_b_flops_per_step if verbose: print(f"gru B: {gru_b_complexity} GFLOPS") gflops += gru_b_complexity # subconditioning b subcond_b_rate = fs subconditioning_b_complexity = 1e-9 * self.subconditioner_b.get_average_flops_per_step() * subcond_b_rate if verbose: print(f"subconditioning B: {subconditioning_b_complexity} GFLOPS") gflops += subconditioning_b_complexity # dual fcs for i, fc in enumerate(self.dual_fc): rate = fs / len(self.dual_fc) input_size = fc.dense1.in_features output_size = fc.dense1.out_features dual_fc_complexity = 1e-9 * (4 * input_size * output_size + 22 * output_size) * rate if hierarchical_sampling: dual_fc_complexity /= 8 if verbose: print(f"dual_fc_{i}: {dual_fc_complexity} GFLOPS") gflops += dual_fc_complexity if verbose: print(f'total: {gflops} GFLOPS') return gflops def sparsify(self): for sparsifier in self.sparsifier: sparsifier.step() def frame_rate_network(self, features, periods): embedded_periods = torch.flatten(self.period_embedding(periods), 2, 3) features = torch.concat((features, embedded_periods), dim=-1) # convert to channels first and calculate conditioning vector c = torch.permute(features, [0, 2, 1]) c = torch.tanh(self.feature_conv1(c)) c = torch.tanh(self.feature_conv2(c)) # back to channels last c = torch.permute(c, [0, 2, 1]) c = torch.tanh(self.feature_dense1(c)) c = torch.tanh(self.feature_dense2(c)) return c def prepare_signals(self, signals, group_size, signal_idx): """ extracts, delays and groups signals """ batch_size, sequence_length, num_signals = signals.shape # extract signals according to position signals = torch.cat([signals[:, :, i : i + 1] for i in signal_idx], dim=-1) # roll back pcm to account for grouping signals = torch.roll(signals, group_size - 1, -2) # reshape signals = torch.reshape(signals, (batch_size, sequence_length // group_size, group_size * len(signal_idx))) return signals def sample_rate_network(self, signals, c, gru_states): signals_a = self.prepare_signals(signals, self.group_size_gru_a, self.signals_idx) embedded_signals = torch.flatten(self.signal_embedding(signals_a), 2, 3) # features at GRU A rate c_upsampled_a = torch.repeat_interleave(c, self.frame_size // self.gru_a_rate_divider, dim=1) # features at GRU B rate c_upsampled_b = torch.repeat_interleave(c, self.frame_size // self.gru_b_rate_divider, dim=1) y = torch.concat((embedded_signals, c_upsampled_a), dim=-1) y, gru_a_state = self.gru_a(y, gru_states[0]) # first round of upsampling and subconditioning c_signals_a = self.prepare_signals(signals, self.group_size_subcondition_a, self.signals_idx_a) y = self.subconditioner_a(y, c_signals_a) y = interleave_tensors(y) y = torch.concat((y, c_upsampled_b), dim=-1) y, gru_b_state = self.gru_b(y, gru_states[1]) c_signals_b = self.prepare_signals(signals, 1, self.signals_idx_b) y = self.subconditioner_b(y, c_signals_b) y = [self.dual_fc[i](y[i]) for i in range(self.substeps_b)] y = interleave_tensors(y) return y, (gru_a_state, gru_b_state) def decoder(self, signals, c, gru_states): embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3) y = torch.concat((embedded_signals, c), dim=-1) y, gru_a_state = self.gru_a(y, gru_states[0]) y = torch.concat((y, c), dim=-1) y, gru_b_state = self.gru_b(y, gru_states[1]) y = self.dual_fc(y) return torch.softmax(y, dim=-1), (gru_a_state, gru_b_state) def forward(self, features, periods, signals, gru_states): c = self.frame_rate_network(features, periods) y, _ = self.sample_rate_network(signals, c, gru_states) log_probs = torch.log_softmax(y, dim=-1) return log_probs def generate(self, features, periods, lpcs): with torch.no_grad(): device = self.parameters().__next__().device num_frames = features.shape[0] - self.feature_history - self.feature_lookahead lpc_order = lpcs.shape[-1] num_input_signals = len(self.signals) pitch_corr_position = self.input_layout['features']['pitch_corr'][0] # signal buffers last_signal = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) prediction = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) last_error = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) output = torch.zeros((num_frames * self.frame_size), dtype=torch.int16) mem = 0 # state buffers gru_a_state = torch.zeros((1, 1, self.gru_a_units)) gru_b_state = torch.zeros((1, 1, self.gru_b_units)) input_signals = 128 + torch.zeros(self.group_size_gru_a * num_input_signals, dtype=torch.long) # conditioning signals for subconditioner a c_signals_a = 128 + torch.zeros(self.group_size_subcondition_a * len(self.signals_idx_a), dtype=torch.long) # conditioning signals for subconditioner b c_signals_b = 128 + torch.zeros(len(self.signals_idx_b), dtype=torch.long) # signal dict signal_dict = { 'prediction' : prediction, 'last_error' : last_error, 'last_signal' : last_signal } # push data to device features = features.to(device) periods = periods.to(device) lpcs = lpcs.to(device) # run feature encoding c = self.frame_rate_network(features.unsqueeze(0), periods.unsqueeze(0)) for frame_index in range(num_frames): frame_start = frame_index * self.frame_size pitch_corr = features[frame_index + self.feature_history, pitch_corr_position] a = - torch.flip(lpcs[frame_index + self.feature_history], [0]) current_c = c[:, frame_index : frame_index + 1, :] for i in range(0, self.frame_size, self.group_size_gru_a): pcm_position = frame_start + i + lpc_order output_position = frame_start + i # calculate newest prediction prediction[pcm_position] = torch.sum(last_signal[pcm_position - lpc_order + 1: pcm_position + 1] * a) # prepare input for slot in range(self.group_size_gru_a): k = slot - self.group_size_gru_a + 1 for idx, name in enumerate(self.signals): input_signals[idx + slot * num_input_signals] = lin2ulawq( signal_dict[name][pcm_position + k] ) # run GRU A embed_signals = self.signal_embedding(input_signals.reshape((1, 1, -1))) embed_signals = torch.flatten(embed_signals, 2) y = torch.cat((embed_signals, current_c), dim=-1) h_a, gru_a_state = self.gru_a(y, gru_a_state) # loop over substeps_a for step_a in range(self.substeps_a): # prepare conditioning input for slot in range(self.group_size_subcondition_a): k = slot - self.group_size_subcondition_a + 1 for idx, name in enumerate(self.subcondition_signals_a): c_signals_a[idx + slot * num_input_signals] = lin2ulawq( signal_dict[name][pcm_position + k] ) # subconditioning h_a = self.subconditioner_a.single_step(step_a, h_a, c_signals_a.reshape((1, 1, -1))) # run GRU B y = torch.cat((h_a, current_c), dim=-1) h_b, gru_b_state = self.gru_b(y, gru_b_state) # loop over substeps b for step_b in range(self.substeps_b): # prepare subconditioning input for idx, name in enumerate(self.subcondition_signals_b): c_signals_b[idx] = lin2ulawq( signal_dict[name][pcm_position] ) # subcondition h_b = self.subconditioner_b.single_step(step_b, h_b, c_signals_b.reshape((1, 1, -1))) # run dual FC probs = torch.softmax(self.dual_fc[step_b](h_b), dim=-1) # sample new_exc = ulaw2lin(sample_excitation(probs, pitch_corr)) # update signals sig = new_exc + prediction[pcm_position] last_error[pcm_position + 1] = new_exc last_signal[pcm_position + 1] = sig mem = 0.85 * mem + float(sig) output[output_position] = clip_to_int16(round(mem)) # increase positions pcm_position += 1 output_position += 1 # calculate next prediction prediction[pcm_position] = torch.sum(last_signal[pcm_position - lpc_order + 1: pcm_position + 1] * a) return output