import torch from torch import nn import numpy as np 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.layers import DualFC from utils.misc import get_pdf_from_tree class LPCNet(nn.Module): def __init__(self, config): super(LPCNet, self).__init__() # self.input_layout = config['input_layout'] self.feature_history = config['feature_history'] self.feature_lookahead = config['feature_lookahead'] # 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'] self.hsampling = config.get('hsampling', False) self.gru_a_input_dim = len(self.input_layout['signals']) * self.signal_embedding_dim + self.feature_conditioning_dim self.gru_b_input_dim = self.gru_a_units + self.feature_conditioning_dim # 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) self.dual_fc = DualFC(self.gru_b_units, self.output_levels) # 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) # inference parameters self.lpc_gamma = config.get('lpc_gamma', 1) def sparsify(self): for sparsifier in self.sparsifier: sparsifier.step() def get_gflops(self, fs, verbose=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 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 # gru b gru_b_rate = fs 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 # dual fcs fc = self.dual_fc rate = fs 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 self.hsampling: dual_fc_complexity /= 8 if verbose: print(f"dual_fc: {dual_fc_complexity} GFLOPS") gflops += dual_fc_complexity if verbose: print(f'total: {gflops} GFLOPS') return gflops 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 sample_rate_network(self, signals, c, gru_states): embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3) c_upsampled = torch.repeat_interleave(c, self.frame_size, dim=1) y = torch.concat((embedded_signals, c_upsampled), dim=-1) y, gru_a_state = self.gru_a(y, gru_states[0]) y = torch.concat((y, c_upsampled), dim=-1) y, gru_b_state = self.gru_b(y, gru_states[1]) y = self.dual_fc(y) if self.hsampling: y = torch.sigmoid(y) log_probs = torch.log(get_pdf_from_tree(y) + 1e-6) else: log_probs = torch.log_softmax(y, dim=-1) return log_probs, (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) if self.hsampling: y = torch.sigmoid(y) probs = get_pdf_from_tree(y) else: probs = torch.softmax(y, dim=-1) return probs, (gru_a_state, gru_b_state) def forward(self, features, periods, signals, gru_states): c = self.frame_rate_network(features, periods) log_probs, _ = self.sample_rate_network(signals, c, gru_states) 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.input_layout['signals']) pitch_corr_position = self.input_layout['features']['pitch_corr'][0] # signal buffers pcm = torch.zeros((num_frames * self.frame_size + lpc_order)) 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)) gru_states = [gru_a_state, gru_b_state] input_signals = torch.zeros((1, 1, num_input_signals), dtype=torch.long) + 128 # push data to device features = features.to(device) periods = periods.to(device) lpcs = lpcs.to(device) # lpc weighting weights = torch.FloatTensor([self.lpc_gamma ** (i + 1) for i in range(lpc_order)]).to(device) lpcs = lpcs * weights # 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(self.frame_size): pcm_position = frame_start + i + lpc_order output_position = frame_start + i # prepare input pred = torch.sum(pcm[pcm_position - lpc_order : pcm_position] * a) if 'prediction' in self.input_layout['signals']: input_signals[0, 0, self.input_layout['signals']['prediction']] = lin2ulawq(pred) # run single step of sample rate network probs, gru_states = self.decoder( input_signals, current_c, gru_states ) # sample from output exc_ulaw = sample_excitation(probs, pitch_corr) # signal generation exc = ulaw2lin(exc_ulaw) sig = exc + pred pcm[pcm_position] = sig mem = 0.85 * mem + float(sig) output[output_position] = clip_to_int16(round(mem)) # buffer update if 'last_signal' in self.input_layout['signals']: input_signals[0, 0, self.input_layout['signals']['last_signal']] = lin2ulawq(sig) if 'last_error' in self.input_layout['signals']: input_signals[0, 0, self.input_layout['signals']['last_error']] = lin2ulawq(exc) return output