ref: 35ee397e060283d30c098ae5e17836316bbec08b
dir: /dnn/torch/osce/models/no_lace.py/
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.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
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):
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
# 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)
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, 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)
# 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, pool_after=pool_after)
self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after)
self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after)
# 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, 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.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, 2)
self.post_cf2 = nn.Conv1d(cond_dim, cond_dim, 2)
self.post_af1 = nn.Conv1d(cond_dim, cond_dim, 2)
self.post_af2 = nn.Conv1d(cond_dim, cond_dim, 2)
self.post_af3 = nn.Conv1d(cond_dim, cond_dim, 2)
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):
f = f.permute(0, 2, 1)
f = F.pad(f, [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