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from typing import *
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import librosa
from torch import Tensor
from torch.nn import Parameter, init
from torch.nn.common_types import _size_1_t
from model.mamba.mamba import Mamba
from model.mamba.utils.generation import InferenceParams
class LinearGroup(nn.Module):
def __init__(self, in_features: int, out_features: int, num_groups: int, bias: bool = True) -> None:
super(LinearGroup, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.num_groups = num_groups
self.weight = Parameter(torch.empty((num_groups, out_features, in_features)))
if bias:
self.bias = Parameter(torch.empty(num_groups, out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self) -> None:
# same as linear
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
init.uniform_(self.bias, -bound, bound)
def forward(self, x: Tensor) -> Tensor:
"""shape [..., group, feature]"""
x = torch.einsum("...gh,gkh->...gk", x, self.weight)
if self.bias is not None:
x = x + self.bias
return x
def extra_repr(self) -> str:
return f"{self.in_features}, {self.out_features}, num_groups={self.num_groups}, bias={True if self.bias is not None else False}"
class LayerNorm(nn.LayerNorm):
def __init__(self, seq_last: bool, **kwargs) -> None:
"""
Arg s:
seq_last (bool): whether the sequence dim is the last dim
"""
super().__init__(**kwargs)
self.seq_last = seq_last
def forward(self, input: Tensor) -> Tensor:
if self.seq_last:
input = input.transpose(-1, 1) # [B, H, Seq] -> [B, Seq, H], or [B,H,w,h] -> [B,h,w,H]
o = super().forward(input)
if self.seq_last:
o = o.transpose(-1, 1)
return o
class CausalConv1d(nn.Conv1d):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_1_t,
stride: _size_1_t = 1,
padding: _size_1_t | str = 0,
dilation: _size_1_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros',
device=None,
dtype=None,
look_ahead: int = 0,
) -> None:
super().__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode, device, dtype)
self.look_ahead = look_ahead
assert look_ahead <= self.kernel_size[0] - 1, (look_ahead, self.kernel_size)
def forward(self, x: Tensor, state: Dict[int, Any] = None) -> Tensor:
# x [B,H,T]
B, H, T = x.shape
if state is None or id(self) not in state:
x = F.pad(x, pad=(self.kernel_size[0] - 1 - self.look_ahead, self.look_ahead))
else:
x = torch.concat([state[id(self)], x], dim=-1)
if state is not None:
state[id(self)] = x[..., -self.kernel_size + 1:]
x = super().forward(x)
return x
class CleanMelLayer(nn.Module):
def __init__(
self,
dim_hidden: int,
dim_squeeze: int,
n_freqs: int,
dropout: Tuple[float, float, float] = (0, 0, 0),
f_kernel_size: int = 5,
f_conv_groups: int = 8,
padding: str = 'zeros',
full: nn.Module = None,
mamba_state: int = None,
mamba_conv_kernel: int = None,
online: bool = False,
) -> None:
super().__init__()
self.online = online
# cross-band block
# frequency-convolutional module
self.fconv1 = nn.ModuleList([
LayerNorm(seq_last=True, normalized_shape=dim_hidden),
nn.Conv1d(in_channels=dim_hidden, out_channels=dim_hidden, kernel_size=f_kernel_size, groups=f_conv_groups, padding='same', padding_mode=padding),
nn.PReLU(dim_hidden),
])
# full-band linear module
self.norm_full = LayerNorm(seq_last=False, normalized_shape=dim_hidden)
self.full_share = False if full == None else True
self.squeeze = nn.Sequential(nn.Conv1d(in_channels=dim_hidden, out_channels=dim_squeeze, kernel_size=1), nn.SiLU())
self.dropout_full = nn.Dropout2d(dropout[2]) if dropout[2] > 0 else None
self.full = LinearGroup(n_freqs, n_freqs, num_groups=dim_squeeze) if full == None else full
self.unsqueeze = nn.Sequential(nn.Conv1d(in_channels=dim_squeeze, out_channels=dim_hidden, kernel_size=1), nn.SiLU())
# frequency-convolutional module
self.fconv2 = nn.ModuleList([
LayerNorm(seq_last=True, normalized_shape=dim_hidden),
nn.Conv1d(in_channels=dim_hidden, out_channels=dim_hidden, kernel_size=f_kernel_size, groups=f_conv_groups, padding='same', padding_mode=padding),
nn.PReLU(dim_hidden),
])
# narrow-band block
self.norm_mamba = LayerNorm(seq_last=False, normalized_shape=dim_hidden)
if online:
self.mamba = Mamba(d_model=dim_hidden, d_state=mamba_state, d_conv=mamba_conv_kernel, layer_idx=0)
else:
self.mamba = nn.ModuleList([
Mamba(d_model=dim_hidden, d_state=mamba_state, d_conv=mamba_conv_kernel, layer_idx=0),
Mamba(d_model=dim_hidden, d_state=mamba_state, d_conv=mamba_conv_kernel, layer_idx=1),
])
self.dropout_mamba = nn.Dropout(dropout[0])
def forward(self, x: Tensor, inference: bool = False) -> Tensor:
x = x + self._fconv(self.fconv1, x)
x = x + self._full(x)
x = x + self._fconv(self.fconv2, x)
if self.online:
x = x + self._mamba(x, self.mamba, self.norm_mamba, self.dropout_mamba, inference)
else:
x_fw = x + self._mamba(x, self.mamba[0], self.norm_mamba, self.dropout_mamba, inference)
x_bw = x.flip(dims=[2]) + self._mamba(x.flip(dims=[2]), self.mamba[1], self.norm_mamba, self.dropout_mamba, inference)
x = (x_fw + x_bw.flip(dims=[2])) / 2
return x
def _mamba(self, x: Tensor, mamba: Mamba, norm: nn.Module, dropout: nn.Module, inference: bool = False):
B, F, T, H = x.shape
x = norm(x)
x = x.reshape(B * F, T, H)
if inference:
inference_params = InferenceParams(T, B * F)
xs = []
for i in range(T):
inference_params.seqlen_offset = i
xi = mamba.forward(x[:, [i], :], inference_params)
xs.append(xi)
x = torch.concat(xs, dim=1)
else:
x = mamba.forward(x)
x = x.reshape(B, F, T, H)
return dropout(x)
def _fconv(self, ml: nn.ModuleList, x: Tensor) -> Tensor:
B, F, T, H = x.shape
x = x.permute(0, 2, 3, 1) # [B,T,H,F]
x = x.reshape(B * T, H, F)
for m in ml:
x = m(x)
x = x.reshape(B, T, H, F)
x = x.permute(0, 3, 1, 2) # [B,F,T,H]
return x
def _full(self, x: Tensor) -> Tensor:
B, F, T, H = x.shape
x = self.norm_full(x)
x = x.permute(0, 2, 3, 1) # [B,T,H,F]
x = x.reshape(B * T, H, F)
x = self.squeeze(x) # [B*T,H',F]
if self.dropout_full:
x = x.reshape(B, T, -1, F)
x = x.transpose(1, 3) # [B,F,H',T]
x = self.dropout_full(x) # dropout some frequencies in one utterance
x = x.transpose(1, 3) # [B,T,H',F]
x = x.reshape(B * T, -1, F)
x = self.full(x) # [B*T,H',F]
x = self.unsqueeze(x) # [B*T,H,F]
x = x.reshape(B, T, H, F)
x = x.permute(0, 3, 1, 2) # [B,F,T,H]
return x
def extra_repr(self) -> str:
return f"full_share={self.full_share}"
class CleanMel(nn.Module):
def __init__(
self,
dim_input: int, # the input dim for each time-frequency point
dim_output: int, # the output dim for each time-frequency point
n_layers: int,
n_freqs: int,
n_mels: int = 80,
layer_linear_freq: int = 1,
encoder_kernel_size: int = 5,
dim_hidden: int = 192,
dropout: Tuple[float, float, float] = (0, 0, 0),
f_kernel_size: int = 5,
f_conv_groups: int = 8,
padding: str = 'zeros',
mamba_state: int = 16,
mamba_conv_kernel: int = 4,
online: bool = True,
sr: int = 16000,
n_fft: int = 512,
):
super().__init__()
self.layer_linear_freq = layer_linear_freq
self.online = online
# encoder
self.encoder = CausalConv1d(in_channels=dim_input, out_channels=dim_hidden, kernel_size=encoder_kernel_size, look_ahead=0)
# cleanmel layers
full = None
layers = []
for l in range(n_layers):
layer = CleanMelLayer(
dim_hidden=dim_hidden,
dim_squeeze=8 if l < layer_linear_freq else dim_hidden,
n_freqs=n_freqs if l < layer_linear_freq else n_mels,
dropout=dropout,
f_kernel_size=f_kernel_size,
f_conv_groups=f_conv_groups,
padding=padding,
full=full if l > layer_linear_freq else None,
online=online,
mamba_conv_kernel=mamba_conv_kernel,
mamba_state=mamba_state,
)
if hasattr(layer, 'full'):
full = layer.full
layers.append(layer)
self.layers = nn.ModuleList(layers)
# Mel filterbank
linear2mel = librosa.filters.mel(**{"sr": sr, "n_fft": n_fft, "n_mels": n_mels})
self.register_buffer("linear2mel", torch.nn.Parameter(torch.tensor(linear2mel.T, dtype=torch.float32)))
# decoder
self.decoder = nn.Linear(in_features=dim_hidden, out_features=dim_output)
def forward(self, x: Tensor, inference: bool = False) -> Tensor:
# x: [Batch, Freq, Time, Feature]
B, F, T, H0 = x.shape
x = self.encoder(x.reshape(B * F, T, H0).permute(0, 2, 1)).permute(0, 2, 1)
H = x.shape[2]
x = x.reshape(B, F, T, H)
# First Cross-Narrow band block in Linear Frequency
for i in range(self.layer_linear_freq):
m = self.layers[i]
x = m(x, inference).contiguous()
# Mel-filterbank
x = torch.einsum("bfth,fm->bmth", x, self.linear2mel)
for i in range(self.layer_linear_freq, len(self.layers)):
m = self.layers[i]
x = m(x, inference).contiguous()
y = self.decoder(x).squeeze(-1)
return y.contiguous()
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