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8e4ece5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """Pure-MLX reference implementation of the DeepFilterNet3 network.
Loads the model.safetensors + config.json shipped in this repository and
mirrors the DeepFilterNet3 inference graph: lookahead shift on the input
features, encoder, ERB decoder, DF decoder, DF-output reshape. The
surrounding DSP (STFT, ERB feature extraction, normalization, deep-filter
application, iSTFT) is the caller's job — auxiliary.npz carries the exact
filterbank / window / normalization constants.
Layout: channels-last [B, T, F, C] throughout (time = conv H axis,
frequency = conv W axis). All GRUs run with a zero initial hidden state.
"""
import json
from pathlib import Path
import mlx.core as mx
import numpy as np
def _relu(x):
return mx.maximum(x, 0)
def grouped_linear(x, w):
"""x: [B, T, I], w: [groups, I/groups, H/groups] -> [B, T, H]."""
b, t, _ = x.shape
g, gi, gh = w.shape
x = x.reshape(b, t, g, gi)
y = mx.einsum("btgi,gih->btgh", x, w)
return y.reshape(b, t, g * gh)
def gru_layer(x, Wx, Wh, b, bhn):
"""mlx.nn.GRU recurrence with an explicit zero initial hidden state.
x: [B, T, I] -> [B, T, H]. Gate order r, z, n (same as PyTorch):
r = sigmoid(x@Wxr.T + h@Whr.T + br)
z = sigmoid(x@Wxz.T + h@Whz.T + bz)
n = tanh(x@Wxn.T + bn + r * (h@Whn.T + bhn))
h' = (1 - z) * n + z * h
"""
hidden = Wh.shape[1]
xp = x @ Wx.T + b # [B, T, 3H]
x_rz = xp[..., : 2 * hidden]
x_n = xp[..., 2 * hidden :]
h = mx.zeros((x.shape[0], hidden), dtype=x.dtype)
outs = []
for t in range(x.shape[1]):
hp = h @ Wh.T
rz = mx.sigmoid(x_rz[:, t] + hp[..., : 2 * hidden])
r = rz[..., :hidden]
z = rz[..., hidden:]
n = mx.tanh(x_n[:, t] + r * (hp[..., 2 * hidden :] + bhn))
h = (1 - z) * n + z * h
outs.append(h)
return mx.stack(outs, axis=1)
def conv_transpose_dw_fstride2(x, w_flipped):
"""Depthwise ConvTranspose2d over frequency, k=(1,3), stride=(1,2),
padding=(0,1), output_padding=(0,1) — via zero-stuffing + correlation.
x: [B, T, F, C] -> [B, T, 2F, C]. w_flipped: [C, 1, 3, 1], kernel
pre-flipped along the frequency axis.
"""
bsz, t, f, c = x.shape
z = mx.stack([x, mx.zeros_like(x)], axis=3).reshape(bsz, t, 2 * f, c)
z = mx.pad(z, [(0, 0), (0, 0), (1, 1), (0, 0)])
return mx.conv2d(z, w_flipped, stride=(1, 1), padding=(0, 0), groups=c)
class DFN3MLX:
"""DeepFilterNet3 network. Inputs are normalized features:
feat_erb [B, T, 32, 1] — ERB features (dB-scaled, mean-normalized)
feat_spec [B, T, 96, 2] — complex spectrogram features (unit-normalized)
Returns (erb_mask [B, T, 32, 1], df_coefs [B, 5, T, 96, 2], lsnr [B, T, 1]).
"""
def __init__(self, model_dir: str | Path):
model_dir = Path(model_dir)
self.w = {k: mx.array(v) for k, v in mx.load(str(model_dir / "model.safetensors")).items()}
with open(model_dir / "config.json") as f:
self.cfg = json.load(f)
# Pre-flip transposed-conv kernels for the zero-stuff formulation
for name in ("erb_dec.convt2.dwt.weight", "erb_dec.convt1.dwt.weight"):
flipped = np.array(self.w[name])[:, :, ::-1, :]
self.w[name] = mx.array(np.ascontiguousarray(flipped))
self.lsnr_scale = self.cfg["lsnr_max"] - self.cfg["lsnr_min"]
self.lsnr_offset = self.cfg["lsnr_min"]
self.conv_lookahead = self.cfg["conv_lookahead"]
# ── building blocks ──
def _inp_conv(self, x, prefix, groups):
"""pad(t=2) + Conv2d k=(3,3) (grouped) [+ pointwise] + ReLU."""
w = self.w
x = mx.pad(x, [(0, 0), (2, 0), (0, 0), (0, 0)])
if f"{prefix}.conv.bias" in w: # erb_conv0: single conv, BN fused into it
y = mx.conv2d(x, w[f"{prefix}.conv.weight"], stride=(1, 1), padding=(0, 1), groups=groups)
y = y + w[f"{prefix}.conv.bias"]
else: # df_conv0: grouped conv + BN-fused pointwise
y = mx.conv2d(x, w[f"{prefix}.conv.weight"], stride=(1, 1), padding=(0, 1), groups=groups)
y = mx.conv2d(y, w[f"{prefix}.pw.weight"], stride=(1, 1), padding=(0, 0))
y = y + w[f"{prefix}.pw.bias"]
return _relu(y)
def _sep_conv(self, x, prefix, fstride):
"""Depthwise k=(1,3) conv + BN-fused pointwise + ReLU."""
w = self.w
c = x.shape[-1]
y = mx.conv2d(x, w[f"{prefix}.dw.weight"], stride=(1, fstride), padding=(0, 1), groups=c)
y = mx.conv2d(y, w[f"{prefix}.pw.weight"], stride=(1, 1), padding=(0, 0))
return _relu(y + w[f"{prefix}.pw.bias"])
def _pathway(self, x, prefix):
"""Depthwise 1x1 conv (BN fused) + ReLU — a per-channel affine."""
w = self.w
y = mx.conv2d(x, w[f"{prefix}.dw.weight"], stride=(1, 1), padding=(0, 0), groups=x.shape[-1])
return _relu(y + w[f"{prefix}.dw.bias"])
def _sep_convt(self, x, prefix):
"""Depthwise transposed conv (fstride=2) + BN-fused pointwise + ReLU."""
w = self.w
y = conv_transpose_dw_fstride2(x, w[f"{prefix}.dwt.weight"])
y = mx.conv2d(y, w[f"{prefix}.pw.weight"], stride=(1, 1), padding=(0, 0))
return _relu(y + w[f"{prefix}.pw.bias"])
def _squeezed_gru(self, x, prefix, num_layers, has_linear_out):
w = self.w
y = _relu(grouped_linear(x, w[f"{prefix}.linear_in.weight"]))
if num_layers == 1:
y = gru_layer(y, w[f"{prefix}.gru.Wx"], w[f"{prefix}.gru.Wh"],
w[f"{prefix}.gru.b"], w[f"{prefix}.gru.bhn"])
else:
for l in range(num_layers):
p = f"{prefix}.gru.layers.{l}"
y = gru_layer(y, w[f"{p}.Wx"], w[f"{p}.Wh"], w[f"{p}.b"], w[f"{p}.bhn"])
if has_linear_out:
y = _relu(grouped_linear(y, w[f"{prefix}.linear_out.weight"]))
return y
# ── model ──
def __call__(self, feat_erb, feat_spec, apply_lookahead_shift: bool = True):
w = self.w
la = self.conv_lookahead
if apply_lookahead_shift and la > 0:
feat_erb = mx.pad(feat_erb[:, la:], [(0, 0), (0, la), (0, 0), (0, 0)])
feat_spec = mx.pad(feat_spec[:, la:], [(0, 0), (0, la), (0, 0), (0, 0)])
# Encoder
e0 = self._inp_conv(feat_erb, "enc.erb_conv0", groups=1) # [B,T,32,64]
e1 = self._sep_conv(e0, "enc.erb_conv1", 2) # [B,T,16,64]
e2 = self._sep_conv(e1, "enc.erb_conv2", 2) # [B,T,8,64]
e3 = self._sep_conv(e2, "enc.erb_conv3", 1) # [B,T,8,64]
c0 = self._inp_conv(feat_spec, "enc.df_conv0", groups=2) # [B,T,96,64]
c1 = self._sep_conv(c0, "enc.df_conv1", 2) # [B,T,48,64]
bsz, t = e3.shape[0], e3.shape[1]
cemb = _relu(grouped_linear(c1.reshape(bsz, t, -1), w["enc.df_fc_emb.weight"]))
emb = e3.reshape(bsz, t, -1) + cemb # [B,T,512]
emb = self._squeezed_gru(emb, "enc.emb_gru", num_layers=1, has_linear_out=True)
lsnr = mx.sigmoid(emb @ w["enc.lsnr_fc.weight"].T + w["enc.lsnr_fc.bias"])
lsnr = lsnr * self.lsnr_scale + self.lsnr_offset # [B,T,1]
# ERB decoder
d = self._squeezed_gru(emb, "erb_dec.emb_gru", num_layers=2, has_linear_out=True)
d = d.reshape(bsz, t, 8, -1) # [B,T,8,64]
d3 = self._sep_conv(self._pathway(e3, "erb_dec.conv3p") + d, "erb_dec.convt3", 1)
d2 = self._sep_convt(self._pathway(e2, "erb_dec.conv2p") + d3, "erb_dec.convt2")
d1 = self._sep_convt(self._pathway(e1, "erb_dec.conv1p") + d2, "erb_dec.convt1")
m = self._pathway(e0, "erb_dec.conv0p") + d1
m = mx.conv2d(m, w["erb_dec.conv0_out.conv.weight"], stride=(1, 1), padding=(0, 1))
erb_mask = mx.sigmoid(m + w["erb_dec.conv0_out.conv.bias"]) # [B,T,32,1]
# DF decoder
c = self._squeezed_gru(emb, "df_dec.df_gru", num_layers=2, has_linear_out=False)
c = c + grouped_linear(emb, w["df_dec.df_skip.weight"]) # [B,T,256]
cp = mx.pad(c0, [(0, 0), (4, 0), (0, 0), (0, 0)])
cp = mx.conv2d(cp, w["df_dec.df_convp.conv.weight"], stride=(1, 1), padding=(0, 0), groups=2)
cp = mx.conv2d(cp, w["df_dec.df_convp.pw.weight"], stride=(1, 1), padding=(0, 0))
cp = _relu(cp + w["df_dec.df_convp.pw.bias"]) # [B,T,96,10]
cf = mx.tanh(grouped_linear(c, w["df_dec.df_out.weight"])) # [B,T,960]
cf = cf.reshape(bsz, t, 96, 10) + cp
df_coefs = cf.reshape(bsz, t, 96, 5, 2).transpose(0, 3, 1, 2, 4) # [B,5,T,96,2]
return erb_mask, df_coefs, lsnr
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