ref: fb570ed8bb2648e07e84faf40f30d93b7a0311d7
parent: 2e0c1ad3aefdc6bf4c30dc6cba44d52e5567cb68
author: Jean-Marc Valin <jmvalin@amazon.com>
date: Sat Sep 2 20:52:53 EDT 2023
5-tap pitch predictor
--- a/dnn/torch/fargan/fargan.py
+++ b/dnn/torch/fargan/fargan.py
@@ -140,10 +140,7 @@
print("has_gain:", self.has_gain) print("passthrough_size:", self.passthrough_size)-
- gain_param = 1 if self.has_gain else 0
-
- self.sig_dense1 = nn.Linear(4*self.subframe_size+self.passthrough_size+self.cond_size+gain_param, self.cond_size, bias=False)
+ self.sig_dense1 = nn.Linear(4*self.subframe_size+self.passthrough_size+self.cond_size+4, self.cond_size, bias=False)
self.sig_dense2 = nn.Linear(self.cond_size, self.cond_size, bias=False)
self.gru1 = nn.GRUCell(self.cond_size, self.cond_size, bias=False)
self.gru2 = nn.GRUCell(self.cond_size, self.cond_size, bias=False)
@@ -154,6 +151,7 @@
self.gru1_glu = GLU(self.cond_size)
self.gru2_glu = GLU(self.cond_size)
self.gru3_glu = GLU(self.cond_size)
+ self.ptaps_dense = nn.Linear(self.cond_size, 5)
self.sig_dense_out = nn.Linear(self.cond_size, self.subframe_size+self.passthrough_size, bias=False)
if self.has_gain:
@@ -168,21 +166,19 @@
dump_signal(prev, 'prev_in.f32')
- idx = 256-torch.maximum(torch.tensor(self.subframe_size, device=device), period[:,None])
- rng = torch.arange(self.subframe_size, device=device)
- idx = idx + rng[None,:]
+ idx = 256-torch.clamp(period[:,None], min=self.subframe_size+2, max=254)
+ rng = torch.arange(self.subframe_size+4, device=device)
+ idx = idx + rng[None,:] - 2
pred = torch.gather(exc_mem, 1, idx)
- prev = torch.cat([pred, prev], 1)
+ pred = pred/(1e-5+gain)
+
+ prev = prev/(1e-5+gain)
#prev = prev*0
dump_signal(prev, 'pitch_exc.f32')
dump_signal(exc_mem, 'exc_mem.f32')
- if self.has_gain:
- #gain = torch.norm(prev, dim=1, p=2, keepdim=True)
- prev = prev/(1e-5+gain)
- prev = torch.cat([prev, torch.log(1e-5+gain)], 1)
passthrough = states[3]
- tmp = torch.cat((cond, prev, passthrough, phase), 1)
+ tmp = torch.cat((cond, pred, prev, passthrough, phase), 1)
tmp = self.dense1_glu(torch.tanh(self.sig_dense1(tmp)))
tmp = self.dense2_glu(torch.tanh(self.sig_dense2(tmp)))
@@ -196,8 +192,14 @@
sig_out = sig_out[:,:self.subframe_size]
dump_signal(sig_out, 'exc_out.f32')
if self.has_gain:
+ taps = self.ptaps_dense(gru3_out)
+ taps = .2*taps + torch.exp(taps)
+ taps = taps / (1e-2 + torch.sum(torch.abs(taps), dim=-1, keepdim=True))
+ dump_signal(taps, 'taps.f32')
+ fpitch = taps[:,0:1]*pred[:,:-4] + taps[:,1:2]*pred[:,1:-3] + taps[:,2:3]*pred[:,2:-2] + taps[:,3:4]*pred[:,3:-1] + taps[:,4:]*pred[:,4:]
pitch_gain = torch.exp(self.gain_dense_out(gru3_out))
- sig_out = (sig_out + pitch_gain*prev[:,:self.subframe_size]) * gain
+ dump_signal(pitch_gain, 'pgain.f32')
+ sig_out = (sig_out + pitch_gain*fpitch) * gain
exc_mem = torch.cat([exc_mem[:,self.subframe_size:], sig_out], 1)
dump_signal(sig_out, 'sig_out.f32')
return sig_out, exc_mem, (gru1_state, gru2_state, gru3_state, passthrough)
--
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