optimize

ddsp/vocoder.py

@@ -634,27 +634,31 @@ def __init__(self,
             'noise_magnitude': win_length // 2 + 1,
             'noise_phase': win_length // 2 + 1
         }
-        self.unit2ctrl = Unit2Control(n_unit, n_spk, split_map, use_pitch_aug=use_pitch_aug, use_naive_v2=True)
-
+        self.unit2ctrl = Unit2Control(n_unit, n_spk, split_map, use_pitch_aug=use_pitch_aug, use_naive_v2=True, use_conv_stack=True)
+
+    def fast_source_gen(self, f0_frames):
+        n = torch.arange(self.block_size, device=f0_frames.device)
+        s0 = f0_frames / self.sampling_rate
+        ds0 = F.pad(s0[:, 1:, :] - s0[:, :-1, :], (0, 0, 0, 1))
+        rad = s0 * (n + 1) + 0.5 * ds0 * n * (n + 1) / self.block_size
+        s0 = s0 + ds0 * n / self.block_size
+        rad2 = torch.fmod(rad[..., -1:].float() + 0.5, 1.0) - 0.5
+        rad_acc = rad2.cumsum(dim=1).fmod(1.0).to(f0_frames)
+        rad += F.pad(rad_acc[:, :-1, :], (0, 0, 1, 0))
+        rad -= torch.round(rad)
+        combtooth = torch.sinc(rad / (s0 + 1e-5)).reshape(f0_frames.shape[0], -1)
+        phase_frames = 2 * np.pi * rad[:, :, :1]
+        return combtooth, phase_frames
+
     def forward(self, units_frames, f0_frames, volume_frames, spk_id=None, spk_mix_dict=None, aug_shift=None, initial_phase=None, infer=True, **kwargs):
         '''
             units_frames: B x n_frames x n_unit
             f0_frames: B x n_frames x 1
             volume_frames: B x n_frames x 1 
             spk_id: B x 1
         '''
-        # exciter phase
-        f0 = upsample(f0_frames, self.block_size)
-        if infer:
-            x = torch.cumsum(f0.double() / self.sampling_rate, axis=1)
-        else:
-            x = torch.cumsum(f0 / self.sampling_rate, axis=1)
-        if initial_phase is not None:
-            x += initial_phase.to(x) / 2 / np.pi
-        x = x - torch.round(x)
-        x = x.to(f0)
-
-        phase_frames = 2 * np.pi * x[:, ::self.block_size, :]
+        # combtooth exciter signal 
+        combtooth, phase_frames = self.fast_source_gen(f0_frames)
 
         # parameter prediction
         ctrls, hidden = self.unit2ctrl(units_frames, f0_frames, phase_frames, volume_frames, spk_id=spk_id, spk_mix_dict=spk_mix_dict, aug_shift=aug_shift)
@@ -664,9 +668,7 @@ def forward(self, units_frames, f0_frames, volume_frames, spk_id=None, spk_mix_d
         noise_filter= torch.exp(ctrls['noise_magnitude'] + 1.j * np.pi * ctrls['noise_phase']) / 128
         noise_filter = torch.cat((noise_filter, noise_filter[:,-1:,:]), 1)
 
-        # combtooth exciter signal 
-        combtooth = torch.sinc(self.sampling_rate * x / (f0 + 1e-3))
-        combtooth = combtooth.squeeze(-1)
+        # harmonic part filter
         if combtooth.shape[-1] > self.win_length // 2:
             pad_mode = 'reflect'
         else:
@@ -681,7 +683,7 @@ def forward(self, units_frames, f0_frames, volume_frames, spk_id=None, spk_mix_d
                             return_complex = True,
                             pad_mode = pad_mode)
 
-        # noise exciter signal
+        # noise part filter
         noise = torch.randn_like(combtooth)
         noise_stft = torch.stft(
                             noise,