| | |
|---|
| | | tf2torch_tensor_name_prefix_tf="seq2seq/cif",
|
|---|
| | | tail_mask=True,
|
|---|
| | | ):
|
|---|
| | | super(CifPredictorV2, self).__init__()
|
|---|
| | | super().__init__()
|
|---|
| | |
|
|---|
| | | self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
|
|---|
| | | self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)
|
|---|
| | |
|---|
| | | predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)
|
|---|
| | | return predictor_alignments.detach(), predictor_alignments_length.detach()
|
|---|
| | |
|
|---|
| | | @tables.register("predictor_classes", "CifPredictorV2Export")
|
|---|
| | | class CifPredictorV2(torch.nn.Module):
|
|---|
| | | def __init__(self, model, **kwargs):
|
|---|
| | | super().__init__()
|
|---|
| | | |
|---|
| | | self.pad = model.pad
|
|---|
| | | self.cif_conv1d = model.cif_conv1d
|
|---|
| | | self.cif_output = model.cif_output
|
|---|
| | | self.threshold = model.threshold
|
|---|
| | | self.smooth_factor = model.smooth_factor
|
|---|
| | | self.noise_threshold = model.noise_threshold
|
|---|
| | | self.tail_threshold = model.tail_threshold
|
|---|
| | | |
|---|
| | | def forward(self, hidden: torch.Tensor,
|
|---|
| | | mask: torch.Tensor,
|
|---|
| | | ):
|
|---|
| | | alphas, token_num = self.forward_cnn(hidden, mask)
|
|---|
| | | mask = mask.transpose(-1, -2).float()
|
|---|
| | | mask = mask.squeeze(-1)
|
|---|
| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
|
|---|
| | | acoustic_embeds, cif_peak = cif_export(hidden, alphas, self.threshold)
|
|---|
| | | |
|---|
| | | return acoustic_embeds, token_num, alphas, cif_peak
|
|---|
| | | |
|---|
| | | def forward_cnn(self, hidden: torch.Tensor,
|
|---|
| | | mask: torch.Tensor,
|
|---|
| | | ):
|
|---|
| | | h = hidden
|
|---|
| | | context = h.transpose(1, 2)
|
|---|
| | | queries = self.pad(context)
|
|---|
| | | output = torch.relu(self.cif_conv1d(queries))
|
|---|
| | | output = output.transpose(1, 2)
|
|---|
| | | |
|---|
| | | output = self.cif_output(output)
|
|---|
| | | alphas = torch.sigmoid(output)
|
|---|
| | | alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
|
|---|
| | | mask = mask.transpose(-1, -2).float()
|
|---|
| | | alphas = alphas * mask
|
|---|
| | | alphas = alphas.squeeze(-1)
|
|---|
| | | token_num = alphas.sum(-1)
|
|---|
| | | |
|---|
| | | return alphas, token_num
|
|---|
| | | |
|---|
| | | def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
|
|---|
| | | b, t, d = hidden.size()
|
|---|
| | | tail_threshold = self.tail_threshold
|
|---|
| | | |
|---|
| | | zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
|
|---|
| | | ones_t = torch.ones_like(zeros_t)
|
|---|
| | | |
|---|
| | | mask_1 = torch.cat([mask, zeros_t], dim=1)
|
|---|
| | | mask_2 = torch.cat([ones_t, mask], dim=1)
|
|---|
| | | mask = mask_2 - mask_1
|
|---|
| | | tail_threshold = mask * tail_threshold
|
|---|
| | | alphas = torch.cat([alphas, zeros_t], dim=1)
|
|---|
| | | alphas = torch.add(alphas, tail_threshold)
|
|---|
| | | |
|---|
| | | zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
|
|---|
| | | hidden = torch.cat([hidden, zeros], dim=1)
|
|---|
| | | token_num = alphas.sum(dim=-1)
|
|---|
| | | token_num_floor = torch.floor(token_num)
|
|---|
| | | |
|---|
| | | return hidden, alphas, token_num_floor
|
|---|
| | |
|
|---|
| | | @torch.jit.script
|
|---|
| | | def cif_export(hidden, alphas, threshold: float):
|
|---|
| | | batch_size, len_time, hidden_size = hidden.size()
|
|---|
| | | threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
|
|---|
| | | |
|---|
| | | # loop varss
|
|---|
| | | integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
|
|---|
| | | frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
|
|---|
| | | # intermediate vars along time
|
|---|
| | | list_fires = []
|
|---|
| | | list_frames = []
|
|---|
| | | |
|---|
| | | for t in range(len_time):
|
|---|
| | | alpha = alphas[:, t]
|
|---|
| | | distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
|
|---|
| | | |
|---|
| | | integrate += alpha
|
|---|
| | | list_fires.append(integrate)
|
|---|
| | | |
|---|
| | | fire_place = integrate >= threshold
|
|---|
| | | integrate = torch.where(fire_place,
|
|---|
| | | integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
|
|---|
| | | integrate)
|
|---|
| | | cur = torch.where(fire_place,
|
|---|
| | | distribution_completion,
|
|---|
| | | alpha)
|
|---|
| | | remainds = alpha - cur
|
|---|
| | | |
|---|
| | | frame += cur[:, None] * hidden[:, t, :]
|
|---|
| | | list_frames.append(frame)
|
|---|
| | | frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
|
|---|
| | | remainds[:, None] * hidden[:, t, :],
|
|---|
| | | frame)
|
|---|
| | | |
|---|
| | | fires = torch.stack(list_fires, 1)
|
|---|
| | | frames = torch.stack(list_frames, 1)
|
|---|
| | | |
|---|
| | | fire_idxs = fires >= threshold
|
|---|
| | | frame_fires = torch.zeros_like(hidden)
|
|---|
| | | max_label_len = frames[0, fire_idxs[0]].size(0)
|
|---|
| | | for b in range(batch_size):
|
|---|
| | | frame_fire = frames[b, fire_idxs[b]]
|
|---|
| | | frame_len = frame_fire.size(0)
|
|---|
| | | frame_fires[b, :frame_len, :] = frame_fire
|
|---|
| | | |
|---|
| | | if frame_len >= max_label_len:
|
|---|
| | | max_label_len = frame_len
|
|---|
| | | frame_fires = frame_fires[:, :max_label_len, :]
|
|---|
| | | return frame_fires, fires
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | class mae_loss(torch.nn.Module):
|
|---|
| | |
|
|---|
