| | |
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| | | from funasr.register import tables
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| | | from funasr.train_utils.device_funcs import to_device
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| | | from funasr.models.transformer.utils.nets_utils import make_pad_mask
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| | |
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| | | from torch.cuda.amp import autocast
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| | |
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| | | @tables.register("predictor_classes", "CifPredictor")
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| | | class CifPredictor(torch.nn.Module):
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| | |
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| | |
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| | | def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
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| | | target_label_length=None):
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| | | h = hidden
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| | | context = h.transpose(1, 2)
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| | | queries = self.pad(context)
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| | | memory = self.cif_conv1d(queries)
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| | | output = memory + context
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| | | output = self.dropout(output)
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| | | output = output.transpose(1, 2)
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| | | output = torch.relu(output)
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| | | output = self.cif_output(output)
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| | | alphas = torch.sigmoid(output)
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| | | alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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| | | if mask is not None:
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| | | mask = mask.transpose(-1, -2).float()
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| | | alphas = alphas * mask
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| | | if mask_chunk_predictor is not None:
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| | | alphas = alphas * mask_chunk_predictor
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| | | alphas = alphas.squeeze(-1)
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| | | mask = mask.squeeze(-1)
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| | | if target_label_length is not None:
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| | | target_length = target_label_length
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| | | elif target_label is not None:
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| | | target_length = (target_label != ignore_id).float().sum(-1)
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| | | else:
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| | | target_length = None
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| | | token_num = alphas.sum(-1)
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| | | if target_length is not None:
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| | | alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
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|---|
| | | elif self.tail_threshold > 0.0:
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| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
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| | | |
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| | | with autocast(False):
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| | | h = hidden
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| | | context = h.transpose(1, 2)
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| | | queries = self.pad(context)
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| | | memory = self.cif_conv1d(queries)
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| | | output = memory + context
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| | | output = self.dropout(output)
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| | | output = output.transpose(1, 2)
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| | | output = torch.relu(output)
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| | | output = self.cif_output(output)
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| | | alphas = torch.sigmoid(output)
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| | | alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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| | | if mask is not None:
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| | | mask = mask.transpose(-1, -2).float()
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| | | alphas = alphas * mask
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| | | if mask_chunk_predictor is not None:
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| | | alphas = alphas * mask_chunk_predictor
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| | | alphas = alphas.squeeze(-1)
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| | | mask = mask.squeeze(-1)
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| | | if target_label_length is not None:
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| | | target_length = target_label_length
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| | | elif target_label is not None:
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| | | target_length = (target_label != ignore_id).float().sum(-1)
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| | | else:
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| | | target_length = None
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| | | token_num = alphas.sum(-1)
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| | | if target_length is not None:
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| | | alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
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| | | elif self.tail_threshold > 0.0:
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| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
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| | | |
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| | | acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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| | |
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| | | acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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| | | |
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| | | if target_length is None and self.tail_threshold > 0.0:
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| | | token_num_int = torch.max(token_num).type(torch.int32).item()
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| | | acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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| | | |
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| | | if target_length is None and self.tail_threshold > 0.0:
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| | | token_num_int = torch.max(token_num).type(torch.int32).item()
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| | | acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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| | | |
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| | | return acoustic_embeds, token_num, alphas, cif_peak
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| | |
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| | | def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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| | |
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| | | tf2torch_tensor_name_prefix_tf="seq2seq/cif",
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| | | tail_mask=True,
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| | | ):
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| | | super(CifPredictorV2, self).__init__()
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| | | super().__init__()
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| | |
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| | | self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
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| | | self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)
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| | |
|---|
| | |
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| | | def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
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| | | target_label_length=None):
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| | | h = hidden
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| | | context = h.transpose(1, 2)
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| | | queries = self.pad(context)
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| | | output = torch.relu(self.cif_conv1d(queries))
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| | | output = output.transpose(1, 2)
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| | |
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| | | output = self.cif_output(output)
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| | | alphas = torch.sigmoid(output)
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| | | alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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| | | if mask is not None:
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| | | mask = mask.transpose(-1, -2).float()
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| | | alphas = alphas * mask
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| | | if mask_chunk_predictor is not None:
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| | | alphas = alphas * mask_chunk_predictor
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| | | alphas = alphas.squeeze(-1)
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| | | mask = mask.squeeze(-1)
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| | | if target_label_length is not None:
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| | | target_length = target_label_length
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|---|
| | | elif target_label is not None:
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| | | target_length = (target_label != ignore_id).float().sum(-1)
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| | | else:
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| | | target_length = None
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| | | token_num = alphas.sum(-1)
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| | | if target_length is not None:
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| | | alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
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| | | elif self.tail_threshold > 0.0:
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| | | if self.tail_mask:
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| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
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| | | |
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| | | with autocast(False):
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| | | h = hidden
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| | | context = h.transpose(1, 2)
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| | | queries = self.pad(context)
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| | | output = torch.relu(self.cif_conv1d(queries))
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| | | output = output.transpose(1, 2)
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| | | |
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| | | output = self.cif_output(output)
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| | | alphas = torch.sigmoid(output)
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| | | alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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| | | if mask is not None:
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| | | mask = mask.transpose(-1, -2).float()
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| | | alphas = alphas * mask
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|---|
| | | if mask_chunk_predictor is not None:
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| | | alphas = alphas * mask_chunk_predictor
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| | | alphas = alphas.squeeze(-1)
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| | | mask = mask.squeeze(-1)
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| | | if target_label_length is not None:
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| | | target_length = target_label_length.squeeze(-1)
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|---|
| | | elif target_label is not None:
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| | | target_length = (target_label != ignore_id).float().sum(-1)
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| | | else:
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| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=None)
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| | |
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| | | acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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| | | if target_length is None and self.tail_threshold > 0.0:
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| | | token_num_int = torch.max(token_num).type(torch.int32).item()
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| | | acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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| | | target_length = None
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| | | token_num = alphas.sum(-1)
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| | | if target_length is not None:
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| | | alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
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|---|
| | | elif self.tail_threshold > 0.0:
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|---|
| | | if self.tail_mask:
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| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
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| | | else:
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| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=None)
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| | | |
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| | | acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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| | | if target_length is None and self.tail_threshold > 0.0:
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| | | token_num_int = torch.max(token_num).type(torch.int32).item()
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| | | acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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| | |
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| | | return acoustic_embeds, token_num, alphas, cif_peak
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| | |
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| | |
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| | | predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)
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| | | return predictor_alignments.detach(), predictor_alignments_length.detach()
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| | |
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| | | def gen_tf2torch_map_dict(self):
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| | | @tables.register("predictor_classes", "CifPredictorV2Export")
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| | | class CifPredictorV2Export(torch.nn.Module):
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| | | def __init__(self, model, **kwargs):
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| | | super().__init__()
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| | | |
|---|
| | | self.pad = model.pad
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| | | self.cif_conv1d = model.cif_conv1d
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| | | self.cif_output = model.cif_output
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| | | self.threshold = model.threshold
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| | | self.smooth_factor = model.smooth_factor
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| | | self.noise_threshold = model.noise_threshold
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| | | self.tail_threshold = model.tail_threshold
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| | |
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| | | tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
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| | | tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
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| | | map_dict_local = {
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| | | ## predictor
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| | | "{}.cif_conv1d.weight".format(tensor_name_prefix_torch):
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| | | {"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),
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| | | "squeeze": None,
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| | | "transpose": (2, 1, 0),
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| | | }, # (256,256,3),(3,256,256)
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| | | "{}.cif_conv1d.bias".format(tensor_name_prefix_torch):
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| | | {"name": "{}/conv1d/bias".format(tensor_name_prefix_tf),
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| | | "squeeze": None,
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|---|
| | | "transpose": None,
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|---|
| | | }, # (256,),(256,)
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|---|
| | | "{}.cif_output.weight".format(tensor_name_prefix_torch):
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| | | {"name": "{}/conv1d_1/kernel".format(tensor_name_prefix_tf),
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| | | "squeeze": 0,
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| | | "transpose": (1, 0),
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|---|
| | | }, # (1,256),(1,256,1)
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|---|
| | | "{}.cif_output.bias".format(tensor_name_prefix_torch):
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| | | {"name": "{}/conv1d_1/bias".format(tensor_name_prefix_tf),
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|---|
| | | "squeeze": None,
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|---|
| | | "transpose": None,
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|---|
| | | }, # (1,),(1,)
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|---|
| | | }
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| | | return map_dict_local
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|---|
| | | def forward(self, hidden: torch.Tensor,
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| | | mask: torch.Tensor,
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|---|
| | | ):
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| | | alphas, token_num = self.forward_cnn(hidden, mask)
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| | | mask = mask.transpose(-1, -2).float()
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| | | mask = mask.squeeze(-1)
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| | | hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
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| | | acoustic_embeds, cif_peak = cif_export(hidden, alphas, self.threshold)
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|---|
| | | |
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| | | return acoustic_embeds, token_num, alphas, cif_peak
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|---|
| | | |
|---|
| | | def forward_cnn(self, hidden: torch.Tensor,
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|---|
| | | mask: torch.Tensor,
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|---|
| | | ):
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|---|
| | | h = hidden
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| | | context = h.transpose(1, 2)
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|---|
| | | queries = self.pad(context)
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|---|
| | | output = torch.relu(self.cif_conv1d(queries))
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|---|
| | | output = output.transpose(1, 2)
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|---|
| | | |
|---|
| | | output = self.cif_output(output)
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|---|
| | | alphas = torch.sigmoid(output)
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|---|
| | | alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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|---|
| | | mask = mask.transpose(-1, -2).float()
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|---|
| | | alphas = alphas * mask
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|---|
| | | alphas = alphas.squeeze(-1)
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|---|
| | | token_num = alphas.sum(-1)
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|---|
| | | |
|---|
| | | return alphas, token_num
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| | | |
|---|
| | | def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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|---|
| | | b, t, d = hidden.size()
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|---|
| | | tail_threshold = self.tail_threshold
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|---|
| | | |
|---|
| | | zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
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|---|
| | | ones_t = torch.ones_like(zeros_t)
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| | | |
|---|
| | | mask_1 = torch.cat([mask, zeros_t], dim=1)
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|---|
| | | mask_2 = torch.cat([ones_t, mask], dim=1)
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|---|
| | | mask = mask_2 - mask_1
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|---|
| | | tail_threshold = mask * tail_threshold
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|---|
| | | alphas = torch.cat([alphas, zeros_t], dim=1)
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|---|
| | | alphas = torch.add(alphas, tail_threshold)
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|---|
| | | |
|---|
| | | zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
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|---|
| | | hidden = torch.cat([hidden, zeros], dim=1)
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|---|
| | | token_num = alphas.sum(dim=-1)
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|---|
| | | token_num_floor = torch.floor(token_num)
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| | | |
|---|
| | | return hidden, alphas, token_num_floor
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|---|
| | |
|
|---|
| | | def convert_tf2torch(self,
|
|---|
| | | var_dict_tf,
|
|---|
| | | var_dict_torch,
|
|---|
| | | ):
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| | | map_dict = self.gen_tf2torch_map_dict()
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|---|
| | | var_dict_torch_update = dict()
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|---|
| | | for name in sorted(var_dict_torch.keys(), reverse=False):
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|---|
| | | names = name.split('.')
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|---|
| | | if names[0] == self.tf2torch_tensor_name_prefix_torch:
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|---|
| | | name_tf = map_dict[name]["name"]
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|---|
| | | data_tf = var_dict_tf[name_tf]
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|---|
| | | if map_dict[name]["squeeze"] is not None:
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|---|
| | | data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
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|---|
| | | if map_dict[name]["transpose"] is not None:
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|---|
| | | data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
|
|---|
| | | data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
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|---|
| | | assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
|
|---|
| | | var_dict_torch[
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|---|
| | | name].size(),
|
|---|
| | | data_tf.size())
|
|---|
| | | var_dict_torch_update[name] = data_tf
|
|---|
| | | logging.info(
|
|---|
| | | "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
|
|---|
| | | var_dict_tf[name_tf].shape))
|
|---|
| | | @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)
|
|---|
| | |
|
|---|
| | | return var_dict_torch_update
|
|---|
| | | # 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):
|
|---|
