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| | | import torch
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| | | from torch import nn
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| | | from torch import Tensor
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| | | import logging
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| | | import numpy as np
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| | | from funasr.torch_utils.device_funcs import to_device
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| | | from funasr.modules.nets_utils import make_pad_mask
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| | | from funasr.modules.streaming_utils.utils import sequence_mask
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| | | from typing import Optional, Tuple
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| | |
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| | | class CifPredictor(nn.Module):
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| | | def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, tail_threshold=0.45):
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| | | acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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| | | return acoustic_embeds, token_num, alphas, cif_peak
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| | |
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| | | def forward_chunk(self, hidden, cache=None):
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| | | batch_size, len_time, hidden_size = hidden.shape
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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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| | |
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| | | alphas = alphas.squeeze(-1)
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| | |
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| | | token_length = []
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| | | list_fires = []
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| | | list_frames = []
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| | | cache_alphas = []
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| | | cache_hiddens = []
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| | |
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| | | if cache is not None and "chunk_size" in cache:
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| | | alphas[:, :cache["chunk_size"][0]] = 0.0
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| | | if "is_final" in cache and not cache["is_final"]:
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| | | alphas[:, sum(cache["chunk_size"][:2]):] = 0.0
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| | | if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
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| | | cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
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| | | cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
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| | | hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
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| | | alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
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| | | if cache is not None and "is_final" in cache and cache["is_final"]:
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| | | tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
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| | | tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
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| | | tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
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| | | hidden = torch.cat((hidden, tail_hidden), dim=1)
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| | | alphas = torch.cat((alphas, tail_alphas), dim=1)
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| | |
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| | | len_time = alphas.shape[1]
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| | | for b in range(batch_size):
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| | | integrate = 0.0
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| | | frames = torch.zeros((hidden_size), device=hidden.device)
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| | | list_frame = []
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| | | list_fire = []
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| | | for t in range(len_time):
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| | | alpha = alphas[b][t]
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| | | if alpha + integrate < self.threshold:
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| | | integrate += alpha
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| | | list_fire.append(integrate)
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| | | frames += alpha * hidden[b][t]
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| | | else:
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| | | frames += (self.threshold - integrate) * hidden[b][t]
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| | | list_frame.append(frames)
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| | | integrate += alpha
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| | | list_fire.append(integrate)
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| | | integrate -= self.threshold
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| | | frames = integrate * hidden[b][t]
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| | |
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| | | cache_alphas.append(integrate)
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| | | if integrate > 0.0:
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| | | cache_hiddens.append(frames / integrate)
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| | | else:
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| | | cache_hiddens.append(frames)
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| | |
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| | | token_length.append(torch.tensor(len(list_frame), device=alphas.device))
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| | | list_fires.append(list_fire)
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| | | list_frames.append(list_frame)
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| | |
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| | | cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
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| | | cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
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| | | cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
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| | | cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
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| | |
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| | | max_token_len = max(token_length)
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| | | if max_token_len == 0:
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| | | return hidden, torch.stack(token_length, 0)
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| | | list_ls = []
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| | | for b in range(batch_size):
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| | | pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)
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| | | if token_length[b] == 0:
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| | | list_ls.append(pad_frames)
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| | | else:
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| | | list_frames[b] = torch.stack(list_frames[b])
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| | | list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0))
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| | |
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| | | cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
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| | | cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
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| | | cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
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| | | cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
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| | | return torch.stack(list_ls, 0), torch.stack(token_length, 0)
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| | |
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| | |
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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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| | | predictor_alignments = index_div_bool_zeros_count_tile_out
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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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| | | class BATPredictor(nn.Module):
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| | | def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, return_accum=False):
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| | | super(BATPredictor, self).__init__()
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| | |
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| | | self.pad = nn.ConstantPad1d((l_order, r_order), 0)
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| | | self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
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| | | self.cif_output = nn.Linear(idim, 1)
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| | | self.dropout = torch.nn.Dropout(p=dropout)
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| | | self.threshold = threshold
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| | | self.smooth_factor = smooth_factor
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| | | self.noise_threshold = noise_threshold
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| | | self.return_accum = return_accum
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| | |
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| | | def cif(
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| | | self,
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| | | input: Tensor,
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| | | alpha: Tensor,
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| | | beta: float = 1.0,
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| | | return_accum: bool = False,
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| | | ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
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| | | B, S, C = input.size()
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| | | assert tuple(alpha.size()) == (B, S), f"{alpha.size()} != {(B, S)}"
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| | |
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| | | dtype = alpha.dtype
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| | | alpha = alpha.float()
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| | |
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| | | alpha_sum = alpha.sum(1)
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| | | feat_lengths = (alpha_sum / beta).floor().long()
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| | | T = feat_lengths.max()
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| | |
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| | | # aggregate and integrate
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| | | csum = alpha.cumsum(-1)
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| | | with torch.no_grad():
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| | | # indices used for scattering
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| | | right_idx = (csum / beta).floor().long().clip(max=T)
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| | | left_idx = right_idx.roll(1, dims=1)
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| | | left_idx[:, 0] = 0
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| | |
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| | | # count # of fires from each source
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| | | fire_num = right_idx - left_idx
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| | | extra_weights = (fire_num - 1).clip(min=0)
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| | | # The extra entry in last dim is for
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| | | output = input.new_zeros((B, T + 1, C))
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| | | source_range = torch.arange(1, 1 + S).unsqueeze(0).type_as(input)
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| | | zero = alpha.new_zeros((1,))
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| | |
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| | | # right scatter
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| | | fire_mask = fire_num > 0
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| | | right_weight = torch.where(
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| | | fire_mask,
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| | | csum - right_idx.type_as(alpha) * beta,
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| | | zero
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| | | ).type_as(input)
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| | | # assert right_weight.ge(0).all(), f"{right_weight} should be non-negative."
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| | | output.scatter_add_(
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| | | 1,
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| | | right_idx.unsqueeze(-1).expand(-1, -1, C),
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| | | right_weight.unsqueeze(-1) * input
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| | | )
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| | |
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| | | # left scatter
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| | | left_weight = (
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| | | alpha - right_weight - extra_weights.type_as(alpha) * beta
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| | | ).type_as(input)
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| | | output.scatter_add_(
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| | | 1,
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| | | left_idx.unsqueeze(-1).expand(-1, -1, C),
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| | | left_weight.unsqueeze(-1) * input
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| | | )
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| | |
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| | | # extra scatters
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| | | if extra_weights.ge(0).any():
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| | | extra_steps = extra_weights.max().item()
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| | | tgt_idx = left_idx
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| | | src_feats = input * beta
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| | | for _ in range(extra_steps):
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| | | tgt_idx = (tgt_idx + 1).clip(max=T)
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| | | # (B, S, 1)
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| | | src_mask = (extra_weights > 0)
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| | | output.scatter_add_(
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| | | 1,
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| | | tgt_idx.unsqueeze(-1).expand(-1, -1, C),
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| | | src_feats * src_mask.unsqueeze(2)
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| | | )
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| | | extra_weights -= 1
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| | |
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| | | output = output[:, :T, :]
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| | |
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| | | if return_accum:
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| | | return output, csum
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| | | else:
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| | | return output, alpha
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| | |
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| | | def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, 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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| | | alphas = alphas * mask.transpose(-1, -2).float()
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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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| | | 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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| | | # logging.info("target_length: {}".format(target_length))
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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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| | | # length_noise = torch.rand(alphas.size(0), device=alphas.device) - 0.5
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| | | # target_length = length_noise + target_length
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| | | alphas *= ((target_length + 1e-4) / token_num)[:, None].repeat(1, alphas.size(1))
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| | | acoustic_embeds, cif_peak = self.cif(hidden, alphas, self.threshold, self.return_accum)
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| | | return acoustic_embeds, token_num, alphas, cif_peak
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