import torch from torch import nn from torch import Tensor import logging import numpy as np from funasr.torch_utils.device_funcs import to_device from funasr.modules.nets_utils import make_pad_mask from funasr.modules.streaming_utils.utils import sequence_mask from typing import Optional, Tuple class CifPredictor(nn.Module): 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): super(CifPredictor, self).__init__() self.pad = nn.ConstantPad1d((l_order, r_order), 0) self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim) self.cif_output = nn.Linear(idim, 1) self.dropout = torch.nn.Dropout(p=dropout) self.threshold = threshold self.smooth_factor = smooth_factor self.noise_threshold = noise_threshold self.tail_threshold = tail_threshold def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, target_label_length=None): h = hidden context = h.transpose(1, 2) queries = self.pad(context) memory = self.cif_conv1d(queries) output = memory + context output = self.dropout(output) output = output.transpose(1, 2) output = torch.relu(output) output = self.cif_output(output) alphas = torch.sigmoid(output) alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold) if mask is not None: mask = mask.transpose(-1, -2).float() alphas = alphas * mask if mask_chunk_predictor is not None: alphas = alphas * mask_chunk_predictor alphas = alphas.squeeze(-1) mask = mask.squeeze(-1) if target_label_length is not None: target_length = target_label_length elif target_label is not None: target_length = (target_label != ignore_id).float().sum(-1) else: target_length = None token_num = alphas.sum(-1) if target_length is not None: alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) elif self.tail_threshold > 0.0: hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask) acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) if target_length is None and self.tail_threshold > 0.0: token_num_int = torch.max(token_num).type(torch.int32).item() acoustic_embeds = acoustic_embeds[:, :token_num_int, :] return acoustic_embeds, token_num, alphas, cif_peak def tail_process_fn(self, hidden, alphas, token_num=None, mask=None): b, t, d = hidden.size() tail_threshold = self.tail_threshold if mask is not None: 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) else: tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device) tail_threshold = torch.reshape(tail_threshold, (1, 1)) alphas = torch.cat([alphas, tail_threshold], dim=1) 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 def gen_frame_alignments(self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None): batch_size, maximum_length = alphas.size() int_type = torch.int32 is_training = self.training if is_training: token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) else: token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) max_token_num = torch.max(token_num).item() alphas_cumsum = torch.cumsum(alphas, dim=1) alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) index = torch.ones([batch_size, max_token_num], dtype=int_type) index = torch.cumsum(index, dim=1) index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) index_div_bool_zeros = index_div.eq(0) index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 index_div_bool_zeros_count = torch.clamp(index_div_bool_zeros_count, 0, encoder_sequence_length.max()) token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device) index_div_bool_zeros_count *= token_num_mask index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(1, 1, maximum_length) ones = torch.ones_like(index_div_bool_zeros_count_tile) zeros = torch.zeros_like(index_div_bool_zeros_count_tile) ones = torch.cumsum(ones, dim=2) cond = index_div_bool_zeros_count_tile == ones index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool) index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type) index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1) index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type) predictor_mask = (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max())).type( int_type).to(encoder_sequence_length.device) index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask predictor_alignments = index_div_bool_zeros_count_tile_out predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype) return predictor_alignments.detach(), predictor_alignments_length.detach() class CifPredictorV2(nn.Module): def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, tail_threshold=0.0, tf2torch_tensor_name_prefix_torch="predictor", tf2torch_tensor_name_prefix_tf="seq2seq/cif", tail_mask=True, ): super(CifPredictorV2, self).__init__() self.pad = nn.ConstantPad1d((l_order, r_order), 0) self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1) self.cif_output = nn.Linear(idim, 1) self.dropout = torch.nn.Dropout(p=dropout) self.threshold = threshold self.smooth_factor = smooth_factor self.noise_threshold = noise_threshold self.tail_threshold = tail_threshold self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf self.tail_mask = tail_mask def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, target_label_length=None): 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) if mask is not None: mask = mask.transpose(-1, -2).float() alphas = alphas * mask if mask_chunk_predictor is not None: alphas = alphas * mask_chunk_predictor alphas = alphas.squeeze(-1) mask = mask.squeeze(-1) if target_label_length is not None: target_length = target_label_length elif target_label is not None: target_length = (target_label != ignore_id).float().sum(-1) else: target_length = None token_num = alphas.sum(-1) if target_length is not None: alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) elif self.tail_threshold > 0.0: if self.tail_mask: hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask) else: hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=None) acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) if target_length is None and self.tail_threshold > 0.0: token_num_int = torch.max(token_num).type(torch.int32).item() acoustic_embeds = acoustic_embeds[:, :token_num_int, :] return acoustic_embeds, token_num, alphas, cif_peak def forward_chunk(self, hidden, cache=None): batch_size, len_time, hidden_size = hidden.shape 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) alphas = alphas.squeeze(-1) token_length = [] list_fires = [] list_frames = [] cache_alphas = [] cache_hiddens = [] if cache is not None and "chunk_size" in cache: alphas[:, :cache["chunk_size"][0]] = 0.0 if "is_final" in cache and not cache["is_final"]: alphas[:, sum(cache["chunk_size"][:2]):] = 0.0 if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache: cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device) cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device) hidden = torch.cat((cache["cif_hidden"], hidden), dim=1) alphas = torch.cat((cache["cif_alphas"], alphas), dim=1) if cache is not None and "is_final" in cache and cache["is_final"]: tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device) tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device) tail_alphas = torch.tile(tail_alphas, (batch_size, 1)) hidden = torch.cat((hidden, tail_hidden), dim=1) alphas = torch.cat((alphas, tail_alphas), dim=1) len_time = alphas.shape[1] for b in range(batch_size): integrate = 0.0 frames = torch.zeros((hidden_size), device=hidden.device) list_frame = [] list_fire = [] for t in range(len_time): alpha = alphas[b][t] if alpha + integrate < self.threshold: integrate += alpha list_fire.append(integrate) frames += alpha * hidden[b][t] else: frames += (self.threshold - integrate) * hidden[b][t] list_frame.append(frames) integrate += alpha list_fire.append(integrate) integrate -= self.threshold frames = integrate * hidden[b][t] cache_alphas.append(integrate) if integrate > 0.0: cache_hiddens.append(frames / integrate) else: cache_hiddens.append(frames) token_length.append(torch.tensor(len(list_frame), device=alphas.device)) list_fires.append(list_fire) list_frames.append(list_frame) cache["cif_alphas"] = torch.stack(cache_alphas, axis=0) cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0) cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0) cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0) max_token_len = max(token_length) if max_token_len == 0: return hidden, torch.stack(token_length, 0) list_ls = [] for b in range(batch_size): pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device) if token_length[b] == 0: list_ls.append(pad_frames) else: list_frames[b] = torch.stack(list_frames[b]) list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0)) cache["cif_alphas"] = torch.stack(cache_alphas, axis=0) cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0) cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0) cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0) return torch.stack(list_ls, 0), torch.stack(token_length, 0) def tail_process_fn(self, hidden, alphas, token_num=None, mask=None): b, t, d = hidden.size() tail_threshold = self.tail_threshold if mask is not None: 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) else: tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device) tail_threshold = torch.reshape(tail_threshold, (1, 1)) if b > 1: alphas = torch.cat([alphas, tail_threshold.repeat(b, 1)], dim=1) else: alphas = torch.cat([alphas, tail_threshold], dim=1) 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 def gen_frame_alignments(self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None): batch_size, maximum_length = alphas.size() int_type = torch.int32 is_training = self.training if is_training: token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) else: token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) max_token_num = torch.max(token_num).item() alphas_cumsum = torch.cumsum(alphas, dim=1) alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) index = torch.ones([batch_size, max_token_num], dtype=int_type) index = torch.cumsum(index, dim=1) index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) index_div_bool_zeros = index_div.eq(0) index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 index_div_bool_zeros_count = torch.clamp(index_div_bool_zeros_count, 0, encoder_sequence_length.max()) token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device) index_div_bool_zeros_count *= token_num_mask index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(1, 1, maximum_length) ones = torch.ones_like(index_div_bool_zeros_count_tile) zeros = torch.zeros_like(index_div_bool_zeros_count_tile) ones = torch.cumsum(ones, dim=2) cond = index_div_bool_zeros_count_tile == ones index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool) index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type) index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1) index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type) predictor_mask = (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max())).type( int_type).to(encoder_sequence_length.device) index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask predictor_alignments = index_div_bool_zeros_count_tile_out predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype) return predictor_alignments.detach(), predictor_alignments_length.detach() def gen_tf2torch_map_dict(self): tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf map_dict_local = { ## predictor "{}.cif_conv1d.weight".format(tensor_name_prefix_torch): {"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf), "squeeze": None, "transpose": (2, 1, 0), }, # (256,256,3),(3,256,256) "{}.cif_conv1d.bias".format(tensor_name_prefix_torch): {"name": "{}/conv1d/bias".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, # (256,),(256,) "{}.cif_output.weight".format(tensor_name_prefix_torch): {"name": "{}/conv1d_1/kernel".format(tensor_name_prefix_tf), "squeeze": 0, "transpose": (1, 0), }, # (1,256),(1,256,1) "{}.cif_output.bias".format(tensor_name_prefix_torch): {"name": "{}/conv1d_1/bias".format(tensor_name_prefix_tf), "squeeze": None, "transpose": None, }, # (1,),(1,) } return map_dict_local def convert_tf2torch(self, var_dict_tf, var_dict_torch, ): map_dict = self.gen_tf2torch_map_dict() var_dict_torch_update = dict() for name in sorted(var_dict_torch.keys(), reverse=False): names = name.split('.') if names[0] == self.tf2torch_tensor_name_prefix_torch: name_tf = map_dict[name]["name"] data_tf = var_dict_tf[name_tf] if map_dict[name]["squeeze"] is not None: data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"]) if map_dict[name]["transpose"] is not None: data_tf = np.transpose(data_tf, map_dict[name]["transpose"]) data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu") assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf, var_dict_torch[ 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)) return var_dict_torch_update class mae_loss(nn.Module): def __init__(self, normalize_length=False): super(mae_loss, self).__init__() self.normalize_length = normalize_length self.criterion = torch.nn.L1Loss(reduction='sum') def forward(self, token_length, pre_token_length): loss_token_normalizer = token_length.size(0) if self.normalize_length: loss_token_normalizer = token_length.sum().type(torch.float32) loss = self.criterion(token_length, pre_token_length) loss = loss / loss_token_normalizer return loss def cif(hidden, alphas, threshold): batch_size, len_time, hidden_size = hidden.size() # loop varss integrate = torch.zeros([batch_size], device=hidden.device) frame = torch.zeros([batch_size, hidden_size], 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], device=hidden.device) - integrate integrate += alpha list_fires.append(integrate) fire_place = integrate >= threshold integrate = torch.where(fire_place, integrate - torch.ones([batch_size], 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) list_ls = [] len_labels = torch.round(alphas.sum(-1)).int() max_label_len = len_labels.max() for b in range(batch_size): fire = fires[b, :] l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze()) pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device) list_ls.append(torch.cat([l, pad_l], 0)) return torch.stack(list_ls, 0), fires def cif_wo_hidden(alphas, threshold): batch_size, len_time = alphas.size() # loop varss integrate = torch.zeros([batch_size], device=alphas.device) # intermediate vars along time list_fires = [] for t in range(len_time): alpha = alphas[:, t] integrate += alpha list_fires.append(integrate) fire_place = integrate >= threshold integrate = torch.where(fire_place, integrate - torch.ones([batch_size], device=alphas.device)*threshold, integrate) fires = torch.stack(list_fires, 1) return fires class CifPredictorV3(nn.Module): def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, tail_threshold=0.0, tf2torch_tensor_name_prefix_torch="predictor", tf2torch_tensor_name_prefix_tf="seq2seq/cif", smooth_factor2=1.0, noise_threshold2=0, upsample_times=5, upsample_type="cnn", use_cif1_cnn=True, tail_mask=True, ): super(CifPredictorV3, self).__init__() self.pad = nn.ConstantPad1d((l_order, r_order), 0) self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1) self.cif_output = nn.Linear(idim, 1) self.dropout = torch.nn.Dropout(p=dropout) self.threshold = threshold self.smooth_factor = smooth_factor self.noise_threshold = noise_threshold self.tail_threshold = tail_threshold self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf self.upsample_times = upsample_times self.upsample_type = upsample_type self.use_cif1_cnn = use_cif1_cnn if self.upsample_type == 'cnn': self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times) self.cif_output2 = nn.Linear(idim, 1) elif self.upsample_type == 'cnn_blstm': self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times) self.blstm = nn.LSTM(idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True) self.cif_output2 = nn.Linear(idim*2, 1) elif self.upsample_type == 'cnn_attn': self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times) from funasr.models.encoder.transformer_encoder import EncoderLayer as TransformerEncoderLayer from funasr.modules.attention import MultiHeadedAttention from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward positionwise_layer_args = ( idim, idim*2, 0.1, ) self.self_attn = TransformerEncoderLayer( idim, MultiHeadedAttention( 4, idim, 0.1 ), PositionwiseFeedForward(*positionwise_layer_args), 0.1, True, #normalize_before, False, #concat_after, ) self.cif_output2 = nn.Linear(idim, 1) self.smooth_factor2 = smooth_factor2 self.noise_threshold2 = noise_threshold2 def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, target_label_length=None): h = hidden context = h.transpose(1, 2) queries = self.pad(context) output = torch.relu(self.cif_conv1d(queries)) # alphas2 is an extra head for timestamp prediction if not self.use_cif1_cnn: _output = context else: _output = output if self.upsample_type == 'cnn': output2 = self.upsample_cnn(_output) output2 = output2.transpose(1,2) elif self.upsample_type == 'cnn_blstm': output2 = self.upsample_cnn(_output) output2 = output2.transpose(1,2) output2, (_, _) = self.blstm(output2) elif self.upsample_type == 'cnn_attn': output2 = self.upsample_cnn(_output) output2 = output2.transpose(1,2) output2, _ = self.self_attn(output2, mask) # import pdb; pdb.set_trace() alphas2 = torch.sigmoid(self.cif_output2(output2)) alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2) # repeat the mask in T demension to match the upsampled length if mask is not None: mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1) mask2 = mask2.unsqueeze(-1) alphas2 = alphas2 * mask2 alphas2 = alphas2.squeeze(-1) token_num2 = alphas2.sum(-1) 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) if mask is not None: mask = mask.transpose(-1, -2).float() alphas = alphas * mask if mask_chunk_predictor is not None: alphas = alphas * mask_chunk_predictor alphas = alphas.squeeze(-1) mask = mask.squeeze(-1) if target_label_length is not None: target_length = target_label_length elif target_label is not None: target_length = (target_label != ignore_id).float().sum(-1) else: target_length = None token_num = alphas.sum(-1) if target_length is not None: alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) elif self.tail_threshold > 0.0: hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask) acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) if target_length is None and self.tail_threshold > 0.0: token_num_int = torch.max(token_num).type(torch.int32).item() acoustic_embeds = acoustic_embeds[:, :token_num_int, :] return acoustic_embeds, token_num, alphas, cif_peak, token_num2 def get_upsample_timestamp(self, hidden, mask=None, token_num=None): h = hidden b = hidden.shape[0] context = h.transpose(1, 2) queries = self.pad(context) output = torch.relu(self.cif_conv1d(queries)) # alphas2 is an extra head for timestamp prediction if not self.use_cif1_cnn: _output = context else: _output = output if self.upsample_type == 'cnn': output2 = self.upsample_cnn(_output) output2 = output2.transpose(1,2) elif self.upsample_type == 'cnn_blstm': output2 = self.upsample_cnn(_output) output2 = output2.transpose(1,2) output2, (_, _) = self.blstm(output2) elif self.upsample_type == 'cnn_attn': output2 = self.upsample_cnn(_output) output2 = output2.transpose(1,2) output2, _ = self.self_attn(output2, mask) alphas2 = torch.sigmoid(self.cif_output2(output2)) alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2) # repeat the mask in T demension to match the upsampled length if mask is not None: mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1) mask2 = mask2.unsqueeze(-1) alphas2 = alphas2 * mask2 alphas2 = alphas2.squeeze(-1) _token_num = alphas2.sum(-1) if token_num is not None: alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1)) # re-downsample ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1) ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4) # upsampled alphas and cif_peak us_alphas = alphas2 us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4) return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak def tail_process_fn(self, hidden, alphas, token_num=None, mask=None): b, t, d = hidden.size() tail_threshold = self.tail_threshold if mask is not None: 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) else: tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device) tail_threshold = torch.reshape(tail_threshold, (1, 1)) alphas = torch.cat([alphas, tail_threshold], dim=1) 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 def gen_frame_alignments(self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None): batch_size, maximum_length = alphas.size() int_type = torch.int32 is_training = self.training if is_training: token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) else: token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) max_token_num = torch.max(token_num).item() alphas_cumsum = torch.cumsum(alphas, dim=1) alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) index = torch.ones([batch_size, max_token_num], dtype=int_type) index = torch.cumsum(index, dim=1) index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) index_div_bool_zeros = index_div.eq(0) index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 index_div_bool_zeros_count = torch.clamp(index_div_bool_zeros_count, 0, encoder_sequence_length.max()) token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device) index_div_bool_zeros_count *= token_num_mask index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(1, 1, maximum_length) ones = torch.ones_like(index_div_bool_zeros_count_tile) zeros = torch.zeros_like(index_div_bool_zeros_count_tile) ones = torch.cumsum(ones, dim=2) cond = index_div_bool_zeros_count_tile == ones index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool) index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type) index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1) index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type) predictor_mask = (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max())).type( int_type).to(encoder_sequence_length.device) index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask predictor_alignments = index_div_bool_zeros_count_tile_out predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype) return predictor_alignments.detach(), predictor_alignments_length.detach() class BATPredictor(nn.Module): def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, return_accum=False): super(BATPredictor, self).__init__() self.pad = nn.ConstantPad1d((l_order, r_order), 0) self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim) self.cif_output = nn.Linear(idim, 1) self.dropout = torch.nn.Dropout(p=dropout) self.threshold = threshold self.smooth_factor = smooth_factor self.noise_threshold = noise_threshold self.return_accum = return_accum def cif( self, input: Tensor, alpha: Tensor, beta: float = 1.0, return_accum: bool = False, ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: B, S, C = input.size() assert tuple(alpha.size()) == (B, S), f"{alpha.size()} != {(B, S)}" dtype = alpha.dtype alpha = alpha.float() alpha_sum = alpha.sum(1) feat_lengths = (alpha_sum / beta).floor().long() T = feat_lengths.max() # aggregate and integrate csum = alpha.cumsum(-1) with torch.no_grad(): # indices used for scattering right_idx = (csum / beta).floor().long().clip(max=T) left_idx = right_idx.roll(1, dims=1) left_idx[:, 0] = 0 # count # of fires from each source fire_num = right_idx - left_idx extra_weights = (fire_num - 1).clip(min=0) # The extra entry in last dim is for output = input.new_zeros((B, T + 1, C)) source_range = torch.arange(1, 1 + S).unsqueeze(0).type_as(input) zero = alpha.new_zeros((1,)) # right scatter fire_mask = fire_num > 0 right_weight = torch.where( fire_mask, csum - right_idx.type_as(alpha) * beta, zero ).type_as(input) # assert right_weight.ge(0).all(), f"{right_weight} should be non-negative." output.scatter_add_( 1, right_idx.unsqueeze(-1).expand(-1, -1, C), right_weight.unsqueeze(-1) * input ) # left scatter left_weight = ( alpha - right_weight - extra_weights.type_as(alpha) * beta ).type_as(input) output.scatter_add_( 1, left_idx.unsqueeze(-1).expand(-1, -1, C), left_weight.unsqueeze(-1) * input ) # extra scatters if extra_weights.ge(0).any(): extra_steps = extra_weights.max().item() tgt_idx = left_idx src_feats = input * beta for _ in range(extra_steps): tgt_idx = (tgt_idx + 1).clip(max=T) # (B, S, 1) src_mask = (extra_weights > 0) output.scatter_add_( 1, tgt_idx.unsqueeze(-1).expand(-1, -1, C), src_feats * src_mask.unsqueeze(2) ) extra_weights -= 1 output = output[:, :T, :] if return_accum: return output, csum else: return output, alpha def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None, target_label_length=None): h = hidden context = h.transpose(1, 2) queries = self.pad(context) memory = self.cif_conv1d(queries) output = memory + context output = self.dropout(output) output = output.transpose(1, 2) output = torch.relu(output) output = self.cif_output(output) alphas = torch.sigmoid(output) alphas = torch.nn.functional.relu(alphas*self.smooth_factor - self.noise_threshold) if mask is not None: alphas = alphas * mask.transpose(-1, -2).float() if mask_chunk_predictor is not None: alphas = alphas * mask_chunk_predictor alphas = alphas.squeeze(-1) if target_label_length is not None: target_length = target_label_length elif target_label is not None: target_length = (target_label != ignore_id).float().sum(-1) # logging.info("target_length: {}".format(target_length)) else: target_length = None token_num = alphas.sum(-1) if target_length is not None: # length_noise = torch.rand(alphas.size(0), device=alphas.device) - 0.5 # target_length = length_noise + target_length alphas *= ((target_length + 1e-4) / token_num)[:, None].repeat(1, alphas.size(1)) acoustic_embeds, cif_peak = self.cif(hidden, alphas, self.threshold, self.return_accum) return acoustic_embeds, token_num, alphas, cif_peak