#!/usr/bin/env python3 # -*- encoding: utf-8 -*- # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. # MIT License (https://opensource.org/licenses/MIT) import torch import logging import numpy as np from funasr.register import tables from funasr.train_utils.device_funcs import to_device from funasr.models.transformer.utils.nets_utils import make_pad_mask from torch.cuda.amp import autocast @tables.register("predictor_classes", "CifPredictor") class CifPredictor(torch.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().__init__() self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0) self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim) self.cif_output = torch.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, ): with autocast(False): 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() @tables.register("predictor_classes", "CifPredictorV2") class CifPredictorV2(torch.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().__init__() self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0) self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1) self.cif_output = torch.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, ): with autocast(False): 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.squeeze(-1) 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, **kwargs): is_final = kwargs.get("is_final", False) 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 not 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: 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), None, None 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), None, None 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() @tables.register("predictor_classes", "CifPredictorV2Export") class CifPredictorV2Export(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): 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