import math
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import torch
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import numpy as np
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import torch.nn.functional as F
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from funasr.models.scama.utils import sequence_mask
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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|
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class overlap_chunk:
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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San-m: Memory equipped self-attention for end-to-end speech recognition
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https://arxiv.org/abs/2006.01713
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"""
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def __init__(
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self,
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chunk_size: tuple = (16,),
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stride: tuple = (10,),
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pad_left: tuple = (0,),
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encoder_att_look_back_factor: tuple = (1,),
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shfit_fsmn: int = 0,
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decoder_att_look_back_factor: tuple = (1,),
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):
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pad_left = self.check_chunk_size_args(chunk_size, pad_left)
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encoder_att_look_back_factor = self.check_chunk_size_args(
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chunk_size, encoder_att_look_back_factor
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)
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decoder_att_look_back_factor = self.check_chunk_size_args(
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chunk_size, decoder_att_look_back_factor
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)
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(
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self.chunk_size,
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self.stride,
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self.pad_left,
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self.encoder_att_look_back_factor,
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self.decoder_att_look_back_factor,
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) = (
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chunk_size,
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stride,
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pad_left,
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encoder_att_look_back_factor,
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decoder_att_look_back_factor,
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)
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self.shfit_fsmn = shfit_fsmn
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self.x_add_mask = None
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self.x_rm_mask = None
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self.x_len = None
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self.mask_shfit_chunk = None
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self.mask_chunk_predictor = None
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self.mask_att_chunk_encoder = None
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self.mask_shift_att_chunk_decoder = None
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self.chunk_outs = None
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(
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self.chunk_size_cur,
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self.stride_cur,
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self.pad_left_cur,
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self.encoder_att_look_back_factor_cur,
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self.chunk_size_pad_shift_cur,
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) = (None, None, None, None, None)
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def check_chunk_size_args(self, chunk_size, x):
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if len(x) < len(chunk_size):
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x = [x[0] for i in chunk_size]
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return x
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def get_chunk_size(self, ind: int = 0):
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# with torch.no_grad:
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chunk_size, stride, pad_left, encoder_att_look_back_factor, decoder_att_look_back_factor = (
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self.chunk_size[ind],
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self.stride[ind],
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self.pad_left[ind],
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self.encoder_att_look_back_factor[ind],
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self.decoder_att_look_back_factor[ind],
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)
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(
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self.chunk_size_cur,
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self.stride_cur,
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self.pad_left_cur,
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self.encoder_att_look_back_factor_cur,
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self.chunk_size_pad_shift_cur,
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self.decoder_att_look_back_factor_cur,
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) = (
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chunk_size,
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stride,
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pad_left,
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encoder_att_look_back_factor,
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chunk_size + self.shfit_fsmn,
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decoder_att_look_back_factor,
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)
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return (
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self.chunk_size_cur,
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self.stride_cur,
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self.pad_left_cur,
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self.encoder_att_look_back_factor_cur,
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self.chunk_size_pad_shift_cur,
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)
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def random_choice(self, training=True, decoding_ind=None):
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chunk_num = len(self.chunk_size)
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ind = 0
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if training and chunk_num > 1:
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ind = torch.randint(0, chunk_num, ()).cpu().item()
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if not training and decoding_ind is not None:
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ind = int(decoding_ind)
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return ind
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def gen_chunk_mask(self, x_len, ind=0, num_units=1, num_units_predictor=1):
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with torch.no_grad():
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x_len = x_len.cpu().numpy()
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x_len_max = x_len.max()
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chunk_size, stride, pad_left, encoder_att_look_back_factor, chunk_size_pad_shift = (
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self.get_chunk_size(ind)
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)
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shfit_fsmn = self.shfit_fsmn
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pad_right = chunk_size - stride - pad_left
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chunk_num_batch = np.ceil(x_len / stride).astype(np.int32)
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x_len_chunk = (
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(chunk_num_batch - 1) * chunk_size_pad_shift
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+ shfit_fsmn
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+ pad_left
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+ 0
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+ x_len
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- (chunk_num_batch - 1) * stride
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)
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x_len_chunk = x_len_chunk.astype(x_len.dtype)
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x_len_chunk_max = x_len_chunk.max()
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chunk_num = int(math.ceil(x_len_max / stride))
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dtype = np.int32
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max_len_for_x_mask_tmp = max(chunk_size, x_len_max + pad_left)
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x_add_mask = np.zeros([0, max_len_for_x_mask_tmp], dtype=dtype)
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x_rm_mask = np.zeros([max_len_for_x_mask_tmp, 0], dtype=dtype)
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mask_shfit_chunk = np.zeros([0, num_units], dtype=dtype)
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mask_chunk_predictor = np.zeros([0, num_units_predictor], dtype=dtype)
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mask_shift_att_chunk_decoder = np.zeros([0, 1], dtype=dtype)
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mask_att_chunk_encoder = np.zeros([0, chunk_num * chunk_size_pad_shift], dtype=dtype)
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for chunk_ids in range(chunk_num):
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# x_mask add
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fsmn_padding = np.zeros((shfit_fsmn, max_len_for_x_mask_tmp), dtype=dtype)
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x_mask_cur = np.diag(np.ones(chunk_size, dtype=np.float32))
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x_mask_pad_left = np.zeros((chunk_size, chunk_ids * stride), dtype=dtype)
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x_mask_pad_right = np.zeros((chunk_size, max_len_for_x_mask_tmp), dtype=dtype)
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x_cur_pad = np.concatenate([x_mask_pad_left, x_mask_cur, x_mask_pad_right], axis=1)
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x_cur_pad = x_cur_pad[:chunk_size, :max_len_for_x_mask_tmp]
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x_add_mask_fsmn = np.concatenate([fsmn_padding, x_cur_pad], axis=0)
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x_add_mask = np.concatenate([x_add_mask, x_add_mask_fsmn], axis=0)
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# x_mask rm
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fsmn_padding = np.zeros((max_len_for_x_mask_tmp, shfit_fsmn), dtype=dtype)
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padding_mask_left = np.zeros((max_len_for_x_mask_tmp, pad_left), dtype=dtype)
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padding_mask_right = np.zeros((max_len_for_x_mask_tmp, pad_right), dtype=dtype)
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x_mask_cur = np.diag(np.ones(stride, dtype=dtype))
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x_mask_cur_pad_top = np.zeros((chunk_ids * stride, stride), dtype=dtype)
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x_mask_cur_pad_bottom = np.zeros((max_len_for_x_mask_tmp, stride), dtype=dtype)
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x_rm_mask_cur = np.concatenate(
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[x_mask_cur_pad_top, x_mask_cur, x_mask_cur_pad_bottom], axis=0
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)
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x_rm_mask_cur = x_rm_mask_cur[:max_len_for_x_mask_tmp, :stride]
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x_rm_mask_cur_fsmn = np.concatenate(
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[fsmn_padding, padding_mask_left, x_rm_mask_cur, padding_mask_right], axis=1
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)
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x_rm_mask = np.concatenate([x_rm_mask, x_rm_mask_cur_fsmn], axis=1)
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# fsmn_padding_mask
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pad_shfit_mask = np.zeros([shfit_fsmn, num_units], dtype=dtype)
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ones_1 = np.ones([chunk_size, num_units], dtype=dtype)
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mask_shfit_chunk_cur = np.concatenate([pad_shfit_mask, ones_1], axis=0)
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mask_shfit_chunk = np.concatenate([mask_shfit_chunk, mask_shfit_chunk_cur], axis=0)
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# predictor mask
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zeros_1 = np.zeros([shfit_fsmn + pad_left, num_units_predictor], dtype=dtype)
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ones_2 = np.ones([stride, num_units_predictor], dtype=dtype)
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zeros_3 = np.zeros(
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[chunk_size - stride - pad_left, num_units_predictor], dtype=dtype
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)
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ones_zeros = np.concatenate([ones_2, zeros_3], axis=0)
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mask_chunk_predictor_cur = np.concatenate([zeros_1, ones_zeros], axis=0)
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mask_chunk_predictor = np.concatenate(
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[mask_chunk_predictor, mask_chunk_predictor_cur], axis=0
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)
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# encoder att mask
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zeros_1_top = np.zeros([shfit_fsmn, chunk_num * chunk_size_pad_shift], dtype=dtype)
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zeros_2_num = max(chunk_ids - encoder_att_look_back_factor, 0)
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zeros_2 = np.zeros([chunk_size, zeros_2_num * chunk_size_pad_shift], dtype=dtype)
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encoder_att_look_back_num = max(chunk_ids - zeros_2_num, 0)
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zeros_2_left = np.zeros([chunk_size, shfit_fsmn], dtype=dtype)
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ones_2_mid = np.ones([stride, stride], dtype=dtype)
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zeros_2_bottom = np.zeros([chunk_size - stride, stride], dtype=dtype)
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zeros_2_right = np.zeros([chunk_size, chunk_size - stride], dtype=dtype)
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ones_2 = np.concatenate([ones_2_mid, zeros_2_bottom], axis=0)
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ones_2 = np.concatenate([zeros_2_left, ones_2, zeros_2_right], axis=1)
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ones_2 = np.tile(ones_2, [1, encoder_att_look_back_num])
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zeros_3_left = np.zeros([chunk_size, shfit_fsmn], dtype=dtype)
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ones_3_right = np.ones([chunk_size, chunk_size], dtype=dtype)
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ones_3 = np.concatenate([zeros_3_left, ones_3_right], axis=1)
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zeros_remain_num = max(chunk_num - 1 - chunk_ids, 0)
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zeros_remain = np.zeros(
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[chunk_size, zeros_remain_num * chunk_size_pad_shift], dtype=dtype
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)
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ones2_bottom = np.concatenate([zeros_2, ones_2, ones_3, zeros_remain], axis=1)
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mask_att_chunk_encoder_cur = np.concatenate([zeros_1_top, ones2_bottom], axis=0)
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mask_att_chunk_encoder = np.concatenate(
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[mask_att_chunk_encoder, mask_att_chunk_encoder_cur], axis=0
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)
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# decoder fsmn_shift_att_mask
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zeros_1 = np.zeros([shfit_fsmn, 1])
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ones_1 = np.ones([chunk_size, 1])
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mask_shift_att_chunk_decoder_cur = np.concatenate([zeros_1, ones_1], axis=0)
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mask_shift_att_chunk_decoder = np.concatenate(
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[mask_shift_att_chunk_decoder, mask_shift_att_chunk_decoder_cur], axis=0
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)
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self.x_add_mask = x_add_mask[:x_len_chunk_max, : x_len_max + pad_left]
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self.x_len_chunk = x_len_chunk
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self.x_rm_mask = x_rm_mask[:x_len_max, :x_len_chunk_max]
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self.x_len = x_len
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self.mask_shfit_chunk = mask_shfit_chunk[:x_len_chunk_max, :]
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self.mask_chunk_predictor = mask_chunk_predictor[:x_len_chunk_max, :]
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self.mask_att_chunk_encoder = mask_att_chunk_encoder[:x_len_chunk_max, :x_len_chunk_max]
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self.mask_shift_att_chunk_decoder = mask_shift_att_chunk_decoder[:x_len_chunk_max, :]
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self.chunk_outs = (
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self.x_add_mask,
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self.x_len_chunk,
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self.x_rm_mask,
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self.x_len,
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self.mask_shfit_chunk,
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self.mask_chunk_predictor,
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self.mask_att_chunk_encoder,
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self.mask_shift_att_chunk_decoder,
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)
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return self.chunk_outs
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def split_chunk(self, x, x_len, chunk_outs):
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"""
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:param x: (b, t, d)
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:param x_length: (b)
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:param ind: int
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:return:
|
"""
|
x = x[:, : x_len.max(), :]
|
b, t, d = x.size()
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x_len_mask = (~make_pad_mask(x_len, maxlen=t)).to(x.device)
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x *= x_len_mask[:, :, None]
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x_add_mask = self.get_x_add_mask(chunk_outs, x.device, dtype=x.dtype)
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x_len_chunk = self.get_x_len_chunk(chunk_outs, x_len.device, dtype=x_len.dtype)
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pad = (0, 0, self.pad_left_cur, 0)
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x = F.pad(x, pad, "constant", 0.0)
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b, t, d = x.size()
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x = torch.transpose(x, 1, 0)
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x = torch.reshape(x, [t, -1])
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x_chunk = torch.mm(x_add_mask, x)
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x_chunk = torch.reshape(x_chunk, [-1, b, d]).transpose(1, 0)
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return x_chunk, x_len_chunk
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def remove_chunk(self, x_chunk, x_len_chunk, chunk_outs):
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x_chunk = x_chunk[:, : x_len_chunk.max(), :]
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b, t, d = x_chunk.size()
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x_len_chunk_mask = (~make_pad_mask(x_len_chunk, maxlen=t)).to(x_chunk.device)
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x_chunk *= x_len_chunk_mask[:, :, None]
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x_rm_mask = self.get_x_rm_mask(chunk_outs, x_chunk.device, dtype=x_chunk.dtype)
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x_len = self.get_x_len(chunk_outs, x_len_chunk.device, dtype=x_len_chunk.dtype)
|
x_chunk = torch.transpose(x_chunk, 1, 0)
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x_chunk = torch.reshape(x_chunk, [t, -1])
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x = torch.mm(x_rm_mask, x_chunk)
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x = torch.reshape(x, [-1, b, d]).transpose(1, 0)
|
|
return x, x_len
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def get_x_add_mask(self, chunk_outs=None, device="cpu", idx=0, dtype=torch.float32):
|
with torch.no_grad():
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x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
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x = torch.from_numpy(x).type(dtype).to(device)
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return x
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def get_x_len_chunk(self, chunk_outs=None, device="cpu", idx=1, dtype=torch.float32):
|
with torch.no_grad():
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x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
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x = torch.from_numpy(x).type(dtype).to(device)
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return x
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def get_x_rm_mask(self, chunk_outs=None, device="cpu", idx=2, dtype=torch.float32):
|
with torch.no_grad():
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x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
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x = torch.from_numpy(x).type(dtype).to(device)
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return x
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def get_x_len(self, chunk_outs=None, device="cpu", idx=3, dtype=torch.float32):
|
with torch.no_grad():
|
x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
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x = torch.from_numpy(x).type(dtype).to(device)
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return x
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def get_mask_shfit_chunk(
|
self, chunk_outs=None, device="cpu", batch_size=1, num_units=1, idx=4, dtype=torch.float32
|
):
|
with torch.no_grad():
|
x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
|
x = np.tile(
|
x[
|
None,
|
:,
|
:,
|
],
|
[batch_size, 1, num_units],
|
)
|
x = torch.from_numpy(x).type(dtype).to(device)
|
return x
|
|
def get_mask_chunk_predictor(
|
self, chunk_outs=None, device="cpu", batch_size=1, num_units=1, idx=5, dtype=torch.float32
|
):
|
with torch.no_grad():
|
x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
|
x = np.tile(
|
x[
|
None,
|
:,
|
:,
|
],
|
[batch_size, 1, num_units],
|
)
|
x = torch.from_numpy(x).type(dtype).to(device)
|
return x
|
|
def get_mask_att_chunk_encoder(
|
self, chunk_outs=None, device="cpu", batch_size=1, idx=6, dtype=torch.float32
|
):
|
with torch.no_grad():
|
x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
|
x = np.tile(
|
x[
|
None,
|
:,
|
:,
|
],
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[batch_size, 1, 1],
|
)
|
x = torch.from_numpy(x).type(dtype).to(device)
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return x
|
|
def get_mask_shift_att_chunk_decoder(
|
self, chunk_outs=None, device="cpu", batch_size=1, idx=7, dtype=torch.float32
|
):
|
with torch.no_grad():
|
x = chunk_outs[idx] if chunk_outs is not None else self.chunk_outs[idx]
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x = np.tile(x[None, None, :, 0], [batch_size, 1, 1])
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x = torch.from_numpy(x).type(dtype).to(device)
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return x
|
|
|
def build_scama_mask_for_cross_attention_decoder(
|
predictor_alignments: torch.Tensor,
|
encoder_sequence_length: torch.Tensor,
|
chunk_size: int = 5,
|
encoder_chunk_size: int = 5,
|
attention_chunk_center_bias: int = 0,
|
attention_chunk_size: int = 1,
|
attention_chunk_type: str = "chunk",
|
step=None,
|
predictor_mask_chunk_hopping: torch.Tensor = None,
|
decoder_att_look_back_factor: int = 1,
|
mask_shift_att_chunk_decoder: torch.Tensor = None,
|
target_length: torch.Tensor = None,
|
is_training=True,
|
dtype: torch.dtype = torch.float32,
|
):
|
with torch.no_grad():
|
device = predictor_alignments.device
|
batch_size, chunk_num = predictor_alignments.size()
|
maximum_encoder_length = encoder_sequence_length.max().item()
|
int_type = predictor_alignments.dtype
|
if not is_training:
|
target_length = predictor_alignments.sum(dim=-1).type(encoder_sequence_length.dtype)
|
maximum_target_length = target_length.max()
|
predictor_alignments_cumsum = torch.cumsum(predictor_alignments, dim=1)
|
predictor_alignments_cumsum = predictor_alignments_cumsum[:, None, :].repeat(
|
1, maximum_target_length, 1
|
)
|
|
index = torch.ones([batch_size, maximum_target_length], dtype=int_type).to(device)
|
index = torch.cumsum(index, dim=1)
|
index = index[:, :, None].repeat(1, 1, chunk_num)
|
|
index_div = torch.floor(torch.divide(predictor_alignments_cumsum, index)).type(int_type)
|
index_div_bool_zeros = index_div == 0
|
index_div_bool_zeros_count = torch.sum(index_div_bool_zeros.type(int_type), dim=-1) + 1
|
|
index_div_bool_zeros_count = torch.clip(index_div_bool_zeros_count, min=1, max=chunk_num)
|
|
index_div_bool_zeros_count *= chunk_size
|
index_div_bool_zeros_count += attention_chunk_center_bias
|
index_div_bool_zeros_count = torch.clip(
|
index_div_bool_zeros_count - 1, min=0, max=maximum_encoder_length
|
)
|
index_div_bool_zeros_count_ori = index_div_bool_zeros_count
|
|
index_div_bool_zeros_count = (
|
torch.floor(index_div_bool_zeros_count / encoder_chunk_size) + 1
|
) * encoder_chunk_size
|
max_len_chunk = math.ceil(maximum_encoder_length / encoder_chunk_size) * encoder_chunk_size
|
|
mask_flip, mask_flip2 = None, None
|
if attention_chunk_size is not None:
|
index_div_bool_zeros_count_beg = index_div_bool_zeros_count - attention_chunk_size
|
index_div_bool_zeros_count_beg = torch.clip(
|
index_div_bool_zeros_count_beg, 0, max_len_chunk
|
)
|
index_div_bool_zeros_count_beg_mask = sequence_mask(
|
index_div_bool_zeros_count_beg, maxlen=max_len_chunk, dtype=int_type, device=device
|
)
|
mask_flip = 1 - index_div_bool_zeros_count_beg_mask
|
attention_chunk_size2 = attention_chunk_size * (decoder_att_look_back_factor + 1)
|
index_div_bool_zeros_count_beg = index_div_bool_zeros_count - attention_chunk_size2
|
|
index_div_bool_zeros_count_beg = torch.clip(
|
index_div_bool_zeros_count_beg, 0, max_len_chunk
|
)
|
index_div_bool_zeros_count_beg_mask = sequence_mask(
|
index_div_bool_zeros_count_beg, maxlen=max_len_chunk, dtype=int_type, device=device
|
)
|
mask_flip2 = 1 - index_div_bool_zeros_count_beg_mask
|
|
mask = sequence_mask(
|
index_div_bool_zeros_count, maxlen=max_len_chunk, dtype=dtype, device=device
|
)
|
|
if predictor_mask_chunk_hopping is not None:
|
b, k, t = mask.size()
|
predictor_mask_chunk_hopping = predictor_mask_chunk_hopping[:, None, :, 0].repeat(
|
1, k, 1
|
)
|
|
mask_mask_flip = mask
|
if mask_flip is not None:
|
mask_mask_flip = mask_flip * mask
|
|
def _fn():
|
mask_sliced = mask[:b, :k, encoder_chunk_size:t]
|
zero_pad_right = torch.zeros(
|
[b, k, encoder_chunk_size], dtype=mask_sliced.dtype
|
).to(device)
|
mask_sliced = torch.cat([mask_sliced, zero_pad_right], dim=2)
|
_, _, tt = predictor_mask_chunk_hopping.size()
|
pad_right_p = max_len_chunk - tt
|
predictor_mask_chunk_hopping_pad = torch.nn.functional.pad(
|
predictor_mask_chunk_hopping, [0, pad_right_p], "constant", 0
|
)
|
masked = mask_sliced * predictor_mask_chunk_hopping_pad
|
|
mask_true = mask_mask_flip + masked
|
return mask_true
|
|
mask = _fn() if t > chunk_size else mask_mask_flip
|
|
if mask_flip2 is not None:
|
mask *= mask_flip2
|
|
mask_target = sequence_mask(
|
target_length, maxlen=maximum_target_length, dtype=mask.dtype, device=device
|
)
|
mask = mask[:, :maximum_target_length, :] * mask_target[:, :, None]
|
|
mask_len = sequence_mask(
|
encoder_sequence_length, maxlen=maximum_encoder_length, dtype=mask.dtype, device=device
|
)
|
mask = mask[:, :, :maximum_encoder_length] * mask_len[:, None, :]
|
|
if attention_chunk_type == "full":
|
mask = torch.ones_like(mask).to(device)
|
if mask_shift_att_chunk_decoder is not None:
|
mask = mask * mask_shift_att_chunk_decoder
|
mask = mask[:, :maximum_target_length, :maximum_encoder_length].type(dtype).to(device)
|
|
return mask
|
