#!/usr/bin/env python3
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# Copyright 2018 Mitsubishi Electric Research Labs (Takaaki Hori)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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import torch
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import numpy as np
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import six
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class CTCPrefixScoreTH(object):
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"""Batch processing of CTCPrefixScore
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which is based on Algorithm 2 in WATANABE et al.
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"HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
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but extended to efficiently compute the label probablities for multiple
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hypotheses simultaneously
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See also Seki et al. "Vectorized Beam Search for CTC-Attention-Based
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Speech Recognition," In INTERSPEECH (pp. 3825-3829), 2019.
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"""
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def __init__(self, x, xlens, blank, eos, margin=0):
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"""Construct CTC prefix scorer
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:param torch.Tensor x: input label posterior sequences (B, T, O)
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:param torch.Tensor xlens: input lengths (B,)
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:param int blank: blank label id
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:param int eos: end-of-sequence id
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:param int margin: margin parameter for windowing (0 means no windowing)
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"""
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# In the comment lines,
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# we assume T: input_length, B: batch size, W: beam width, O: output dim.
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self.logzero = -10000000000.0
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self.blank = blank
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self.eos = eos
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self.batch = x.size(0)
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self.input_length = x.size(1)
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self.odim = x.size(2)
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self.dtype = x.dtype
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self.device = (
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torch.device("cuda:%d" % x.get_device())
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if x.is_cuda
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else torch.device("cpu")
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)
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# Pad the rest of posteriors in the batch
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# TODO(takaaki-hori): need a better way without for-loops
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for i, l in enumerate(xlens):
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if l < self.input_length:
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x[i, l:, :] = self.logzero
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x[i, l:, blank] = 0
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# Reshape input x
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xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O)
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xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim)
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self.x = torch.stack([xn, xb]) # (2, T, B, O)
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self.end_frames = torch.as_tensor(xlens) - 1
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# Setup CTC windowing
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self.margin = margin
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if margin > 0:
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self.frame_ids = torch.arange(
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self.input_length, dtype=self.dtype, device=self.device
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)
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# Base indices for index conversion
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self.idx_bh = None
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self.idx_b = torch.arange(self.batch, device=self.device)
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self.idx_bo = (self.idx_b * self.odim).unsqueeze(1)
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def __call__(self, y, state, scoring_ids=None, att_w=None):
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"""Compute CTC prefix scores for next labels
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:param list y: prefix label sequences
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:param tuple state: previous CTC state
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:param torch.Tensor pre_scores: scores for pre-selection of hypotheses (BW, O)
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:param torch.Tensor att_w: attention weights to decide CTC window
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:return new_state, ctc_local_scores (BW, O)
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"""
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output_length = len(y[0]) - 1 # ignore sos
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last_ids = [yi[-1] for yi in y] # last output label ids
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n_bh = len(last_ids) # batch * hyps
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n_hyps = n_bh // self.batch # assuming each utterance has the same # of hyps
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self.scoring_num = scoring_ids.size(-1) if scoring_ids is not None else 0
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# prepare state info
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if state is None:
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r_prev = torch.full(
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(self.input_length, 2, self.batch, n_hyps),
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self.logzero,
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dtype=self.dtype,
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device=self.device,
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)
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r_prev[:, 1] = torch.cumsum(self.x[0, :, :, self.blank], 0).unsqueeze(2)
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r_prev = r_prev.view(-1, 2, n_bh)
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s_prev = 0.0
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f_min_prev = 0
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f_max_prev = 1
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else:
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r_prev, s_prev, f_min_prev, f_max_prev = state
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# select input dimensions for scoring
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if self.scoring_num > 0:
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scoring_idmap = torch.full(
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(n_bh, self.odim), -1, dtype=torch.long, device=self.device
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)
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snum = self.scoring_num
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if self.idx_bh is None or n_bh > len(self.idx_bh):
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self.idx_bh = torch.arange(n_bh, device=self.device).view(-1, 1)
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scoring_idmap[self.idx_bh[:n_bh], scoring_ids] = torch.arange(
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snum, device=self.device
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)
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scoring_idx = (
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scoring_ids + self.idx_bo.repeat(1, n_hyps).view(-1, 1)
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).view(-1)
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x_ = torch.index_select(
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self.x.view(2, -1, self.batch * self.odim), 2, scoring_idx
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).view(2, -1, n_bh, snum)
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else:
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scoring_ids = None
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scoring_idmap = None
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snum = self.odim
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x_ = self.x.unsqueeze(3).repeat(1, 1, 1, n_hyps, 1).view(2, -1, n_bh, snum)
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# new CTC forward probs are prepared as a (T x 2 x BW x S) tensor
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# that corresponds to r_t^n(h) and r_t^b(h) in a batch.
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r = torch.full(
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(self.input_length, 2, n_bh, snum),
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self.logzero,
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dtype=self.dtype,
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device=self.device,
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)
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if output_length == 0:
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r[0, 0] = x_[0, 0]
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r_sum = torch.logsumexp(r_prev, 1)
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log_phi = r_sum.unsqueeze(2).repeat(1, 1, snum)
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if scoring_ids is not None:
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for idx in range(n_bh):
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pos = scoring_idmap[idx, last_ids[idx]]
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if pos >= 0:
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log_phi[:, idx, pos] = r_prev[:, 1, idx]
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else:
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for idx in range(n_bh):
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log_phi[:, idx, last_ids[idx]] = r_prev[:, 1, idx]
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# decide start and end frames based on attention weights
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if att_w is not None and self.margin > 0:
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f_arg = torch.matmul(att_w, self.frame_ids)
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f_min = max(int(f_arg.min().cpu()), f_min_prev)
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f_max = max(int(f_arg.max().cpu()), f_max_prev)
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start = min(f_max_prev, max(f_min - self.margin, output_length, 1))
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end = min(f_max + self.margin, self.input_length)
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else:
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f_min = f_max = 0
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start = max(output_length, 1)
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end = self.input_length
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# compute forward probabilities log(r_t^n(h)) and log(r_t^b(h))
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for t in range(start, end):
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rp = r[t - 1]
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rr = torch.stack([rp[0], log_phi[t - 1], rp[0], rp[1]]).view(
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2, 2, n_bh, snum
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)
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r[t] = torch.logsumexp(rr, 1) + x_[:, t]
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# compute log prefix probabilities log(psi)
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log_phi_x = torch.cat((log_phi[0].unsqueeze(0), log_phi[:-1]), dim=0) + x_[0]
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if scoring_ids is not None:
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log_psi = torch.full(
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(n_bh, self.odim), self.logzero, dtype=self.dtype, device=self.device
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)
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log_psi_ = torch.logsumexp(
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torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0),
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dim=0,
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)
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for si in range(n_bh):
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log_psi[si, scoring_ids[si]] = log_psi_[si]
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else:
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log_psi = torch.logsumexp(
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torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0),
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dim=0,
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)
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for si in range(n_bh):
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log_psi[si, self.eos] = r_sum[self.end_frames[si // n_hyps], si]
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# exclude blank probs
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log_psi[:, self.blank] = self.logzero
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return (log_psi - s_prev), (r, log_psi, f_min, f_max, scoring_idmap)
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def index_select_state(self, state, best_ids):
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"""Select CTC states according to best ids
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:param state : CTC state
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:param best_ids : index numbers selected by beam pruning (B, W)
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:return selected_state
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"""
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r, s, f_min, f_max, scoring_idmap = state
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# convert ids to BHO space
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n_bh = len(s)
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n_hyps = n_bh // self.batch
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vidx = (best_ids + (self.idx_b * (n_hyps * self.odim)).view(-1, 1)).view(-1)
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# select hypothesis scores
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s_new = torch.index_select(s.view(-1), 0, vidx)
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s_new = s_new.view(-1, 1).repeat(1, self.odim).view(n_bh, self.odim)
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# convert ids to BHS space (S: scoring_num)
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if scoring_idmap is not None:
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snum = self.scoring_num
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hyp_idx = (best_ids // self.odim + (self.idx_b * n_hyps).view(-1, 1)).view(
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-1
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)
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label_ids = torch.fmod(best_ids, self.odim).view(-1)
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score_idx = scoring_idmap[hyp_idx, label_ids]
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score_idx[score_idx == -1] = 0
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vidx = score_idx + hyp_idx * snum
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else:
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snum = self.odim
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# select forward probabilities
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r_new = torch.index_select(r.view(-1, 2, n_bh * snum), 2, vidx).view(
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-1, 2, n_bh
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)
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return r_new, s_new, f_min, f_max
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def extend_prob(self, x):
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"""Extend CTC prob.
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:param torch.Tensor x: input label posterior sequences (B, T, O)
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"""
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if self.x.shape[1] < x.shape[1]: # self.x (2,T,B,O); x (B,T,O)
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# Pad the rest of posteriors in the batch
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# TODO(takaaki-hori): need a better way without for-loops
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xlens = [x.size(1)]
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for i, l in enumerate(xlens):
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if l < self.input_length:
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x[i, l:, :] = self.logzero
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x[i, l:, self.blank] = 0
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tmp_x = self.x
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xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O)
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xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim)
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self.x = torch.stack([xn, xb]) # (2, T, B, O)
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self.x[:, : tmp_x.shape[1], :, :] = tmp_x
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self.input_length = x.size(1)
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self.end_frames = torch.as_tensor(xlens) - 1
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def extend_state(self, state):
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"""Compute CTC prefix state.
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:param state : CTC state
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:return ctc_state
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"""
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if state is None:
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# nothing to do
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return state
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else:
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r_prev, s_prev, f_min_prev, f_max_prev = state
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r_prev_new = torch.full(
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(self.input_length, 2),
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self.logzero,
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dtype=self.dtype,
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device=self.device,
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)
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start = max(r_prev.shape[0], 1)
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r_prev_new[0:start] = r_prev
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for t in six.moves.range(start, self.input_length):
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r_prev_new[t, 1] = r_prev_new[t - 1, 1] + self.x[0, t, :, self.blank]
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return (r_prev_new, s_prev, f_min_prev, f_max_prev)
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class CTCPrefixScore(object):
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"""Compute CTC label sequence scores
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which is based on Algorithm 2 in WATANABE et al.
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"HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
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but extended to efficiently compute the probablities of multiple labels
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simultaneously
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"""
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def __init__(self, x, blank, eos, xp):
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self.xp = xp
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self.logzero = -10000000000.0
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self.blank = blank
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self.eos = eos
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self.input_length = len(x)
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self.x = x
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def initial_state(self):
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"""Obtain an initial CTC state
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:return: CTC state
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"""
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# initial CTC state is made of a frame x 2 tensor that corresponds to
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# r_t^n(<sos>) and r_t^b(<sos>), where 0 and 1 of axis=1 represent
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# superscripts n and b (non-blank and blank), respectively.
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r = self.xp.full((self.input_length, 2), self.logzero, dtype=np.float32)
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r[0, 1] = self.x[0, self.blank]
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for i in six.moves.range(1, self.input_length):
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r[i, 1] = r[i - 1, 1] + self.x[i, self.blank]
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return r
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def __call__(self, y, cs, r_prev):
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"""Compute CTC prefix scores for next labels
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:param y : prefix label sequence
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:param cs : array of next labels
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:param r_prev: previous CTC state
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:return ctc_scores, ctc_states
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"""
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# initialize CTC states
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output_length = len(y) - 1 # ignore sos
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# new CTC states are prepared as a frame x (n or b) x n_labels tensor
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# that corresponds to r_t^n(h) and r_t^b(h).
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r = self.xp.ndarray((self.input_length, 2, len(cs)), dtype=np.float32)
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xs = self.x[:, cs]
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if output_length == 0:
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r[0, 0] = xs[0]
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r[0, 1] = self.logzero
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else:
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r[output_length - 1] = self.logzero
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# prepare forward probabilities for the last label
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r_sum = self.xp.logaddexp(
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r_prev[:, 0], r_prev[:, 1]
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) # log(r_t^n(g) + r_t^b(g))
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last = y[-1]
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if output_length > 0 and last in cs:
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log_phi = self.xp.ndarray((self.input_length, len(cs)), dtype=np.float32)
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for i in six.moves.range(len(cs)):
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log_phi[:, i] = r_sum if cs[i] != last else r_prev[:, 1]
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else:
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log_phi = r_sum
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# compute forward probabilities log(r_t^n(h)), log(r_t^b(h)),
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# and log prefix probabilities log(psi)
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start = max(output_length, 1)
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log_psi = r[start - 1, 0]
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for t in six.moves.range(start, self.input_length):
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r[t, 0] = self.xp.logaddexp(r[t - 1, 0], log_phi[t - 1]) + xs[t]
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r[t, 1] = (
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self.xp.logaddexp(r[t - 1, 0], r[t - 1, 1]) + self.x[t, self.blank]
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)
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log_psi = self.xp.logaddexp(log_psi, log_phi[t - 1] + xs[t])
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# get P(...eos|X) that ends with the prefix itself
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eos_pos = self.xp.where(cs == self.eos)[0]
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if len(eos_pos) > 0:
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log_psi[eos_pos] = r_sum[-1] # log(r_T^n(g) + r_T^b(g))
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# exclude blank probs
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blank_pos = self.xp.where(cs == self.blank)[0]
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if len(blank_pos) > 0:
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log_psi[blank_pos] = self.logzero
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# return the log prefix probability and CTC states, where the label axis
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# of the CTC states is moved to the first axis to slice it easily
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return log_psi, self.xp.rollaxis(r, 2)
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