def recognize_beam(self, h, lpz, recog_args, char_list):
'''beam search implementation
:param Variable h:
:param Namespace recog_args:
:param char_list:
:return:
'''
logging.info('input lengths: ' + str(h.size(0)))
# initialization
c_list = [self.zero_state(h.unsqueeze(0))]
z_list = [self.zero_state(h.unsqueeze(0))]
for l in six.moves.range(1, self.dlayers):
c_list.append(self.zero_state(h.unsqueeze(0)))
z_list.append(self.zero_state(h.unsqueeze(0)))
a = None
self.att.reset() # reset pre-computation of h
# search parms
beam = recog_args.beam_size
penalty = recog_args.penalty
ctc_weight = recog_args.ctc_weight
# preprate sos
y = self.sos
vy = Variable(h.data.new(1).zero_().long(), volatile=True)
if recog_args.maxlenratio == 0:
maxlen = h.shape[0]
else:
# maxlen >= 1
maxlen = max(1, int(recog_args.maxlenratio * h.size(0)))
minlen = int(recog_args.minlenratio * h.size(0))
logging.info('max output length: ' + str(maxlen))
logging.info('min output length: ' + str(minlen))
# initialize hypothesis
hyp = {
'score': 0.0,
'yseq': [y],
'c_prev': c_list,
'z_prev': z_list,
'a_prev': a
}
if lpz is not None:
ctc_prefix_score = CTCPrefixScore(lpz.numpy(), 0, self.eos, np)
hyp['ctc_state_prev'] = ctc_prefix_score.initial_state()
hyp['ctc_score_prev'] = 0.0
ctc_beam = min(lpz.shape[-1], int(beam * CTC_SCORING_RATIO))
hyps = [hyp]
ended_hyps = []
for i in six.moves.range(maxlen):
logging.debug('position ' + str(i))
hyps_best_kept = []
for hyp in hyps:
vy.unsqueeze(1)
vy[0] = hyp['yseq'][i]
ey = self.embed(vy) # utt list (1) x zdim
ey.unsqueeze(0)
att_c, att_w = self.att(h.unsqueeze(0), [h.size(0)],
hyp['z_prev'][0], hyp['a_prev'])
ey = torch.cat((ey, att_c), dim=1) # utt(1) x (zdim + hdim)
z_list[0], c_list[0] = self.decoder[0](
ey, (hyp['z_prev'][0], hyp['c_prev'][0]))
for l in six.moves.range(1, self.dlayers):
z_list[l], c_list[l] = self.decoder[l](
z_list[l - 1], (hyp['z_prev'][l], hyp['c_prev'][l]))
# get nbest local scores and their ids
local_att_scores = F.log_softmax(self.output(z_list[-1]),
dim=1).data
if lpz is not None:
local_best_scores, local_best_ids = torch.topk(
local_att_scores, ctc_beam, dim=1)
ctc_scores, ctc_states = ctc_prefix_score(
hyp['yseq'], local_best_ids[0], hyp['ctc_state_prev'])
local_scores = \
(1.0 - ctc_weight) * local_att_scores[:, local_best_ids[0]] \
+ ctc_weight * torch.from_numpy(ctc_scores - hyp['ctc_score_prev'])
local_best_scores, joint_best_ids = torch.topk(
local_scores, beam, dim=1)
local_best_ids = local_best_ids[:, joint_best_ids[0]]
else:
local_best_scores, local_best_ids = torch.topk(
local_att_scores, beam, dim=1)
for j in six.moves.range(beam):
new_hyp = {}
new_hyp['z_prev'] = [z_list[0]]
new_hyp['c_prev'] = [c_list[0]]
for l in six.moves.range(1, self.dlayers):
new_hyp['z_prev'].append(z_list[l])
new_hyp['c_prev'].append(c_list[l])
new_hyp['a_prev'] = att_w
new_hyp['score'] = hyp['score'] + local_best_scores[0, j]
new_hyp['yseq'] = [0] * (1 + len(hyp['yseq']))
new_hyp['yseq'][:len(hyp['yseq'])] = hyp['yseq']
new_hyp['yseq'][len(hyp['yseq'])] = local_best_ids[0, j]
if lpz is not None:
new_hyp['ctc_state_prev'] = ctc_states[joint_best_ids[
0, j]]
new_hyp['ctc_score_prev'] = ctc_scores[joint_best_ids[
0, j]]
# will be (2 x beam) hyps at most
hyps_best_kept.append(new_hyp)
hyps_best_kept = sorted(hyps_best_kept,
key=lambda x: x['score'],
reverse=True)[:beam]
# sort and get nbest
hyps = hyps_best_kept
logging.debug('number of pruned hypothes: ' + str(len(hyps)))
logging.debug(
'best hypo: ' +
''.join([char_list[int(x)] for x in hyps[0]['yseq'][1:]]))
# add eos in the final loop to avoid that there are no ended hyps
if i == maxlen - 1:
logging.info('adding <eos> in the last postion in the loop')
for hyp in hyps:
hyp['yseq'].append(self.eos)
# add ended hypothes to a final list, and removed them from current hypothes
# (this will be a probmlem, number of hyps < beam)
remained_hyps = []
for hyp in hyps:
if hyp['yseq'][-1] == self.eos:
# only store the sequence that has more than minlen outputs
# also add penalty
if len(hyp['yseq']) > minlen:
hyp['score'] += (i + 1) * penalty
ended_hyps.append(hyp)
else:
remained_hyps.append(hyp)
# end detection
if end_detect(ended_hyps, i) and recog_args.maxlenratio == 0.0:
logging.info('end detected at %d', i)
break
hyps = remained_hyps
if len(hyps) > 0:
logging.debug('remeined hypothes: ' + str(len(hyps)))
else:
logging.info('no hypothesis. Finish decoding.')
break
for hyp in hyps:
logging.debug(
'hypo: ' +
''.join([char_list[int(x)] for x in hyp['yseq'][1:]]))
logging.debug('number of ended hypothes: ' + str(len(ended_hyps)))
best_hyp = sorted(ended_hyps, key=lambda x: x['score'],
reverse=True)[0]
logging.info('total log probability: ' + str(best_hyp['score']))
logging.info('normalized log probability: ' +
str(best_hyp['score'] / len(best_hyp['yseq'])))
# remove sos
return best_hyp['yseq'][1:]
def recognize_beam(self, h, lpz, recog_args, char_list, rnnlm=None):
'''beam search implementation
:param h:
:param recog_args:
:param char_list:
:return:
'''
logging.info('input lengths: ' + str(h.shape[0]))
# initialization
c_list = [None] # list of cell state of each layer
z_list = [None] # list of hidden state of each layer
for l in six.moves.range(1, self.dlayers):
c_list.append(None)
z_list.append(None)
a = None
self.att.reset() # reset pre-computation of h
# search parms
beam = recog_args.beam_size
penalty = recog_args.penalty
ctc_weight = recog_args.ctc_weight
# preprate sos
y = self.xp.full(1, self.sos, 'i')
if recog_args.maxlenratio == 0:
maxlen = h.shape[0]
else:
# maxlen >= 1
maxlen = max(1, int(recog_args.maxlenratio * h.shape[0]))
minlen = int(recog_args.minlenratio * h.shape[0])
logging.info('max output length: ' + str(maxlen))
logging.info('min output length: ' + str(minlen))
# initialize hypothesis
if rnnlm:
hyp = {
'score': 0.0,
'yseq': [y],
'c_prev': c_list,
'z_prev': z_list,
'a_prev': a,
'rnnlm_prev': None
}
else:
hyp = {
'score': 0.0,
'yseq': [y],
'c_prev': c_list,
'z_prev': z_list,
'a_prev': a
}
if lpz is not None:
ctc_prefix_score = CTCPrefixScore(lpz, 0, self.eos, self.xp)
hyp['ctc_state_prev'] = ctc_prefix_score.initial_state()
hyp['ctc_score_prev'] = 0.0
if ctc_weight != 1.0:
# pre-pruning based on attention scores
ctc_beam = min(lpz.shape[-1], int(beam * CTC_SCORING_RATIO))
else:
ctc_beam = lpz.shape[-1]
hyps = [hyp]
ended_hyps = []
for i in six.moves.range(maxlen):
logging.debug('position ' + str(i))
hyps_best_kept = []
for hyp in hyps:
ey = self.embed(hyp['yseq'][i]) # utt list (1) x zdim
att_c, att_w = self.att([h], hyp['z_prev'][0], hyp['a_prev'])
ey = F.hstack((ey, att_c)) # utt(1) x (zdim + hdim)
c_list[0], z_list[0] = self.lstm0(hyp['c_prev'][0],
hyp['z_prev'][0], ey)
for l in six.moves.range(1, self.dlayers):
c_list[l], z_list[l] = self['lstm%d' % l](hyp['c_prev'][l],
hyp['z_prev'][l],
z_list[l - 1])
# get nbest local scores and their ids
local_att_scores = F.log_softmax(self.output(z_list[-1])).data
if rnnlm:
rnnlm_state, local_lm_scores = rnnlm.predict(
hyp['rnnlm_prev'], hyp['yseq'][i])
local_scores = local_att_scores + recog_args.lm_weight * local_lm_scores
else:
local_scores = local_att_scores
if lpz is not None:
local_best_ids = self.xp.argsort(
local_scores, axis=1)[0, ::-1][:ctc_beam]
ctc_scores, ctc_states = ctc_prefix_score(
hyp['yseq'], local_best_ids, hyp['ctc_state_prev'])
local_scores = \
(1.0 - ctc_weight) * local_att_scores[:, local_best_ids] \
+ ctc_weight * (ctc_scores - hyp['ctc_score_prev'])
if rnnlm:
local_scores += recog_args.lm_weight * local_lm_scores[:,
local_best_ids]
joint_best_ids = self.xp.argsort(local_scores,
axis=1)[0, ::-1][:beam]
local_best_scores = local_scores[:, joint_best_ids]
local_best_ids = local_best_ids[joint_best_ids]
else:
local_best_ids = self.xp.argsort(local_scores,
axis=1)[0, ::-1][:beam]
local_best_scores = local_scores[:, local_best_ids]
for j in six.moves.range(beam):
new_hyp = {}
# do not copy {z,c}_list directly
new_hyp['z_prev'] = z_list[:]
new_hyp['c_prev'] = c_list[:]
new_hyp['a_prev'] = att_w
new_hyp['score'] = hyp['score'] + local_best_scores[0, j]
new_hyp['yseq'] = [0] * (1 + len(hyp['yseq']))
new_hyp['yseq'][:len(hyp['yseq'])] = hyp['yseq']
new_hyp['yseq'][len(hyp['yseq'])] = self.xp.full(
1, local_best_ids[j], 'i')
if rnnlm:
new_hyp['rnnlm_prev'] = rnnlm_state
if lpz is not None:
new_hyp['ctc_state_prev'] = ctc_states[
joint_best_ids[j]]
new_hyp['ctc_score_prev'] = ctc_scores[
joint_best_ids[j]]
# will be (2 x beam) hyps at most
hyps_best_kept.append(new_hyp)
hyps_best_kept = sorted(hyps_best_kept,
key=lambda x: x['score'],
reverse=True)[:beam]
# sort and get nbest
hyps = hyps_best_kept
logging.debug('number of pruned hypothes: ' + str(len(hyps)))
logging.debug('best hypo: ' + ''.join(
[char_list[int(x)]
for x in hyps[0]['yseq'][1:]]).replace('<space>', ' '))
# add eos in the final loop to avoid that there are no ended hyps
if i == maxlen - 1:
logging.info('adding <eos> in the last postion in the loop')
for hyp in hyps:
hyp['yseq'].append(self.xp.full(1, self.eos, 'i'))
# add ended hypothes to a final list, and removed them from current hypothes
# (this will be a problem, number of hyps < beam)
remained_hyps = []
for hyp in hyps:
if hyp['yseq'][-1] == self.eos:
# only store the sequence that has more than minlen outputs
# also add penalty
if len(hyp['yseq']) > minlen:
hyp['score'] += (i + 1) * penalty
ended_hyps.append(hyp)
else:
remained_hyps.append(hyp)
# end detection
if end_detect(ended_hyps, i) and recog_args.maxlenratio == 0.0:
logging.info('end detected at %d', i)
break
hyps = remained_hyps
if len(hyps) > 0:
logging.debug('remeined hypothes: ' + str(len(hyps)))
else:
logging.info('no hypothesis. Finish decoding.')
break
for hyp in hyps:
logging.debug('hypo: ' + ''.join(
[char_list[int(x)]
for x in hyp['yseq'][1:]]).replace('<space>', ' '))
logging.debug('number of ended hypothes: ' + str(len(ended_hyps)))
nbest_hyps = sorted(
ended_hyps, key=lambda x: x['score'],
reverse=True)[:min(len(ended_hyps), recog_args.nbest)]
logging.info('total log probability: ' + str(nbest_hyps[0]['score']))
logging.info('normalized log probability: ' +
str(nbest_hyps[0]['score'] / len(nbest_hyps[0]['yseq'])))
return nbest_hyps
def recognize_beam(self, encoder_outputs, char_list, lpz, aligns_pad,
args):
"""Beam search, decode one utterence now.
Args:
encoder_outputs: T x H
char_list: list of character
args: args.beam
Returns:
nbest_hyps:
"""
# search params
#import pdb
#pdb.set_trace()
beam = args.beam_size
nbest = args.nbest
ctc_weight = args.ctc_weight
CTC_SCORING_RATIO = 1.5
if args.decode_max_len != 0:
maxlen = args.decode_max_len
elif lpz is not None:
maxlen = int(len(torch.nonzero(torch.max(lpz, dim=-1)[1])) * 1.5)
elif args.align_trun:
maxlen = int(aligns_pad.size(1) * 1.5)
# *********Init decoder rnn
h_list = [self.zero_state(encoder_outputs.unsqueeze(0))]
c_list = [self.zero_state(encoder_outputs.unsqueeze(0))]
for l in range(1, self.num_layers):
h_list.append(self.zero_state(encoder_outputs.unsqueeze(0)))
c_list.append(self.zero_state(encoder_outputs.unsqueeze(0)))
att_c = self.zero_state(encoder_outputs.unsqueeze(0),
H=encoder_outputs.unsqueeze(0).size(2))
# prepare sos
y = self.sos_id
vy = encoder_outputs.new_zeros(1).long()
hyp = {
'score': 0.0,
'yseq': [y],
'c_prev': c_list,
'h_prev': h_list,
'a_prev': att_c
}
if lpz is not None:
#import pdb
#pdb.set_trace()
ctc_prefix_score = CTCPrefixScore(lpz.detach().cpu().numpy(), 0,
self.eos_id, np)
hyp['ctc_state_prev'] = ctc_prefix_score.initial_state()
hyp['ctc_score_prev'] = 0.0
if ctc_weight != 1.0:
ctc_beam = min(lpz.shape[-1], int(beam * CTC_SCORING_RATIO))
else:
ctc_beam = lpz.shape[-1]
if args.trun:
ctc_greedy = torch.max(lpz, dim=-1)[1].unsqueeze(dim=0)
#print(ctc_greedy)
aligns = []
for k in range(ctc_greedy.size()[0]):
align = (torch.nonzero(ctc_greedy[k]) +
1).reshape(-1).cpu().numpy().tolist()
align.insert(0, 0)
aligns.append(align)
#print(aligns[0:2])
#print(np.shape(aligns))
#aligns = torch.Tensor(aligns).long().cuda()
aligns_pad = pad_list([torch.Tensor(y).long() for y in aligns],
IGNORE_ID)
hyps = [hyp]
ended_hyps = []
for i in range(maxlen):
hyps_best_kept = []
for hyp in hyps:
# vy.unsqueeze(1)
vy[0] = hyp['yseq'][i]
embedded = self.embedding(vy)
# embedded.unsqueeze(0)
# step 1. decoder RNN: s_i = RNN(s_i−1,y_i−1,c_i−1)
rnn_input = torch.cat((embedded, hyp['a_prev']), dim=1)
h_list[0], c_list[0] = self.rnn[0](
rnn_input, (hyp['h_prev'][0], hyp['c_prev'][0]))
for l in range(1, self.num_layers):
h_list[l], c_list[l] = self.rnn[l](
h_list[l - 1], (hyp['h_prev'][l], hyp['c_prev'][l]))
rnn_output = h_list[-1]
# step 2. attention: c_i = AttentionContext(s_i,h)
# below unsqueeze: (N x H) -> (N x 1 x H)
#import pdb
#pdb.set_trace()
mask = None
if args.trun or args.align_trun:
mask = torch.ones(encoder_outputs.unsqueeze(0).size(0),
encoder_outputs.unsqueeze(0).size(1),
dtype=torch.uint8).cuda()
#mask = torch.zeros(encoder_outputs.unsqueeze(0).size(0),encoder_outputs.unsqueeze(0).size(1),dtype=torch.uint8).cuda()
if i + 1 < aligns_pad.size(1):
for m in range(mask.size(0)):
if self.peak_left != 0:
left_id = max(i - self.peak_left + 1, 0)
else:
left_id = 0
right_id = min(i + 1 + self.peak_right,
aligns_pad.size(1) - 1)
left_bound = min(
aligns_pad[m][left_id] + self.offset,
rnn_output.size(1))
right_bound = max(
min(aligns_pad[m][right_id] + self.offset,
rnn_output.size(1)), 0)
#right_bound = max(min(aligns_pad[m][i+1] + self.offset, rnn_output.size(1)), 0)
#left_bound = 0
#mask[m][0:right_bound] = 0
#mask[m][right_bound:-1] = 1
mask[m][left_bound:right_bound] = 0
att_c, att_w = self.attention(rnn_output.unsqueeze(dim=1),
encoder_outputs.unsqueeze(0),
mask)
att_c = att_c.squeeze(dim=1)
# step 3. concate s_i and c_i, and input to MLP
mlp_input = torch.cat((rnn_output, att_c), dim=1)
predicted_y_t = self.mlp(mlp_input)
local_att_scores = F.log_softmax(predicted_y_t, dim=1)
local_scores = local_att_scores
if args.ctc_weight > 0:
#import pdb
#pdb.set_trace()
local_best_scores, local_best_ids = torch.topk(
local_att_scores, ctc_beam, dim=1)
ctc_scores, ctc_states = ctc_prefix_score(
hyp['yseq'], local_best_ids[0], hyp['ctc_state_prev'])
local_scores = (
1.0 - ctc_weight) * local_att_scores[:, local_best_ids[
0]] + ctc_weight * torch.from_numpy(
ctc_scores - hyp['ctc_score_prev']).cuda()
local_best_scores, joint_best_ids = torch.topk(
local_scores, beam, dim=1)
local_best_ids = local_best_ids[:, joint_best_ids[0]]
else:
# topk scores
local_best_scores, local_best_ids = torch.topk(
local_scores, beam, dim=1)
for j in range(beam):
new_hyp = {}
new_hyp['h_prev'] = h_list[:]
new_hyp['c_prev'] = c_list[:]
new_hyp['a_prev'] = att_c[:]
new_hyp['score'] = hyp['score'] + local_best_scores[0, j]
new_hyp['yseq'] = [0] * (1 + len(hyp['yseq']))
new_hyp['yseq'][:len(hyp['yseq'])] = hyp['yseq']
new_hyp['yseq'][len(hyp['yseq'])] = int(local_best_ids[0,
j])
# will be (2 x beam) hyps at most
if args.ctc_weight > 0:
new_hyp['ctc_state_prev'] = ctc_states[joint_best_ids[
0, j]]
new_hyp['ctc_score_prev'] = ctc_scores[joint_best_ids[
0, j]]
hyps_best_kept.append(new_hyp)
hyps_best_kept = sorted(hyps_best_kept,
key=lambda x: x['score'],
reverse=True)[:beam]
# end for hyp in hyps
hyps = hyps_best_kept
# add eos in the final loop to avoid that there are no ended hyps
if i == maxlen - 1:
for hyp in hyps:
hyp['yseq'].append(self.eos_id)
# add ended hypothes to a final list, and removed them from current hypothes
# (this will be a probmlem, number of hyps < beam)
remained_hyps = []
for hyp in hyps:
if hyp['yseq'][-1] == self.eos_id:
# hyp['score'] += (i + 1) * penalty
ended_hyps.append(hyp)
else:
remained_hyps.append(hyp)
hyps = remained_hyps
if len(hyps) > 0:
print('remeined hypothes: ' + str(len(hyps)))
else:
print('no hypothesis. Finish decoding.')
break
#import pdb
#pdb.set_trace()
for hyp in hyps:
print('hypo: ' +
' '.join([char_list[int(x)] for x in hyp['yseq'][1:]]))
# end for i in range(maxlen)
nbest_hyps = sorted(ended_hyps, key=lambda x: x['score'],
reverse=True)[:min(len(ended_hyps), nbest)]
#print(nbest_hyps)
return nbest_hyps
def recognize_beam(self, encoder_outputs, char_list, lpz, args):
"""Beam search, decode one utterence now.
Args:
encoder_outputs: T x H
char_list: list of character
args: args.beam
Returns:
nbest_hyps:
"""
# search params
beam = args.beam_size
nbest = args.nbest
ctc_weight = args.ctc_weight
CTC_SCORING_RATIO = 1.5
if args.decode_max_len == 0:
maxlen = encoder_outputs.size(0)
else:
maxlen = args.decode_max_len
# *********Init decoder rnn
h_list = [self.zero_state(encoder_outputs.unsqueeze(0))]
c_list = [self.zero_state(encoder_outputs.unsqueeze(0))]
for l in range(1, self.num_layers):
h_list.append(self.zero_state(encoder_outputs.unsqueeze(0)))
c_list.append(self.zero_state(encoder_outputs.unsqueeze(0)))
att_c = self.zero_state(encoder_outputs.unsqueeze(0),
H=encoder_outputs.unsqueeze(0).size(2))
# prepare sos
y = self.sos_id
vy = encoder_outputs.new_zeros(1).long()
hyp = {
'score': 0.0,
'yseq': [y],
'c_prev': c_list,
'h_prev': h_list,
'a_prev': att_c
}
if lpz is not None:
import pdb
pdb.set_trace()
ctc_prefix_score = CTCPrefixScore(lpz.detach().cpu().numpy(), 0,
self.eos_id, np)
hyp['ctc_state_prev'] = ctc_prefix_score.initial_state()
hyp['ctc_score_prev'] = 0.0
if ctc_weight != 1.0:
ctc_beam = min(lpz.shape[-1], int(beam * CTC_SCORING_RATIO))
else:
ctc_beam = lpz.shape[-1]
hyps = [hyp]
ended_hyps = []
for i in range(maxlen):
hyps_best_kept = []
for hyp in hyps:
# vy.unsqueeze(1)
vy[0] = hyp['yseq'][i]
embedded = self.embedding(vy)
# embedded.unsqueeze(0)
# step 1. decoder RNN: s_i = RNN(s_i−1,y_i−1,c_i−1)
rnn_input = torch.cat((embedded, hyp['a_prev']), dim=1)
h_list[0], c_list[0] = self.rnn[0](
rnn_input, (hyp['h_prev'][0], hyp['c_prev'][0]))
for l in range(1, self.num_layers):
h_list[l], c_list[l] = self.rnn[l](
h_list[l - 1], (hyp['h_prev'][l], hyp['c_prev'][l]))
rnn_output = h_list[-1]
# step 2. attention: c_i = AttentionContext(s_i,h)
# below unsqueeze: (N x H) -> (N x 1 x H)
att_c, att_w = self.attention(rnn_output.unsqueeze(dim=1),
encoder_outputs.unsqueeze(0))
att_c = att_c.squeeze(dim=1)
# step 3. concate s_i and c_i, and input to MLP
mlp_input = torch.cat((rnn_output, att_c), dim=1)
predicted_y_t = self.mlp(mlp_input)
local_att_scores = F.log_softmax(predicted_y_t, dim=1)
local_scores = local_att_scores
if lpz is not None:
#import pdb
#pdb.set_trace()
local_best_scores, local_best_ids = torch.topk(
local_att_scores, ctc_beam, dim=1)
ctc_scores, ctc_states = ctc_prefix_score(
hyp['yseq'], local_best_ids[0], hyp['ctc_state_prev'])
local_scores = (
1.0 - ctc_weight) * local_att_scores[:, local_best_ids[
0]] + ctc_weight * torch.from_numpy(
ctc_scores - hyp['ctc_score_prev']).cuda()
local_best_scores, joint_best_ids = torch.topk(
local_scores, beam, dim=1)
local_best_ids = local_best_ids[:, joint_best_ids[0]]
else:
# topk scores
local_best_scores, local_best_ids = torch.topk(
local_scores, beam, dim=1)
for j in range(beam):
new_hyp = {}
new_hyp['h_prev'] = h_list[:]
new_hyp['c_prev'] = c_list[:]
new_hyp['a_prev'] = att_c[:]
new_hyp['score'] = hyp['score'] + local_best_scores[0, j]
new_hyp['yseq'] = [0] * (1 + len(hyp['yseq']))
new_hyp['yseq'][:len(hyp['yseq'])] = hyp['yseq']
new_hyp['yseq'][len(hyp['yseq'])] = int(local_best_ids[0,
j])
# will be (2 x beam) hyps at most
if lpz is not None:
new_hyp['ctc_state_prev'] = ctc_states[joint_best_ids[
0, j]]
new_hyp['ctc_score_prev'] = ctc_scores[joint_best_ids[
0, j]]
hyps_best_kept.append(new_hyp)
hyps_best_kept = sorted(hyps_best_kept,
key=lambda x: x['score'],
reverse=True)[:beam]
# end for hyp in hyps
hyps = hyps_best_kept
# add eos in the final loop to avoid that there are no ended hyps
if i == maxlen - 1:
for hyp in hyps:
hyp['yseq'].append(self.eos_id)
# add ended hypothes to a final list, and removed them from current hypothes
# (this will be a probmlem, number of hyps < beam)
remained_hyps = []
for hyp in hyps:
if hyp['yseq'][-1] == self.eos_id:
# hyp['score'] += (i + 1) * penalty
ended_hyps.append(hyp)
else:
remained_hyps.append(hyp)
hyps = remained_hyps
if len(hyps) > 0:
print('remeined hypothes: ' + str(len(hyps)))
else:
print('no hypothesis. Finish decoding.')
break
for hyp in hyps:
print('hypo: ' +
' '.join([char_list[int(x)] for x in hyp['yseq'][1:]]))
# end for i in range(maxlen)
nbest_hyps = sorted(ended_hyps, key=lambda x: x['score'],
reverse=True)[:min(len(ended_hyps), nbest)]
#print(nbest_hyps)
return nbest_hyps