reader.initialize()
since = time.time()
batch_size = args.batch_size
fout = open(args.output, 'w')
while True:
src_seq, src_mask, utts = reader.read_batch_utt(batch_size)
if len(utts) == 0: break
with torch.no_grad():
src_seq, src_mask = src_seq.to(device), src_mask.to(device)
hypos, scores = beam_search(model,
src_seq,
src_mask,
device,
args.beam_size,
args.max_len,
len_norm=args.len_norm,
coverage=args.coverage,
lm=lm,
lm_scale=args.lm_scale)
hypos, scores = hypos.tolist(), scores.tolist()
if args.format == 'ctm':
write_ctm(hypos, scores, fout, utts, dic, word_dic, args.space)
else:
write_text(hypos, scores, fout, utts, dic, args.space)
fout.close()
time_elapsed = time.time() - since
print(" Elapsed Time: %.0fm %.0fs" %
(time_elapsed // 60, time_elapsed % 60))
since = time.time()
batch_size = args.batch_size
fout = open(args.output, 'w')
while True:
src_seq, src_mask, utts = reader.read_batch_utt(batch_size)
if len(utts) == 0: break
with torch.no_grad():
src_seq, src_mask = src_seq.to(device), src_mask.to(device)
hypos, scores = beam_search(model,
src_seq,
src_mask,
device,
args.beam_size,
args.max_len,
len_norm=args.len_norm,
coverage=args.coverage,
lm=lm,
lm_scale=args.lm_scale)
hypos, scores = hypos.tolist(), scores.tolist()
if args.format == 'ctm':
write_ctm(hypos, scores, fout, utts, dic, word_dic, args.space)
elif args.format == 'stm':
write_stm(hypos, fout, utts, dic, word_dic, args.space)
else:
write_text(hypos, fout, utts, dic, word_dic, args.space)
fout.close()
time_elapsed = time.time() - since
print(" Elapsed Time: %.0fm %.0fs" %
(time_elapsed // 60, time_elapsed % 60))