def main():
args = parse()
# Load a conf file
dir_name = os.path.dirname(args.recog_model[0])
conf = load_config(os.path.join(dir_name, 'conf.yml'))
# Overwrite conf
for k, v in conf.items():
if 'recog' not in k:
setattr(args, k, v)
recog_params = vars(args)
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
logger = set_logger(os.path.join(args.recog_dir, 'plot.log'),
key='decoding')
for i, s in enumerate(args.recog_sets):
# Load dataset
dataset = Dataset(
corpus=args.corpus,
tsv_path=s,
dict_path=os.path.join(dir_name, 'dict.txt'),
dict_path_sub1=os.path.join(dir_name, 'dict_sub1.txt') if
os.path.isfile(os.path.join(dir_name, 'dict_sub1.txt')) else False,
nlsyms=args.nlsyms,
wp_model=os.path.join(dir_name, 'wp.model'),
unit=args.unit,
unit_sub1=args.unit_sub1,
batch_size=args.recog_batch_size,
is_test=True)
if i == 0:
# Load the ASR model
model = Speech2Text(args, dir_name)
model, checkpoint = load_checkpoint(model, args.recog_model[0])
epoch = checkpoint['epoch']
# ensemble (different models)
ensemble_models = [model]
if len(args.recog_model) > 1:
for recog_model_e in args.recog_model[1:]:
# Load the ASR model
conf_e = load_config(
os.path.join(os.path.dirname(recog_model_e),
'conf.yml'))
args_e = copy.deepcopy(args)
for k, v in conf_e.items():
if 'recog' not in k:
setattr(args_e, k, v)
model_e = Speech2Text(args_e)
model_e, _ = load_checkpoint(model_e, recog_model_e)
model_e.cuda()
ensemble_models += [model_e]
# Load the LM for shallow fusion
if not args.lm_fusion:
if args.recog_lm is not None and args.recog_lm_weight > 0:
conf_lm = load_config(
os.path.join(os.path.dirname(args.recog_lm),
'conf.yml'))
args_lm = argparse.Namespace()
for k, v in conf_lm.items():
setattr(args_lm, k, v)
lm = select_lm(args_lm)
lm, _ = load_checkpoint(lm, args.recog_lm)
if args_lm.backward:
model.lm_bwd = lm
else:
model.lm_fwd = lm
if args.recog_lm_bwd is not None and args.recog_lm_weight > 0 and \
(args.recog_fwd_bwd_attention or args.recog_reverse_lm_rescoring):
conf_lm = load_config(
os.path.join(args.recog_lm_bwd, 'conf.yml'))
args_lm_bwd = argparse.Namespace()
for k, v in conf_lm.items():
setattr(args_lm_bwd, k, v)
lm_bwd = select_lm(args_lm_bwd)
lm_bwd, _ = load_checkpoint(lm_bwd, args.recog_lm_bwd)
model.lm_bwd = lm_bwd
if not args.recog_unit:
args.recog_unit = args.unit
logger.info('recog unit: %s' % args.recog_unit)
logger.info('recog metric: %s' % args.recog_metric)
logger.info('recog oracle: %s' % args.recog_oracle)
logger.info('epoch: %d' % (epoch - 1))
logger.info('batch size: %d' % args.recog_batch_size)
logger.info('beam width: %d' % args.recog_beam_width)
logger.info('min length ratio: %.3f' % args.recog_min_len_ratio)
logger.info('max length ratio: %.3f' % args.recog_max_len_ratio)
logger.info('length penalty: %.3f' % args.recog_length_penalty)
logger.info('coverage penalty: %.3f' % args.recog_coverage_penalty)
logger.info('coverage threshold: %.3f' %
args.recog_coverage_threshold)
logger.info('CTC weight: %.3f' % args.recog_ctc_weight)
logger.info('LM path: %s' % args.recog_lm)
logger.info('LM path (bwd): %s' % args.recog_lm_bwd)
logger.info('LM weight: %.3f' % args.recog_lm_weight)
logger.info('GNMT: %s' % args.recog_gnmt_decoding)
logger.info('forward-backward attention: %s' %
args.recog_fwd_bwd_attention)
logger.info('reverse LM rescoring: %s' %
args.recog_reverse_lm_rescoring)
logger.info('resolving UNK: %s' % args.recog_resolving_unk)
logger.info('ensemble: %d' % (len(ensemble_models)))
logger.info('ASR decoder state carry over: %s' %
(args.recog_asr_state_carry_over))
logger.info('LM state carry over: %s' %
(args.recog_lm_state_carry_over))
logger.info('cache size: %d' % (args.recog_n_caches))
logger.info('cache type: %s' % (args.recog_cache_type))
logger.info('cache word frequency threshold: %s' %
(args.recog_cache_word_freq))
logger.info('cache theta (speech): %.3f' %
(args.recog_cache_theta_speech))
logger.info('cache lambda (speech): %.3f' %
(args.recog_cache_lambda_speech))
logger.info('cache theta (lm): %.3f' % (args.recog_cache_theta_lm))
logger.info('cache lambda (lm): %.3f' %
(args.recog_cache_lambda_lm))
# GPU setting
model.cuda()
# TODO(hirofumi): move this
save_path = mkdir_join(args.recog_dir, 'att_weights')
if args.recog_n_caches > 0:
save_path_cache = mkdir_join(args.recog_dir, 'cache')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
if args.recog_n_caches > 0:
shutil.rmtree(save_path_cache)
os.mkdir(save_path_cache)
while True:
batch, is_new_epoch = dataset.next(
recog_params['recog_batch_size'])
best_hyps_id, aws, (cache_attn_hist, cache_id_hist) = model.decode(
batch['xs'],
recog_params,
dataset.idx2token[0],
exclude_eos=False,
refs_id=batch['ys'],
ensemble_models=ensemble_models[1:]
if len(ensemble_models) > 1 else [],
speakers=batch['sessions']
if dataset.corpus == 'swbd' else batch['speakers'])
if model.bwd_weight > 0.5:
# Reverse the order
best_hyps_id = [hyp[::-1] for hyp in best_hyps_id]
aws = [aw[::-1] for aw in aws]
for b in range(len(batch['xs'])):
tokens = dataset.idx2token[0](best_hyps_id[b],
return_list=True)
spk = batch['speakers'][b]
plot_attention_weights(
aws[b][:len(tokens)],
tokens,
spectrogram=batch['xs'][b][:, :dataset.input_dim]
if args.input_type == 'speech' else None,
save_path=mkdir_join(save_path, spk,
batch['utt_ids'][b] + '.png'),
figsize=(20, 8))
if args.recog_n_caches > 0 and cache_id_hist is not None and cache_attn_hist is not None:
n_keys, n_queries = cache_attn_hist[0].shape
# mask = np.ones((n_keys, n_queries))
# for i in range(n_queries):
# mask[:n_keys - i, -(i + 1)] = 0
mask = np.zeros((n_keys, n_queries))
plot_cache_weights(
cache_attn_hist[0],
keys=dataset.idx2token[0](cache_id_hist[-1],
return_list=True), # fifo
# keys=dataset.idx2token[0](cache_id_hist, return_list=True), # dict
queries=tokens,
save_path=mkdir_join(save_path_cache, spk,
batch['utt_ids'][b] + '.png'),
figsize=(40, 16),
mask=mask)
if model.bwd_weight > 0.5:
hyp = ' '.join(tokens[::-1])
else:
hyp = ' '.join(tokens)
logger.info('utt-id: %s' % batch['utt_ids'][b])
logger.info('Ref: %s' % batch['text'][b].lower())
logger.info('Hyp: %s' % hyp)
logger.info('-' * 50)
if is_new_epoch:
break
def main():
# Load configuration
args, dir_name = parse_args_eval(sys.argv[1:])
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
set_logger(os.path.join(args.recog_dir, 'plot.log'), stdout=args.recog_stdout)
for i, s in enumerate(args.recog_sets):
# Load dataloader
dataloader = build_dataloader(args=args,
tsv_path=s,
batch_size=1,
is_test=True,
first_n_utterances=args.recog_first_n_utt,
longform_max_n_frames=args.recog_longform_max_n_frames)
if i == 0:
# Load ASR model
model = Speech2Text(args, dir_name)
epoch = int(float(args.recog_model[0].split('-')[-1]) * 10) / 10
if args.recog_n_average > 1:
# Model averaging for Transformer
model = average_checkpoints(model, args.recog_model[0],
n_average=args.recog_n_average)
else:
load_checkpoint(args.recog_model[0], model)
# Ensemble (different models)
ensemble_models = [model]
if len(args.recog_model) > 1:
for recog_model_e in args.recog_model[1:]:
conf_e = load_config(os.path.join(os.path.dirname(recog_model_e), 'conf.yml'))
args_e = copy.deepcopy(args)
for k, v in conf_e.items():
if 'recog' not in k:
setattr(args_e, k, v)
model_e = Speech2Text(args_e)
load_checkpoint(recog_model_e, model_e)
if args.recog_n_gpus >= 1:
model_e.cuda()
ensemble_models += [model_e]
# Load LM for shallow fusion
if not args.lm_fusion:
# first path
if args.recog_lm is not None and args.recog_lm_weight > 0:
conf_lm = load_config(os.path.join(os.path.dirname(args.recog_lm), 'conf.yml'))
args_lm = argparse.Namespace()
for k, v in conf_lm.items():
setattr(args_lm, k, v)
lm = build_lm(args_lm)
load_checkpoint(args.recog_lm, lm)
if args_lm.backward:
model.lm_bwd = lm
else:
model.lm_fwd = lm
# NOTE: only support for first path
if not args.recog_unit:
args.recog_unit = args.unit
logger.info('recog unit: %s' % args.recog_unit)
logger.info('recog oracle: %s' % args.recog_oracle)
logger.info('epoch: %d' % epoch)
logger.info('batch size: %d' % args.recog_batch_size)
logger.info('beam width: %d' % args.recog_beam_width)
logger.info('min length ratio: %.3f' % args.recog_min_len_ratio)
logger.info('max length ratio: %.3f' % args.recog_max_len_ratio)
logger.info('length penalty: %.3f' % args.recog_length_penalty)
logger.info('length norm: %s' % args.recog_length_norm)
logger.info('coverage penalty: %.3f' % args.recog_coverage_penalty)
logger.info('coverage threshold: %.3f' % args.recog_coverage_threshold)
logger.info('CTC weight: %.3f' % args.recog_ctc_weight)
logger.info('fist LM path: %s' % args.recog_lm)
logger.info('LM weight: %.3f' % args.recog_lm_weight)
logger.info('GNMT: %s' % args.recog_gnmt_decoding)
logger.info('forward-backward attention: %s' % args.recog_fwd_bwd_attention)
logger.info('resolving UNK: %s' % args.recog_resolving_unk)
logger.info('ensemble: %d' % (len(ensemble_models)))
logger.info('ASR decoder state carry over: %s' % (args.recog_asr_state_carry_over))
logger.info('LM state carry over: %s' % (args.recog_lm_state_carry_over))
logger.info('model average (Transformer): %d' % (args.recog_n_average))
# GPU setting
if args.recog_n_gpus >= 1:
model.cudnn_setting(deterministic=True, benchmark=False)
model.cuda()
save_path = mkdir_join(args.recog_dir, 'att_weights')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
for batch in dataloader:
nbest_hyps_id, aws = model.decode(
batch['xs'], args, dataloader.idx2token[0],
exclude_eos=False,
refs_id=batch['ys'],
ensemble_models=ensemble_models[1:] if len(ensemble_models) > 1 else [],
speakers=batch['sessions'] if dataloader.corpus == 'swbd' else batch['speakers'])
best_hyps_id = [h[0] for h in nbest_hyps_id]
# Get CTC probs
ctc_probs, topk_ids = None, None
if args.ctc_weight > 0:
ctc_probs, topk_ids, xlens = model.get_ctc_probs(
batch['xs'], task='ys', temperature=1, topk=min(100, model.vocab))
# NOTE: ctc_probs: '[B, T, topk]'
ctc_probs_sub1, topk_ids_sub1 = None, None
if args.ctc_weight_sub1 > 0:
ctc_probs_sub1, topk_ids_sub1, xlens_sub1 = model.get_ctc_probs(
batch['xs'], task='ys_sub1', temperature=1, topk=min(100, model.vocab_sub1))
if model.bwd_weight > 0.5:
# Reverse the order
best_hyps_id = [hyp[::-1] for hyp in best_hyps_id]
aws = [[aw[0][:, ::-1]] for aw in aws]
for b in range(len(batch['xs'])):
tokens = dataloader.idx2token[0](best_hyps_id[b], return_list=True)
spk = batch['speakers'][b]
plot_attention_weights(
aws[b][0][:, :len(tokens)], tokens,
spectrogram=batch['xs'][b][:, :dataloader.input_dim] if args.input_type == 'speech' else None,
factor=args.subsample_factor,
ref=batch['text'][b].lower(),
save_path=mkdir_join(save_path, spk, batch['utt_ids'][b] + '.png'),
figsize=(20, 8),
ctc_probs=ctc_probs[b, :xlens[b]] if ctc_probs is not None else None,
ctc_topk_ids=topk_ids[b] if topk_ids is not None else None,
ctc_probs_sub1=ctc_probs_sub1[b, :xlens_sub1[b]] if ctc_probs_sub1 is not None else None,
ctc_topk_ids_sub1=topk_ids_sub1[b] if topk_ids_sub1 is not None else None)
if model.bwd_weight > 0.5:
hyp = ' '.join(tokens[::-1])
else:
hyp = ' '.join(tokens)
logger.info('utt-id: %s' % batch['utt_ids'][b])
logger.info('Ref: %s' % batch['text'][b].lower())
logger.info('Hyp: %s' % hyp)
logger.info('-' * 50)
def main():
args = parse()
# Load a conf file
dir_name = os.path.dirname(args.recog_model[0])
conf = load_config(os.path.join(dir_name, 'conf.yml'))
# Overwrite conf
for k, v in conf.items():
if 'recog' not in k:
setattr(args, k, v)
recog_params = vars(args)
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
set_logger(os.path.join(args.recog_dir, 'plot.log'), stdout=args.recog_stdout)
for i, s in enumerate(args.recog_sets):
# Load dataset
dataset = Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=os.path.join(dir_name, 'dict.txt'),
dict_path_sub1=os.path.join(dir_name, 'dict_sub1.txt') if os.path.isfile(
os.path.join(dir_name, 'dict_sub1.txt')) else False,
nlsyms=args.nlsyms,
wp_model=os.path.join(dir_name, 'wp.model'),
unit=args.unit,
unit_sub1=args.unit_sub1,
batch_size=args.recog_batch_size,
is_test=True)
if i == 0:
# Load the ASR model
model = Speech2Text(args, dir_name)
topk_list = load_checkpoint(model, args.recog_model[0])
epoch = int(args.recog_model[0].split('-')[-1])
# Model averaging for Transformer
if 'transformer' in conf['enc_type'] and conf['dec_type'] == 'transformer':
model = average_checkpoints(model, args.recog_model[0],
n_average=args.recog_n_average,
topk_list=topk_list)
# ensemble (different models)
ensemble_models = [model]
if len(args.recog_model) > 1:
for recog_model_e in args.recog_model[1:]:
conf_e = load_config(os.path.join(os.path.dirname(recog_model_e), 'conf.yml'))
args_e = copy.deepcopy(args)
for k, v in conf_e.items():
if 'recog' not in k:
setattr(args_e, k, v)
model_e = Speech2Text(args_e)
load_checkpoint(model_e, recog_model_e)
if args.recog_n_gpus > 0:
model_e.cuda()
ensemble_models += [model_e]
# Load the LM for shallow fusion
if not args.lm_fusion:
# first path
if args.recog_lm is not None and args.recog_lm_weight > 0:
conf_lm = load_config(os.path.join(os.path.dirname(args.recog_lm), 'conf.yml'))
args_lm = argparse.Namespace()
for k, v in conf_lm.items():
setattr(args_lm, k, v)
lm = build_lm(args_lm)
load_checkpoint(lm, args.recog_lm)
if args_lm.backward:
model.lm_bwd = lm
else:
model.lm_fwd = lm
# NOTE: only support for first path
if not args.recog_unit:
args.recog_unit = args.unit
logger.info('recog unit: %s' % args.recog_unit)
logger.info('recog oracle: %s' % args.recog_oracle)
logger.info('epoch: %d' % epoch)
logger.info('batch size: %d' % args.recog_batch_size)
logger.info('beam width: %d' % args.recog_beam_width)
logger.info('min length ratio: %.3f' % args.recog_min_len_ratio)
logger.info('max length ratio: %.3f' % args.recog_max_len_ratio)
logger.info('length penalty: %.3f' % args.recog_length_penalty)
logger.info('length norm: %s' % args.recog_length_norm)
logger.info('coverage penalty: %.3f' % args.recog_coverage_penalty)
logger.info('coverage threshold: %.3f' % args.recog_coverage_threshold)
logger.info('CTC weight: %.3f' % args.recog_ctc_weight)
logger.info('fist LM path: %s' % args.recog_lm)
logger.info('LM weight: %.3f' % args.recog_lm_weight)
logger.info('GNMT: %s' % args.recog_gnmt_decoding)
logger.info('forward-backward attention: %s' % args.recog_fwd_bwd_attention)
logger.info('resolving UNK: %s' % args.recog_resolving_unk)
logger.info('ensemble: %d' % (len(ensemble_models)))
logger.info('ASR decoder state carry over: %s' % (args.recog_asr_state_carry_over))
logger.info('LM state carry over: %s' % (args.recog_lm_state_carry_over))
logger.info('model average (Transformer): %d' % (args.recog_n_average))
# GPU setting
if args.recog_n_gpus > 0:
model.cuda()
save_path = mkdir_join(args.recog_dir, 'att_weights')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
while True:
batch, is_new_epoch = dataset.next(recog_params['recog_batch_size'])
best_hyps_id, aws = model.decode(
batch['xs'], recog_params, dataset.idx2token[0],
exclude_eos=False,
refs_id=batch['ys'],
ensemble_models=ensemble_models[1:] if len(ensemble_models) > 1 else [],
speakers=batch['sessions'] if dataset.corpus == 'swbd' else batch['speakers'])
# Get CTC probs
ctc_probs, topk_ids = None, None
if args.ctc_weight > 0:
ctc_probs, topk_ids, xlens = model.get_ctc_probs(
batch['xs'], temperature=1, topk=min(100, model.vocab))
# NOTE: ctc_probs: '[B, T, topk]'
if model.bwd_weight > 0.5:
# Reverse the order
best_hyps_id = [hyp[::-1] for hyp in best_hyps_id]
aws = [aw[:, ::-1] for aw in aws]
for b in range(len(batch['xs'])):
tokens = dataset.idx2token[0](best_hyps_id[b], return_list=True)
spk = batch['speakers'][b]
plot_attention_weights(
aws[b][:, :len(tokens)], tokens,
spectrogram=batch['xs'][b][:, :dataset.input_dim] if args.input_type == 'speech' else None,
ref=batch['text'][b].lower(),
save_path=mkdir_join(save_path, spk, batch['utt_ids'][b] + '.png'),
figsize=(20, 8),
ctc_probs=ctc_probs[b, :xlens[b]] if ctc_probs is not None else None,
ctc_topk_ids=topk_ids[b] if topk_ids is not None else None)
if model.bwd_weight > 0.5:
hyp = ' '.join(tokens[::-1])
else:
hyp = ' '.join(tokens)
logger.info('utt-id: %s' % batch['utt_ids'][b])
logger.info('Ref: %s' % batch['text'][b].lower())
logger.info('Hyp: %s' % hyp)
logger.info('-' * 50)
if is_new_epoch:
break
