def recog(args):
'''Run recognition'''
# display chainer version
logging.info('chainer version = ' + chainer.__version__)
# seed setting (chainer seed may not need it)
os.environ["CHAINER_SEED"] = str(args.seed)
logging.info('chainer seed = ' + os.environ['CHAINER_SEED'])
# read training config
idim, odim, train_args = get_model_conf(args.model, args.model_conf)
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# specify model architecture
logging.info('reading model parameters from ' + args.model)
e2e = E2E(idim, odim, train_args)
model = Loss(e2e, train_args.mtlalpha)
chainer_load(args.model, model)
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
rnnlm = lm_chainer.ClassifierWithState(
lm_chainer.RNNLM(len(train_args.char_list), rnnlm_args.unit))
chainer_load(args.rnnlm, rnnlm)
else:
rnnlm = None
if args.word_rnnlm:
if not args.word_dict:
logging.error(
'word dictionary file is not specified for the word RNNLM.')
sys.exit(1)
rnnlm_args = get_model_conf(args.word_rnnlm, args.rnnlm_conf)
word_dict = load_labeldict(args.word_dict)
char_dict = {x: i for i, x in enumerate(train_args.char_list)}
word_rnnlm = lm_chainer.ClassifierWithState(
lm_chainer.RNNLM(len(word_dict), rnnlm_args.unit))
chainer_load(args.word_rnnlm, word_rnnlm)
if rnnlm is not None:
rnnlm = lm_chainer.ClassifierWithState(
extlm_chainer.MultiLevelLM(word_rnnlm.predictor,
rnnlm.predictor, word_dict,
char_dict))
else:
rnnlm = lm_chainer.ClassifierWithState(
extlm_chainer.LookAheadWordLM(word_rnnlm.predictor, word_dict,
char_dict))
# read json data
with open(args.recog_json, 'rb') as f:
js = json.load(f)['utts']
# decode each utterance
new_js = {}
with chainer.no_backprop_mode():
for idx, name in enumerate(js.keys(), 1):
logging.info('(%d/%d) decoding ' + name, idx, len(js.keys()))
feat = kaldi_io_py.read_mat(js[name]['input'][0]['feat'])
nbest_hyps = e2e.recognize(feat, args, train_args.char_list, rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
# TODO(watanabe) fix character coding problems when saving it
with open(args.result_label, 'wb') as f:
f.write(
json.dumps({
'utts': new_js
}, indent=4, sort_keys=True).encode('utf_8'))
def recog(args):
'''Run recognition'''
# seed setting
torch.manual_seed(args.seed)
# read training config
idim, odim, train_args = get_model_conf(args.model, args.model_conf)
# load trained model parameters
logging.info('reading model parameters from ' + args.model)
e2e = E2E(idim, odim, train_args)
model = Loss(e2e, train_args.mtlalpha)
torch_load(args.model, model)
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(train_args.char_list), rnnlm_args.unit))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
if args.word_rnnlm:
if not args.word_dict:
logging.error(
'word dictionary file is not specified for the word RNNLM.')
sys.exit(1)
rnnlm_args = get_model_conf(args.word_rnnlm, args.rnnlm_conf)
word_dict = load_labeldict(args.word_dict)
char_dict = {x: i for i, x in enumerate(train_args.char_list)}
word_rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(word_dict), rnnlm_args.unit))
torch_load(args.word_rnnlm, word_rnnlm)
word_rnnlm.eval()
if rnnlm is not None:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.MultiLevelLM(word_rnnlm.predictor,
rnnlm.predictor, word_dict,
char_dict))
else:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.LookAheadWordLM(word_rnnlm.predictor, word_dict,
char_dict))
# read json data
with open(args.recog_json, 'rb') as f:
js = json.load(f)['utts']
# decode each utterance
new_js = {}
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info('(%d/%d) decoding ' + name, idx, len(js.keys()))
feat = kaldi_io_py.read_mat(js[name]['input'][0]['feat'])
nbest_hyps = e2e.recognize(feat, args, train_args.char_list, rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
# TODO(watanabe) fix character coding problems when saving it
with open(args.result_label, 'wb') as f:
f.write(
json.dumps({
'utts': new_js
}, indent=4, sort_keys=True).encode('utf_8'))
def recog(args):
'''Run recognition'''
# seed setting
torch.manual_seed(args.seed)
# read training config
idim, odim, train_args = get_model_conf(args.model, args.model_conf)
# load trained model parameters
logging.info('reading model parameters from ' + args.model)
e2e = E2E(idim, odim, train_args)
model = Loss(e2e, train_args.mtlalpha)
torch_load(args.model, model)
e2e.recog_args = args
# read rnnlm
if args.rnnlm:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(
len(train_args.char_list), rnnlm_args.layer, rnnlm_args.unit))
torch_load(args.rnnlm, rnnlm)
rnnlm.eval()
else:
rnnlm = None
if args.word_rnnlm:
rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf)
word_dict = rnnlm_args.char_list_dict
char_dict = {x: i for i, x in enumerate(train_args.char_list)}
word_rnnlm = lm_pytorch.ClassifierWithState(lm_pytorch.RNNLM(
len(word_dict), rnnlm_args.layer, rnnlm_args.unit))
torch_load(args.word_rnnlm, word_rnnlm)
word_rnnlm.eval()
if rnnlm is not None:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.MultiLevelLM(word_rnnlm.predictor,
rnnlm.predictor, word_dict, char_dict))
else:
rnnlm = lm_pytorch.ClassifierWithState(
extlm_pytorch.LookAheadWordLM(word_rnnlm.predictor,
word_dict, char_dict))
# gpu
if args.ngpu == 1:
gpu_id = range(args.ngpu)
logging.info('gpu id: ' + str(gpu_id))
model.cuda()
if rnnlm:
rnnlm.cuda()
# read json data
with open(args.recog_json, 'rb') as f:
js = json.load(f)['utts']
new_js = {}
if args.batchsize is None:
with torch.no_grad():
for idx, name in enumerate(js.keys(), 1):
logging.info('(%d/%d) decoding ' + name, idx, len(js.keys()))
feat = kaldi_io_py.read_mat(js[name]['input'][0]['feat'])
nbest_hyps = e2e.recognize(feat, args, train_args.char_list, rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps, train_args.char_list)
else:
try:
from itertools import zip_longest as zip_longest
except Exception:
from itertools import izip_longest as zip_longest
def grouper(n, iterable, fillvalue=None):
kargs = [iter(iterable)] * n
return zip_longest(*kargs, fillvalue=fillvalue)
# sort data
keys = js.keys()
feat_lens = [js[key]['input'][0]['shape'][0] for key in keys]
sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i])
keys = [keys[i] for i in sorted_index]
with torch.no_grad():
for names in grouper(args.batchsize, keys, None):
names = [name for name in names if name]
feats = [kaldi_io_py.read_mat(js[name]['input'][0]['feat'])
for name in names]
nbest_hyps = e2e.recognize_batch(feats, args, train_args.char_list, rnnlm=rnnlm)
for i, nbest_hyp in enumerate(nbest_hyps):
name = names[i]
new_js[name] = add_results_to_json(js[name], nbest_hyp, train_args.char_list)
# TODO(watanabe) fix character coding problems when saving it
with open(args.result_label, 'wb') as f:
f.write(json.dumps({'utts': new_js}, indent=4, sort_keys=True).encode('utf_8'))
