def test_chainer_save_and_load():
args = make_arg()
model = ch_asr.E2E(10, 5, args)
# initialize randomly
for p in model.params():
p.data = np.random.randn(*p.data.shape)
tmppath = tempfile.mktemp()
chainer.serializers.save_npz(tmppath, model)
p_saved = [p.data for p in model.params()]
# set constant value
for p in model.params():
p.data = np.zeros_like(p.data)
asr_utils.chainer_load(tmppath, model)
for p1, p2 in zip(p_saved, model.params()):
np.testing.assert_array_equal(p1, p2.data)
if os.path.exists(tmppath):
os.remove(tmppath)
def test_downloaded_asr_model_decodable(module, download_info):
# download model
print(download_info[0])
tmpdir = tempfile.mkdtemp(prefix="tmp_", dir=".")
model_path = download_zip_from_google_drive(tmpdir, download_info[1])
# load trained model parameters
m = importlib.import_module(module)
idim, odim, train_args = get_model_conf(model_path)
model = m.E2E(idim, odim, train_args)
if "chainer" in module:
chainer_load(model_path, model)
else:
torch_load(model_path, model)
with torch.no_grad(), chainer.no_backprop_mode():
in_data = np.random.randn(128, idim)
model.recognize(in_data, train_args, train_args.char_list) # decodable
# remove
if os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
def recog(args):
"""Decode with the given args.
Args:
args (namespace): The program arguments.
"""
# display chainer version
logging.info('chainer version = ' + chainer.__version__)
set_deterministic_chainer(args)
# 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)
# To be compatible with v.0.3.0 models
if hasattr(train_args, "model_module"):
model_module = train_args.model_module
else:
model_module = "espnet.nets.chainer_backend.e2e_asr:E2E"
model_class = dynamic_import(model_module)
model = model_class(idim, odim, train_args)
assert isinstance(model, ASRInterface)
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.layer,
rnnlm_args.unit))
chainer_load(args.rnnlm, rnnlm)
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_chainer.ClassifierWithState(
lm_chainer.RNNLM(len(word_dict), rnnlm_args.layer,
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']
load_inputs_and_targets = LoadInputsAndTargets(
mode='asr',
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf,
preprocess_args={'train': False} # Switch the mode of preprocessing
)
# 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()))
batch = [(name, js[name])]
feat = load_inputs_and_targets(batch)[0][0]
nbest_hyps = model.recognize(feat, args, train_args.char_list,
rnnlm)
new_js[name] = add_results_to_json(js[name], nbest_hyps,
train_args.char_list)
with open(args.result_label, 'wb') as f:
f.write(
json.dumps({
'utts': new_js
},
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
def recog(args):
"""Decode with the given args
:param Namespace args: The program arguments
"""
# display chainer version
logging.info('chainer version = ' + chainer.__version__)
set_deterministic_chainer(args)
# 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)
model = E2E(idim, odim, train_args)
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.layer,
rnnlm_args.unit))
chainer_load(args.rnnlm, rnnlm)
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_chainer.ClassifierWithState(
lm_chainer.RNNLM(len(word_dict), rnnlm_args.layer,
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']
load_inputs_and_targets = LoadInputsAndTargets(
mode='asr',
load_output=False,
sort_in_input_length=False,
preprocess_conf=train_args.preprocess_conf
if args.preprocess_conf is None else args.preprocess_conf)
# 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()))
batch = [(name, js[name])]
with using_transform_config({'train': False}):
feat = load_inputs_and_targets(batch)[0][0]
nbest_hyps = model.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'''
# 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.layer,
rnnlm_args.unit))
chainer_load(args.rnnlm, rnnlm)
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_chainer.ClassifierWithState(
lm_chainer.RNNLM(len(word_dict), rnnlm_args.layer,
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'))
