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Zero
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# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import math
import numpy as np
def _load_json_cmvn(json_cmvn_file):
""" Load the json format cmvn stats file and calculate cmvn
Args:
json_cmvn_file: cmvn stats file in json format
Returns:
a numpy array of [means, vars]
"""
with open(json_cmvn_file) as f:
cmvn_stats = json.load(f)
means = cmvn_stats['mean_stat']
variance = cmvn_stats['var_stat']
count = cmvn_stats['frame_num']
for i in range(len(means)):
means[i] /= count
variance[i] = variance[i] / count - means[i] * means[i]
if variance[i] < 1.0e-20:
variance[i] = 1.0e-20
variance[i] = 1.0 / math.sqrt(variance[i])
cmvn = np.array([means, variance])
return cmvn
def _load_kaldi_cmvn(kaldi_cmvn_file):
""" Load the kaldi format cmvn stats file and calculate cmvn
Args:
kaldi_cmvn_file: kaldi text style global cmvn file, which
is generated by:
compute-cmvn-stats --binary=false scp:feats.scp global_cmvn
Returns:
a numpy array of [means, vars]
"""
means = []
variance = []
with open(kaldi_cmvn_file, 'r') as fid:
# kaldi binary file start with '\0B'
if fid.read(2) == '\0B':
logging.error('kaldi cmvn binary file is not supported, please '
'recompute it by: compute-cmvn-stats --binary=false '
' scp:feats.scp global_cmvn')
sys.exit(1)
fid.seek(0)
arr = fid.read().split()
assert (arr[0] == '[')
assert (arr[-2] == '0')
assert (arr[-1] == ']')
feat_dim = int((len(arr) - 2 - 2) / 2)
for i in range(1, feat_dim + 1):
means.append(float(arr[i]))
count = float(arr[feat_dim + 1])
for i in range(feat_dim + 2, 2 * feat_dim + 2):
variance.append(float(arr[i]))
for i in range(len(means)):
means[i] /= count
variance[i] = variance[i] / count - means[i] * means[i]
if variance[i] < 1.0e-20:
variance[i] = 1.0e-20
variance[i] = 1.0 / math.sqrt(variance[i])
cmvn = np.array([means, variance])
return cmvn
def load_cmvn(cmvn_file, is_json):
if is_json:
cmvn = _load_json_cmvn(cmvn_file)
else:
cmvn = _load_kaldi_cmvn(cmvn_file)
return cmvn[0], cmvn[1]
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