95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import math
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import numpy as np
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def _load_json_cmvn(json_cmvn_file):
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""" Load the json format cmvn stats file and calculate cmvn
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Args:
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json_cmvn_file: cmvn stats file in json format
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Returns:
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a numpy array of [means, vars]
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"""
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with open(json_cmvn_file) as f:
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cmvn_stats = json.load(f)
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means = cmvn_stats['mean_stat']
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variance = cmvn_stats['var_stat']
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count = cmvn_stats['frame_num']
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for i in range(len(means)):
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means[i] /= count
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variance[i] = variance[i] / count - means[i] * means[i]
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if variance[i] < 1.0e-20:
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variance[i] = 1.0e-20
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variance[i] = 1.0 / math.sqrt(variance[i])
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cmvn = np.array([means, variance])
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return cmvn
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def _load_kaldi_cmvn(kaldi_cmvn_file):
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""" Load the kaldi format cmvn stats file and calculate cmvn
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Args:
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kaldi_cmvn_file: kaldi text style global cmvn file, which
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is generated by:
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compute-cmvn-stats --binary=false scp:feats.scp global_cmvn
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Returns:
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a numpy array of [means, vars]
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"""
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means = []
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variance = []
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with open(kaldi_cmvn_file, 'r') as fid:
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# kaldi binary file start with '\0B'
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if fid.read(2) == '\0B':
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logging.error('kaldi cmvn binary file is not supported, please '
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'recompute it by: compute-cmvn-stats --binary=false '
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' scp:feats.scp global_cmvn')
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sys.exit(1)
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fid.seek(0)
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arr = fid.read().split()
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assert (arr[0] == '[')
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assert (arr[-2] == '0')
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assert (arr[-1] == ']')
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feat_dim = int((len(arr) - 2 - 2) / 2)
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for i in range(1, feat_dim + 1):
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means.append(float(arr[i]))
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count = float(arr[feat_dim + 1])
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for i in range(feat_dim + 2, 2 * feat_dim + 2):
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variance.append(float(arr[i]))
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for i in range(len(means)):
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means[i] /= count
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variance[i] = variance[i] / count - means[i] * means[i]
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if variance[i] < 1.0e-20:
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variance[i] = 1.0e-20
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variance[i] = 1.0 / math.sqrt(variance[i])
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cmvn = np.array([means, variance])
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return cmvn
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def load_cmvn(cmvn_file, is_json):
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if is_json:
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cmvn = _load_json_cmvn(cmvn_file)
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else:
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cmvn = _load_kaldi_cmvn(cmvn_file)
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return cmvn[0], cmvn[1]
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