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53 lines
1.8 KiB
53 lines
1.8 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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 numpy as np
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__all__ = ["label_smoothing_dist"]
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# TODO(takaaki-hori): add different smoothing methods
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def label_smoothing_dist(odim, lsm_type, transcript=None, blank=0):
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"""Obtain label distribution for loss smoothing.
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:param odim:
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:param lsm_type:
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:param blank:
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:param transcript:
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:return:
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"""
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if transcript is not None:
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with open(transcript, "rb") as f:
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trans_json = json.load(f)["utts"]
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if lsm_type == "unigram":
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assert transcript is not None, (
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"transcript is required for %s label smoothing" % lsm_type)
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labelcount = np.zeros(odim)
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for k, v in trans_json.items():
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ids = np.array([int(n) for n in v["output"][0]["tokenid"].split()])
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# to avoid an error when there is no text in an uttrance
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if len(ids) > 0:
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labelcount[ids] += 1
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labelcount[odim - 1] = len(transcript) # count <eos>
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labelcount[labelcount == 0] = 1 # flooring
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labelcount[blank] = 0 # remove counts for blank
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labeldist = labelcount.astype(np.float32) / np.sum(labelcount)
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else:
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logging.error("Error: unexpected label smoothing type: %s" % lsm_type)
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sys.exit()
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return labeldist
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