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# 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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__all__ = ["end_detect"]
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def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))):
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"""End detection.
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described in Eq. (50) of S. Watanabe et al
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"Hybrid CTC/Attention Architecture for End-to-End Speech Recognition"
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:param ended_hyps: dict
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:param i: int
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:param M: int
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:param D_end: float
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:return: bool
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"""
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if len(ended_hyps) == 0:
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return False
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count = 0
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best_hyp = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[0]
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for m in range(M):
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# get ended_hyps with their length is i - m
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hyp_length = i - m
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hyps_same_length = [
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x for x in ended_hyps if len(x["yseq"]) == hyp_length
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]
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if len(hyps_same_length) > 0:
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best_hyp_same_length = sorted(
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hyps_same_length, key=lambda x: x["score"], reverse=True)[0]
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if best_hyp_same_length["score"] - best_hyp["score"] < D_end:
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count += 1
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if count == M:
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return True
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else:
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return False
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