48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
# Copyright (c) 2020 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 numpy as np
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def get_alignment(attn_probs, mel_lens, n_head):
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max_F = 0
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assert attn_probs[0].shape[0] % n_head == 0
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batch_size = int(attn_probs[0].shape[0] // n_head)
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for i in range(len(attn_probs)):
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multi_attn = attn_probs[i].numpy()
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for j in range(n_head):
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attn = multi_attn[j * batch_size:(j + 1) * batch_size]
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F = score_F(attn)
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if max_F < F:
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max_F = F
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max_attn = attn
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alignment = compute_duration(max_attn, mel_lens)
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return alignment, max_attn
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def score_F(attn):
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max = np.max(attn, axis=-1)
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mean = np.mean(max)
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return mean
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def compute_duration(attn, mel_lens):
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alignment = np.zeros([attn.shape[0], attn.shape[2]])
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mel_lens = mel_lens.numpy()
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for i in range(attn.shape[0]):
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for j in range(mel_lens[i]):
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max_index = np.argmax(attn[i, j])
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alignment[i, max_index] += 1
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return alignment
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