4f2ea4913c
* squash Signed-off-by: Jason <jasoli@nvidia.com> * add comments Signed-off-by: Jason <jasoli@nvidia.com> * style and cleanup Signed-off-by: Jason <jasoli@nvidia.com> * cleanup Signed-off-by: Jason <jasoli@nvidia.com> * add new test file Signed-off-by: Jason <jasoli@nvidia.com> * syntax Signed-off-by: Jason <jasoli@nvidia.com> * style Signed-off-by: Jason <jasoli@nvidia.com> * typo Signed-off-by: Jason <jasoli@nvidia.com> * update Signed-off-by: Jason <jasoli@nvidia.com> * update Signed-off-by: Jason <jasoli@nvidia.com> * update Signed-off-by: Jason <jasoli@nvidia.com> * try again Signed-off-by: Jason <jasoli@nvidia.com> * wip Signed-off-by: Jason <jasoli@nvidia.com> * style; ci should fail Signed-off-by: Jason <jasoli@nvidia.com> * final Signed-off-by: Jason <jasoli@nvidia.com>
27 lines
1 KiB
Python
27 lines
1 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. 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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from scipy.stats import betabinom
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def beta_binomial_prior_distribution(phoneme_count, mel_count, scaling_factor=1.0):
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x = np.arange(0, phoneme_count)
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mel_text_probs = []
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for i in range(1, mel_count + 1):
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a, b = scaling_factor * i, scaling_factor * (mel_count + 1 - i)
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mel_i_prob = betabinom(phoneme_count, a, b).pmf(x)
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mel_text_probs.append(mel_i_prob)
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return np.array(mel_text_probs)
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