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46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo)
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#
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# See ../../LICENSE for clarification regarding multiple authors
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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 torch
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def encode_supervisions(nnet_output_shape, batch):
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"""
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In GRID, the lengths of all samples are same.
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And here, we don't deploy cut operation on it.
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So, the start frame is always 0 among all samples.
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"""
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N, T, D = nnet_output_shape
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supervisions_idx = torch.arange(0, N).to(torch.int32)
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start_frames = [0 for _ in range(N)]
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supervisions_start_frame = torch.tensor(start_frames).to(torch.int32)
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num_frames = [T for _ in range(N)]
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supervisions_num_frames = torch.tensor(num_frames).to(torch.int32)
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supervision_segments = torch.stack(
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(
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supervisions_idx,
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supervisions_start_frame,
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supervisions_num_frames,
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),
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1,
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).to(torch.int32)
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texts = batch["txt"]
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return supervision_segments, texts
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