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Running
on
Zero
Running
on
Zero
File size: 1,294 Bytes
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# Copyright 2025 ByteDance and/or its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
def mel2token_to_dur(mel2token, T_txt=None, max_dur=None):
is_torch = isinstance(mel2token, torch.Tensor)
has_batch_dim = True
if not is_torch:
mel2token = torch.LongTensor(mel2token)
if T_txt is None:
T_txt = mel2token.max()
if len(mel2token.shape) == 1:
mel2token = mel2token[None, ...]
has_batch_dim = False
B, _ = mel2token.shape
dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token))
dur = dur[:, 1:]
if max_dur is not None:
dur = dur.clamp(max=max_dur)
if not is_torch:
dur = dur.numpy()
if not has_batch_dim:
dur = dur[0]
return dur
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