mirror of
https://github.com/FunAudioLLM/CosyVoice.git
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94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
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# 2024 Alibaba Inc (authors: Xiang Lyu)
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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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# Modified from ESPnet(https://github.com/espnet/espnet)
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"""Unility functions for Transformer."""
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from typing import List
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import torch
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IGNORE_ID = -1
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def pad_list(xs: List[torch.Tensor], pad_value: int):
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"""Perform padding for the list of tensors.
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Args:
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xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
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pad_value (float): Value for padding.
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Returns:
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Tensor: Padded tensor (B, Tmax, `*`).
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Examples:
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>>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
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>>> x
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[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
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>>> pad_list(x, 0)
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tensor([[1., 1., 1., 1.],
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[1., 1., 0., 0.],
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[1., 0., 0., 0.]])
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"""
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max_len = max([len(item) for item in xs])
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batchs = len(xs)
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ndim = xs[0].ndim
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if ndim == 1:
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pad_res = torch.zeros(batchs,
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max_len,
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dtype=xs[0].dtype,
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device=xs[0].device)
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elif ndim == 2:
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pad_res = torch.zeros(batchs,
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max_len,
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xs[0].shape[1],
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dtype=xs[0].dtype,
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device=xs[0].device)
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elif ndim == 3:
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pad_res = torch.zeros(batchs,
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max_len,
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xs[0].shape[1],
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xs[0].shape[2],
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dtype=xs[0].dtype,
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device=xs[0].device)
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else:
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raise ValueError(f"Unsupported ndim: {ndim}")
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pad_res.fill_(pad_value)
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for i in range(batchs):
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pad_res[i, :len(xs[i])] = xs[i]
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return pad_res
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def th_accuracy(pad_outputs: torch.Tensor, pad_targets: torch.Tensor,
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ignore_label: int) -> torch.Tensor:
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"""Calculate accuracy.
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Args:
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pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
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pad_targets (LongTensor): Target label tensors (B, Lmax).
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ignore_label (int): Ignore label id.
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Returns:
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torch.Tensor: Accuracy value (0.0 - 1.0).
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"""
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pad_pred = pad_outputs.view(pad_targets.size(0), pad_targets.size(1),
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pad_outputs.size(1)).argmax(2)
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mask = pad_targets != ignore_label
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numerator = torch.sum(
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pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
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denominator = torch.sum(mask)
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return (numerator / denominator).detach()
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