forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathaudio_processor.py
More file actions
71 lines (57 loc) · 2.15 KB
/
audio_processor.py
File metadata and controls
71 lines (57 loc) · 2.15 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import List, Union
import numpy as np
import torch
PipelineAudioInput = Union[
np.ndarray,
torch.Tensor,
List[np.ndarray],
List[torch.Tensor],
]
def is_valid_audio(audio) -> bool:
r"""
Checks if the input is a valid audio.
A valid audio can be:
- A 2D or 3D `np.ndarray` or `torch.Tensor` (e.g., mono or multi-channel waveform, or batched audio data).
Args:
audio (`Union[np.ndarray, torch.Tensor]`):
The audio to validate. It can be a NumPy array or a torch tensor.
Returns:
`bool`:
`True` if the input is a valid audio, `False` otherwise.
"""
return isinstance(audio, (np.ndarray, torch.Tensor)) and audio.ndim in (2, 3)
def is_valid_audio_audiolist(audios):
r"""
Checks if the input is a valid audio or list of audios.
The input can be one of the following formats:
- A 4D tensor or numpy array (batch of audios).
- A valid single audio: `np.ndarray` or `torch.Tensor`.
- A list of valid audios.
Args:
audios (`Union[np.ndarray, torch.Tensor, List]`):
The audio(s) to check. Can be a batch of audios (4D tensor/array), a single audio, or a list of valid
audios.
Returns:
`bool`:
`True` if the input is valid, `False` otherwise.
"""
if isinstance(audios, (np.ndarray, torch.Tensor)) and audios.ndim == 4:
return True
elif is_valid_audio(audios):
return True
elif isinstance(audios, list):
return all(is_valid_audio(audio) for audio in audios)
return False