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
|
import os
import tempfile
from typing import Optional
from packaging import version
import torch
import torchaudio
_MIN_VER = version.parse('0.6.0a0')
_MAX_VER = version.parse('0.7.0')
_RUNTIME_VER = version.parse(torchaudio.__version__)
def info(filepath: str) -> torchaudio.backend.sox_io_backend.AudioMetaData:
return torchaudio.info(filepath)
def load(
filepath: str,
frame_offset: int,
num_frames: int,
normalize: bool,
channels_first: bool):
return torchaudio.load(filepath, frame_offset, num_frames, normalize, channels_first)
def save(
filepath: str,
tensor: torch.Tensor,
sample_rate: int,
channels_first: bool = True,
compression: Optional[float] = None,
):
torchaudio.save(filepath, tensor, sample_rate, channels_first, compression)
def generate(output_dir):
if not (_MIN_VER <= _RUNTIME_VER < _MAX_VER):
raise RuntimeError(f'Invalid torchaudio runtime version: {_RUNTIME_VER}')
torchaudio.set_audio_backend('sox_io')
funcs = [
info,
load,
save,
]
os.makedirs(output_dir, exist_ok=True)
for func in funcs:
torch.jit.script(func).save(os.path.join(output_dir, f'{func.__name__}.zip'))
def validate(input_dir):
torchaudio.set_audio_backend('sox_io')
# See https://github.com/pytorch/pytorch/issues/42258
# info_ = torch.jit.load(os.path.join(input_dir, 'info.zip'))
load_ = torch.jit.load(os.path.join(input_dir, 'load.zip'))
save_ = torch.jit.load(os.path.join(input_dir, 'save.zip'))
sample_rate = 44100
normalize = True
channels_first = True
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = os.path.join(temp_dir, 'test.wav')
temp_data = torch.rand(2, sample_rate, dtype=torch.float32)
save_(temp_file, temp_data, sample_rate, channels_first, 0.)
# info_(temp_file)
load_(temp_file, 0, -1, normalize, channels_first)
|
