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import itertools
from typing import Optional
import torch
import torchaudio
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
skipIfNoExec,
skipIfNoExtension,
get_wav_data,
save_wav,
load_wav,
sox_utils,
)
from .common import (
name_func,
)
def py_info_func(filepath: str) -> torchaudio.backend.sox_io_backend.AudioMetaData:
return torchaudio.info(filepath)
def py_load_func(filepath: str, normalize: bool, channels_first: bool):
return torchaudio.load(
filepath, normalize=normalize, channels_first=channels_first)
def py_save_func(
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)
@skipIfNoExec('sox')
@skipIfNoExtension
class SoxIO(TempDirMixin, TorchaudioTestCase):
"""TorchScript-ability Test suite for `sox_io_backend`"""
backend = 'sox_io'
@parameterized.expand(list(itertools.product(
['float32', 'int32', 'int16', 'uint8'],
[8000, 16000],
[1, 2],
)), name_func=name_func)
def test_info_wav(self, dtype, sample_rate, num_channels):
"""`sox_io_backend.info` is torchscript-able and returns the same result"""
audio_path = self.get_temp_path(f'{dtype}_{sample_rate}_{num_channels}.wav')
data = get_wav_data(dtype, num_channels, normalize=False, num_frames=1 * sample_rate)
save_wav(audio_path, data, sample_rate)
script_path = self.get_temp_path('info_func.zip')
torch.jit.script(py_info_func).save(script_path)
ts_info_func = torch.jit.load(script_path)
py_info = py_info_func(audio_path)
ts_info = ts_info_func(audio_path)
assert py_info.sample_rate == ts_info.sample_rate
assert py_info.num_frames == ts_info.num_frames
assert py_info.num_channels == ts_info.num_channels
@parameterized.expand(list(itertools.product(
['float32', 'int32', 'int16', 'uint8'],
[8000, 16000],
[1, 2],
[False, True],
[False, True],
)), name_func=name_func)
def test_load_wav(self, dtype, sample_rate, num_channels, normalize, channels_first):
"""`sox_io_backend.load` is torchscript-able and returns the same result"""
audio_path = self.get_temp_path(f'test_load_{dtype}_{sample_rate}_{num_channels}_{normalize}.wav')
data = get_wav_data(dtype, num_channels, normalize=False, num_frames=1 * sample_rate)
save_wav(audio_path, data, sample_rate)
script_path = self.get_temp_path('load_func.zip')
torch.jit.script(py_load_func).save(script_path)
ts_load_func = torch.jit.load(script_path)
py_data, py_sr = py_load_func(
audio_path, normalize=normalize, channels_first=channels_first)
ts_data, ts_sr = ts_load_func(
audio_path, normalize=normalize, channels_first=channels_first)
self.assertEqual(py_sr, ts_sr)
self.assertEqual(py_data, ts_data)
@parameterized.expand(list(itertools.product(
['float32', 'int32', 'int16', 'uint8'],
[8000, 16000],
[1, 2],
)), name_func=name_func)
def test_save_wav(self, dtype, sample_rate, num_channels):
script_path = self.get_temp_path('save_func.zip')
torch.jit.script(py_save_func).save(script_path)
ts_save_func = torch.jit.load(script_path)
expected = get_wav_data(dtype, num_channels)
py_path = self.get_temp_path(f'test_save_py_{dtype}_{sample_rate}_{num_channels}.wav')
ts_path = self.get_temp_path(f'test_save_ts_{dtype}_{sample_rate}_{num_channels}.wav')
py_save_func(py_path, expected, sample_rate, True, None)
ts_save_func(ts_path, expected, sample_rate, True, None)
py_data, py_sr = load_wav(py_path)
ts_data, ts_sr = load_wav(ts_path)
self.assertEqual(sample_rate, py_sr)
self.assertEqual(sample_rate, ts_sr)
self.assertEqual(expected, py_data)
self.assertEqual(expected, ts_data)
@parameterized.expand(list(itertools.product(
[8000, 16000],
[1, 2],
list(range(9)),
)), name_func=name_func)
def test_save_flac(self, sample_rate, num_channels, compression_level):
script_path = self.get_temp_path('save_func.zip')
torch.jit.script(py_save_func).save(script_path)
ts_save_func = torch.jit.load(script_path)
expected = get_wav_data('float32', num_channels)
py_path = self.get_temp_path(f'test_save_py_{sample_rate}_{num_channels}_{compression_level}.flac')
ts_path = self.get_temp_path(f'test_save_ts_{sample_rate}_{num_channels}_{compression_level}.flac')
py_save_func(py_path, expected, sample_rate, True, compression_level)
ts_save_func(ts_path, expected, sample_rate, True, compression_level)
# converting to 32 bit because flac file has 24 bit depth which scipy cannot handle.
py_path_wav = f'{py_path}.wav'
ts_path_wav = f'{ts_path}.wav'
sox_utils.convert_audio_file(py_path, py_path_wav, bit_depth=32)
sox_utils.convert_audio_file(ts_path, ts_path_wav, bit_depth=32)
py_data, py_sr = load_wav(py_path_wav, normalize=True)
ts_data, ts_sr = load_wav(ts_path_wav, normalize=True)
self.assertEqual(sample_rate, py_sr)
self.assertEqual(sample_rate, ts_sr)
self.assertEqual(expected, py_data)
self.assertEqual(expected, ts_data)
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