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Unverified Commit 0e5581cb authored by moto's avatar moto Committed by GitHub
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Simplify and abstract away asset access in test (#542) 

This PR aims the following things;
1. Introduce and adopt helper function `get_asset_path` that abstract the logic to construct path to test assets.
2. Remove `create_temp_assets_dir` anywhere except `test_io`.

The benefits of doing so are,
a. the test code becomes simpler (no manual construction of asset path with `os.path.join`)
b. No unnecessary directory creation and file copies.

For 2. and b. tests in `test_io.py` (or tests that use `torchaudio.save`) are the only tests that need to write file to the disc, where the use of temporary directory makes it cleaner, therefore, `create_temp_assets_dir` is not necessary elsewhere. (still, `test_io` does not need to copy the entire asset directory, but that's not the point here.)

Also if any test is accidentally overwriting an asset data, not using a copy will make us aware of such behavior, so it is better to get rid of `create_temp_assets_dir`.
parent 0fa07595
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Showing with 83 additions and 111 deletions
test/common_utils.py
View file @ 0e5581cb
......@@ -6,10 +6,15 @@ from shutil import copytree
import torch
import torchaudio
TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__))
_TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__))
BACKENDS = torchaudio._backend._audio_backends
def get_asset_path(*paths):
"""Return full path of a test asset"""
return os.path.join(_TEST_DIR_PATH, 'assets', *paths)
def create_temp_assets_dir():
"""
Creates a temporary directory and moves all files from test/assets there.
......@@ -17,7 +22,7 @@ def create_temp_assets_dir():
and object.
"""
tmp_dir = tempfile.TemporaryDirectory()
copytree(os.path.join(TEST_DIR_PATH, "assets"),
copytree(os.path.join(_TEST_DIR_PATH, "assets"),
os.path.join(tmp_dir.name, "assets"))
return tmp_dir.name, tmp_dir
......@@ -65,11 +70,7 @@ def AudioBackendScope(new_backend):
def filter_backends_with_mp3(backends):
# Filter out backends that do not support mp3
test_dirpath, _ = create_temp_assets_dir()
test_filepath = os.path.join(
test_dirpath, "assets", "steam-train-whistle-daniel_simon.mp3"
)
test_filepath = get_asset_path('steam-train-whistle-daniel_simon.mp3')
def supports_mp3(backend):
try:
......
test/test_batch_consistency.py
View file @ 0e5581cb
"""Test numerical consistency among single input and batched input."""
import os
import unittest
import torch
......@@ -54,7 +53,7 @@ class TestFunctional(unittest.TestCase):
'440Hz_44100Hz_16bit_05sec.wav', # 1ch
]
for filename in filenames:
filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', filename)
filepath = common_utils.get_asset_path(filename)
waveform, sample_rate = torchaudio.load(filepath)
_test_batch(F.detect_pitch_frequency, waveform, sample_rate)
......@@ -133,8 +132,7 @@ class TestTransforms(unittest.TestCase):
torch.testing.assert_allclose(computed, expected)
def test_batch_mulaw(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100
# Single then transform then batch
......@@ -159,8 +157,7 @@ class TestTransforms(unittest.TestCase):
torch.testing.assert_allclose(computed, expected)
def test_batch_spectrogram(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100
# Single then transform then batch
......@@ -171,8 +168,7 @@ class TestTransforms(unittest.TestCase):
torch.testing.assert_allclose(computed, expected)
def test_batch_melspectrogram(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100
# Single then transform then batch
......@@ -185,8 +181,7 @@ class TestTransforms(unittest.TestCase):
@unittest.skipIf("sox" not in BACKENDS, "sox not available")
@AudioBackendScope("sox")
def test_batch_mfcc(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.mp3')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3')
waveform, _ = torchaudio.load(test_filepath)
# Single then transform then batch
......@@ -197,8 +192,7 @@ class TestTransforms(unittest.TestCase):
torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5)
def test_batch_TimeStretch(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100
kwargs = {
......@@ -232,8 +226,7 @@ class TestTransforms(unittest.TestCase):
torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5)
def test_batch_Fade(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100
fade_in_len = 3000
fade_out_len = 3000
......@@ -246,8 +239,7 @@ class TestTransforms(unittest.TestCase):
torch.testing.assert_allclose(computed, expected)
def test_batch_Vol(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100
# Single then transform then batch
......
test/test_compliance_kaldi.py
View file @ 0e5581cb
......@@ -5,7 +5,9 @@ import torch
import torchaudio
import torchaudio.compliance.kaldi as kaldi
import unittest
from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir
import common_utils
from common_utils import AudioBackendScope, BACKENDS
def extract_window(window, wave, f, frame_length, frame_shift, snip_edges):
......@@ -45,10 +47,9 @@ def extract_window(window, wave, f, frame_length, frame_shift, snip_edges):
class Test_Kaldi(unittest.TestCase):
test_dirpath, test_dir = create_temp_assets_dir()
test_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file.wav')
test_8000_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file_8000.wav')
kaldi_output_dir = os.path.join(test_dirpath, 'assets', 'kaldi')
test_filepath = common_utils.get_asset_path('kaldi_file.wav')
test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav')
kaldi_output_dir = common_utils.get_asset_path('kaldi')
test_filepaths = {prefix: [] for prefix in compliance.utils.TEST_PREFIX}
# separating test files by their types (e.g 'spec', 'fbank', etc.)
......@@ -90,8 +91,7 @@ class Test_Kaldi(unittest.TestCase):
def _create_data_set(self):
# used to generate the dataset to test on. this is not used in testing (offline procedure)
test_dirpath = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
test_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file.wav')
test_filepath = common_utils.get_asset_path('kaldi_file.wav')
sr = 16000
x = torch.arange(0, 20).float()
# between [-6,6]
......
test/test_dataloader.py
View file @ 0e5581cb
import unittest
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import torchaudio
import math
import os
from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir
from torch.utils.data import Dataset, DataLoader
import common_utils
from common_utils import AudioBackendScope, BACKENDS
@unittest.skipIf("sox" not in BACKENDS, "sox not available")
class TORCHAUDIODS(Dataset):
test_dirpath, test_dir = create_temp_assets_dir()
def __init__(self):
self.asset_dirpath = os.path.join(self.test_dirpath, "assets")
sound_files = ["sinewave.wav", "steam-train-whistle-daniel_simon.mp3"]
self.data = [os.path.join(self.asset_dirpath, fn) for fn in sound_files]
self.si, self.ei = torchaudio.info(os.path.join(self.asset_dirpath, "sinewave.wav"))
self.data = [common_utils.get_asset_path(fn) for fn in sound_files]
self.si, self.ei = torchaudio.info(common_utils.get_asset_path("sinewave.wav"))
self.si.precision = 16
self.E = torchaudio.sox_effects.SoxEffectsChain()
self.E.append_effect_to_chain("rate", [self.si.rate]) # resample to 16000hz
......
test/test_datasets.py
View file @ 0e5581cb
import os
import unittest
from torchaudio.datasets.commonvoice import COMMONVOICE
......@@ -13,8 +12,7 @@ import common_utils
class TestDatasets(unittest.TestCase):
test_dirpath, test_dir = common_utils.create_temp_assets_dir()
path = os.path.join(test_dirpath, "assets")
path = common_utils.get_asset_path()
def test_yesno(self):
data = YESNO(self.path)
......
test/test_functional.py
View file @ 0e5581cb
import math
import os
import unittest
import torch
......@@ -301,10 +300,8 @@ class TestIstft(unittest.TestCase):
class TestDetectPitchFrequency(unittest.TestCase):
def test_pitch(self):
test_filepath_100 = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', "100Hz_44100Hz_16bit_05sec.wav")
test_filepath_440 = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', "440Hz_44100Hz_16bit_05sec.wav")
test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav")
test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav")
# Files from https://www.mediacollege.com/audio/tone/download/
tests = [
......
test/test_kaldi_io.py
View file @ 0e5581cb
import os
import unittest
import torch
import torchaudio.kaldi_io as kio
import unittest
import common_utils
class Test_KaldiIO(unittest.TestCase):
data1 = [[1, 2, 3], [11, 12, 13], [21, 22, 23]]
data2 = [[31, 32, 33], [41, 42, 43], [51, 52, 53]]
test_dirpath, test_dir = common_utils.create_temp_assets_dir()
def _test_helper(self, file_name, expected_data, fn, expected_dtype):
""" Takes a file_name to the input data and a function fn to extract the
data. It compares the extracted data to the expected_data. The expected_dtype
will be used to check that the extracted data is of the right type.
"""
test_filepath = os.path.join(self.test_dirpath, "assets", file_name)
test_filepath = common_utils.get_asset_path(file_name)
expected_output = {'key' + str(idx + 1): torch.tensor(val, dtype=expected_dtype)
for idx, val in enumerate(expected_data)}
......
test/test_librosa_compatibility.py
View file @ 0e5581cb
......@@ -149,7 +149,7 @@ def test_phase_vocoder(complex_specgrams, rate, hop_length):
def _load_audio_asset(*asset_paths, **kwargs):
file_path = os.path.join(common_utils.TEST_DIR_PATH, 'assets', *asset_paths)
file_path = common_utils.get_asset_path(*asset_paths)
sound, sample_rate = torchaudio.load(file_path, **kwargs)
return sound, sample_rate
......
test/test_sox_compatibility.py
View file @ 0e5581cb
import os
import unittest
import torch
......@@ -6,16 +5,15 @@ import torchaudio
import torchaudio.functional as F
import torchaudio.transforms as T
from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir
import common_utils
from common_utils import AudioBackendScope, BACKENDS
class TestFunctionalFiltering(unittest.TestCase):
test_dirpath, test_dir = create_temp_assets_dir()
@unittest.skipIf("sox" not in BACKENDS, "sox not available")
@AudioBackendScope("sox")
def test_gain(self):
test_filepath = os.path.join(self.test_dirpath, "assets", "steam-train-whistle-daniel_simon.wav")
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath)
waveform_gain = F.gain(waveform, 3)
......@@ -31,7 +29,7 @@ class TestFunctionalFiltering(unittest.TestCase):
@unittest.skipIf("sox" not in BACKENDS, "sox not available")
@AudioBackendScope("sox")
def test_dither(self):
test_filepath = os.path.join(self.test_dirpath, "assets", "steam-train-whistle-daniel_simon.wav")
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath)
waveform_dithered = F.dither(waveform)
......@@ -53,7 +51,7 @@ class TestFunctionalFiltering(unittest.TestCase):
@unittest.skipIf("sox" not in BACKENDS, "sox not available")
@AudioBackendScope("sox")
def test_vctk_transform_pipeline(self):
test_filepath_vctk = os.path.join(self.test_dirpath, "assets/VCTK-Corpus/wav48/p224/", "p224_002.wav")
test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav')
wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk)
# rate
......@@ -76,14 +74,13 @@ class TestFunctionalFiltering(unittest.TestCase):
@unittest.skipIf("sox" not in BACKENDS, "sox not available")
@AudioBackendScope("sox")
def test_lowpass(self):
"""
Test biquad lowpass filter, compare to SoX implementation
"""
CUTOFF_FREQ = 3000
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("lowpass", [CUTOFF_FREQ])
......@@ -103,7 +100,7 @@ class TestFunctionalFiltering(unittest.TestCase):
CUTOFF_FREQ = 2000
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("highpass", [CUTOFF_FREQ])
......@@ -125,7 +122,7 @@ class TestFunctionalFiltering(unittest.TestCase):
CENTRAL_FREQ = 1000
Q = 0.707
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("allpass", [CENTRAL_FREQ, str(Q) + 'q'])
......@@ -147,7 +144,7 @@ class TestFunctionalFiltering(unittest.TestCase):
Q = 0.707
CONST_SKIRT_GAIN = True
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("bandpass", ["-c", CENTRAL_FREQ, str(Q) + 'q'])
......@@ -169,7 +166,7 @@ class TestFunctionalFiltering(unittest.TestCase):
Q = 0.707
CONST_SKIRT_GAIN = False
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("bandpass", [CENTRAL_FREQ, str(Q) + 'q'])
......@@ -190,7 +187,7 @@ class TestFunctionalFiltering(unittest.TestCase):
CENTRAL_FREQ = 1000
Q = 0.707
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("bandreject", [CENTRAL_FREQ, str(Q) + 'q'])
......@@ -212,7 +209,7 @@ class TestFunctionalFiltering(unittest.TestCase):
Q = 0.707
NOISE = True
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("band", ["-n", CENTRAL_FREQ, str(Q) + 'q'])
......@@ -234,7 +231,7 @@ class TestFunctionalFiltering(unittest.TestCase):
Q = 0.707
NOISE = False
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("band", [CENTRAL_FREQ, str(Q) + 'q'])
......@@ -256,7 +253,7 @@ class TestFunctionalFiltering(unittest.TestCase):
Q = 0.707
GAIN = 40
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("treble", [GAIN, CENTRAL_FREQ, str(Q) + 'q'])
......@@ -274,7 +271,7 @@ class TestFunctionalFiltering(unittest.TestCase):
Test biquad deemph filter, compare to SoX implementation
"""
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("deemph")
......@@ -292,7 +289,7 @@ class TestFunctionalFiltering(unittest.TestCase):
Test biquad riaa filter, compare to SoX implementation
"""
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("riaa")
......@@ -314,7 +311,7 @@ class TestFunctionalFiltering(unittest.TestCase):
Q = 0.707
GAIN = 1
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("equalizer", [CENTER_FREQ, Q, GAIN])
......@@ -329,7 +326,7 @@ class TestFunctionalFiltering(unittest.TestCase):
@AudioBackendScope("sox")
def test_perf_biquad_filtering(self):
fn_sine = os.path.join(self.test_dirpath, "assets", "whitenoise.wav")
fn_sine = common_utils.get_asset_path('whitenoise.wav')
b0 = 0.4
b1 = 0.2
......
test/test_sox_effects.py
View file @ 0e5581cb
......@@ -2,16 +2,14 @@ import unittest
import torch
import torchaudio
import math
import os
from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir
import common_utils
from common_utils import AudioBackendScope, BACKENDS
@unittest.skipIf("sox" not in BACKENDS, "sox not available")
class Test_SoxEffectsChain(unittest.TestCase):
test_dirpath, test_dir = create_temp_assets_dir()
test_filepath = os.path.join(test_dirpath, "assets",
"steam-train-whistle-daniel_simon.mp3")
test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3")
@classmethod
def setUpClass(cls):
......@@ -22,7 +20,7 @@ class Test_SoxEffectsChain(unittest.TestCase):
torchaudio.shutdown_sox()
def test_single_channel(self):
fn_sine = os.path.join(self.test_dirpath, "assets", "sinewave.wav")
fn_sine = common_utils.get_asset_path("sinewave.wav")
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(fn_sine)
E.append_effect_to_chain("echos", [0.8, 0.7, 40, 0.25, 63, 0.3])
......
test/test_torchscript_consistency.py
View file @ 0e5581cb
"""Test suites for jit-ability and its numerical compatibility"""
import os
import unittest
import torch
......@@ -81,8 +80,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, tensor)
def test_detect_pitch_frequency(self):
filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.mp3')
filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3')
waveform, _ = torchaudio.load(filepath)
def func(tensor):
......@@ -213,7 +211,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, tensor, shape_only=True)
def test_lfilter(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav')
filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -254,7 +252,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_lowpass(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav')
filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -265,7 +263,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_highpass(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav')
filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -276,7 +274,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_allpass(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav')
filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -288,7 +286,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_bandpass_with_csg(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -301,7 +299,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_bandpass_withou_csg(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -314,7 +312,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_bandreject(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -326,7 +324,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_band_with_noise(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -339,7 +337,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_band_without_noise(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -352,7 +350,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_treble(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -365,7 +363,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_deemph(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -375,7 +373,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_riaa(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -385,7 +383,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_equalizer(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -398,7 +396,7 @@ class _FunctionalTestMixin:
self._assert_consistency(func, waveform)
def test_perf_biquad_filtering(self):
filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
def func(tensor):
......@@ -469,8 +467,7 @@ class _TransformsTestMixin:
)
def test_Fade(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath)
fade_in_len = 3000
fade_out_len = 3000
......@@ -485,8 +482,7 @@ class _TransformsTestMixin:
self._assert_consistency(T.TimeMasking(time_mask_param=30, iid_masks=False), tensor)
def test_Vol(self):
test_filepath = os.path.join(
common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath)
self._assert_consistency(T.Vol(1.1), waveform)
......
test/test_transforms.py
View file @ 0e5581cb
import math
import os
import unittest
import torch
......@@ -7,7 +6,7 @@ import torchaudio
import torchaudio.transforms as transforms
import torchaudio.functional as F
from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir
import common_utils
class Tester(unittest.TestCase):
......@@ -19,10 +18,6 @@ class Tester(unittest.TestCase):
waveform = (torch.cos(2 * math.pi * torch.arange(0, 4 * sample_rate).float() * freq / sample_rate))
waveform.unsqueeze_(0) # (1, 64000)
waveform = (waveform * volume * 2**31).long()
# file for stereo stft test
test_dirpath, test_dir = create_temp_assets_dir()
test_filepath = os.path.join(test_dirpath, 'assets',
'steam-train-whistle-daniel_simon.wav')
def scale(self, waveform, factor=2.0**31):
# scales a waveform by a factor
......@@ -45,7 +40,8 @@ class Tester(unittest.TestCase):
self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.)
def test_AmplitudeToDB(self):
waveform, sample_rate = torchaudio.load(self.test_filepath)
filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, sample_rate = torchaudio.load(filepath)
mag_to_db_transform = transforms.AmplitudeToDB('magnitude', 80.)
power_to_db_transform = transforms.AmplitudeToDB('power', 80.)
......@@ -114,7 +110,8 @@ class Tester(unittest.TestCase):
self.assertTrue(mel_transform2.mel_scale.fb.sum(1).le(1.).all())
self.assertTrue(mel_transform2.mel_scale.fb.sum(1).ge(0.).all())
# check on multi-channel audio
x_stereo, sr_stereo = torchaudio.load(self.test_filepath) # (2, 278756), 44100
filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
x_stereo, sr_stereo = torchaudio.load(filepath) # (2, 278756), 44100
spectrogram_stereo = s2db(mel_transform(x_stereo)) # (2, 128, 1394)
self.assertTrue(spectrogram_stereo.dim() == 3)
self.assertTrue(spectrogram_stereo.size(0) == 2)
......@@ -164,7 +161,7 @@ class Tester(unittest.TestCase):
self.assertTrue(torch_mfcc_norm_none.allclose(norm_check))
def test_resample_size(self):
input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav')
input_path = common_utils.get_asset_path('sinewave.wav')
waveform, sample_rate = torchaudio.load(input_path)
upsample_rate = sample_rate * 2
......
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