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Padding amount was too high for loading files with FFMPEG. (#72)
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* Getting the pad amount as low as possible for mp3 to pass.

* Fleshing out comment.

* Bumping version

* Raising threshold.

* Adding source

* updating comment

* Adding offset option to dataset

* Changing to hann window

* Update regression data.

* Raising atol but why

---------

Co-authored-by: pseeth <[email protected]>
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pseeth and pseeth authored Feb 14, 2023
1 parent 5f31615 commit 3ec92c5
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Showing 45 changed files with 93 additions and 82 deletions.
1 change: 1 addition & 0 deletions .gitignore
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Expand Up @@ -140,3 +140,4 @@ test_out.md

# generated test data
tests/audio/chords/
notebooks/*
2 changes: 1 addition & 1 deletion .pre-commit-config.yaml
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Expand Up @@ -4,7 +4,7 @@ repos:
hooks:
- id: reorder-python-imports
- repo: https://github.com/psf/black
rev: 22.3.0
rev: 23.1.0
hooks:
- id: black
language_version: python3
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2 changes: 1 addition & 1 deletion audiotools/__init__.py
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@@ -1,4 +1,4 @@
__version__ = "0.6.0"
__version__ = "0.6.1"
from .core import AudioSignal
from .core import STFTParams
from .core import Meter
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2 changes: 1 addition & 1 deletion audiotools/core/audio_signal.py
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Expand Up @@ -1063,7 +1063,7 @@ def stft_params(self):
def stft_params(self, value: STFTParams):
default_win_len = int(2 ** (np.ceil(np.log2(0.032 * self.sample_rate))))
default_hop_len = default_win_len // 4
default_win_type = "sqrt_hann"
default_win_type = "hann"
default_match_stride = False
default_padding_type = "reflect"

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8 changes: 6 additions & 2 deletions audiotools/core/ffmpeg.py
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Expand Up @@ -178,8 +178,12 @@ def load_from_file_with_ffmpeg(cls, audio_path: str, quiet: bool = True, **kwarg
# offset in a video container.
pad = ffprobe_offset(audio_path)
# Don't pad files with discrepancies less than
# 0.1s - it's likely due to codec latency.
if pad < 0.1:
# 0.0.27s - it's likely due to codec latency.
# The amount of latency introduced by mp3 is
# 1152, which is 0.0261 44khz. So we
# set the threshold here slightly above that.
# Source: https://lame.sourceforge.io/tech-FAQ.txt.
if pad < 0.027:
pad = 0.0
ff = ffmpy.FFmpeg(
inputs={wav_file: None},
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4 changes: 3 additions & 1 deletion audiotools/data/datasets.py
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Expand Up @@ -334,6 +334,7 @@ def __init__(
sample_rate: int,
n_examples: int = 1000,
duration: float = 0.5,
offset: float = None,
loudness_cutoff: float = -40,
num_channels: int = 1,
transform: Callable = None,
Expand All @@ -355,6 +356,7 @@ def __init__(
self.transform = transform
self.sample_rate = sample_rate
self.duration = duration
self.offset = offset
self.aligned = aligned
self.shuffle_loaders = shuffle_loaders

Expand All @@ -367,7 +369,7 @@ def __init__(

def __getitem__(self, idx):
state = util.random_state(idx)
offset = None
offset = None if self.offset is None else self.offset
item = {}

keys = list(self.loaders.keys())
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2 changes: 0 additions & 2 deletions audiotools/data/transforms.py
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Expand Up @@ -493,7 +493,6 @@ def __init__(
name: str = None,
prob: float = 1.0,
):

transforms = [copy.copy(transform) for _ in range(n_repeat)]
super().__init__(transforms, name=name, prob=prob)

Expand Down Expand Up @@ -1479,7 +1478,6 @@ def __init__(
name: str = None,
prob: float = 1,
):

super().__init__(t_center=t_center, t_width=t_width, name=name, prob=prob)

def _transform(self, signal, tmin_s: float, tmax_s: float):
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16 changes: 14 additions & 2 deletions audiotools/metrics/spectral.py
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Expand Up @@ -48,10 +48,16 @@ def __init__(
pow: float = 2.0,
weight: float = 1.0,
match_stride: bool = False,
window_type: str = None,
):
super().__init__()
self.stft_params = [
STFTParams(window_length=w, hop_length=w // 4, match_stride=match_stride)
STFTParams(
window_length=w,
hop_length=w // 4,
match_stride=match_stride,
window_type=window_type,
)
for w in window_lengths
]
self.loss_fn = loss_fn
Expand Down Expand Up @@ -128,10 +134,16 @@ def __init__(
match_stride: bool = False,
mel_fmin: List[float] = [0.0, 0.0],
mel_fmax: List[float] = [None, None],
window_type: str = None,
):
super().__init__()
self.stft_params = [
STFTParams(window_length=w, hop_length=w // 4, match_stride=match_stride)
STFTParams(
window_length=w,
hop_length=w // 4,
match_stride=match_stride,
window_type=window_type,
)
for w in window_lengths
]
self.n_mels = n_mels
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1 change: 0 additions & 1 deletion audiotools/ml/layers/spectral_gate.py
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Expand Up @@ -35,7 +35,6 @@ class SpectralGate(nn.Module):
"""

def __init__(self, n_freq: int = 3, n_time: int = 5):

super().__init__()

smoothing_filter = torch.outer(
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1 change: 0 additions & 1 deletion audiotools/post.py
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Expand Up @@ -111,7 +111,6 @@ def _default_format_fn(label, x, **kwargs):

v_keys = list(v.keys())
if columns is None:

columns = [first_column] + v_keys
output.append(" | ".join(columns))

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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -6,7 +6,7 @@

setup(
name="audiotools",
version="0.6.0",
version="0.6.1",
classifiers=[
"Intended Audience :: Developers",
"Intended Audience :: Education",
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4 changes: 0 additions & 4 deletions tests/core/test_grad.py
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Expand Up @@ -9,12 +9,10 @@


def test_audio_grad():

audio_path = "tests/audio/spk/f10_script4_produced.wav"
ir_path = "tests/audio/ir/h179_Bar_1txts.wav"

def _test_audio_grad(attr: str, target=True, kwargs: dict = {}):

signal = AudioSignal(audio_path)
signal.audio_data.requires_grad = True

Expand All @@ -26,7 +24,6 @@ def _test_audio_grad(attr: str, target=True, kwargs: dict = {}):

try:
if isinstance(result, AudioSignal):

# If necessary, propagate spectrogram changes to waveform
if result.stft_data is not None:
result.istft()
Expand Down Expand Up @@ -88,7 +85,6 @@ def _test_audio_grad(attr: str, target=True, kwargs: dict = {}):


def test_batch_grad():

audio_path = "tests/audio/spk/f10_script4_produced.wav"

signal = AudioSignal(audio_path)
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2 changes: 1 addition & 1 deletion tests/data/test_transforms.py
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Expand Up @@ -25,7 +25,7 @@ def _compare_transform(transform_name, signal):
if regression_data.exists():
regression_signal = AudioSignal(regression_data)
assert torch.allclose(
signal.audio_data, regression_signal.audio_data, atol=1e-6
signal.audio_data, regression_signal.audio_data, atol=1e-4
)
else:
signal.write(regression_data)
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