Merge pull request #113 from sp-nitech/ruff

Strip isort

.github/workflows/ci.yml

@@ -21,8 +21,8 @@ jobs:
             torch: 2.0.0
             torchaudio: 2.0.1
           - python: 3.12
-            torch: 2.5.1
-            torchaudio: 2.5.1
+            torch: 2.6.0
+            torchaudio: 2.6.0
 
     steps:
       - name: Clone
@@ -49,10 +49,10 @@ jobs:
         run: make test
 
       - name: Coverage
-        uses: codecov/codecov-action@v4
+        uses: codecov/codecov-action@v5
         with:
           fail_ci_if_error: true
-          file: ./coverage.xml
+          files: ./coverage.xml
           flags: unittests
           name: codecov-umbrella
           token: ${{ secrets.CODECOV_TOKEN }}

Makefile

@@ -49,16 +49,15 @@ doc-clean:
 check: tool
 	. ./venv/bin/activate && python -m ruff check $(PROJECT) tests
 	. ./venv/bin/activate && python -m ruff format --check $(PROJECT) tests docs
-	. ./venv/bin/activate && python -m isort --check $(PROJECT) tests
 	. ./venv/bin/activate && python -m mdformat --check *.md
 	. ./venv/bin/activate && cd docs && python -m docstrfmt --check .
+	./venv/bin/codespell
 	./tools/taplo/taplo fmt --check *.toml
 	./tools/yamlfmt/yamlfmt --lint *.cff *.yml .github/workflows/*.yml
 
 format: tool
 	. ./venv/bin/activate && python -m ruff check --fix $(PROJECT) tests
 	. ./venv/bin/activate && python -m ruff format $(PROJECT) tests docs
-	. ./venv/bin/activate && python -m isort $(PROJECT) tests
 	. ./venv/bin/activate && python -m mdformat *.md
 	. ./venv/bin/activate && cd docs && python -m docstrfmt .
 	./tools/taplo/taplo fmt *.toml

README.md

@@ -20,7 +20,7 @@
 
 ## Documentation
 
-- See [this page](https://sp-nitech.github.io/diffsptk/latest/) for a reference manual.
+- See [this page](https://sp-nitech.github.io/diffsptk/latest/) for the reference manual.
 - Our [paper](https://www.isca-speech.org/archive/ssw_2023/yoshimura23_ssw.html) is available on the ISCA Archive.
 
 ## Installation

diffsptk/functional.py

@@ -584,7 +584,7 @@ def fbank(x, n_channel, sample_rate, f_min=0, f_max=None, floor=1e-5, out_format
         Minimum mel-filter bank output in linear scale.
 
     out_format : ['y', 'yE', 'y,E']
-        `y` is mel-filber bank outpus and `E` is energy. If this is `yE`, the two output
+        `y` is mel-filber bank output and `E` is energy. If this is `yE`, the two output
         tensors are concatenated and return the tensor instead of the tuple.
 
     Returns
@@ -711,7 +711,7 @@ def freqt(c, out_order, alpha=0):
         Order of output cepstrum, :math:`M_2`.
 
     alpha : float in (-1, 1)
-        Frquency warping factor, :math:`\\alpha`.
+        Frequency warping factor, :math:`\\alpha`.
 
     Returns
     -------

diffsptk/misc/utils.py

@@ -14,16 +14,16 @@
 # limitations under the License.                                           #
 # ------------------------------------------------------------------------ #
 
-from importlib import import_module
 import logging
 import math
+from importlib import import_module
 
 import numpy as np
 import soundfile as sf
 import torch
-from torch import nn
 import torch.nn.functional as F
 import torchaudio
+from torch import nn
 
 UNVOICED_SYMBOL = 0
 TWO_PI = math.tau

diffsptk/modules/acr2csm.py

@@ -14,15 +14,12 @@
 # limitations under the License.                                           #
 # ------------------------------------------------------------------------ #
 
-from scipy.special import comb
 import torch
-from torch import nn
 import torch.nn.functional as F
+from scipy.special import comb
+from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import hankel
-from ..misc.utils import to
-from ..misc.utils import vander
+from ..misc.utils import check_size, hankel, to, vander
 from .root_pol import PolynomialToRoots
 
 

diffsptk/modules/ap.py

@@ -16,11 +16,10 @@
 
 import numpy as np
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
-from ..misc.utils import UNVOICED_SYMBOL
-from ..misc.utils import numpy_to_torch
+from ..misc.utils import UNVOICED_SYMBOL, numpy_to_torch
 
 
 class Aperiodicity(nn.Module):

diffsptk/modules/b2mc.py

@@ -15,11 +15,10 @@
 # ------------------------------------------------------------------------ #
 
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import to
+from ..misc.utils import check_size, to
 
 
 class MLSADigitalFilterCoefficientsToMelCepstrum(nn.Module):

diffsptk/modules/c2mpir.py

@@ -17,8 +17,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import cexp
-from ..misc.utils import check_size
+from ..misc.utils import cexp, check_size
 
 
 class CepstrumToMinimumPhaseImpulseResponse(nn.Module):

diffsptk/modules/c2ndps.py

@@ -17,8 +17,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import to
+from ..misc.utils import check_size, to
 
 
 class CepstrumToNegativeDerivativeOfPhaseSpectrum(nn.Module):

diffsptk/modules/chroma.py

@@ -16,11 +16,10 @@
 
 import numpy as np
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import to
+from ..misc.utils import check_size, to
 
 
 class ChromaFilterBankAnalysis(nn.Module):

diffsptk/modules/cqt.py

@@ -32,13 +32,10 @@
 
 import numpy as np
 import torch
-from torch import nn
 import torchaudio
+from torch import nn
 
-from ..misc.utils import Lambda
-from ..misc.utils import delayed_import
-from ..misc.utils import get_resample_params
-from ..misc.utils import numpy_to_torch
+from ..misc.utils import Lambda, delayed_import, get_resample_params, numpy_to_torch
 from .stft import ShortTimeFourierTransform as STFT
 
 
@@ -78,7 +75,7 @@ class ConstantQTransform(nn.Module):
         Window function for the basis.
 
     scale : bool
-        If True, scale the CQT responce by the length of filter.
+        If True, scale the CQT response by the length of filter.
 
     res_type : ['kaiser_best', 'kaiser_fast'] or None
         Resampling type.

diffsptk/modules/csm2acr.py

@@ -17,8 +17,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import to
+from ..misc.utils import check_size, to
 
 
 class CompositeSinusoidalModelCoefficientsToAutocorrelation(nn.Module):

diffsptk/modules/dct.py

@@ -17,9 +17,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import plateau
-from ..misc.utils import to
+from ..misc.utils import check_size, plateau, to
 
 
 class DiscreteCosineTransform(nn.Module):

diffsptk/modules/delta.py

@@ -15,8 +15,8 @@
 # ------------------------------------------------------------------------ #
 
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from ..misc.utils import to
 

diffsptk/modules/dfs.py

@@ -15,12 +15,10 @@
 # ------------------------------------------------------------------------ #
 
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
-from ..misc.utils import iir
-from ..misc.utils import to
-from ..misc.utils import to_3d
+from ..misc.utils import iir, to, to_3d
 
 
 class InfiniteImpulseResponseDigitalFilter(nn.Module):

diffsptk/modules/dht.py

@@ -17,9 +17,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import cas
-from ..misc.utils import check_size
-from ..misc.utils import to
+from ..misc.utils import cas, check_size, to
 
 
 class DiscreteHartleyTransform(nn.Module):

diffsptk/modules/drc.py

@@ -16,8 +16,8 @@
 
 import numpy as np
 import torch
-from torch import nn
 import torchcomp
+from torch import nn
 
 from ..misc.utils import to_2d
 

diffsptk/modules/dst.py

@@ -17,9 +17,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import plateau
-from ..misc.utils import to
+from ..misc.utils import check_size, plateau, to
 
 
 class DiscreteSineTransform(nn.Module):

diffsptk/modules/excite.py

@@ -15,11 +15,10 @@
 # ------------------------------------------------------------------------ #
 
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
-from ..misc.utils import TWO_PI
-from ..misc.utils import UNVOICED_SYMBOL
+from ..misc.utils import TWO_PI, UNVOICED_SYMBOL
 from .linear_intpl import LinearInterpolation
 
 

diffsptk/modules/fbank.py

@@ -18,8 +18,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import to
+from ..misc.utils import check_size, to
 
 
 class MelFilterBankAnalysis(nn.Module):
@@ -50,7 +49,7 @@ class MelFilterBankAnalysis(nn.Module):
         If True, use power spectrum instead of amplitude spectrum.
 
     out_format : ['y', 'yE', 'y,E']
-        `y` is mel-filber bank outpus and `E` is energy. If this is `yE`, the two output
+        `y` is mel-filber bank output and `E` is energy. If this is `yE`, the two output
         tensors are concatenated and return the tensor instead of the tuple.
 
     References

diffsptk/modules/fftcep.py

@@ -15,8 +15,8 @@
 # ------------------------------------------------------------------------ #
 
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 from ..misc.utils import check_size
 

diffsptk/modules/frame.py

@@ -14,8 +14,8 @@
 # limitations under the License.                                           #
 # ------------------------------------------------------------------------ #
 
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
 
 class Frame(nn.Module):

diffsptk/modules/freqt.py

@@ -17,8 +17,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import to
+from ..misc.utils import check_size, to
 
 
 class FrequencyTransform(nn.Module):

diffsptk/modules/freqt2.py

@@ -17,12 +17,11 @@
 import torch
 from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import to
+from ..misc.utils import check_size, to
 
 
 class SecondOrderAllPassFrequencyTransform(nn.Module):
-    """Second-order all-pass frequecy transform module.
+    """Second-order all-pass frequency transform module.
 
     Parameters
     ----------

diffsptk/modules/gmm.py

@@ -19,9 +19,7 @@
 from torch import nn
 from tqdm import tqdm
 
-from ..misc.utils import get_logger
-from ..misc.utils import outer
-from ..misc.utils import to_dataloader
+from ..misc.utils import get_logger, outer, to_dataloader
 
 
 class GaussianMixtureModeling(nn.Module):
@@ -338,7 +336,7 @@ def forward(self, x, return_posterior=False):
 
             # Check convergence.
             if self.verbose:
-                self.logger.info(f"iter {n+1:5d}: average = {log_likelihood / T:g}")
+                self.logger.info(f"iter {n + 1:5d}: average = {log_likelihood / T:g}")
             change = log_likelihood - prev_log_likelihood
             if n and change < self.eps:
                 break

diffsptk/modules/gnorm.py

@@ -17,8 +17,7 @@
 import torch
 from torch import nn
 
-from ..misc.utils import check_size
-from ..misc.utils import get_gamma
+from ..misc.utils import check_size, get_gamma
 
 
 class GeneralizedCepstrumGainNormalization(nn.Module):

diffsptk/modules/grpdelay.py

@@ -15,11 +15,10 @@
 # ------------------------------------------------------------------------ #
 
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 
-from ..misc.utils import remove_gain
-from ..misc.utils import to
+from ..misc.utils import remove_gain, to
 
 
 class GroupDelay(nn.Module):