diff --git a/.flake8 b/.flake8
deleted file mode 100644
index cb2ed23..0000000
--- a/.flake8
+++ /dev/null
@@ -1,10 +0,0 @@
-[flake8]
-exclude =
-    .eggs,
-    build,
-extend-ignore =
-    # math, https://github.com/PyCQA/pycodestyle/issues/513
-    W503,
-per-file-ignores =
-    # ignore unused imports
-    __init__.py: F401
diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml
index 2f8baef..1f5613e 100644
--- a/.github/workflows/linter.yml
+++ b/.github/workflows/linter.yml
@@ -14,10 +14,10 @@ jobs:
     steps:
     - uses: actions/checkout@v3
 
-    - name: Set up Python 3.8
+    - name: Set up Python 3.10
       uses: actions/setup-python@v4
       with:
-        python-version: '3.8'
+        python-version: '3.10'
 
     - name: Install pre-commit hooks
       run: |
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 92a17ee..119cb13 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,13 +9,15 @@
 #
 #
 default_language_version:
-  python: python3.8
+  python: python3.10
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.276
+    rev: v0.1.8
     hooks:
       - id: ruff
+        args: [ --fix ]
+      - id: ruff-format
   - repo: https://github.com/codespell-project/codespell
     rev: v2.2.4
     hooks:
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index d910255..5f33f4f 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -41,7 +41,7 @@ Coding Convention
 -----------------
 
 We follow the PEP8_ convention for Python code
-and check for correct syntax with ruff_.
+and use ruff_ as a linter and code formatter.
 In addition,
 we check for common spelling errors with codespell_.
 Both tools and possible exceptions
@@ -61,13 +61,14 @@ You can also install ruff_ and codespell_
 and call it directly::
 
     pip install ruff codespell  # consider system wide installation
-    ruff check .
+    ruff check --fix .  # lint all Python files, and fix any fixable errors
+    ruff format .  # format code of all Python files
     codespell
 
 It can be restricted to specific folders::
 
-    ruff check audfoo/ tests/
-    codespell audfoo/ tests/
+    ruff check audplot/ tests/
+    codespell audplot/ tests/
 
 
 .. _codespell: https://github.com/codespell-project/codespell/
diff --git a/audplot/__init__.py b/audplot/__init__.py
index c0ac3c2..4f8abcd 100644
--- a/audplot/__init__.py
+++ b/audplot/__init__.py
@@ -17,6 +17,7 @@
 # Dynamically get the version of the installed module
 try:
     import importlib.metadata
+
     __version__ = importlib.metadata.version(__name__)
 except Exception:  # pragma: no cover
     importlib = None  # pragma: no cover
diff --git a/audplot/core/api.py b/audplot/core/api.py
index 18625e6..eadacb9 100644
--- a/audplot/core/api.py
+++ b/audplot/core/api.py
@@ -19,12 +19,12 @@
 
 
 def cepstrum(
-        cc_matrix: np.ndarray,
-        hop_duration: float,
-        *,
-        channel: int = 0,
-        ax: matplotlib.axes._axes.Axes = None,
-        cmap: str = 'magma',
+    cc_matrix: np.ndarray,
+    hop_duration: float,
+    *,
+    channel: int = 0,
+    ax: matplotlib.axes._axes.Axes = None,
+    cmap: str = "magma",
 ) -> matplotlib.image.AxesImage:
     r"""Cepstrum.
 
@@ -51,7 +51,7 @@ def cepstrum(
 
             >>> import librosa
             >>> import matplotlib.pyplot as plt
-            >>> x, sr = librosa.load(librosa.ex('trumpet'))
+            >>> x, sr = librosa.load(librosa.ex("trumpet"))
             >>> y = librosa.feature.mfcc(y=x, sr=sr)
             >>> hop_dur = 512 / sr  # default hop length is 512
             >>> image = cepstrum(y, hop_dur)
@@ -65,16 +65,16 @@ def cepstrum(
 
     n_cc, n_cepstra = cc_matrix.shape
     extent = [0, n_cepstra * hop_duration, -0.5, n_cc - 0.5]
-    ax.set_ylabel('Cepstral Coefficients')
-    ax.set_xlabel('Time / s')
+    ax.set_ylabel("Cepstral Coefficients")
+    ax.set_xlabel("Time / s")
 
     ax.margins(x=0)
     image = ax.imshow(
         cc_matrix,
-        aspect='auto',
-        origin='lower',
+        aspect="auto",
+        origin="lower",
         cmap=cmap,
-        interpolation='none',
+        interpolation="none",
         extent=extent,
     )
 
@@ -91,14 +91,14 @@ def cepstrum(
 
 
 def confusion_matrix(
-        truth: typing.Union[typing.Sequence, pd.Series],
-        prediction: typing.Union[typing.Sequence, pd.Series],
-        *,
-        labels: typing.Sequence = None,
-        label_aliases: typing.Dict = None,
-        percentage: bool = False,
-        show_both: bool = False,
-        ax: matplotlib.axes.Axes = None,
+    truth: typing.Union[typing.Sequence, pd.Series],
+    prediction: typing.Union[typing.Sequence, pd.Series],
+    *,
+    labels: typing.Sequence = None,
+    label_aliases: typing.Dict = None,
+    percentage: bool = False,
+    show_both: bool = False,
+    ax: matplotlib.axes.Axes = None,
 ):
     r"""Confusion matrix between ground truth and prediction.
 
@@ -158,9 +158,9 @@ def confusion_matrix(
         .. plot::
             :context: close-figs
 
-            >>> confusion_matrix(truth, prediction, label_aliases={0: 'A', 1: 'B', 2: 'C'})
+            >>> confusion_matrix(truth, prediction, label_aliases={0: "A", 1: "B", 2: "C"})
 
-    """  # noqa: 501
+    """  # noqa: E501
     ax = ax or plt.gca()
     if labels is None:
         labels = audmetric.utils.infer_labels(truth, prediction)
@@ -175,7 +175,7 @@ def confusion_matrix(
 
     # Set format of first row labels in confusion matrix
     if percentage:
-        annot = cm.applymap(lambda x: f'{100 * x:.0f}%')
+        annot = cm.applymap(lambda x: f"{100 * x:.0f}%")
     else:
         annot = cm.applymap(lambda x: human_format(x))
 
@@ -191,24 +191,21 @@ def confusion_matrix(
         if percentage:
             annot2 = cm2.applymap(lambda x: human_format(x))
         else:
-            annot2 = cm2.applymap(lambda x: f'{100 * x:.0f}%')
+            annot2 = cm2.applymap(lambda x: f"{100 * x:.0f}%")
 
         # Combine strings from two dataframes
         # by vectorizing the underlying function.
         # See: https://stackoverflow.com/a/42277839
 
         def combine_string(x, y):
-            return f'{x}\n({y})'
+            return f"{x}\n({y})"
 
         combine_string = np.vectorize(combine_string)
         annot = pd.DataFrame(combine_string(annot, annot2), index=labels)
 
     # Get label names to present on x- and y-axis
     if label_aliases is not None:
-        labels = [
-            label_aliases.get(label, label)
-            for label in labels
-        ]
+        labels = [label_aliases.get(label, label) for label in labels]
 
     sns.heatmap(
         cm,
@@ -216,24 +213,24 @@ def combine_string(x, y):
         xticklabels=labels,
         yticklabels=labels,
         cbar=False,
-        fmt='',
-        cmap='Blues',
+        fmt="",
+        cmap="Blues",
         ax=ax,
     )
-    ax.tick_params(axis='y', rotation=0)
-    ax.set_xlabel('Prediction')
-    ax.set_ylabel('Truth')
+    ax.tick_params(axis="y", rotation=0)
+    ax.set_xlabel("Prediction")
+    ax.set_ylabel("Truth")
 
 
 def detection_error_tradeoff(
-        x: typing.Union[typing.Sequence, pd.Series],
-        y: typing.Union[typing.Sequence, pd.Series],
-        *,
-        error_rates: bool = False,
-        xlim: typing.Sequence = [0.001, 0.5],
-        ylim: typing.Sequence = [0.001, 0.5],
-        label: str = None,
-        ax: matplotlib.axes.Axes = None,
+    x: typing.Union[typing.Sequence, pd.Series],
+    y: typing.Union[typing.Sequence, pd.Series],
+    *,
+    error_rates: bool = False,
+    xlim: typing.Sequence = [0.001, 0.5],
+    ylim: typing.Sequence = [0.001, 0.5],
+    label: str = None,
+    ax: matplotlib.axes.Axes = None,
 ) -> typing.Callable:
     r"""Detection error tradeoff curve.
 
@@ -298,7 +295,9 @@ def detection_error_tradeoff(
             >>> # Random prediction
             >>> pred1 = np.random.random_sample(2000)
             >>> # Better than random prediction
-            >>> pred2 = np.zeros(2000,)
+            >>> pred2 = np.zeros(
+            ...     2000,
+            ... )
             >>> pred2[:1000] = np.random.normal(loc=0.6, scale=0.1, size=1000)
             >>> pred2[1000:] = np.random.normal(loc=0.4, scale=0.1, size=1000)
             >>> pred2 = np.clip(pred2, 0, 1)
@@ -307,12 +306,12 @@ def detection_error_tradeoff(
             ...     pred1,
             ...     xlim=[0.01, 0.99],  # use large limits for random
             ...     ylim=[0.01, 0.99],
-            ...     label='pred1',
+            ...     label="pred1",
             ... )
             >>> # Add pred2 to plot using transformed FMR and FNMR values
             >>> import audmetric
             >>> fmr, fnmr, _ = audmetric.detection_error_tradeoff(truth, pred2)
-            >>> _ = plt.plot(transform(fmr), transform(fnmr), label='pred2')
+            >>> _ = plt.plot(transform(fmr), transform(fnmr), label="pred2")
             >>> _ = plt.legend()
             >>> plt.tight_layout()
 
@@ -330,17 +329,14 @@ def detection_error_tradeoff(
         y=transform(y),
         label=label,
     )
-    ax.set_title('Detection Error Tradeoff (DET) Curve')
-    ax.set_xlabel('False Match Rate')
-    ax.set_ylabel('False Non-Match Rate')
+    ax.set_title("Detection Error Tradeoff (DET) Curve")
+    ax.set_xlabel("False Match Rate")
+    ax.set_ylabel("False Non-Match Rate")
     ax.grid(alpha=0.4)
 
     ticks = [0.001, 0.01, 0.05, 0.2, 0.4, 0.6, 0.8, 0.95, 0.99]
     tick_locations = transform(ticks)
-    tick_labels = [
-        f'{t:.0%}' if (100 * t).is_integer() else f'{t:.1%}'
-        for t in ticks
-    ]
+    tick_labels = [f"{t:.0%}" if (100 * t).is_integer() else f"{t:.1%}" for t in ticks]
     g.set(xticks=tick_locations, xticklabels=tick_labels)
     g.set(yticks=tick_locations, yticklabels=tick_labels)
     ax.set_xlim(transform(xlim[0]), transform(xlim[1]))
@@ -352,10 +348,10 @@ def detection_error_tradeoff(
 
 
 def distribution(
-        truth: typing.Union[typing.Sequence, pd.Series],
-        prediction: typing.Union[typing.Sequence, pd.Series],
-        *,
-        ax: matplotlib.axes.Axes = None,
+    truth: typing.Union[typing.Sequence, pd.Series],
+    prediction: typing.Union[typing.Sequence, pd.Series],
+    *,
+    ax: matplotlib.axes.Axes = None,
 ):
     r"""Distribution of truth and predicted values.
 
@@ -385,15 +381,15 @@ def distribution(
     ax = ax or plt.gca()
     data = pd.DataFrame(
         data=np.array([truth, prediction]).T,
-        columns=['Truth', 'Prediction'],
+        columns=["Truth", "Prediction"],
     )
     sns.histplot(
         data,
         common_bins=False,
-        stat='frequency',
+        stat="frequency",
         kde=True,
         edgecolor=None,
-        kde_kws={'cut': 3},  # hard code like in distplot()
+        kde_kws={"cut": 3},  # hard code like in distplot()
         ax=ax,
     )
     ax.grid(alpha=0.4)
@@ -403,7 +399,7 @@ def distribution(
 
 
 def human_format(
-        number: typing.Union[int, float],
+    number: typing.Union[int, float],
 ) -> str:
     r"""Display large or small numbers in a human readable way.
 
@@ -457,48 +453,48 @@ def human_format(
         '-1k'
 
     """
-    sign = ''
+    sign = ""
     if number == 0:
-        return '0'
+        return "0"
     if number < 0:
-        sign = '-'
+        sign = "-"
         number = -1 * number
     units = [
-        'n',  # 10^-9  nano
-        'u',  # 10^-6  micro
-        'm',  # 10^-3  milli
-        '',   # 0
-        'k',  # 10^3   thousand
-        'M',  # 10^6   Million      Mega
-        'B',  # 10^9   Billion      Giga
-        'T',  # 10^12  Trillion     Tera
-        'P',  # 10^15  Quadrillion  Peta
-        'E',  # 10^18  Quintillion  Exa
-        'Z',  # 10^21  Sextillion   Zetta
-        'Y',  # 10^24  Septillion   Yotta
+        "n",  # 10^-9  nano
+        "u",  # 10^-6  micro
+        "m",  # 10^-3  milli
+        "",  # 0
+        "k",  # 10^3   thousand
+        "M",  # 10^6   Million      Mega
+        "B",  # 10^9   Billion      Giga
+        "T",  # 10^12  Trillion     Tera
+        "P",  # 10^15  Quadrillion  Peta
+        "E",  # 10^18  Quintillion  Exa
+        "Z",  # 10^21  Sextillion   Zetta
+        "Y",  # 10^24  Septillion   Yotta
     ]
     k = 1000.0
     magnitude = int(math.floor(math.log(number, k)))
-    number = f'{number / k**magnitude:.1f}'
+    number = f"{number / k**magnitude:.1f}"
     if magnitude >= 9:
-        raise ValueError('Only magnitudes < 1000 ** 9 are supported.')
+        raise ValueError("Only magnitudes < 1000 ** 9 are supported.")
     if magnitude <= -4:
-        raise ValueError('Only magnitudes > 1000 ** -4 are supported.')
+        raise ValueError("Only magnitudes > 1000 ** -4 are supported.")
     # Make sure we show only up to 3 significant digits
     if len(number) > 4:
         number = number[:-2]
-    if number.endswith('.0'):
+    if number.endswith(".0"):
         number = number[:-2]
-    return f'{sign}{number}{units[magnitude + 3]}'
+    return f"{sign}{number}{units[magnitude + 3]}"
 
 
 def scatter(
-        truth: typing.Union[typing.Sequence, pd.Series],
-        prediction: typing.Union[typing.Sequence, pd.Series],
-        *,
-        fit: bool = False,
-        order: int = 1,
-        ax: matplotlib.axes.Axes = None,
+    truth: typing.Union[typing.Sequence, pd.Series],
+    prediction: typing.Union[typing.Sequence, pd.Series],
+    *,
+    fit: bool = False,
+    order: int = 1,
+    ax: matplotlib.axes.Axes = None,
 ):
     r"""Scatter plot of truth and predicted values.
 
@@ -532,22 +528,22 @@ def scatter(
         x=truth,
         y=prediction,
         fit_reg=fit,
-        line_kws={'color': 'r'},
+        line_kws={"color": "r"},
         order=order,
         ax=ax,
         seed=0,
     )
-    ax.set_xlabel('Truth')
-    ax.set_ylabel('Prediction')
+    ax.set_xlabel("Truth")
+    ax.set_ylabel("Prediction")
     ax.grid(alpha=0.4)
     sns.despine(ax=ax)
 
 
 def series(
-        truth: typing.Union[typing.Sequence, pd.Series],
-        prediction: typing.Union[typing.Sequence, pd.Series],
-        *,
-        ax: matplotlib.axes.Axes = None,
+    truth: typing.Union[typing.Sequence, pd.Series],
+    prediction: typing.Union[typing.Sequence, pd.Series],
+    *,
+    ax: matplotlib.axes.Axes = None,
 ):
     r"""Time series plot of truth and predicted values.
 
@@ -578,17 +574,17 @@ def series(
     ax.plot(truth)
     ax.plot(prediction)
     ax.set_ylim(minimum, maximum)
-    ax.legend(['Truth', 'Prediction'])
+    ax.legend(["Truth", "Prediction"])
     ax.grid(alpha=0.4)
     sns.despine(ax=ax)
 
 
 def signal(
-        x: np.ndarray,
-        sampling_rate: float,
-        *,
-        channel: int = 0,
-        ax: plt.Axes = None,
+    x: np.ndarray,
+    sampling_rate: float,
+    *,
+    channel: int = 0,
+    ax: plt.Axes = None,
 ):
     r"""Time signal.
 
@@ -610,7 +606,7 @@ def signal(
             :context: close-figs
 
             >>> import librosa
-            >>> x, sr = librosa.load(librosa.ex('trumpet'))
+            >>> x, sr = librosa.load(librosa.ex("trumpet"))
             >>> signal(x, sr)
 
     """
@@ -618,7 +614,7 @@ def signal(
     x = x[channel] if x.ndim == 2 else x
 
     time = np.arange(len(x)) / sampling_rate
-    ax.set_xlabel('Time / s')
+    ax.set_xlabel("Time / s")
 
     ax.margins(x=0)
     ax.plot(time, x)
@@ -627,13 +623,13 @@ def signal(
 
 
 def spectrum(
-        magnitude: np.ndarray,
-        hop_duration: float,
-        centers: np.ndarray,
-        *,
-        channel: int = 0,
-        cmap: str = 'magma',
-        ax: matplotlib.axes.Axes = None,
+    magnitude: np.ndarray,
+    hop_duration: float,
+    centers: np.ndarray,
+    *,
+    channel: int = 0,
+    cmap: str = "magma",
+    ax: matplotlib.axes.Axes = None,
 ) -> matplotlib.image.AxesImage:
     r"""Plot spectrum.
 
@@ -662,13 +658,13 @@ def spectrum(
 
             >>> import librosa
             >>> import matplotlib.pyplot as plt
-            >>> x, sr = librosa.load(librosa.ex('trumpet'))
+            >>> x, sr = librosa.load(librosa.ex("trumpet"))
             >>> y = librosa.feature.melspectrogram(y=x, sr=sr, n_mels=40, fmax=4000)
             >>> y_db = librosa.power_to_db(y, ref=np.max)
             >>> hop_dur = 512 / sr  # default hop length is 512
             >>> centers = librosa.mel_frequencies(n_mels=40, fmax=4000)
             >>> image = spectrum(y_db, hop_dur, centers)
-            >>> cb = plt.colorbar(image, format='%+2.0f dB')
+            >>> cb = plt.colorbar(image, format="%+2.0f dB")
             >>> cb.outline.set_visible(False)
             >>> plt.tight_layout()
 
@@ -678,16 +674,16 @@ def spectrum(
 
     frequencies, times = magnitude.shape
     extent = [0, times * hop_duration, -0.5, frequencies - 0.5]
-    ax.set_ylabel('Frequency / Hz')
-    ax.set_xlabel('Time / s')
+    ax.set_ylabel("Frequency / Hz")
+    ax.set_xlabel("Time / s")
 
     ax.margins(x=0)
     image = ax.imshow(
         magnitude,
-        aspect='auto',
-        origin='lower',
+        aspect="auto",
+        origin="lower",
         cmap=cmap,
-        interpolation='none',
+        interpolation="none",
         extent=extent,
     )
 
@@ -709,14 +705,14 @@ def spectrum(
 
 
 def waveform(
-        x: np.ndarray,
-        *,
-        text: str = None,
-        color: typing.Union[str, typing.Sequence[float]] = '#E13B41',
-        background: typing.Union[str, typing.Sequence[float]] = '#FFFFFF00',
-        linewidth: float = 1.5,
-        ylim: typing.Sequence[float] = (-1, 1),
-        ax: matplotlib.axes.Axes = None,
+    x: np.ndarray,
+    *,
+    text: str = None,
+    color: typing.Union[str, typing.Sequence[float]] = "#E13B41",
+    background: typing.Union[str, typing.Sequence[float]] = "#FFFFFF00",
+    linewidth: float = 1.5,
+    ylim: typing.Sequence[float] = (-1, 1),
+    ax: matplotlib.axes.Axes = None,
 ):
     r"""Plot waveform of a mono signal.
 
@@ -748,38 +744,38 @@ def waveform(
             :context: close-figs
 
             >>> import librosa
-            >>> x, _ = librosa.load(librosa.ex('trumpet'))
-            >>> waveform(x, text='Trumpet')
+            >>> x, _ = librosa.load(librosa.ex("trumpet"))
+            >>> waveform(x, text="Trumpet")
 
         .. plot::
             :context: close-figs
 
             >>> import librosa
-            >>> x, _ = librosa.load(librosa.ex('trumpet'))
-            >>> waveform(x, background='#363636', color='#f6f6f6')
+            >>> x, _ = librosa.load(librosa.ex("trumpet"))
+            >>> waveform(x, background="#363636", color="#f6f6f6")
 
         .. plot::
             :context: close-figs
 
             >>> import librosa
             >>> import matplotlib.pyplot as plt
-            >>> x, _ = librosa.load(librosa.ex('trumpet', hq=True), mono=False)
+            >>> x, _ = librosa.load(librosa.ex("trumpet", hq=True), mono=False)
             >>> _, axs = plt.subplots(2, figsize=(8, 3))
             >>> plt.subplots_adjust(hspace=0)
             >>> waveform(
             ...     x[0, :],
-            ...     text='Left ',  # empty space for same size as 'Right'
+            ...     text="Left ",  # empty space for same size as 'Right'
             ...     linewidth=0.5,
-            ...     background='#389DCD',
-            ...     color='#1B5975',
+            ...     background="#389DCD",
+            ...     color="#1B5975",
             ...     ax=axs[0],
             ... )
             >>> waveform(
             ...     x[1, :],
-            ...     text='Right',
+            ...     text="Right",
             ...     linewidth=0.5,
-            ...     background='#CA5144',
-            ...     color='#742A23',
+            ...     background="#CA5144",
+            ...     color="#742A23",
             ...     ax=axs[1],
             ... )
 
@@ -789,8 +785,8 @@ def waveform(
     # If axis/figure exist already it will have no effect
 
     # Set default figsize if no existing figure is used
-    default_figsize = plt.rcParams['figure.figsize']
-    plt.rcParams['figure.figsize'] = (8, 1)
+    default_figsize = plt.rcParams["figure.figsize"]
+    plt.rcParams["figure.figsize"] = (8, 1)
 
     fig = plt.gcf()
     ax = ax or plt.gca()
@@ -798,7 +794,7 @@ def waveform(
     x = np.atleast_2d(x)
     channels, samples = x.shape
     if channels > 1:
-        raise RuntimeError('Only mono signals are supported.')
+        raise RuntimeError("Only mono signals are supported.")
     x = x[0]
 
     # Set colors
@@ -849,11 +845,11 @@ def waveform(
             -space_around_text,
             0,
             text,
-            fontsize='large',
-            fontweight='semibold',
+            fontsize="large",
+            fontweight="semibold",
             color=color,
-            horizontalalignment='right',
-            verticalalignment='center',
+            horizontalalignment="right",
+            verticalalignment="center",
         )
         # Get left position of text and adjust xlim accordingly
         fig = ax.get_figure()
@@ -866,4 +862,4 @@ def waveform(
     ax.set(xlim=xlim)
 
     # Restore default figure size
-    plt.rcParams['figure.figsize'] = default_figsize
+    plt.rcParams["figure.figsize"] = default_figsize
diff --git a/docs/conf.py b/docs/conf.py
index 08c3fb8..f30c751 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -7,70 +7,70 @@
 import audeer
 
 
-config = toml.load(audeer.path('..', 'pyproject.toml'))
+config = toml.load(audeer.path("..", "pyproject.toml"))
 
 
 # Project -----------------------------------------------------------------
-author = ', '.join(author['name'] for author in config['project']['authors'])
-copyright = f'2020-{date.today().year} audEERING GmbH'
-project = config['project']['name']
+author = ", ".join(author["name"] for author in config["project"]["authors"])
+copyright = f"2020-{date.today().year} audEERING GmbH"
+project = config["project"]["name"]
 version = audeer.git_repo_version()
-title = 'Documentation'
+title = "Documentation"
 
 
 # General -----------------------------------------------------------------
-master_doc = 'index'
-source_suffix = '.rst'
+master_doc = "index"
+source_suffix = ".rst"
 exclude_patterns = [
-    'api-src',
-    'build',
-    'tests',
-    'Thumbs.db',
-    '.DS_Store',
+    "api-src",
+    "build",
+    "tests",
+    "Thumbs.db",
+    ".DS_Store",
 ]
-templates_path = ['_templates']
+templates_path = ["_templates"]
 pygments_style = None
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',  # support for Google-style docstrings
-    'sphinx.ext.autosummary',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.intersphinx',
-    'sphinx_autodoc_typehints',
-    'sphinx_copybutton',  # for "copy to clipboard" buttons
-    'sphinxcontrib.katex',  # has to be before jupyter_sphinx
-    'matplotlib.sphinxext.plot_directive',  # include resulting figures in doc
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",  # support for Google-style docstrings
+    "sphinx.ext.autosummary",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.intersphinx",
+    "sphinx_autodoc_typehints",
+    "sphinx_copybutton",  # for "copy to clipboard" buttons
+    "sphinxcontrib.katex",  # has to be before jupyter_sphinx
+    "matplotlib.sphinxext.plot_directive",  # include resulting figures in doc
 ]
 
 # Do not copy prompot output
-copybutton_prompt_text = r'>>> |\.\.\. '
+copybutton_prompt_text = r">>> |\.\.\. "
 copybutton_prompt_is_regexp = True
 
 # Mapping to external documentation
 intersphinx_mapping = {
-    'audmath': ('https://audeering.github.io/audmath/', None),
-    'audmetric': ('https://audeering.github.io/audmetric/', None),
-    'matplotlib': ('https://matplotlib.org/stable/', None),
-    'numpy': ('https://docs.scipy.org/doc/numpy/', None),
-    'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),
-    'python': ('https://docs.python.org/3/', None),
-    'scipy': ('https://docs.scipy.org/doc/scipy/', None),
+    "audmath": ("https://audeering.github.io/audmath/", None),
+    "audmetric": ("https://audeering.github.io/audmetric/", None),
+    "matplotlib": ("https://matplotlib.org/stable/", None),
+    "numpy": ("https://docs.scipy.org/doc/numpy/", None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
+    "python": ("https://docs.python.org/3/", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
 }
 
 # Disable Gitlab as we need to sign in
 linkcheck_ignore = [
-    'https://gitlab.audeering.com',
+    "https://gitlab.audeering.com",
 ]
 
 # Plotting
 plot_include_source = True
 plot_html_show_source_link = False
 plot_html_show_formats = False
-plot_pre_code = ''
+plot_pre_code = ""
 plot_rcparams = {
-    'figure.figsize': '5, 3.8',  # inch
+    "figure.figsize": "5, 3.8",  # inch
 }
-plot_formats = ['svg']
+plot_formats = ["svg"]
 
 # Disable auto-generation of TOC entries in the API
 # https://github.com/sphinx-doc/sphinx/issues/6316
@@ -78,25 +78,24 @@
 
 
 # HTML --------------------------------------------------------------------
-html_theme = 'sphinx_audeering_theme'
+html_theme = "sphinx_audeering_theme"
 html_theme_options = {
-    'display_version': True,
-    'logo_only': False,
-    'footer_links': False,
+    "display_version": True,
+    "logo_only": False,
+    "footer_links": False,
 }
 html_context = {
-    'display_github': True,
+    "display_github": True,
 }
 html_title = title
 
 
 # Copy API (sub-)module RST files to docs/api/ folder ---------------------
-audeer.rmdir('api')
-audeer.mkdir('api')
-api_src_files = audeer.list_file_names('api-src')
+audeer.rmdir("api")
+audeer.mkdir("api")
+api_src_files = audeer.list_file_names("api-src")
 api_dst_files = [
-    audeer.path('api', os.path.basename(src_file))
-    for src_file in api_src_files
+    audeer.path("api", os.path.basename(src_file)) for src_file in api_src_files
 ]
 for src_file, dst_file in zip(api_src_files, api_dst_files):
     shutil.copyfile(src_file, dst_file)
diff --git a/docs/usage.rst b/docs/usage.rst
index 53a2a71..d3ada50 100644
--- a/docs/usage.rst
+++ b/docs/usage.rst
@@ -20,12 +20,12 @@ you can change the labels after plotting.
 
     sns.set()  # get prettier plots
 
-    truth = ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C']
-    prediction = ['A', 'A', 'B', 'B', 'C', 'C', 'A', 'A', 'C']
-    label_aliases = {'A': 'c1', 'B': 'c2', 'C': 'c3'}
+    truth = ["A", "A", "A", "B", "B", "B", "C", "C", "C"]
+    prediction = ["A", "A", "B", "B", "C", "C", "A", "A", "C"]
+    label_aliases = {"A": "c1", "B": "c2", "C": "c3"}
 
     plt.figure(figsize=[2.8, 2.5])
-    plt.title('Confusion Matrix')
+    plt.title("Confusion Matrix")
     audplot.confusion_matrix(truth, prediction, label_aliases=label_aliases)
 
     plt.tight_layout()
@@ -50,7 +50,7 @@ you can specify the axes to draw on.
         audplot.series,
     ]
     fig, axs = plt.subplots(1, len(plot_funcs), figsize=[12, 3])
-    plt.suptitle('Multiple plots in one figure')
+    plt.suptitle("Multiple plots in one figure")
     for plot_func, ax in zip(plot_funcs, axs):
         plot_func(truth, prediction, ax=ax)
 
diff --git a/pyproject.toml b/pyproject.toml
index 73d695a..a7961c3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -77,6 +77,12 @@ addopts = '''
 # ----- ruff --------------------------------------------------------------
 #
 [tool.ruff]
+cache-dir = '.cache/ruff'
+
+[tool.ruff.format]
+docstring-code-format = true
+
+[tool.ruff.lint]
 select = [
     'D',     # pydocstyle
     'E',     # pycodestyle errors
@@ -93,11 +99,7 @@ extend-ignore = [
     'D107',  # Missing docstring in `__init__`
 ]
 
-line-length = 79
-
-cache-dir = '.cache/ruff'
-
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 '__init__.py' = [
     'F401',  # * imported but unused
 ]
@@ -111,7 +113,7 @@ cache-dir = '.cache/ruff'
 #
 # Check correct order/syntax of import statements
 #
-[tool.ruff.isort]
+[tool.ruff.lint.isort]
 
 # All from imports have their own line, e.g.
 #
@@ -148,7 +150,7 @@ section-order = [
     'first-party',
     'local-folder',
 ]
-[tool.ruff.isort.sections]
+[tool.ruff.lint.isort.sections]
 'audeering' = [
     'audb',
     'audbackend',
@@ -173,7 +175,7 @@ section-order = [
 #
 # Check variable/class names follow PEP8 naming convention
 #
-[tool.ruff.pep8-naming]
+[tool.ruff.lint.pep8-naming]
 ignore-names = [
     'config',  # allow lowercase class name
     'test_*',  # allow uppercase name when testing a class
@@ -184,7 +186,7 @@ ignore-names = [
 #
 # Check docstrings follow selected convention
 #
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = 'google'
 
 
diff --git a/tests/test_api.py b/tests/test_api.py
index eac0b0c..c0bf74e 100644
--- a/tests/test_api.py
+++ b/tests/test_api.py
@@ -9,35 +9,35 @@
 
 
 @pytest.mark.parametrize(
-    'number, expected_string',
+    "number, expected_string",
     [
-        (0, '0'),
-        (1, '1'),
-        (10, '10'),
-        (100, '100'),
-        (1000, '1k'),
-        (10000, '10k'),
-        (100000, '100k'),
-        (1000000, '1M'),
-        (0.1, '100m'),
-        (0.01, '10m'),
-        (0.001, '1m'),
-        (0.0015, '1.5m'),
-        (0.0001, '100u'),
-        (-1, '-1'),
-        (-0.001, '-1m'),
-        (-0.0015, '-1.5m'),
+        (0, "0"),
+        (1, "1"),
+        (10, "10"),
+        (100, "100"),
+        (1000, "1k"),
+        (10000, "10k"),
+        (100000, "100k"),
+        (1000000, "1M"),
+        (0.1, "100m"),
+        (0.01, "10m"),
+        (0.001, "1m"),
+        (0.0015, "1.5m"),
+        (0.0001, "100u"),
+        (-1, "-1"),
+        (-0.001, "-1m"),
+        (-0.0015, "-1.5m"),
         pytest.param(
-            1000 ** 9,
-            '',
+            1000**9,
+            "",
             marks=pytest.mark.xfail(raises=ValueError),
         ),
         pytest.param(
-            1000 ** -4,
-            '',
+            1000**-4,
+            "",
             marks=pytest.mark.xfail(raises=ValueError),
         ),
-    ]
+    ],
 )
 def test_human_format(number, expected_string):
     string = audplot.human_format(number)