Merge branch 'main' into train

.bumpversion.cfg

@@ -0,0 +1,8 @@
+[bumpversion]
+current_version = 1.1.1
+commit = True
+tag = True
+
+[bumpversion:file:batdetect2/__init__.py]
+
+[bumpversion:file:pyproject.toml]

.github/workflows/python-package.yml

@@ -1,34 +1,29 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
-# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
-
 name: Python package
 
 on:
   push:
-    branches: [ "main" ]
+    branches: ["main"]
   pull_request:
-    branches: [ "main" ]
+    branches: ["main"]
 
 jobs:
   build:
-
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install pytest
-        pip install .
-    - name: Test with pytest
-      run: |
-        pytest
+      - uses: actions/checkout@v4
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          enable-cache: true
+          cache-dependency-glob: "uv.lock"
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+      - name: Install the project
+        run: uv sync --all-extras --dev
+      - name: Test with pytest
+        run: uv run pytest

.github/workflows/python-publish.yml

@@ -1,11 +1,3 @@
-# This workflow will upload a Python Package using Twine when a release is created
-# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
-
-# This workflow uses actions that are not certified by GitHub.
-# They are provided by a third-party and are governed by
-# separate terms of service, privacy policy, and support
-# documentation.
-
 name: Upload Python Package
 
 on:
@@ -17,23 +9,22 @@ permissions:
 
 jobs:
   deploy:
-
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python
-      uses: actions/setup-python@v3
-      with:
-        python-version: '3.x'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install build
-    - name: Build package
-      run: python -m build
-    - name: Publish package
-      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
-      with:
-        user: __token__
-        password: ${{ secrets.PYPI_API_TOKEN }}
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.x"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build
+      - name: Build package
+        run: python -m build
+      - name: Publish package
+        uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -103,13 +103,11 @@ experiments/*
 .ipynb_checkpoints
 *.ipynb
 
-# Bump2version
-.bumpversion.cfg
-
 # DO Include
 !batdetect2_notebook.ipynb
 !batdetect2/models/checkpoints/*.pth.tar
 !tests/data/*.wav
 !notebooks/*.ipynb
+!tests/data/**/*.wav
 notebooks/lightning_logs
 example_data/preprocessed

batdetect2/__init__.py

@@ -1 +1,6 @@
-__version__ = '1.0.8'
+import logging
+
+numba_logger = logging.getLogger("numba")
+numba_logger.setLevel(logging.WARNING)
+
+__version__ = "1.1.1"

batdetect2/cli/compat.py

@@ -1,5 +1,3 @@
-"""BatDetect2 command line interface."""
-
 import click
 
 from batdetect2 import api
@@ -114,10 +112,9 @@ def detect(
             ):
                 results_path = audio_file.replace(audio_dir, ann_dir)
                 save_results_to_file(results, results_path)
-        except (RuntimeError, ValueError, LookupError) as err:
+        except (RuntimeError, ValueError, LookupError, EOFError) as err:
             error_files.append(audio_file)
-            click.secho(f"Error processing file!: {err}", fg="red")
-            raise err
+            click.secho(f"Error processing file {audio_file}: {err}", fg="red")
 
     click.echo(f"\nResults saved to: {ann_dir}")
 

batdetect2/types.py

@@ -1,13 +1,11 @@
 """Types used in the code base."""
 from typing import Any, List, NamedTuple, Optional
 
+
 import numpy as np
 import torch
 
-try:
-    from typing import TypedDict
-except ImportError:
-    from typing_extensions import TypedDict
+from typing import TypedDict
 
 
 try:
@@ -15,9 +13,8 @@ try:
 except ImportError:
     from typing_extensions import Protocol
 
-
 try:
-    from typing import NotRequired
+    from typing import NotRequired  # type: ignore
 except ImportError:
     from typing_extensions import NotRequired
 

batdetect2/utils/audio_utils.py

@@ -6,6 +6,8 @@ import librosa.core.spectrum
 import numpy as np
 import torch
 
+from batdetect2.detector import parameters
+
 from . import wavfile
 
 __all__ = [
@@ -15,20 +17,44 @@ __all__ = [
 ]
 
 
-def time_to_x_coords(time_in_file, sampling_rate, fft_win_length, fft_overlap):
-    nfft = np.floor(fft_win_length * sampling_rate)  # int() uses floor
-    noverlap = np.floor(fft_overlap * nfft)
-    return (time_in_file * sampling_rate - noverlap) / (nfft - noverlap)
+def time_to_x_coords(
+    time_in_file: float,
+    samplerate: float = parameters.TARGET_SAMPLERATE_HZ,
+    window_duration: float = parameters.FFT_WIN_LENGTH_S,
+    window_overlap: float = parameters.FFT_OVERLAP,
+) -> float:
+    nfft = np.floor(window_duration * samplerate)  # int() uses floor
+    noverlap = np.floor(window_overlap * nfft)
+    return (time_in_file * samplerate - noverlap) / (nfft - noverlap)
 
 
-# NOTE this is also defined in post_process
-def x_coords_to_time(x_pos, sampling_rate, fft_win_length, fft_overlap):
-    nfft = np.floor(fft_win_length * sampling_rate)
-    noverlap = np.floor(fft_overlap * nfft)
-    return ((x_pos * (nfft - noverlap)) + noverlap) / sampling_rate
+def x_coords_to_time(
+    x_pos: int,
+    samplerate: float = parameters.TARGET_SAMPLERATE_HZ,
+    window_duration: float = parameters.FFT_WIN_LENGTH_S,
+    window_overlap: float = parameters.FFT_OVERLAP,
+) -> float:
+    n_fft = np.floor(window_duration * samplerate)
+    n_overlap = np.floor(window_overlap * n_fft)
+    n_step = n_fft - n_overlap
+    return ((x_pos * n_step) + n_overlap) / samplerate
     # return (1.0 - fft_overlap) * fft_win_length * (x_pos + 0.5)  # 0.5 is for center of temporal window
 
 
+def x_coord_to_sample(
+    x_pos: int,
+    samplerate: float = parameters.TARGET_SAMPLERATE_HZ,
+    window_duration: float = parameters.FFT_WIN_LENGTH_S,
+    window_overlap: float = parameters.FFT_OVERLAP,
+    resize_factor: float = parameters.RESIZE_FACTOR,
+) -> int:
+    n_fft = np.floor(window_duration * samplerate)
+    n_overlap = np.floor(window_overlap * n_fft)
+    n_step = n_fft - n_overlap
+    x_pos = int(x_pos / resize_factor)
+    return int((x_pos * n_step) + n_overlap)
+
+
 def generate_spectrogram(
     audio,
     sampling_rate,
@@ -194,55 +220,118 @@ def load_audio(
     return sampling_rate, audio_raw
 
 
+def compute_spectrogram_width(
+    length: int,
+    samplerate: int = parameters.TARGET_SAMPLERATE_HZ,
+    window_duration: float = parameters.FFT_WIN_LENGTH_S,
+    window_overlap: float = parameters.FFT_OVERLAP,
+    resize_factor: float = parameters.RESIZE_FACTOR,
+) -> int:
+    n_fft = int(window_duration * samplerate)
+    n_overlap = int(window_overlap * n_fft)
+    n_step = n_fft - n_overlap
+    width = (length - n_overlap) // n_step
+    return int(width * resize_factor)
+
+
 def pad_audio(
-    audio_raw,
-    fs,
-    ms,
-    overlap_perc,
-    resize_factor,
-    divide_factor,
-    fixed_width=None,
+    audio: np.ndarray,
+    samplerate: int = parameters.TARGET_SAMPLERATE_HZ,
+    window_duration: float = parameters.FFT_WIN_LENGTH_S,
+    window_overlap: float = parameters.FFT_OVERLAP,
+    resize_factor: float = parameters.RESIZE_FACTOR,
+    divide_factor: int = parameters.SPEC_DIVIDE_FACTOR,
+    fixed_width: Optional[int] = None,
 ):
-    # Adds zeros to the end of the raw data so that the generated sepctrogram
-    # will be evenly divisible by `divide_factor`
-    # Also deals with very short audio clips and fixed_width during training
+    """Pad audio to be evenly divisible by `divide_factor`.
 
-    # This code could be clearer, clean up
-    nfft = int(ms * fs)
-    noverlap = int(overlap_perc * nfft)
-    step = nfft - noverlap
-    min_size = int(divide_factor * (1.0 / resize_factor))
-    spec_width = (audio_raw.shape[0] - noverlap) // step
-    spec_width_rs = spec_width * resize_factor
+    This function pads the audio signal with zeros to ensure that the
+    generated spectrogram length will be evenly divisible by `divide_factor`.
+    This is important for the model to work correctly.
 
-    if fixed_width is not None and spec_width < fixed_width:
-        # too small
-        # used during training to ensure all the batches are the same size
-        diff = fixed_width * step + noverlap - audio_raw.shape[0]
-        audio_raw = np.hstack(
-            (audio_raw, np.zeros(diff, dtype=audio_raw.dtype))
+    This `divide_factor` comes from the model architecture as it downscales
+    the spectrogram by this factor, so the input must be divisible by this
+    integer number.
+
+    Parameters
+    ----------
+    audio : np.ndarray
+        The audio signal.
+    samplerate : int
+        The sampling rate of the audio signal.
+    window_size : float
+        The window size in seconds used for the spectrogram computation.
+    window_overlap : float
+        The overlap between windows in the spectrogram computation.
+    resize_factor : float
+        This factor is used to resize the spectrogram after the STFT
+        computation. Default is 0.5 which means that the spectrogram will be
+        reduced by half. Important to take into account for the final size of
+        the spectrogram.
+    divide_factor : int
+        The factor by which the spectrogram will be divided.
+    fixed_width : int, optional
+        If provided, the audio will be padded or cut so that the resulting
+        spectrogram width will be equal to this value.
+
+    Returns
+    -------
+    np.ndarray
+        The padded audio signal.
+    """
+    spec_width = compute_spectrogram_width(
+        audio.shape[0],
+        samplerate=samplerate,
+        window_duration=window_duration,
+        window_overlap=window_overlap,
+        resize_factor=resize_factor,
+    )
+
+    if fixed_width:
+        target_samples = x_coord_to_sample(
+            fixed_width,
+            samplerate=samplerate,
+            window_duration=window_duration,
+            window_overlap=window_overlap,
+            resize_factor=resize_factor,
         )
 
-    elif fixed_width is not None and spec_width > fixed_width:
-        # too big
-        # used during training to ensure all the batches are the same size
-        diff = fixed_width * step + noverlap - audio_raw.shape[0]
-        audio_raw = audio_raw[:diff]
+        if spec_width < fixed_width:
+            # need to be at least min_size
+            diff = target_samples - audio.shape[0]
+            return np.hstack((audio, np.zeros(diff, dtype=audio.dtype)))
 
-    elif (
-        spec_width_rs < min_size
-        or (np.floor(spec_width_rs) % divide_factor) != 0
-    ):
-        # need to be at least min_size
-        div_amt = np.ceil(spec_width_rs / float(divide_factor))
-        div_amt = np.maximum(1, div_amt)
-        target_size = int(div_amt * divide_factor * (1.0 / resize_factor))
-        diff = target_size * step + noverlap - audio_raw.shape[0]
-        audio_raw = np.hstack(
-            (audio_raw, np.zeros(diff, dtype=audio_raw.dtype))
+        if spec_width > fixed_width:
+            return audio[:target_samples]
+
+        return audio
+
+    min_width = int(divide_factor / resize_factor)
+
+    if spec_width < min_width:
+        target_samples = x_coord_to_sample(
+            min_width,
+            samplerate=samplerate,
+            window_duration=window_duration,
+            window_overlap=window_overlap,
+            resize_factor=resize_factor,
         )
+        diff = target_samples - audio.shape[0]
+        return np.hstack((audio, np.zeros(diff, dtype=audio.dtype)))
 
-    return audio_raw
+    if (spec_width % divide_factor) == 0:
+        return audio
+
+    target_width = int(np.ceil(spec_width / divide_factor)) * divide_factor
+    target_samples = x_coord_to_sample(
+        target_width,
+        samplerate=samplerate,
+        window_duration=window_duration,
+        window_overlap=window_overlap,
+        resize_factor=resize_factor,
+    )
+    diff = target_samples - audio.shape[0]
+    return np.hstack((audio, np.zeros(diff, dtype=audio.dtype)))
 
 
 def gen_mag_spectrogram(x, fs, ms, overlap_perc):

batdetect2/utils/detector_utils.py

@@ -8,6 +8,11 @@ import pandas as pd
 import torch
 import torch.nn.functional as F
 
+try:
+    from numpy.exceptions import AxisError
+except ImportError:
+    from numpy import AxisError  # type: ignore
+
 import batdetect2.detector.compute_features as feats
 import batdetect2.detector.post_process as pp
 import batdetect2.utils.audio_utils as au
@@ -80,6 +85,7 @@ def load_model(
     model_path: str = DEFAULT_MODEL_PATH,
     load_weights: bool = True,
     device: Union[torch.device, str, None] = None,
+    weights_only: bool = True,
 ) -> Tuple[DetectionModel, ModelParameters]:
     """Load model from file.
 
@@ -100,7 +106,11 @@ def load_model(
     if not os.path.isfile(model_path):
         raise FileNotFoundError("Model file not found.")
 
-    net_params = torch.load(model_path, map_location=device)
+    net_params = torch.load(
+        model_path,
+        map_location=device,
+        weights_only=weights_only,
+    )
 
     params = net_params["params"]
 
@@ -242,7 +252,7 @@ def format_single_result(
         )
         class_name = class_names[np.argmax(class_overall)]
         annotations = get_annotations_from_preds(predictions, class_names)
-    except (np.AxisError, ValueError):
+    except (AxisError, ValueError):
         # No detections
         class_overall = np.zeros(len(class_names))
         class_name = "None"
@@ -738,9 +748,7 @@ def process_file(
 
     # Get original sampling rate
     file_samp_rate = librosa.get_samplerate(audio_file)
-    orig_samp_rate = file_samp_rate * float(
-        config.get("time_expansion", 1.0) or 1.0
-    )
+    orig_samp_rate = file_samp_rate * (config.get("time_expansion") or 1)
 
     # load audio file
     sampling_rate, audio_full = au.load_audio(

pyproject.toml

@@ -1,103 +1,93 @@
-[tool]
-uv = { dev-dependencies = [
-    "ipykernel>=6.29.4",
-    "setuptools>=69.5.1",
-    "pytest>=8.1.1",
-] }
-[tool.pdm]
-[tool.pdm.dev-dependencies]
-dev = [
-    "pytest>=7.2.2",
-]
-
 [project]
 name = "batdetect2"
-version = "1.0.8"
+version = "1.1.1"
 description = "Deep learning model for detecting and classifying bat echolocation calls in high frequency audio recordings."
 authors = [
-    { "name" = "Oisin Mac Aodha", "email" = "oisin.macaodha@ed.ac.uk" },
-    { "name" = "Santiago Martinez Balvanera", "email" = "santiago.balvanera.20@ucl.ac.uk" }
+  { "name" = "Oisin Mac Aodha", "email" = "oisin.macaodha@ed.ac.uk" },
+  { "name" = "Santiago Martinez Balvanera", "email" = "santiago.balvanera.20@ucl.ac.uk" },
 ]
 dependencies = [
-    "librosa>=0.10.1",
-    "matplotlib>=3.7.1",
-    "numpy>=1.23.5",
-    "pandas>=1.5.3",
-    "scikit-learn>=1.2.2",
-    "scipy>=1.10.1",
-    "torch>=1.13.1",
-    "torchaudio",
-    "torchvision",
-    "soundevent[audio,geometry,plot]>=2.0.1",
-    "click>=8.1.7",
-    "netcdf4>=1.6.5",
-    "tqdm>=4.66.2",
-    "pytorch-lightning>=2.2.2",
-    "cf-xarray>=0.9.0",
-    "onnx>=1.16.0",
-    "lightning[extra]>=2.2.2",
-    "tensorboard>=2.16.2",
+  "click>=8.1.7",
+  "librosa>=0.10.1",
+  "matplotlib>=3.7.1",
+  "numpy>=1.23.5",
+  "pandas>=1.5.3",
+  "scikit-learn>=1.2.2",
+  "scipy>=1.10.1",
+  "torch>=1.13.1,<2.5.0",
+  "torchaudio>=1.13.1,<2.5.0",
+  "torchvision>=0.14.0",
+  "soundevent[audio,geometry,plot]>=2.0.1",
+  "click>=8.1.7",
+  "netcdf4>=1.6.5",
+  "tqdm>=4.66.2",
+  "pytorch-lightning>=2.2.2",
+  "cf-xarray>=0.9.0",
+  "onnx>=1.16.0",
+  "lightning[extra]>=2.2.2",
+  "tensorboard>=2.16.2",
 ]
-requires-python = ">=3.9"
+requires-python = ">=3.9,<3.13"
 readme = "README.md"
 license = { text = "CC-by-nc-4" }
 classifiers = [
-    "Development Status :: 4 - Beta",
-    "Intended Audience :: Science/Research",
-    "Natural Language :: English",
-    "Operating System :: OS Independent",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
-    "Topic :: Scientific/Engineering :: Artificial Intelligence",
-    "Topic :: Software Development :: Libraries :: Python Modules",
-    "Topic :: Multimedia :: Sound/Audio :: Analysis",
+  "Development Status :: 4 - Beta",
+  "Intended Audience :: Science/Research",
+  "Natural Language :: English",
+  "Operating System :: OS Independent",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Topic :: Scientific/Engineering :: Artificial Intelligence",
+  "Topic :: Software Development :: Libraries :: Python Modules",
+  "Topic :: Multimedia :: Sound/Audio :: Analysis",
 ]
 keywords = [
-    "bat",
-    "echolocation",
-    "deep learning",
-    "audio",
-    "machine learning",
-    "classification",
-    "detection",
+  "bat",
+  "echolocation",
+  "deep learning",
+  "audio",
+  "machine learning",
+  "classification",
+  "detection",
 ]
 
 [build-system]
-requires = ["pdm-pep517>=1.0.0"]
-build-backend = "pdm.pep517.api"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
 [project.scripts]
 batdetect2 = "batdetect2.cli:cli"
 
-[tool.black]
-line-length = 79
-
-[tool.isort]
-profile = "black"
-line_length = 79
+[tool.uv]
+dev-dependencies = [
+  "debugpy>=1.8.8",
+  "hypothesis>=6.118.7",
+  "pyright>=1.1.388",
+  "pytest>=7.2.2",
+  "ruff>=0.7.3",
+  "ipykernel>=6.29.4",
+  "setuptools>=69.5.1",
+]
 
 [tool.ruff]
 line-length = 79
+target-version = "py39"
 
-[[tool.mypy.overrides]]
-module = [
-    "librosa",
-    "pandas",
-]
-ignore_missing_imports = true
+[tool.ruff.format]
+docstring-code-format = true
+docstring-code-line-length = 79
 
-[tool.pylsp-mypy]
-enabled = false
-live_mode = true
-strict = true
+[tool.ruff.lint]
+select = ["E4", "E7", "E9", "F", "B", "Q", "I", "NPY201"]
 
-[tool.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = "numpy"
 
 [tool.pyright]
-include = [
-    "bat_detect",
-    "tests",
-]
+include = ["batdetect2", "tests"]
 venvPath = "."
 venv = ".venv"
+pythonVersion = "3.9"
+pythonPlatform = "All"

tests/conftest.py

@@ -0,0 +1,40 @@
+from pathlib import Path
+from typing import List
+
+import pytest
+
+
+@pytest.fixture
+def example_data_dir() -> Path:
+    pkg_dir = Path(__file__).parent.parent
+    example_data_dir = pkg_dir / "example_data"
+    assert example_data_dir.exists()
+    return example_data_dir
+
+
+@pytest.fixture
+def example_audio_dir(example_data_dir: Path) -> Path:
+    example_audio_dir = example_data_dir / "audio"
+    assert example_audio_dir.exists()
+    return example_audio_dir
+
+
+@pytest.fixture
+def example_audio_files(example_audio_dir: Path) -> List[Path]:
+    audio_files = list(example_audio_dir.glob("*.[wW][aA][vV]"))
+    assert len(audio_files) == 3
+    return audio_files
+
+
+@pytest.fixture
+def data_dir() -> Path:
+    dir = Path(__file__).parent / "data"
+    assert dir.exists()
+    return dir
+
+
+@pytest.fixture
+def contrib_dir(data_dir) -> Path:
+    dir = data_dir / "contrib"
+    assert dir.exists()
+    return dir

tests/test_api.py

@@ -1,14 +1,13 @@
 """Test bat detect module API."""
 
-from pathlib import Path
-
 import os
 from glob import glob
+from pathlib import Path
 
 import numpy as np
+import soundfile as sf
 import torch
 from torch import nn
-import soundfile as sf
 
 from batdetect2 import api
 
@@ -267,7 +266,6 @@ def test_process_file_with_spec_slices():
     assert len(results["spec_slices"]) == len(detections)
 
 
-
 def test_process_file_with_empty_predictions_does_not_fail(
     tmp_path: Path,
 ):

tests/test_audio_utils.py

@@ -0,0 +1,136 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from hypothesis import given
+from hypothesis import strategies as st
+
+from batdetect2.detector import parameters
+from batdetect2.utils import audio_utils, detector_utils
+
+
+@given(duration=st.floats(min_value=0.1, max_value=2))
+def test_can_compute_correct_spectrogram_width(duration: float):
+    samplerate = parameters.TARGET_SAMPLERATE_HZ
+    params = parameters.DEFAULT_SPECTROGRAM_PARAMETERS
+
+    length = int(duration * samplerate)
+    audio = np.random.rand(length)
+
+    spectrogram, _ = audio_utils.generate_spectrogram(
+        audio,
+        samplerate,
+        params,
+    )
+
+    # convert to pytorch
+    spectrogram = torch.from_numpy(spectrogram)
+
+    # add batch and channel dimensions
+    spectrogram = spectrogram.unsqueeze(0).unsqueeze(0)
+
+    # resize the spec
+    resize_factor = params["resize_factor"]
+    spec_op_shape = (
+        int(params["spec_height"] * resize_factor),
+        int(spectrogram.shape[-1] * resize_factor),
+    )
+    spectrogram = F.interpolate(
+        spectrogram,
+        size=spec_op_shape,
+        mode="bilinear",
+        align_corners=False,
+    )
+
+    expected_width = audio_utils.compute_spectrogram_width(
+        length,
+        samplerate=parameters.TARGET_SAMPLERATE_HZ,
+        window_duration=params["fft_win_length"],
+        window_overlap=params["fft_overlap"],
+        resize_factor=params["resize_factor"],
+    )
+
+    assert spectrogram.shape[-1] == expected_width
+
+
+@given(duration=st.floats(min_value=0.1, max_value=2))
+def test_pad_audio_without_fixed_size(duration: float):
+    # Test the pad_audio function
+    # This function is used to pad audio with zeros to a specific length
+    # It is used in the generate_spectrogram function
+    # The function is tested with a simplepas
+    samplerate = parameters.TARGET_SAMPLERATE_HZ
+    params = parameters.DEFAULT_SPECTROGRAM_PARAMETERS
+
+    length = int(duration * samplerate)
+    audio = np.random.rand(length)
+
+    # pad the audio to be divisible by divide factor
+    padded_audio = audio_utils.pad_audio(
+        audio,
+        samplerate=samplerate,
+        window_duration=params["fft_win_length"],
+        window_overlap=params["fft_overlap"],
+        resize_factor=params["resize_factor"],
+        divide_factor=params["spec_divide_factor"],
+    )
+
+    # check that the padded audio is divisible by the divide factor
+    expected_width = audio_utils.compute_spectrogram_width(
+        len(padded_audio),
+        samplerate=parameters.TARGET_SAMPLERATE_HZ,
+        window_duration=params["fft_win_length"],
+        window_overlap=params["fft_overlap"],
+        resize_factor=params["resize_factor"],
+    )
+
+    assert expected_width % params["spec_divide_factor"] == 0
+
+
+@given(duration=st.floats(min_value=0.1, max_value=2))
+def test_computed_spectrograms_are_actually_divisible_by_the_spec_divide_factor(
+    duration: float,
+):
+    samplerate = parameters.TARGET_SAMPLERATE_HZ
+    params = parameters.DEFAULT_SPECTROGRAM_PARAMETERS
+    length = int(duration * samplerate)
+    audio = np.random.rand(length)
+    _, spectrogram, _ = detector_utils.compute_spectrogram(
+        audio,
+        samplerate,
+        params,
+        torch.device("cpu"),
+    )
+    assert spectrogram.shape[-1] % params["spec_divide_factor"] == 0
+
+
+@given(
+    duration=st.floats(min_value=0.1, max_value=2),
+    width=st.integers(min_value=128, max_value=1024),
+)
+def test_pad_audio_with_fixed_width(duration: float, width: int):
+    samplerate = parameters.TARGET_SAMPLERATE_HZ
+    params = parameters.DEFAULT_SPECTROGRAM_PARAMETERS
+
+    length = int(duration * samplerate)
+    audio = np.random.rand(length)
+
+    # pad the audio to be divisible by divide factor
+    padded_audio = audio_utils.pad_audio(
+        audio,
+        samplerate=samplerate,
+        window_duration=params["fft_win_length"],
+        window_overlap=params["fft_overlap"],
+        resize_factor=params["resize_factor"],
+        divide_factor=params["spec_divide_factor"],
+        fixed_width=width,
+    )
+
+    # check that the padded audio is divisible by the divide factor
+    expected_width = audio_utils.compute_spectrogram_width(
+        len(padded_audio),
+        samplerate=parameters.TARGET_SAMPLERATE_HZ,
+        window_duration=params["fft_win_length"],
+        window_overlap=params["fft_overlap"],
+        resize_factor=params["resize_factor"],
+    )
+    assert expected_width == width

tests/test_cli.py

@@ -1,22 +1,26 @@
 """Test the command line interface."""
+
 from pathlib import Path
-from click.testing import CliRunner
+
 import pandas as pd
+from click.testing import CliRunner
 
 from batdetect2.cli import cli
 
+runner = CliRunner()
+
 
 def test_cli_base_command():
     """Test the base command."""
-    runner = CliRunner()
     result = runner.invoke(cli, ["--help"])
     assert result.exit_code == 0
-    assert "BatDetect2 - Bat Call Detection and Classification" in result.output
+    assert (
+        "BatDetect2 - Bat Call Detection and Classification" in result.output
+    )
 
 
 def test_cli_detect_command_help():
     """Test the detect command help."""
-    runner = CliRunner()
     result = runner.invoke(cli, ["detect", "--help"])
     assert result.exit_code == 0
     assert "Detect bat calls in files in AUDIO_DIR" in result.output
@@ -30,7 +34,6 @@ def test_cli_detect_command_on_test_audio(tmp_path):
     if results_dir.exists():
         results_dir.rmdir()
 
-    runner = CliRunner()
     result = runner.invoke(
         cli,
         [
@@ -54,7 +57,6 @@ def test_cli_detect_command_with_non_trivial_time_expansion(tmp_path):
     if results_dir.exists():
         results_dir.rmdir()
 
-    runner = CliRunner()
     result = runner.invoke(
         cli,
         [
@@ -68,8 +70,7 @@ def test_cli_detect_command_with_non_trivial_time_expansion(tmp_path):
     )
 
     assert result.exit_code == 0
-    assert 'Time Expansion Factor: 10' in result.stdout
-
+    assert "Time Expansion Factor: 10" in result.stdout
 
 
 def test_cli_detect_command_with_the_spec_feature_flag(tmp_path: Path):
@@ -80,7 +81,6 @@ def test_cli_detect_command_with_the_spec_feature_flag(tmp_path: Path):
     if results_dir.exists():
         results_dir.rmdir()
 
-    runner = CliRunner()
     result = runner.invoke(
         cli,
         [
@@ -94,13 +94,12 @@ def test_cli_detect_command_with_the_spec_feature_flag(tmp_path: Path):
     assert result.exit_code == 0
     assert results_dir.exists()
 
-
     csv_files = [path.name for path in results_dir.glob("*.csv")]
 
     expected_files = [
         "20170701_213954-MYOMYS-LR_0_0.5.wav_spec_features.csv",
         "20180530_213516-EPTSER-LR_0_0.5.wav_spec_features.csv",
-        "20180627_215323-RHIFER-LR_0_0.5.wav_spec_features.csv"
+        "20180627_215323-RHIFER-LR_0_0.5.wav_spec_features.csv",
     ]
 
     for expected_file in expected_files:
@@ -108,3 +107,26 @@ def test_cli_detect_command_with_the_spec_feature_flag(tmp_path: Path):
 
         df = pd.read_csv(results_dir / expected_file)
         assert not (df.duration == -1).any()
+
+
+def test_cli_detect_fails_gracefully_on_empty_file(tmp_path: Path):
+    results_dir = tmp_path / "results"
+    target = tmp_path / "audio"
+    target.mkdir()
+
+    # Create an empty file with the .wav extension
+    empty_file = target / "empty.wav"
+    empty_file.touch()
+
+    result = runner.invoke(
+        cli,
+        args=[
+            "detect",
+            str(target),
+            str(results_dir),
+            "0.3",
+            "--spec_features",
+        ],
+    )
+    assert result.exit_code == 0
+    assert f"Error processing file {empty_file}" in result.output

tests/test_contrib.py

@@ -0,0 +1,73 @@
+"""Test suite to ensure user provided files are correctly processed."""
+
+from pathlib import Path
+
+from click.testing import CliRunner
+
+from batdetect2.cli import cli
+
+runner = CliRunner()
+
+
+def test_can_process_jeff37_files(
+    contrib_dir: Path,
+    tmp_path: Path,
+):
+    """This test stems from issue #31.
+
+    A user provided a set of files which which batdetect2 cli failed and
+    generated the following error message:
+
+        [2272] "Error processing file!: negative dimensions are not allowed"
+
+    This test ensures that the error message is not generated when running
+    batdetect2 cli with the same set of files.
+    """
+    path = contrib_dir / "jeff37"
+    assert path.exists()
+
+    results_dir = tmp_path / "results"
+    result = runner.invoke(
+        cli,
+        [
+            "detect",
+            str(path),
+            str(results_dir),
+            "0.3",
+        ],
+    )
+    assert result.exit_code == 0
+    assert results_dir.exists()
+    assert len(list(results_dir.glob("*.csv"))) == 5
+    assert len(list(results_dir.glob("*.json"))) == 5
+
+
+def test_can_process_padpadpadpad_files(
+    contrib_dir: Path,
+    tmp_path: Path,
+):
+    """This test stems from issue #29.
+
+    Batdetect2 cli failed on the files provided by the user @padpadpadpad
+    with the following error message:
+
+        AttributeError: module 'numpy' has no attribute 'AxisError'
+
+    This test ensures that the files are processed without any error.
+    """
+    path = contrib_dir / "padpadpadpad"
+    assert path.exists()
+    results_dir = tmp_path / "results"
+    result = runner.invoke(
+        cli,
+        [
+            "detect",
+            str(path),
+            str(results_dir),
+            "0.3",
+        ],
+    )
+    assert result.exit_code == 0
+    assert results_dir.exists()
+    assert len(list(results_dir.glob("*.csv"))) == 2
+    assert len(list(results_dir.glob("*.json"))) == 2

tests/test_model.py

@@ -0,0 +1,78 @@
+"""Test suite for model functions."""
+
+import warnings
+from pathlib import Path
+from typing import List
+
+import numpy as np
+from hypothesis import given, settings
+from hypothesis import strategies as st
+
+from batdetect2 import api
+from batdetect2.detector import parameters
+
+
+def test_can_import_model_without_warnings():
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        api.load_model()
+
+
+@settings(deadline=None, max_examples=5)
+@given(duration=st.floats(min_value=0.1, max_value=2))
+def test_can_import_model_without_pickle(duration: float):
+    # NOTE: remove this test once no other issues are found This is a temporary
+    # test to check that change in model loading did not impact model behaviour
+    # in any way.
+
+    samplerate = parameters.TARGET_SAMPLERATE_HZ
+    audio = np.random.rand(int(duration * samplerate))
+
+    model_without_pickle, model_params_without_pickle = api.load_model(
+        weights_only=True
+    )
+    model_with_pickle, model_params_with_pickle = api.load_model(
+        weights_only=False
+    )
+
+    assert model_params_without_pickle == model_params_with_pickle
+
+    predictions_without_pickle, _, _ = api.process_audio(
+        audio,
+        model=model_without_pickle,
+    )
+    predictions_with_pickle, _, _ = api.process_audio(
+        audio,
+        model=model_with_pickle,
+    )
+
+    assert predictions_without_pickle == predictions_with_pickle
+
+
+def test_can_import_model_without_pickle_on_test_data(
+    example_audio_files: List[Path],
+):
+    # NOTE: remove this test once no other issues are found This is a temporary
+    # test to check that change in model loading did not impact model behaviour
+    # in any way.
+
+    model_without_pickle, model_params_without_pickle = api.load_model(
+        weights_only=True
+    )
+    model_with_pickle, model_params_with_pickle = api.load_model(
+        weights_only=False
+    )
+
+    assert model_params_without_pickle == model_params_with_pickle
+
+    for audio_file in example_audio_files:
+        audio = api.load_audio(str(audio_file))
+        predictions_without_pickle, _, _ = api.process_audio(
+            audio,
+            model=model_without_pickle,
+        )
+        predictions_with_pickle, _, _ = api.process_audio(
+            audio,
+            model=model_with_pickle,
+        )
+        assert predictions_without_pickle == predictions_with_pickle