Merge pull request #36 from macaodha/fix/GH-31-negative-dimension-are-not-allowed

fix: Resolve detect Command Failure with Specific Audio Files (GH-31)

.gitignore

@@ -110,5 +110,6 @@ experiments/*
 !batdetect2_notebook.ipynb
 !batdetect2/models/*.pth.tar
 !tests/data/*.wav
+!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__ = "1.0.8"

batdetect2/train/train_utils.py

@@ -1,7 +1,5 @@
 import glob
 import json
-import os
-import random
 
 import numpy as np
 

batdetect2/types.py

@@ -1,4 +1,5 @@
 """Types used in the code base."""
+
 from typing import List, NamedTuple, Optional, Union
 
 import numpy as np
@@ -17,7 +18,7 @@ except ImportError:
 
 
 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):
+def time_to_x_coords(
-    nfft = np.floor(fft_win_length * sampling_rate)  # int() uses floor
+    time_in_file: float,
-    noverlap = np.floor(fft_overlap * nfft)
+    samplerate: float = parameters.TARGET_SAMPLERATE_HZ,
-    return (time_in_file * sampling_rate - noverlap) / (nfft - noverlap)
+    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(
-def x_coords_to_time(x_pos, sampling_rate, fft_win_length, fft_overlap):
+    x_pos: int,
-    nfft = np.floor(fft_win_length * sampling_rate)
+    samplerate: float = parameters.TARGET_SAMPLERATE_HZ,
-    noverlap = np.floor(fft_overlap * nfft)
+    window_duration: float = parameters.FFT_WIN_LENGTH_S,
-    return ((x_pos * (nfft - noverlap)) + noverlap) / sampling_rate
+    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,
@@ -184,55 +210,118 @@ 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,
+    audio: np.ndarray,
-    fs,
+    samplerate: int = parameters.TARGET_SAMPLERATE_HZ,
-    ms,
+    window_duration: float = parameters.FFT_WIN_LENGTH_S,
-    overlap_perc,
+    window_overlap: float = parameters.FFT_OVERLAP,
-    resize_factor,
+    resize_factor: float = parameters.RESIZE_FACTOR,
-    divide_factor,
+    divide_factor: int = parameters.SPEC_DIVIDE_FACTOR,
-    fixed_width=None,
+    fixed_width: Optional[int] = None,
 ):
-    # Adds zeros to the end of the raw data so that the generated sepctrogram
+    """Pad audio to be evenly divisible by `divide_factor`.
-    # will be evenly divisible by `divide_factor`
-    # Also deals with very short audio clips and fixed_width during training
 
-    # This code could be clearer, clean up
+    This function pads the audio signal with zeros to ensure that the
-    nfft = int(ms * fs)
+    generated spectrogram length will be evenly divisible by `divide_factor`.
-    noverlap = int(overlap_perc * nfft)
+    This is important for the model to work correctly.
-    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
 
-    if fixed_width is not None and spec_width < fixed_width:
+    This `divide_factor` comes from the model architecture as it downscales
-        # too small
+    the spectrogram by this factor, so the input must be divisible by this
-        # used during training to ensure all the batches are the same size
+    integer number.
-        diff = fixed_width * step + noverlap - audio_raw.shape[0]
+
-        audio_raw = np.hstack(
+    Parameters
-            (audio_raw, np.zeros(diff, dtype=audio_raw.dtype))
+    ----------
+    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:
+        if spec_width < fixed_width:
-        # too big
+            # need to be at least min_size
-        # used during training to ensure all the batches are the same size
+            diff = target_samples - audio.shape[0]
-        diff = fixed_width * step + noverlap - audio_raw.shape[0]
+            return np.hstack((audio, np.zeros(diff, dtype=audio.dtype)))
-        audio_raw = audio_raw[:diff]
 
-    elif (
+        if spec_width > fixed_width:
-        spec_width_rs < min_size
+            return audio[:target_samples]
-        or (np.floor(spec_width_rs) % divide_factor) != 0
+
-    ):
+        return audio
-        # need to be at least min_size
+
-        div_amt = np.ceil(spec_width_rs / float(divide_factor))
+    min_width = int(divide_factor / resize_factor)
-        div_amt = np.maximum(1, div_amt)
+
-        target_size = int(div_amt * divide_factor * (1.0 / resize_factor))
+    if spec_width < min_width:
-        diff = target_size * step + noverlap - audio_raw.shape[0]
+        target_samples = x_coord_to_sample(
-        audio_raw = np.hstack(
+            min_width,
-            (audio_raw, np.zeros(diff, dtype=audio_raw.dtype))
+            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):
@@ -247,7 +336,11 @@ def gen_mag_spectrogram(x, fs, ms, overlap_perc):
 
     # compute spec
     spec, _ = librosa.core.spectrum._spectrogram(
-        y=x, power=1, n_fft=nfft, hop_length=step, center=False
+        y=x,
+        power=1,
+        n_fft=nfft,
+        hop_length=step,
+        center=False,
     )
 
     # remove DC component and flip vertical orientation

batdetect2/utils/detector_utils.py

@@ -11,7 +11,7 @@ import torch.nn.functional as F
 try:
     from numpy.exceptions import AxisError
 except ImportError:
-    from numpy import AxisError
+    from numpy import AxisError  # type: ignore
 
 import batdetect2.detector.compute_features as feats
 import batdetect2.detector.post_process as pp
@@ -759,7 +759,7 @@ def process_file(
 
     # Get original sampling rate
     file_samp_rate = librosa.get_samplerate(audio_file)
-    orig_samp_rate = file_samp_rate * config.get("time_expansion", 1) or 1
+    orig_samp_rate = file_samp_rate * (config.get("time_expansion") or 1)
 
     # load audio file
     sampling_rate, audio_full = au.load_audio(

pyproject.toml

@@ -53,6 +53,8 @@ batdetect2 = "batdetect2.cli:cli"
 
 [tool.uv]
 dev-dependencies = [
+    "debugpy>=1.8.8",
+    "hypothesis>=6.118.7",
     "pyright>=1.1.388",
     "pytest>=7.2.2",
     "ruff>=0.7.3",

tests/conftest.py

@@ -0,0 +1,17 @@
+from pathlib import Path
+
+import pytest
+
+
+@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_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_contrib.py

@@ -0,0 +1,42 @@
+"""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_files_negative_dimensions_are_not_allowed(
+    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

uv.lock

@@ -6,6 +6,15 @@ resolution-markers = [
     "python_full_version >= '3.12'",
 ]
 
+[[package]]
+name = "attrs"
+version = "24.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fc/0f/aafca9af9315aee06a89ffde799a10a582fe8de76c563ee80bbcdc08b3fb/attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346", size = 792678 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6a/21/5b6702a7f963e95456c0de2d495f67bf5fd62840ac655dc451586d23d39a/attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2", size = 63001 },
+]
+
 [[package]]
 name = "audioread"
 version = "3.0.1"
@@ -34,6 +43,8 @@ dependencies = [
 
 [package.dependency-groups]
 dev = [
+    { name = "debugpy" },
+    { name = "hypothesis" },
     { name = "pyright" },
     { name = "pytest" },
     { name = "ruff" },
@@ -55,6 +66,8 @@ requires-dist = [
 
 [package.metadata.dependency-groups]
 dev = [
+    { name = "debugpy", specifier = ">=1.8.8" },
+    { name = "hypothesis", specifier = ">=6.118.7" },
     { name = "pyright", specifier = ">=1.1.388" },
     { name = "pytest", specifier = ">=7.2.2" },
     { name = "ruff", specifier = ">=0.7.3" },
@@ -283,6 +296,31 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 },
 ]
 
+[[package]]
+name = "debugpy"
+version = "1.8.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e4/5e/7667b95c9d7ddb25c047143a3a47685f9be2a5d3d177a85a730b22dc6e5c/debugpy-1.8.8.zip", hash = "sha256:e6355385db85cbd666be703a96ab7351bc9e6c61d694893206f8001e22aee091", size = 4928684 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/77/79/677d71c342d5f24baf81d262c9e0c19cac3b17b4e4587c0574eaa3964ab1/debugpy-1.8.8-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:e59b1607c51b71545cb3496876544f7186a7a27c00b436a62f285603cc68d1c6", size = 2088337 },
+    { url = "https://files.pythonhosted.org/packages/11/b3/4119fa89b66bcc64a3b186ea52ee7c22bccc5d1765ee890887678b0e3e76/debugpy-1.8.8-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6531d952b565b7cb2fbd1ef5df3d333cf160b44f37547a4e7cf73666aca5d8d", size = 3567953 },
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