batdetect2/data/iterators.py

@@ -1,79 +0,0 @@
-from collections.abc import Generator
-from typing import Optional, Tuple
-
-from soundevent import data
-
-from batdetect2.data.datasets import Dataset
-from batdetect2.targets.types import TargetProtocol
-
-
-def iterate_over_sound_events(
-    dataset: Dataset,
-    targets: TargetProtocol,
-    apply_filter: bool = True,
-    apply_transform: bool = True,
-    exclude_generic: bool = True,
-) -> Generator[Tuple[Optional[str], data.SoundEventAnnotation], None, None]:
-    """Iterate over sound events in a dataset, applying filtering and
-    transformations.
-
-    This generator function processes sound event annotations from a given
-    dataset, allowing for optional filtering, transformation, and exclusion of
-    unclassifiable (generic) events based on the provided target definitions.
-
-    Parameters
-    ----------
-    dataset : Dataset
-        The dataset containing clip annotations, each of which may contain
-        multiple sound event annotations.
-    targets : TargetProtocol
-        An object implementing the `TargetProtocol`, which provides methods
-        for filtering, transforming, and encoding sound events.
-    apply_filter : bool, optional
-        If True, sound events will be filtered using `targets.filter()`.
-        Only events for which `targets.filter()` returns True will be yielded.
-        Defaults to True.
-    apply_transform : bool, optional
-        If True, sound events will be transformed using `targets.transform()`
-        before being yielded. Defaults to True.
-    exclude_generic : bool, optional
-        If True, sound events that result in a `None` class name after
-        `targets.encode()` will be excluded. This is typically used to
-        filter out events that cannot be mapped to a specific target class.
-        Defaults to True.
-
-    Yields
-    ------
-    Tuple[Optional[str], data.SoundEventAnnotation]
-        A tuple containing:
-        - The encoded class name (str) for the sound event, or None if it
-          cannot be encoded to a specific class.
-        - The sound event annotation itself, after passing all specified
-          filtering and transformation steps.
-
-    Notes
-    -----
-    The processing order for each sound event is:
-    1. Filtering (if `apply_filter` is True). Events failing the filter are
-       skipped.
-    2. Transformation (if `apply_transform` is True).
-    3. Encoding to determine class name and check for genericity (if
-       `exclude_generic` is True). Events with a `None` class name are skipped
-        if `exclude_generic` is True.
-    """
-    for clip_annotation in dataset:
-        for sound_event_annotation in clip_annotation.sound_events:
-            if apply_filter:
-                if not targets.filter(sound_event_annotation):
-                    continue
-
-            if apply_transform:
-                sound_event_annotation = targets.transform(
-                    sound_event_annotation
-                )
-
-            class_name = targets.encode(sound_event_annotation)
-            if class_name is None and exclude_generic:
-                continue
-
-            yield class_name, sound_event_annotation

batdetect2/postprocess/remapping.py

@@ -84,7 +84,7 @@ def features_to_xarray(
     freqs = np.linspace(min_freq, max_freq, height, endpoint=False)
 
     return xr.DataArray(
-        data=features.detach().cpu().numpy(),
+        data=features.detach().numpy(),
         dims=[
             Dimensions.feature.value,
             Dimensions.frequency.value,
@@ -157,7 +157,7 @@ def detection_to_xarray(
     freqs = np.linspace(min_freq, max_freq, height, endpoint=False)
 
     return xr.DataArray(
-        data=detection.squeeze(dim=0).detach().cpu().numpy(),
+        data=detection.squeeze(dim=0).detach().numpy(),
         dims=[
             Dimensions.frequency.value,
             Dimensions.time.value,
@@ -233,7 +233,7 @@ def classification_to_xarray(
     freqs = np.linspace(min_freq, max_freq, height, endpoint=False)
 
     return xr.DataArray(
-        data=classes.detach().cpu().numpy(),
+        data=classes.detach().numpy(),
         dims=[
             "category",
             Dimensions.frequency.value,
@@ -302,7 +302,7 @@ def sizes_to_xarray(
     freqs = np.linspace(min_freq, max_freq, height, endpoint=False)
 
     return xr.DataArray(
-        data=sizes.detach().cpu().numpy(),
+        data=sizes.detach().numpy(),
         dims=[
             "dimension",
             Dimensions.frequency.value,

batdetect2/preprocess/spectrogram.py

@@ -19,7 +19,7 @@ reproducible spectrogram generation consistent between training and inference.
 The core computation is performed by `compute_spectrogram`.
 """
 
-from typing import Callable, Literal, Optional, Union
+from typing import Literal, Optional, Union
 
 import numpy as np
 import xarray as xr
@@ -327,45 +327,44 @@ def compute_spectrogram(
     """
     config = config or SpectrogramConfig()
 
-    with xr.set_options(keep_attrs=True):
+    spec = stft(
-        spec = stft(
+        wav,
-            wav,
+        window_duration=config.stft.window_duration,
-            window_duration=config.stft.window_duration,
+        window_overlap=config.stft.window_overlap,
-            window_overlap=config.stft.window_overlap,
+        window_fn=config.stft.window_fn,
-            window_fn=config.stft.window_fn,
+    )
-        )
 
-        spec = crop_spectrogram_frequencies(
+    spec = crop_spectrogram_frequencies(
+        spec,
+        min_freq=config.frequencies.min_freq,
+        max_freq=config.frequencies.max_freq,
+    )
+
+    if config.pcen:
+        spec = apply_pcen(
             spec,
-            min_freq=config.frequencies.min_freq,
+            time_constant=config.pcen.time_constant,
-            max_freq=config.frequencies.max_freq,
+            gain=config.pcen.gain,
+            power=config.pcen.power,
+            bias=config.pcen.bias,
         )
 
-        if config.pcen:
+    spec = scale_spectrogram(spec, scale=config.scale)
-            spec = apply_pcen(
-                spec,
-                time_constant=config.pcen.time_constant,
-                gain=config.pcen.gain,
-                power=config.pcen.power,
-                bias=config.pcen.bias,
-            )
 
-        spec = scale_spectrogram(spec, scale=config.scale)
+    if config.spectral_mean_substraction:
+        spec = remove_spectral_mean(spec)
 
-        if config.spectral_mean_substraction:
+    if config.size:
-            spec = remove_spectral_mean(spec)
+        spec = resize_spectrogram(
+            spec,
+            height=config.size.height,
+            resize_factor=config.size.resize_factor,
+        )
 
-        if config.size:
+    if config.peak_normalize:
-            spec = resize_spectrogram(
+        spec = ops.scale(spec, 1 / (10e-6 + np.max(spec)))
-                spec,
-                height=config.size.height,
-                resize_factor=config.size.resize_factor,
-            )
 
-        if config.peak_normalize:
+    return spec.astype(dtype)
-            spec = ops.scale(spec, 1 / (10e-6 + np.max(spec)))
-
-        return spec.astype(dtype)
 
 
 def crop_spectrogram_frequencies(
@@ -478,7 +477,7 @@ def scale_spectrogram(
     """Apply final amplitude scaling/representation to the spectrogram.
 
     Converts the input magnitude spectrogram based on the `scale` type:
-    - "dB": Applies logarithmic scaling `log10(S)`.
+    - "dB": Applies logarithmic scaling `log1p(C * S)`.
     - "power": Squares the magnitude values `S^2`.
     - "amplitude": Returns the input magnitude values `S` unchanged.
 
@@ -497,7 +496,7 @@ def scale_spectrogram(
         Spectrogram with the specified amplitude scaling applied.
     """
     if scale == "dB":
-        return arrays.to_db(spec).astype(dtype)
+        return scale_log(spec, dtype=dtype)
 
     if scale == "power":
         return spec**2
@@ -562,13 +561,12 @@ def apply_pcen(
 def scale_log(
     spec: xr.DataArray,
     dtype: DTypeLike = np.float32,  # type: ignore
-    ref: Union[float, Callable] = np.max,
-    amin: float = 1e-10,
-    top_db: Optional[float] = 80.0,
 ) -> xr.DataArray:
     """Apply logarithmic scaling to a magnitude spectrogram.
 
-    Calculates `log10(S)`, where S is the input magnitude spectrogram.
+    Calculates `log(1 + C * S)`, where S is the input magnitude spectrogram
+    and C is a scaling factor derived from the original STFT parameters
+    (sample rate, N-FFT, window function) stored in `spec.attrs`.
 
     Parameters
     ----------
@@ -589,29 +587,12 @@ def scale_log(
         If required attributes are missing from `spec.attrs`.
     ValueError
         If attributes are non-numeric or window function is invalid.
-
-
-    Notes
-    -----
-    Implementation mainly taken from librosa `power_to_db` function
     """
-
+    samplerate = spec.attrs["original_samplerate"]
-    if callable(ref):
+    nfft = spec.attrs["nfft"]
-        ref_value = ref(spec)
+    log_scaling = 2 / (samplerate * (np.abs(np.hanning(nfft)) ** 2).sum())
-    else:
-        ref_value = np.abs(ref)
-
-    log_spec = 10.0 * np.log10(np.maximum(amin, spec)) - np.log10(
-        np.maximum(amin, ref_value)
-    )
-
-    if top_db is not None:
-        if top_db < 0:
-            raise ValueError("top_db must be non-negative")
-        log_spec = np.maximum(log_spec, log_spec.max() - top_db)
-
     return xr.DataArray(
-        data=log_spec.astype(dtype),
+        data=np.log1p(log_scaling * spec).astype(dtype),
         dims=spec.dims,
         coords=spec.coords,
         attrs=spec.attrs,

batdetect2/train/dataset.py

@@ -9,7 +9,6 @@ from torch.utils.data import Dataset
 
 from batdetect2.train.augmentations import Augmentation
 from batdetect2.train.types import ClipperProtocol, TrainExample
-from batdetect2.utils.tensors import adjust_width
 
 __all__ = [
     "LabeledDataset",
@@ -88,26 +87,6 @@ class LabeledDataset(Dataset):
         )
 
 
-def collate_fn(batch: List[TrainExample]):
-    width = 512
-
-    return TrainExample(
-        spec=torch.stack([adjust_width(x.spec, width) for x in batch]),
-        detection_heatmap=torch.stack(
-            [adjust_width(x.detection_heatmap, width) for x in batch]
-        ),
-        class_heatmap=torch.stack(
-            [adjust_width(x.class_heatmap, width) for x in batch]
-        ),
-        size_heatmap=torch.stack(
-            [adjust_width(x.size_heatmap, width) for x in batch]
-        ),
-        idx=torch.stack([x.idx for x in batch]),
-        start_time=torch.stack([x.start_time for x in batch]),
-        end_time=torch.stack([x.end_time for x in batch]),
-    )
-
-
 def list_preprocessed_files(
     directory: data.PathLike, extension: str = ".nc"
 ) -> List[Path]:

batdetect2/train/preprocess.py

@@ -276,11 +276,8 @@ def preprocess_single_annotation(
             labeller=labeller,
         )
     except Exception as error:
-        logger.error(
+        raise RuntimeError(
-            "Failed to process annotation: {uuid}. Error {error}",
+            f"Failed to process annotation: {clip_annotation.uuid}"
-            uuid=clip_annotation.uuid,
+        ) from error
-            error=error,
-        )
-        return
 
     _save_xr_dataset_to_file(sample, path)

batdetect2/train/train.py

@@ -17,11 +17,7 @@ from batdetect2.train.augmentations import (
 )
 from batdetect2.train.clips import build_clipper
 from batdetect2.train.config import TrainingConfig
-from batdetect2.train.dataset import (
+from batdetect2.train.dataset import LabeledDataset, RandomExampleSource
-    LabeledDataset,
-    RandomExampleSource,
-    collate_fn,
-)
 from batdetect2.train.lightning import TrainingModule
 from batdetect2.train.losses import build_loss
 
@@ -92,7 +88,6 @@ def train(
         batch_size=config.batch_size,
         shuffle=True,
         num_workers=train_workers,
-        collate_fn=collate_fn,
     )
 
     val_dataloader = None
@@ -106,7 +101,6 @@ def train(
             batch_size=config.batch_size,
             shuffle=False,
             num_workers=val_workers,
-            collate_fn=collate_fn,
         )
 
     trainer.fit(

requirements-dev.lock

@@ -0,0 +1,540 @@
+# generated by rye
+# use `rye lock` or `rye sync` to update this lockfile
+#
+# last locked with the following flags:
+#   pre: false
+#   features: []
+#   all-features: false
+#   with-sources: false
+#   generate-hashes: false
+#   universal: false
+
+-e file:.
+absl-py==2.1.0
+    # via tensorboard
+affine==2.4.0
+    # via rasterio
+aiobotocore==2.12.3
+    # via s3fs
+aiohttp==3.9.5
+    # via aiobotocore
+    # via fsspec
+    # via lightning
+    # via s3fs
+aioitertools==0.11.0
+    # via aiobotocore
+aiosignal==1.3.1
+    # via aiohttp
+annotated-types==0.6.0
+    # via pydantic
+antlr4-python3-runtime==4.9.3
+    # via hydra-core
+    # via omegaconf
+anyio==4.3.0
+    # via starlette
+arrow==1.3.0
+    # via lightning
+asttokens==2.4.1
+    # via stack-data
+async-timeout==4.0.3
+    # via aiohttp
+    # via redis
+attrs==23.2.0
+    # via aiohttp
+    # via rasterio
+audioread==3.0.1
+    # via librosa
+backcall==0.2.0
+    # via ipython
+backoff==2.2.1
+    # via lightning
+beautifulsoup4==4.12.3
+    # via lightning
+bitsandbytes==0.41.0
+    # via lightning
+blessed==1.20.0
+    # via inquirer
+boto3==1.34.69
+    # via lightning-cloud
+botocore==1.34.69
+    # via aiobotocore
+    # via boto3
+    # via s3transfer
+certifi==2024.2.2
+    # via netcdf4
+    # via rasterio
+    # via requests
+cf-xarray==0.9.0
+    # via batdetect2
+cffi==1.16.0
+    # via soundfile
+cftime==1.6.3
+    # via netcdf4
+charset-normalizer==3.3.2
+    # via requests
+click==8.1.7
+    # via batdetect2
+    # via click-plugins
+    # via cligj
+    # via lightning
+    # via lightning-cloud
+    # via rasterio
+    # via uvicorn
+click-plugins==1.1.1
+    # via rasterio
+cligj==0.7.2
+    # via rasterio
+comm==0.2.2
+    # via ipykernel
+contourpy==1.1.1
+    # via matplotlib
+croniter==1.4.1
+    # via lightning
+cycler==0.12.1
+    # via matplotlib
+cython==3.0.10
+    # via soundevent
+dateutils==0.6.12
+    # via lightning
+debugpy==1.8.1
+    # via ipykernel
+decorator==5.1.1
+    # via ipython
+    # via librosa
+deepdiff==6.7.1
+    # via lightning
+dnspython==2.6.1
+    # via email-validator
+docker==6.1.3
+    # via lightning
+docstring-parser==0.16
+    # via jsonargparse
+editor==1.6.6
+    # via inquirer
+email-validator==2.1.1
+    # via soundevent
+exceptiongroup==1.2.1
+    # via anyio
+    # via pytest
+executing==2.0.1
+    # via stack-data
+fastapi==0.110.2
+    # via lightning
+    # via lightning-cloud
+filelock==3.13.4
+    # via torch
+    # via triton
+fonttools==4.51.0
+    # via matplotlib
+frozenlist==1.4.1
+    # via aiohttp
+    # via aiosignal
+fsspec==2023.12.2
+    # via lightning
+    # via lightning-fabric
+    # via pytorch-lightning
+    # via s3fs
+    # via torch
+grpcio==1.62.2
+    # via tensorboard
+h11==0.14.0
+    # via uvicorn
+hydra-core==1.3.2
+    # via lightning
+idna==3.7
+    # via anyio
+    # via email-validator
+    # via requests
+    # via yarl
+importlib-metadata==7.1.0
+    # via jupyter-client
+    # via markdown
+    # via rasterio
+importlib-resources==6.4.0
+    # via matplotlib
+    # via typeshed-client
+iniconfig==2.0.0
+    # via pytest
+inquirer==3.2.4
+    # via lightning
+ipykernel==6.29.4
+ipython==8.12.3
+    # via ipykernel
+jedi==0.19.1
+    # via ipython
+jinja2==3.1.3
+    # via lightning
+    # via torch
+jmespath==1.0.1
+    # via boto3
+    # via botocore
+joblib==1.4.0
+    # via librosa
+    # via scikit-learn
+jsonargparse==4.28.0
+    # via lightning
+jupyter-client==8.6.1
+    # via ipykernel
+jupyter-core==5.7.2
+    # via ipykernel
+    # via jupyter-client
+kiwisolver==1.4.5
+    # via matplotlib
+lazy-loader==0.4
+    # via librosa
+librosa==0.10.1
+    # via batdetect2
+lightning==2.2.2
+    # via batdetect2
+lightning-api-access==0.0.5
+    # via lightning
+lightning-cloud==0.5.65
+    # via lightning
+lightning-fabric==2.2.2
+    # via lightning
+lightning-utilities==0.11.2
+    # via lightning
+    # via lightning-fabric
+    # via pytorch-lightning
+    # via torchmetrics
+llvmlite==0.41.1
+    # via numba
+markdown==3.6
+    # via tensorboard
+markdown-it-py==3.0.0
+    # via rich
+markupsafe==2.1.5
+    # via jinja2
+    # via werkzeug
+matplotlib==3.7.5
+    # via batdetect2
+    # via lightning
+    # via soundevent
+matplotlib-inline==0.1.7
+    # via ipykernel
+    # via ipython
+mdurl==0.1.2
+    # via markdown-it-py
+mpmath==1.3.0
+    # via sympy
+msgpack==1.0.8
+    # via librosa
+multidict==6.0.5
+    # via aiohttp
+    # via yarl
+nest-asyncio==1.6.0
+    # via ipykernel
+netcdf4==1.6.5
+    # via batdetect2
+networkx==3.1
+    # via torch
+numba==0.58.1
+    # via librosa
+numpy==1.24.4
+    # via batdetect2
+    # via cftime
+    # via contourpy
+    # via librosa
+    # via lightning
+    # via lightning-fabric
+    # via matplotlib
+    # via netcdf4
+    # via numba
+    # via onnx
+    # via pandas
+    # via pytorch-lightning
+    # via rasterio
+    # via scikit-learn
+    # via scipy
+    # via shapely
+    # via snuggs
+    # via soxr
+    # via tensorboard
+    # via tensorboardx
+    # via torchmetrics
+    # via torchvision
+    # via xarray
+nvidia-cublas-cu12==12.1.3.1
+    # via nvidia-cudnn-cu12
+    # via nvidia-cusolver-cu12
+    # via torch
+nvidia-cuda-cupti-cu12==12.1.105
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.1.105
+    # via torch
+nvidia-cuda-runtime-cu12==12.1.105
+    # via torch
+nvidia-cudnn-cu12==8.9.2.26
+    # via torch
+nvidia-cufft-cu12==11.0.2.54
+    # via torch
+nvidia-curand-cu12==10.3.2.106
+    # via torch
+nvidia-cusolver-cu12==11.4.5.107
+    # via torch
+nvidia-cusparse-cu12==12.1.0.106
+    # via nvidia-cusolver-cu12
+    # via torch
+nvidia-nccl-cu12==2.19.3
+    # via torch
+nvidia-nvjitlink-cu12==12.4.127
+    # via nvidia-cusolver-cu12
+    # via nvidia-cusparse-cu12
+nvidia-nvtx-cu12==12.1.105
+    # via torch
+omegaconf==2.3.0
+    # via hydra-core
+    # via lightning
+onnx==1.16.0
+    # via batdetect2
+ordered-set==4.1.0
+    # via deepdiff
+packaging==24.0
+    # via docker
+    # via hydra-core
+    # via ipykernel
+    # via lazy-loader
+    # via lightning
+    # via lightning-fabric
+    # via lightning-utilities
+    # via matplotlib
+    # via pooch
+    # via pytest
+    # via pytorch-lightning
+    # via tensorboardx
+    # via torchmetrics
+    # via xarray
+pandas==2.0.3
+    # via batdetect2
+    # via xarray
+parso==0.8.4
+    # via jedi
+pexpect==4.9.0
+    # via ipython
+pickleshare==0.7.5
+    # via ipython
+pillow==10.3.0
+    # via matplotlib
+    # via torchvision
+platformdirs==4.2.0
+    # via jupyter-core
+    # via pooch
+pluggy==1.4.0
+    # via pytest
+pooch==1.8.1
+    # via librosa
+prompt-toolkit==3.0.43
+    # via ipython
+protobuf==5.26.1
+    # via onnx
+    # via tensorboard
+    # via tensorboardx
+psutil==5.9.8
+    # via ipykernel
+    # via lightning
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.2
+    # via stack-data
+pycparser==2.22
+    # via cffi
+pydantic==2.7.0
+    # via fastapi
+    # via lightning
+    # via soundevent
+pydantic-core==2.18.1
+    # via pydantic
+pygments==2.17.2
+    # via ipython
+    # via rich
+pyjwt==2.8.0
+    # via lightning-cloud
+pyparsing==3.1.2
+    # via matplotlib
+    # via snuggs
+pytest==8.1.1
+python-dateutil==2.9.0.post0
+    # via arrow
+    # via botocore
+    # via croniter
+    # via dateutils
+    # via jupyter-client
+    # via matplotlib
+    # via pandas
+python-multipart==0.0.9
+    # via lightning
+    # via lightning-cloud
+pytorch-lightning==2.2.2
+    # via batdetect2
+    # via lightning
+pytz==2024.1
+    # via dateutils
+    # via pandas
+pyyaml==6.0.1
+    # via jsonargparse
+    # via lightning
+    # via omegaconf
+    # via pytorch-lightning
+pyzmq==26.0.0
+    # via ipykernel
+    # via jupyter-client
+rasterio==1.3.10
+    # via soundevent
+readchar==4.0.6
+    # via inquirer
+redis==5.0.4
+    # via lightning
+requests==2.31.0
+    # via docker
+    # via fsspec
+    # via lightning
+    # via lightning-cloud
+    # via pooch
+rich==13.7.1
+    # via lightning
+    # via lightning-cloud
+runs==1.2.2
+    # via editor
+s3fs==2023.12.2
+    # via lightning
+s3transfer==0.10.1
+    # via boto3
+scikit-learn==1.3.2
+    # via batdetect2
+    # via librosa
+scipy==1.10.1
+    # via batdetect2
+    # via librosa
+    # via scikit-learn
+    # via soundevent
+setuptools==69.5.1
+    # via lightning-utilities
+    # via rasterio
+    # via readchar
+    # via tensorboard
+shapely==2.0.3
+    # via soundevent
+six==1.16.0
+    # via asttokens
+    # via blessed
+    # via lightning-cloud
+    # via python-dateutil
+    # via tensorboard
+sniffio==1.3.1
+    # via anyio
+snuggs==1.4.7
+    # via rasterio
+soundevent==2.0.1
+    # via batdetect2
+soundfile==0.12.1
+    # via librosa
+    # via soundevent
+soupsieve==2.5
+    # via beautifulsoup4
+soxr==0.3.7
+    # via librosa
+stack-data==0.6.3
+    # via ipython
+starlette==0.37.2
+    # via fastapi
+    # via lightning
+sympy==1.12
+    # via torch
+tensorboard==2.16.2
+    # via batdetect2
+tensorboard-data-server==0.7.2
+    # via tensorboard
+tensorboardx==2.6.2.2
+    # via lightning
+threadpoolctl==3.4.0
+    # via scikit-learn
+tomli==2.0.1
+    # via pytest
+torch==2.2.2
+    # via batdetect2
+    # via lightning
+    # via lightning-fabric
+    # via pytorch-lightning
+    # via torchaudio
+    # via torchmetrics
+    # via torchvision
+torchaudio==2.2.2
+    # via batdetect2
+torchmetrics==1.3.2
+    # via lightning
+    # via pytorch-lightning
+torchvision==0.17.2
+    # via batdetect2
+tornado==6.4
+    # via ipykernel
+    # via jupyter-client
+tqdm==4.66.2
+    # via batdetect2
+    # via lightning
+    # via pytorch-lightning
+traitlets==5.14.2
+    # via comm
+    # via ipykernel
+    # via ipython
+    # via jupyter-client
+    # via jupyter-core
+    # via lightning
+    # via matplotlib-inline
+triton==2.2.0
+    # via torch
+types-python-dateutil==2.9.0.20240316
+    # via arrow
+typeshed-client==2.5.1
+    # via jsonargparse
+typing-extensions==4.11.0
+    # via aioitertools
+    # via anyio
+    # via fastapi
+    # via ipython
+    # via jsonargparse
+    # via librosa
+    # via lightning
+    # via lightning-fabric
+    # via lightning-utilities
+    # via pydantic
+    # via pydantic-core
+    # via pytorch-lightning
+    # via starlette
+    # via torch
+    # via typeshed-client
+    # via uvicorn
+tzdata==2024.1
+    # via pandas
+urllib3==1.26.18
+    # via botocore
+    # via docker
+    # via lightning
+    # via lightning-cloud
+    # via requests
+uvicorn==0.29.0
+    # via lightning
+    # via lightning-cloud
+wcwidth==0.2.13
+    # via blessed
+    # via prompt-toolkit
+websocket-client==1.7.0
+    # via docker
+    # via lightning
+    # via lightning-cloud
+websockets==11.0.3
+    # via lightning
+werkzeug==3.0.2
+    # via tensorboard
+wrapt==1.16.0
+    # via aiobotocore
+xarray==2023.1.0
+    # via cf-xarray
+    # via soundevent
+xmod==1.8.1
+    # via editor
+    # via runs
+yarl==1.9.4
+    # via aiohttp
+zipp==3.18.1
+    # via importlib-metadata
+    # via importlib-resources

requirements.lock

@@ -0,0 +1,470 @@
+# generated by rye
+# use `rye lock` or `rye sync` to update this lockfile
+#
+# last locked with the following flags:
+#   pre: false
+#   features: []
+#   all-features: false
+#   with-sources: false
+#   generate-hashes: false
+#   universal: false
+
+-e file:.
+absl-py==2.1.0
+    # via tensorboard
+affine==2.4.0
+    # via rasterio
+aiobotocore==2.12.3
+    # via s3fs
+aiohttp==3.9.5
+    # via aiobotocore
+    # via fsspec
+    # via lightning
+    # via s3fs
+aioitertools==0.11.0
+    # via aiobotocore
+aiosignal==1.3.1
+    # via aiohttp
+annotated-types==0.6.0
+    # via pydantic
+antlr4-python3-runtime==4.9.3
+    # via hydra-core
+    # via omegaconf
+anyio==4.3.0
+    # via starlette
+arrow==1.3.0
+    # via lightning
+async-timeout==4.0.3
+    # via aiohttp
+    # via redis
+attrs==23.2.0
+    # via aiohttp
+    # via rasterio
+audioread==3.0.1
+    # via librosa
+backoff==2.2.1
+    # via lightning
+beautifulsoup4==4.12.3
+    # via lightning
+bitsandbytes==0.41.0
+    # via lightning
+blessed==1.20.0
+    # via inquirer
+boto3==1.34.69
+    # via lightning-cloud
+botocore==1.34.69
+    # via aiobotocore
+    # via boto3
+    # via s3transfer
+certifi==2024.2.2
+    # via netcdf4
+    # via rasterio
+    # via requests
+cf-xarray==0.9.0
+    # via batdetect2
+cffi==1.16.0
+    # via soundfile
+cftime==1.6.3
+    # via netcdf4
+charset-normalizer==3.3.2
+    # via requests
+click==8.1.7
+    # via batdetect2
+    # via click-plugins
+    # via cligj
+    # via lightning
+    # via lightning-cloud
+    # via rasterio
+    # via uvicorn
+click-plugins==1.1.1
+    # via rasterio
+cligj==0.7.2
+    # via rasterio
+contourpy==1.1.1
+    # via matplotlib
+croniter==1.4.1
+    # via lightning
+cycler==0.12.1
+    # via matplotlib
+cython==3.0.10
+    # via soundevent
+dateutils==0.6.12
+    # via lightning
+decorator==5.1.1
+    # via librosa
+deepdiff==6.7.1
+    # via lightning
+dnspython==2.6.1
+    # via email-validator
+docker==6.1.3
+    # via lightning
+docstring-parser==0.16
+    # via jsonargparse
+editor==1.6.6
+    # via inquirer
+email-validator==2.1.1
+    # via soundevent
+exceptiongroup==1.2.1
+    # via anyio
+fastapi==0.110.2
+    # via lightning
+    # via lightning-cloud
+filelock==3.13.4
+    # via torch
+    # via triton
+fonttools==4.51.0
+    # via matplotlib
+frozenlist==1.4.1
+    # via aiohttp
+    # via aiosignal
+fsspec==2023.12.2
+    # via lightning
+    # via lightning-fabric
+    # via pytorch-lightning
+    # via s3fs
+    # via torch
+grpcio==1.62.2
+    # via tensorboard
+h11==0.14.0
+    # via uvicorn
+hydra-core==1.3.2
+    # via lightning
+idna==3.7
+    # via anyio
+    # via email-validator
+    # via requests
+    # via yarl
+importlib-metadata==7.1.0
+    # via markdown
+    # via rasterio
+importlib-resources==6.4.0
+    # via matplotlib
+    # via typeshed-client
+inquirer==3.2.4
+    # via lightning
+jinja2==3.1.3
+    # via lightning
+    # via torch
+jmespath==1.0.1
+    # via boto3
+    # via botocore
+joblib==1.4.0
+    # via librosa
+    # via scikit-learn
+jsonargparse==4.28.0
+    # via lightning
+kiwisolver==1.4.5
+    # via matplotlib
+lazy-loader==0.4
+    # via librosa
+librosa==0.10.1
+    # via batdetect2
+lightning==2.2.2
+    # via batdetect2
+lightning-api-access==0.0.5
+    # via lightning
+lightning-cloud==0.5.65
+    # via lightning
+lightning-fabric==2.2.2
+    # via lightning
+lightning-utilities==0.11.2
+    # via lightning
+    # via lightning-fabric
+    # via pytorch-lightning
+    # via torchmetrics
+llvmlite==0.41.1
+    # via numba
+markdown==3.6
+    # via tensorboard
+markdown-it-py==3.0.0
+    # via rich
+markupsafe==2.1.5
+    # via jinja2
+    # via werkzeug
+matplotlib==3.7.5
+    # via batdetect2
+    # via lightning
+    # via soundevent
+mdurl==0.1.2
+    # via markdown-it-py
+mpmath==1.3.0
+    # via sympy
+msgpack==1.0.8
+    # via librosa
+multidict==6.0.5
+    # via aiohttp
+    # via yarl
+netcdf4==1.6.5
+    # via batdetect2
+networkx==3.1
+    # via torch
+numba==0.58.1
+    # via librosa
+numpy==1.24.4
+    # via batdetect2
+    # via cftime
+    # via contourpy
+    # via librosa
+    # via lightning
+    # via lightning-fabric
+    # via matplotlib
+    # via netcdf4
+    # via numba
+    # via onnx
+    # via pandas
+    # via pytorch-lightning
+    # via rasterio
+    # via scikit-learn
+    # via scipy
+    # via shapely
+    # via snuggs
+    # via soxr
+    # via tensorboard
+    # via tensorboardx
+    # via torchmetrics
+    # via torchvision
+    # via xarray
+nvidia-cublas-cu12==12.1.3.1
+    # via nvidia-cudnn-cu12
+    # via nvidia-cusolver-cu12
+    # via torch
+nvidia-cuda-cupti-cu12==12.1.105
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.1.105
+    # via torch
+nvidia-cuda-runtime-cu12==12.1.105
+    # via torch
+nvidia-cudnn-cu12==8.9.2.26
+    # via torch
+nvidia-cufft-cu12==11.0.2.54
+    # via torch
+nvidia-curand-cu12==10.3.2.106
+    # via torch
+nvidia-cusolver-cu12==11.4.5.107
+    # via torch
+nvidia-cusparse-cu12==12.1.0.106
+    # via nvidia-cusolver-cu12
+    # via torch
+nvidia-nccl-cu12==2.19.3
+    # via torch
+nvidia-nvjitlink-cu12==12.4.127
+    # via nvidia-cusolver-cu12
+    # via nvidia-cusparse-cu12
+nvidia-nvtx-cu12==12.1.105
+    # via torch
+omegaconf==2.3.0
+    # via hydra-core
+    # via lightning
+onnx==1.16.0
+    # via batdetect2
+ordered-set==4.1.0
+    # via deepdiff
+packaging==24.0
+    # via docker
+    # via hydra-core
+    # via lazy-loader
+    # via lightning
+    # via lightning-fabric
+    # via lightning-utilities
+    # via matplotlib
+    # via pooch
+    # via pytorch-lightning
+    # via tensorboardx
+    # via torchmetrics
+    # via xarray
+pandas==2.0.3
+    # via batdetect2
+    # via xarray
+pillow==10.3.0
+    # via matplotlib
+    # via torchvision
+platformdirs==4.2.0
+    # via pooch
+pooch==1.8.1
+    # via librosa
+protobuf==5.26.1
+    # via onnx
+    # via tensorboard
+    # via tensorboardx
+psutil==5.9.8
+    # via lightning
+pycparser==2.22
+    # via cffi
+pydantic==2.7.0
+    # via fastapi
+    # via lightning
+    # via soundevent
+pydantic-core==2.18.1
+    # via pydantic
+pygments==2.17.2
+    # via rich
+pyjwt==2.8.0
+    # via lightning-cloud
+pyparsing==3.1.2
+    # via matplotlib
+    # via snuggs
+python-dateutil==2.9.0.post0
+    # via arrow
+    # via botocore
+    # via croniter
+    # via dateutils
+    # via matplotlib
+    # via pandas
+python-multipart==0.0.9
+    # via lightning
+    # via lightning-cloud
+pytorch-lightning==2.2.2
+    # via batdetect2
+    # via lightning
+pytz==2024.1
+    # via dateutils
+    # via pandas
+pyyaml==6.0.1
+    # via jsonargparse
+    # via lightning
+    # via omegaconf
+    # via pytorch-lightning
+rasterio==1.3.10
+    # via soundevent
+readchar==4.0.6
+    # via inquirer
+redis==5.0.4
+    # via lightning
+requests==2.31.0
+    # via docker
+    # via fsspec
+    # via lightning
+    # via lightning-cloud
+    # via pooch
+rich==13.7.1
+    # via lightning
+    # via lightning-cloud
+runs==1.2.2
+    # via editor
+s3fs==2023.12.2
+    # via lightning
+s3transfer==0.10.1
+    # via boto3
+scikit-learn==1.3.2
+    # via batdetect2
+    # via librosa
+scipy==1.10.1
+    # via batdetect2
+    # via librosa
+    # via scikit-learn
+    # via soundevent
+setuptools==69.5.1
+    # via lightning-utilities
+    # via rasterio
+    # via readchar
+    # via tensorboard
+shapely==2.0.3
+    # via soundevent
+six==1.16.0
+    # via blessed
+    # via lightning-cloud
+    # via python-dateutil
+    # via tensorboard
+sniffio==1.3.1
+    # via anyio
+snuggs==1.4.7
+    # via rasterio
+soundevent==2.0.1
+    # via batdetect2
+soundfile==0.12.1
+    # via librosa
+    # via soundevent
+soupsieve==2.5
+    # via beautifulsoup4
+soxr==0.3.7
+    # via librosa
+starlette==0.37.2
+    # via fastapi
+    # via lightning
+sympy==1.12
+    # via torch
+tensorboard==2.16.2
+    # via batdetect2
+tensorboard-data-server==0.7.2
+    # via tensorboard
+tensorboardx==2.6.2.2
+    # via lightning
+threadpoolctl==3.4.0
+    # via scikit-learn
+torch==2.2.2
+    # via batdetect2
+    # via lightning
+    # via lightning-fabric
+    # via pytorch-lightning
+    # via torchaudio
+    # via torchmetrics
+    # via torchvision
+torchaudio==2.2.2
+    # via batdetect2
+torchmetrics==1.3.2
+    # via lightning
+    # via pytorch-lightning
+torchvision==0.17.2
+    # via batdetect2
+tqdm==4.66.2
+    # via batdetect2
+    # via lightning
+    # via pytorch-lightning
+traitlets==5.14.3
+    # via lightning
+triton==2.2.0
+    # via torch
+types-python-dateutil==2.9.0.20240316
+    # via arrow
+typeshed-client==2.5.1
+    # via jsonargparse
+typing-extensions==4.11.0
+    # via aioitertools
+    # via anyio
+    # via fastapi
+    # via jsonargparse
+    # via librosa
+    # via lightning
+    # via lightning-fabric
+    # via lightning-utilities
+    # via pydantic
+    # via pydantic-core
+    # via pytorch-lightning
+    # via starlette
+    # via torch
+    # via typeshed-client
+    # via uvicorn
+tzdata==2024.1
+    # via pandas
+urllib3==1.26.18
+    # via botocore
+    # via docker
+    # via lightning
+    # via lightning-cloud
+    # via requests
+uvicorn==0.29.0
+    # via lightning
+    # via lightning-cloud
+wcwidth==0.2.13
+    # via blessed
+websocket-client==1.7.0
+    # via docker
+    # via lightning
+    # via lightning-cloud
+websockets==11.0.3
+    # via lightning
+werkzeug==3.0.2
+    # via tensorboard
+wrapt==1.16.0
+    # via aiobotocore
+xarray==2023.1.0
+    # via cf-xarray
+    # via soundevent
+xmod==1.8.1
+    # via editor
+    # via runs
+yarl==1.9.4
+    # via aiohttp
+zipp==3.18.1
+    # via importlib-metadata
+    # via importlib-resources