Eval

docs/source/targets/tags_and_terms.md

@@ -133,7 +133,7 @@ When you need to specify a tag, you typically use a structure with two fields:
   **It defaults to `class`** if you omit it, which is common when defining the main target classes.
 - `value`: The specific _value_ of the tag (e.g., `Myotis daubentonii`, `Good`, `Rain`).
 
-**Example YAML Configuration using TagInfo (e.g., inside a filter rule):**
+**Example YAML Configuration (e.g., inside a filter rule):**
 
 ```yaml
 # ... inside a filtering configuration section ...

example_data/config.yaml

@@ -1,44 +1,47 @@
 targets:
-  classes:
-    classes:
-      - name: myomys
-        tags:
-          - value: Myotis mystacinus
-      - name: pippip
-        tags:
-          - value: Pipistrellus pipistrellus
-      - name: eptser
-        tags:
-          - value: Eptesicus serotinus
-      - name: rhifer
-        tags:
-          - value: Rhinolophus ferrumequinum
-        roi:
-          name: anchor_bbox
-          anchor: top-left
-    generic_class:
+  detection_target:
+    name: bat
+    match_if:
+      name: all_of
+      conditions:
+        - name: has_tag
+          tag: { key: event, value: Echolocation }
+        - name: not
+          condition:
+            name: has_tag
+            tag: { key: class, value: Unknown }
+    assign_tags:
       - key: class
         value: Bat
 
-  filtering:
-    rules:
-      - match_type: all
-        tags:
-          - key: event
-            value: Echolocation
-      - match_type: exclude
-        tags:
-          - key: class
-            value: Unknown
+  classification_targets:
+    - name: myomys
+      tags:
+        - key: class
+          value: Myotis mystacinus
+    - name: pippip
+      tags:
+        - key: class
+          value: Pipistrellus pipistrellus
+    - name: eptser
+      tags:
+        - key: class
+          value: Eptesicus serotinus
+    - name: rhifer
+      tags:
+        - key: class
+          value: Rhinolophus ferrumequinum
+
+  roi:
+    name: anchor_bbox
+    anchor: top-left
 
 preprocess:
   audio:
+    samplerate: 256000
     resample:
-      samplerate: 256000
+      enabled: True
       method: "poly"
-    scale: false
-    center: true
-    duration: null
 
   spectrogram:
     stft:
@@ -48,66 +51,66 @@ preprocess:
     frequencies:
       max_freq: 120000
       min_freq: 10000
-    pcen:
-      time_constant: 0.1
-      gain: 0.98
-      bias: 2
-      power: 0.5
-    scale: "amplitude"
     size:
       height: 128
       resize_factor: 0.5
-    spectral_mean_substraction: true
-    peak_normalize: false
+    transforms:
+      - name: pcen
+        time_constant: 0.1
+        gain: 0.98
+        bias: 2
+        power: 0.5
+      - name: spectral_mean_substraction
 
 postprocess:
   nms_kernel_size: 9
   detection_threshold: 0.01
-  min_freq: 10000
-  max_freq: 120000
   top_k_per_sec: 200
 
-labels:
-  sigma: 3
-
 model:
   input_height: 128
   in_channels: 1
   out_channels: 32
   encoder:
     layers:
-      - block_type: FreqCoordConvDown
+      - name: FreqCoordConvDown
         out_channels: 32
-      - block_type: FreqCoordConvDown
+      - name: FreqCoordConvDown
         out_channels: 64
-      - block_type: LayerGroup
+      - name: LayerGroup
         layers:
-          - block_type: FreqCoordConvDown
+          - name: FreqCoordConvDown
             out_channels: 128
-          - block_type: ConvBlock
+          - name: ConvBlock
             out_channels: 256
   bottleneck:
     channels: 256
     layers:
-      - block_type: SelfAttention
+      - name: SelfAttention
         attention_channels: 256
   decoder:
     layers:
-      - block_type: FreqCoordConvUp
+      - name: FreqCoordConvUp
         out_channels: 64
-      - block_type: FreqCoordConvUp
+      - name: FreqCoordConvUp
         out_channels: 32
-      - block_type: LayerGroup
+      - name: LayerGroup
         layers:
-          - block_type: FreqCoordConvUp
+          - name: FreqCoordConvUp
             out_channels: 32
-          - block_type: ConvBlock
+          - name: ConvBlock
             out_channels: 32
 
 train:
   learning_rate: 0.001
   t_max: 100
 
+  labels:
+    sigma: 3
+
+  trainer:
+    max_epochs: 40
+
   dataloaders:
     train:
       batch_size: 8
@@ -115,7 +118,7 @@ train:
       shuffle: True
 
     val:
-      batch_size: 8
+      batch_size: 1
       num_workers: 2
 
   loss:
@@ -134,32 +137,34 @@ train:
 
   logger:
     logger_type: csv
-    save_dir: outputs/log/
-    name: logs
+    # save_dir: outputs/log/
+    # name: logs
 
   augmentations:
-    steps:
-      - augmentation_type: mix_audio
+    enabled: true
+    audio:
+      - name: mix_audio
         probability: 0.2
         min_weight: 0.3
         max_weight: 0.7
-      - augmentation_type: add_echo
+      - name: add_echo
         probability: 0.2
         max_delay: 0.005
         min_weight: 0.0
         max_weight: 1.0
-      - augmentation_type: scale_volume
+    spectrogram:
+      - name: scale_volume
         probability: 0.2
         min_scaling: 0.0
         max_scaling: 2.0
-      - augmentation_type: warp
+      - name: warp
         probability: 0.2
         delta: 0.04
-      - augmentation_type: mask_time
+      - name: mask_time
         probability: 0.2
         max_perc: 0.05
         max_masks: 3
-      - augmentation_type: mask_freq
+      - name: mask_freq
         probability: 0.2
         max_perc: 0.10
         max_masks: 3

justfile

@@ -92,19 +92,11 @@ clean-build:
 clean: clean-build clean-pyc clean-test clean-docs
 
 # Examples
-# Preprocess example data.
-example-preprocess OPTIONS="":
-    batdetect2 preprocess \
-        --base-dir . \
-        --dataset-field datasets.train \
-        --config example_data/config.yaml \
-        {{OPTIONS}} \
-        example_data/config.yaml example_data/preprocessed
 
 # Train on example data.
 example-train OPTIONS="":
     batdetect2 train \
-        --val-dir example_data/preprocessed \
+        --val-dataset example_data/dataset.yaml \
         --config example_data/config.yaml \
         {{OPTIONS}} \
-        example_data/preprocessed
+        example_data/dataset.yaml

pyproject.toml

@@ -17,7 +17,7 @@ dependencies = [
   "torch>=1.13.1,<2.5.0",
   "torchaudio>=1.13.1,<2.5.0",
   "torchvision>=0.14.0",
-  "soundevent[audio,geometry,plot]>=2.8.1",
+  "soundevent[audio,geometry,plot]>=2.9.1",
   "click>=8.1.7",
   "netcdf4>=1.6.5",
   "tqdm>=4.66.2",

src/batdetect2/cli/__init__.py

@@ -1,7 +1,7 @@
 from batdetect2.cli.base import cli
 from batdetect2.cli.compat import detect
 from batdetect2.cli.data import data
-from batdetect2.cli.preprocess import preprocess
+from batdetect2.cli.evaluate import evaluate_command
 from batdetect2.cli.train import train_command
 
 __all__ = [
@@ -9,7 +9,7 @@ __all__ = [
     "detect",
     "data",
     "train_command",
-    "preprocess",
+    "evaluate_command",
 ]
 
 

src/batdetect2/cli/evaluate.py

@@ -0,0 +1,63 @@
+import sys
+from pathlib import Path
+from typing import Optional
+
+import click
+from loguru import logger
+
+from batdetect2.cli.base import cli
+from batdetect2.data import load_dataset_from_config
+from batdetect2.evaluate.evaluate import evaluate
+from batdetect2.train.lightning import load_model_from_checkpoint
+
+__all__ = ["evaluate_command"]
+
+
+@cli.command(name="evaluate")
+@click.argument("model-path", type=click.Path(exists=True))
+@click.argument("test_dataset", type=click.Path(exists=True))
+@click.option("--output-dir", type=click.Path())
+@click.option("--workers", type=int)
+@click.option(
+    "-v",
+    "--verbose",
+    count=True,
+    help="Increase verbosity. -v for INFO, -vv for DEBUG.",
+)
+def evaluate_command(
+    model_path: Path,
+    test_dataset: Path,
+    output_dir: Optional[Path] = None,
+    workers: Optional[int] = None,
+    verbose: int = 0,
+):
+    logger.remove()
+    if verbose == 0:
+        log_level = "WARNING"
+    elif verbose == 1:
+        log_level = "INFO"
+    else:
+        log_level = "DEBUG"
+    logger.add(sys.stderr, level=log_level)
+
+    logger.info("Initiating evaluation process...")
+
+    test_annotations = load_dataset_from_config(test_dataset)
+    logger.debug(
+        "Loaded {num_annotations} test examples",
+        num_annotations=len(test_annotations),
+    )
+
+    model, train_config = load_model_from_checkpoint(model_path)
+
+    df, results = evaluate(
+        model,
+        test_annotations,
+        config=train_config,
+        num_workers=workers,
+    )
+
+    print(results)
+
+    if output_dir:
+        df.to_csv(output_dir / "results.csv")

src/batdetect2/cli/preprocess.py

@@ -1,142 +0,0 @@
-import sys
-from pathlib import Path
-from typing import Optional
-
-import click
-import yaml
-from loguru import logger
-
-from batdetect2.cli.base import cli
-from batdetect2.data import load_dataset_from_config
-from batdetect2.train.preprocess import (
-    TrainPreprocessConfig,
-    load_train_preprocessing_config,
-    preprocess_dataset,
-)
-
-__all__ = ["preprocess"]
-
-
-@cli.command()
-@click.argument(
-    "dataset_config",
-    type=click.Path(exists=True),
-)
-@click.argument(
-    "output",
-    type=click.Path(),
-)
-@click.option(
-    "--dataset-field",
-    type=str,
-    help=(
-        "Specifies the key to access the dataset information within the "
-        "dataset configuration file, if the information is nested inside a "
-        "dictionary. If the dataset information is at the top level of the "
-        "config file, you don't need to specify this."
-    ),
-)
-@click.option(
-    "--base-dir",
-    type=click.Path(exists=True),
-    help=(
-        "The main directory where your audio recordings and annotation "
-        "files are stored. This helps the program find your data, "
-        "especially if the paths in your dataset configuration file "
-        "are relative."
-    ),
-)
-@click.option(
-    "--config",
-    type=click.Path(exists=True),
-    help=(
-        "Path to the configuration file. This file tells "
-        "the program how to prepare your audio data before training, such "
-        "as resampling or applying filters."
-    ),
-)
-@click.option(
-    "--config-field",
-    type=str,
-    help=(
-        "If the preprocessing settings are inside a nested dictionary "
-        "within the preprocessing configuration file, specify the key "
-        "here to access them. If the preprocessing settings are at the "
-        "top level, you don't need to specify this."
-    ),
-)
-@click.option(
-    "--num-workers",
-    type=int,
-    help=(
-        "The maximum number of computer cores to use when processing "
-        "your audio data. Using more cores can speed up the preprocessing, "
-        "but don't use more than your computer has available. By default, "
-        "the program will use all available cores."
-    ),
-)
-@click.option(
-    "-v",
-    "--verbose",
-    count=True,
-    help="Increase verbosity. -v for INFO, -vv for DEBUG.",
-)
-def preprocess(
-    dataset_config: Path,
-    output: Path,
-    base_dir: Optional[Path] = None,
-    config: Optional[Path] = None,
-    config_field: Optional[str] = None,
-    num_workers: Optional[int] = None,
-    dataset_field: Optional[str] = None,
-    verbose: int = 0,
-):
-    logger.remove()
-    if verbose == 0:
-        log_level = "WARNING"
-    elif verbose == 1:
-        log_level = "INFO"
-    else:
-        log_level = "DEBUG"
-    logger.add(sys.stderr, level=log_level)
-
-    logger.info("Starting preprocessing.")
-
-    output = Path(output)
-    logger.info("Will save outputs to {output}", output=output)
-
-    base_dir = base_dir or Path.cwd()
-    logger.debug("Current working directory: {base_dir}", base_dir=base_dir)
-
-    if config:
-        logger.info(
-            "Loading preprocessing config from: {config}", config=config
-        )
-
-    conf = (
-        load_train_preprocessing_config(config, field=config_field)
-        if config is not None
-        else TrainPreprocessConfig()
-    )
-    logger.debug(
-        "Preprocessing config:\n{conf}",
-        conf=yaml.dump(conf.model_dump()),
-    )
-
-    dataset = load_dataset_from_config(
-        dataset_config,
-        field=dataset_field,
-        base_dir=base_dir,
-    )
-
-    logger.info(
-        "Loaded {num_examples} annotated clips from the configured dataset",
-        num_examples=len(dataset),
-    )
-
-    preprocess_dataset(
-        dataset,
-        conf,
-        output=output,
-        max_workers=num_workers,
-    )

src/batdetect2/cli/train.py

@@ -20,6 +20,8 @@ __all__ = ["train_command"]
 @click.argument("train_dataset", type=click.Path(exists=True))
 @click.option("--val-dataset", type=click.Path(exists=True))
 @click.option("--model-path", type=click.Path(exists=True))
+@click.option("--ckpt-dir", type=click.Path(exists=True))
+@click.option("--log-dir", type=click.Path(exists=True))
 @click.option("--config", type=click.Path(exists=True))
 @click.option("--config-field", type=str)
 @click.option("--train-workers", type=int)
@@ -34,6 +36,8 @@ def train_command(
     train_dataset: Path,
     val_dataset: Optional[Path] = None,
     model_path: Optional[Path] = None,
+    ckpt_dir: Optional[Path] = None,
+    log_dir: Optional[Path] = None,
     config: Optional[Path] = None,
     config_field: Optional[str] = None,
     train_workers: int = 0,
@@ -83,4 +87,6 @@ def train_command(
         model_path=model_path,
         train_workers=train_workers,
         val_workers=val_workers,
+        log_dir=log_dir,
+        checkpoint_dir=ckpt_dir,
     )

src/batdetect2/data/_core.py

@@ -0,0 +1,61 @@
+from typing import Generic, Protocol, Type, TypeVar
+
+from pydantic import BaseModel
+
+__all__ = [
+    "Registry",
+]
+
+T_Config = TypeVar("T_Config", bound=BaseModel, contravariant=True)
+T_Type = TypeVar("T_Type", covariant=True)
+
+
+class LogicProtocol(Generic[T_Config, T_Type], Protocol):
+    """A generic protocol for the logic classes (conditions or transforms)."""
+
+    @classmethod
+    def from_config(cls, config: T_Config) -> T_Type: ...
+
+
+T_Proto = TypeVar("T_Proto", bound=LogicProtocol)
+
+
+class Registry(Generic[T_Type]):
+    """A generic class to create and manage a registry of items."""
+
+    def __init__(self, name: str):
+        self._name = name
+        self._registry = {}
+
+    def register(self, config_cls: Type[T_Config]):
+        """A decorator factory to register a new item."""
+        fields = config_cls.model_fields
+
+        if "name" not in fields:
+            raise ValueError("Configuration object must have a 'name' field.")
+
+        name = fields["name"].default
+
+        if not isinstance(name, str):
+            raise ValueError("'name' field must be a string literal.")
+
+        def decorator(logic_cls: Type[T_Proto]) -> Type[T_Proto]:
+            self._registry[name] = logic_cls
+            return logic_cls
+
+        return decorator
+
+    def build(self, config: BaseModel) -> T_Type:
+        """Builds a logic instance from a config object."""
+
+        name = getattr(config, "name")  # noqa: B009
+
+        if name is None:
+            raise ValueError("Config does not have a name field")
+
+        if name not in self._registry:
+            raise NotImplementedError(
+                f"No {self._name} with name '{name}' is registered."
+            )
+
+        return self._registry[name].from_config(config)

src/batdetect2/data/annotations/__init__.py

@@ -14,8 +14,9 @@ format-specific loading function to retrieve the annotations as a standard
 """
 
 from pathlib import Path
-from typing import Optional, Union
+from typing import Annotated, Optional, Union
 
+from pydantic import Field
 from soundevent import data
 
 from batdetect2.data.annotations.aoef import (
@@ -42,10 +43,13 @@ __all__ = [
 ]
 
 
-AnnotationFormats = Union[
-    BatDetect2MergedAnnotations,
-    BatDetect2FilesAnnotations,
-    AOEFAnnotations,
+AnnotationFormats = Annotated[
+    Union[
+        BatDetect2MergedAnnotations,
+        BatDetect2FilesAnnotations,
+        AOEFAnnotations,
+    ],
+    Field(discriminator="format"),
 ]
 """Type Alias representing all supported data source configurations.
 

src/batdetect2/data/annotations/legacy.py

@@ -8,8 +8,6 @@ from typing import Callable, List, Optional, Union
 from pydantic import BaseModel, Field
 from soundevent import data
 
-from batdetect2.targets import get_term_from_key
-
 PathLike = Union[Path, str, os.PathLike]
 
 __all__ = []
@@ -92,15 +90,15 @@ def annotation_to_sound_event(
         sound_event=sound_event,
         tags=[
             data.Tag(
-                term=get_term_from_key(label_key),
+                key=label_key,  # type: ignore
                 value=annotation.label,
             ),
             data.Tag(
-                term=get_term_from_key(event_key),
+                key=event_key,  # type: ignore
                 value=annotation.event,
             ),
             data.Tag(
-                term=get_term_from_key(individual_key),
+                key=individual_key,  # type: ignore
                 value=str(annotation.individual),
             ),
         ],
@@ -125,7 +123,7 @@ def file_annotation_to_clip(
         time_expansion=file_annotation.time_exp,
         tags=[
             data.Tag(
-                term=get_term_from_key(label_key),
+                key=label_key,  # type: ignore
                 value=file_annotation.label,
             )
         ],
@@ -157,7 +155,8 @@ def file_annotation_to_clip_annotation(
         notes=notes,
         tags=[
             data.Tag(
-                term=get_term_from_key(label_key), value=file_annotation.label
+                key=label_key,  # type: ignore
+                value=file_annotation.label,
             )
         ],
         sound_events=[

src/batdetect2/data/conditions.py

@@ -0,0 +1,287 @@
+from collections.abc import Callable
+from typing import Annotated, List, Literal, Sequence, Union
+
+from pydantic import Field
+from soundevent import data
+from soundevent.geometry import compute_bounds
+
+from batdetect2.configs import BaseConfig
+from batdetect2.data._core import Registry
+
+SoundEventCondition = Callable[[data.SoundEventAnnotation], bool]
+
+_conditions: Registry[SoundEventCondition] = Registry("condition")
+
+
+class HasTagConfig(BaseConfig):
+    name: Literal["has_tag"] = "has_tag"
+    tag: data.Tag
+
+
+@_conditions.register(HasTagConfig)
+class HasTag:
+    def __init__(self, tag: data.Tag):
+        self.tag = tag
+
+    def __call__(
+        self, sound_event_annotation: data.SoundEventAnnotation
+    ) -> bool:
+        return self.tag in sound_event_annotation.tags
+
+    @classmethod
+    def from_config(cls, config: HasTagConfig):
+        return cls(tag=config.tag)
+
+
+class HasAllTagsConfig(BaseConfig):
+    name: Literal["has_all_tags"] = "has_all_tags"
+    tags: List[data.Tag]
+
+
+@_conditions.register(HasAllTagsConfig)
+class HasAllTags:
+    def __init__(self, tags: List[data.Tag]):
+        if not tags:
+            raise ValueError("Need to specify at least one tag")
+
+        self.tags = set(tags)
+
+    def __call__(
+        self, sound_event_annotation: data.SoundEventAnnotation
+    ) -> bool:
+        return self.tags.issubset(sound_event_annotation.tags)
+
+    @classmethod
+    def from_config(cls, config: HasAllTagsConfig):
+        return cls(tags=config.tags)
+
+
+class HasAnyTagConfig(BaseConfig):
+    name: Literal["has_any_tag"] = "has_any_tag"
+    tags: List[data.Tag]
+
+
+@_conditions.register(HasAnyTagConfig)
+class HasAnyTag:
+    def __init__(self, tags: List[data.Tag]):
+        if not tags:
+            raise ValueError("Need to specify at least one tag")
+
+        self.tags = set(tags)
+
+    def __call__(
+        self, sound_event_annotation: data.SoundEventAnnotation
+    ) -> bool:
+        return bool(self.tags.intersection(sound_event_annotation.tags))
+
+    @classmethod
+    def from_config(cls, config: HasAnyTagConfig):
+        return cls(tags=config.tags)
+
+
+Operator = Literal["gt", "gte", "lt", "lte", "eq"]
+
+
+class DurationConfig(BaseConfig):
+    name: Literal["duration"] = "duration"
+    operator: Operator
+    seconds: float
+
+
+def _build_comparator(
+    operator: Operator, value: float
+) -> Callable[[float], bool]:
+    if operator == "gt":
+        return lambda x: x > value
+
+    if operator == "gte":
+        return lambda x: x >= value
+
+    if operator == "lt":
+        return lambda x: x < value
+
+    if operator == "lte":
+        return lambda x: x <= value
+
+    if operator == "eq":
+        return lambda x: x == value
+
+    raise ValueError(f"Invalid operator {operator}")
+
+
+@_conditions.register(DurationConfig)
+class Duration:
+    def __init__(self, operator: Operator, seconds: float):
+        self.operator = operator
+        self.seconds = seconds
+        self._comparator = _build_comparator(self.operator, self.seconds)
+
+    def __call__(
+        self,
+        sound_event_annotation: data.SoundEventAnnotation,
+    ) -> bool:
+        geometry = sound_event_annotation.sound_event.geometry
+
+        if geometry is None:
+            return False
+
+        start_time, _, end_time, _ = compute_bounds(geometry)
+        duration = end_time - start_time
+
+        return self._comparator(duration)
+
+    @classmethod
+    def from_config(cls, config: DurationConfig):
+        return cls(operator=config.operator, seconds=config.seconds)
+
+
+class FrequencyConfig(BaseConfig):
+    name: Literal["frequency"] = "frequency"
+    boundary: Literal["low", "high"]
+    operator: Operator
+    hertz: float
+
+
+@_conditions.register(FrequencyConfig)
+class Frequency:
+    def __init__(
+        self,
+        operator: Operator,
+        boundary: Literal["low", "high"],
+        hertz: float,
+    ):
+        self.operator = operator
+        self.hertz = hertz
+        self.boundary = boundary
+        self._comparator = _build_comparator(self.operator, self.hertz)
+
+    def __call__(
+        self,
+        sound_event_annotation: data.SoundEventAnnotation,
+    ) -> bool:
+        geometry = sound_event_annotation.sound_event.geometry
+
+        if geometry is None:
+            return False
+
+        # Automatically false if geometry does not have a frequency range
+        if isinstance(geometry, (data.TimeInterval, data.TimeStamp)):
+            return False
+
+        _, low_freq, _, high_freq = compute_bounds(geometry)
+
+        if self.boundary == "low":
+            return self._comparator(low_freq)
+
+        return self._comparator(high_freq)
+
+    @classmethod
+    def from_config(cls, config: FrequencyConfig):
+        return cls(
+            operator=config.operator,
+            boundary=config.boundary,
+            hertz=config.hertz,
+        )
+
+
+class AllOfConfig(BaseConfig):
+    name: Literal["all_of"] = "all_of"
+    conditions: Sequence["SoundEventConditionConfig"]
+
+
+@_conditions.register(AllOfConfig)
+class AllOf:
+    def __init__(self, conditions: List[SoundEventCondition]):
+        self.conditions = conditions
+
+    def __call__(
+        self, sound_event_annotation: data.SoundEventAnnotation
+    ) -> bool:
+        return all(c(sound_event_annotation) for c in self.conditions)
+
+    @classmethod
+    def from_config(cls, config: AllOfConfig):
+        conditions = [
+            build_sound_event_condition(cond) for cond in config.conditions
+        ]
+        return cls(conditions)
+
+
+class AnyOfConfig(BaseConfig):
+    name: Literal["any_of"] = "any_of"
+    conditions: List["SoundEventConditionConfig"]
+
+
+@_conditions.register(AnyOfConfig)
+class AnyOf:
+    def __init__(self, conditions: List[SoundEventCondition]):
+        self.conditions = conditions
+
+    def __call__(
+        self, sound_event_annotation: data.SoundEventAnnotation
+    ) -> bool:
+        return any(c(sound_event_annotation) for c in self.conditions)
+
+    @classmethod
+    def from_config(cls, config: AnyOfConfig):
+        conditions = [
+            build_sound_event_condition(cond) for cond in config.conditions
+        ]
+        return cls(conditions)
+
+
+class NotConfig(BaseConfig):
+    name: Literal["not"] = "not"
+    condition: "SoundEventConditionConfig"
+
+
+@_conditions.register(NotConfig)
+class Not:
+    def __init__(self, condition: SoundEventCondition):
+        self.condition = condition
+
+    def __call__(
+        self, sound_event_annotation: data.SoundEventAnnotation
+    ) -> bool:
+        return not self.condition(sound_event_annotation)
+
+    @classmethod
+    def from_config(cls, config: NotConfig):
+        condition = build_sound_event_condition(config.condition)
+        return cls(condition)
+
+
+SoundEventConditionConfig = Annotated[
+    Union[
+        HasTagConfig,
+        HasAllTagsConfig,
+        HasAnyTagConfig,
+        DurationConfig,
+        FrequencyConfig,
+        AllOfConfig,
+        AnyOfConfig,
+        NotConfig,
+    ],
+    Field(discriminator="name"),
+]
+
+
+def build_sound_event_condition(
+    config: SoundEventConditionConfig,
+) -> SoundEventCondition:
+    return _conditions.build(config)
+
+
+def filter_clip_annotation(
+    clip_annotation: data.ClipAnnotation,
+    condition: SoundEventCondition,
+) -> data.ClipAnnotation:
+    return clip_annotation.model_copy(
+        update=dict(
+            sound_events=[
+                sound_event
+                for sound_event in clip_annotation.sound_events
+                if condition(sound_event)
+            ]
+        )
+    )

src/batdetect2/data/datasets.py

@@ -19,7 +19,7 @@ The core components are:
 """
 
 from pathlib import Path
-from typing import Annotated, List, Optional
+from typing import List, Optional
 
 from loguru import logger
 from pydantic import Field
@@ -31,6 +31,17 @@ from batdetect2.data.annotations import (
     AnnotationFormats,
     load_annotated_dataset,
 )
+from batdetect2.data.conditions import (
+    SoundEventConditionConfig,
+    build_sound_event_condition,
+    filter_clip_annotation,
+)
+from batdetect2.data.transforms import (
+    ApplyAll,
+    SoundEventTransformConfig,
+    build_sound_event_transform,
+    transform_clip_annotation,
+)
 from batdetect2.targets.terms import data_source
 
 __all__ = [
@@ -52,79 +63,68 @@ sources.
 
 
 class DatasetConfig(BaseConfig):
-    """Configuration model defining the structure of a BatDetect2 dataset.
-
-    This class is typically loaded from a YAML file and describes the components
-    of the dataset, including metadata and a list of data sources.
-
-    Attributes
-    ----------
-    name : str
-        A descriptive name for the dataset (e.g., "UK_Bats_Project_2024").
-    description : str
-        A longer description of the dataset's contents, origin, purpose, etc.
-    sources : List[AnnotationFormats]
-        A list defining the different data sources contributing to this
-        dataset. Each item in the list must conform to one of the Pydantic
-        models defined in the `AnnotationFormats` type union. The specific
-        model used for each source is determined by the mandatory `format`
-        field within the source's configuration, allowing BatDetect2 to use the
-        correct parser for different annotation styles.
-    """
+    """Configuration model defining the structure of a BatDetect2 dataset."""
 
     name: str
     description: str
-    sources: List[
-        Annotated[AnnotationFormats, Field(..., discriminator="format")]
-    ]
+    sources: List[AnnotationFormats]
+
+    sound_event_filter: Optional[SoundEventConditionConfig] = None
+    sound_event_transforms: List[SoundEventTransformConfig] = Field(
+        default_factory=list
+    )
 
 
 def load_dataset(
-    dataset: DatasetConfig,
+    config: DatasetConfig,
     base_dir: Optional[Path] = None,
 ) -> Dataset:
-    """Load all clip annotations from the sources defined in a DatasetConfig.
-
-    Iterates through each data source specified in the `dataset_config`,
-    delegates the loading and parsing of that source's annotations to
-    `batdetect2.data.annotations.load_annotated_dataset` (which handles
-    different data formats), and aggregates all resulting `ClipAnnotation`
-    objects into a single flat list.
-
-    Parameters
-    ----------
-    dataset_config : DatasetConfig
-        The configuration object describing the dataset and its sources.
-    base_dir : Path, optional
-        An optional base directory path. If provided, relative paths for
-        metadata files or data directories within the `dataset_config`'s
-        sources might be resolved relative to this directory. Defaults to None.
-
-    Returns
-    -------
-    Dataset (List[data.ClipAnnotation])
-        A flat list containing all loaded `ClipAnnotation` metadata objects
-        from all specified sources.
-
-    Raises
-    ------
-    Exception
-        Can raise various exceptions during the delegated loading process
-        (`load_annotated_dataset`) if files are not found, cannot be parsed
-        according to the specified format, or other I/O errors occur.
-    """
+    """Load all clip annotations from the sources defined in a DatasetConfig."""
     clip_annotations = []
-    for source in dataset.sources:
+
+    condition = (
+        build_sound_event_condition(config.sound_event_filter)
+        if config.sound_event_filter is not None
+        else None
+    )
+
+    transform = (
+        ApplyAll(
+            [
+                build_sound_event_transform(step)
+                for step in config.sound_event_transforms
+            ]
+        )
+        if config.sound_event_transforms
+        else None
+    )
+
+    for source in config.sources:
         annotated_source = load_annotated_dataset(source, base_dir=base_dir)
+
         logger.debug(
             "Loaded {num_examples} from dataset source '{source_name}'",
             num_examples=len(annotated_source.clip_annotations),
             source_name=source.name,
         )
-        clip_annotations.extend(
-            insert_source_tag(clip_annotation, source)
-            for clip_annotation in annotated_source.clip_annotations
-        )
+
+        for clip_annotation in annotated_source.clip_annotations:
+            clip_annotation = insert_source_tag(clip_annotation, source)
+
+            if condition is not None:
+                clip_annotation = filter_clip_annotation(
+                    clip_annotation,
+                    condition,
+                )
+
+            if transform is not None:
+                clip_annotation = transform_clip_annotation(
+                    clip_annotation,
+                    transform,
+                )
+
+            clip_annotations.append(clip_annotation)
+
     return clip_annotations
 
 
@@ -161,7 +161,6 @@ def insert_source_tag(
     )
 
 
-# TODO: add documentation
 def load_dataset_config(path: data.PathLike, field: Optional[str] = None):
     return load_config(path=path, schema=DatasetConfig, field=field)
 

src/batdetect2/data/iterators.py

@@ -10,16 +10,8 @@ from batdetect2.typing.targets 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.
+    """Iterate over sound events in a dataset.
 
     Parameters
     ----------
@@ -29,18 +21,6 @@ def iterate_over_sound_events(
     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
     ------
@@ -63,17 +43,9 @@ def iterate_over_sound_events(
     """
     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_class(sound_event_annotation)
-            if class_name is None and exclude_generic:
+            if not targets.filter(sound_event_annotation):
                 continue
 
+            class_name = targets.encode_class(sound_event_annotation)
+
             yield class_name, sound_event_annotation

src/batdetect2/data/transforms.py

@@ -0,0 +1,250 @@
+from collections.abc import Callable
+from typing import Annotated, Dict, List, Literal, Optional, Union
+
+from pydantic import Field
+from soundevent import data
+
+from batdetect2.configs import BaseConfig
+from batdetect2.data._core import Registry
+from batdetect2.data.conditions import (
+    SoundEventCondition,
+    SoundEventConditionConfig,
+    build_sound_event_condition,
+)
+
+SoundEventTransform = Callable[
+    [data.SoundEventAnnotation],
+    data.SoundEventAnnotation,
+]
+
+_transforms: Registry[SoundEventTransform] = Registry("transform")
+
+
+class SetFrequencyBoundConfig(BaseConfig):
+    name: Literal["set_frequency"] = "set_frequency"
+    boundary: Literal["low", "high"] = "low"
+    hertz: float
+
+
+@_transforms.register(SetFrequencyBoundConfig)
+class SetFrequencyBound:
+    def __init__(self, hertz: float, boundary: Literal["low", "high"] = "low"):
+        self.hertz = hertz
+        self.boundary = boundary
+
+    def __call__(
+        self,
+        sound_event_annotation: data.SoundEventAnnotation,
+    ) -> data.SoundEventAnnotation:
+        sound_event = sound_event_annotation.sound_event
+        geometry = sound_event.geometry
+
+        if geometry is None:
+            return sound_event_annotation
+
+        if not isinstance(geometry, data.BoundingBox):
+            return sound_event_annotation
+
+        start_time, low_freq, end_time, high_freq = geometry.coordinates
+
+        if self.boundary == "low":
+            low_freq = self.hertz
+            high_freq = max(high_freq, low_freq)
+
+        elif self.boundary == "high":
+            high_freq = self.hertz
+            low_freq = min(high_freq, low_freq)
+
+        geometry = data.BoundingBox(
+            coordinates=[start_time, low_freq, end_time, high_freq],
+        )
+
+        sound_event = sound_event.model_copy(update=dict(geometry=geometry))
+        return sound_event_annotation.model_copy(
+            update=dict(sound_event=sound_event)
+        )
+
+    @classmethod
+    def from_config(cls, config: SetFrequencyBoundConfig):
+        return cls(hertz=config.hertz, boundary=config.boundary)
+
+
+class ApplyIfConfig(BaseConfig):
+    name: Literal["apply_if"] = "apply_if"
+    transform: "SoundEventTransformConfig"
+    condition: SoundEventConditionConfig
+
+
+@_transforms.register(ApplyIfConfig)
+class ApplyIf:
+    def __init__(
+        self,
+        condition: SoundEventCondition,
+        transform: SoundEventTransform,
+    ):
+        self.condition = condition
+        self.transform = transform
+
+    def __call__(
+        self,
+        sound_event_annotation: data.SoundEventAnnotation,
+    ) -> data.SoundEventAnnotation:
+        if not self.condition(sound_event_annotation):
+            return sound_event_annotation
+
+        return self.transform(sound_event_annotation)
+
+    @classmethod
+    def from_config(cls, config: ApplyIfConfig):
+        transform = build_sound_event_transform(config.transform)
+        condition = build_sound_event_condition(config.condition)
+        return cls(condition=condition, transform=transform)
+
+
+class ReplaceTagConfig(BaseConfig):
+    name: Literal["replace_tag"] = "replace_tag"
+    original: data.Tag
+    replacement: data.Tag
+
+
+@_transforms.register(ReplaceTagConfig)
+class ReplaceTag:
+    def __init__(
+        self,
+        original: data.Tag,
+        replacement: data.Tag,
+    ):
+        self.original = original
+        self.replacement = replacement
+
+    def __call__(
+        self,
+        sound_event_annotation: data.SoundEventAnnotation,
+    ) -> data.SoundEventAnnotation:
+        tags = []
+
+        for tag in sound_event_annotation.tags:
+            if tag == self.original:
+                tags.append(self.replacement)
+            else:
+                tags.append(tag)
+
+        return sound_event_annotation.model_copy(update=dict(tags=tags))
+
+    @classmethod
+    def from_config(cls, config: ReplaceTagConfig):
+        return cls(original=config.original, replacement=config.replacement)
+
+
+class MapTagValueConfig(BaseConfig):
+    name: Literal["map_tag_value"] = "map_tag_value"
+    tag_key: str
+    value_mapping: Dict[str, str]
+    target_key: Optional[str] = None
+
+
+@_transforms.register(MapTagValueConfig)
+class MapTagValue:
+    def __init__(
+        self,
+        tag_key: str,
+        value_mapping: Dict[str, str],
+        target_key: Optional[str] = None,
+    ):
+        self.tag_key = tag_key
+        self.value_mapping = value_mapping
+        self.target_key = target_key
+
+    def __call__(
+        self,
+        sound_event_annotation: data.SoundEventAnnotation,
+    ) -> data.SoundEventAnnotation:
+        tags = []
+
+        for tag in sound_event_annotation.tags:
+            if tag.key != self.tag_key:
+                tags.append(tag)
+                continue
+
+            value = self.value_mapping.get(tag.value)
+
+            if value is None:
+                tags.append(tag)
+                continue
+
+            if self.target_key is None:
+                tags.append(tag.model_copy(update=dict(value=value)))
+            else:
+                tags.append(
+                    data.Tag(
+                        key=self.target_key,  # type: ignore
+                        value=value,
+                    )
+                )
+
+        return sound_event_annotation.model_copy(update=dict(tags=tags))
+
+    @classmethod
+    def from_config(cls, config: MapTagValueConfig):
+        return cls(
+            tag_key=config.tag_key,
+            value_mapping=config.value_mapping,
+            target_key=config.target_key,
+        )
+
+
+class ApplyAllConfig(BaseConfig):
+    name: Literal["apply_all"] = "apply_all"
+    steps: List["SoundEventTransformConfig"] = Field(default_factory=list)
+
+
+@_transforms.register(ApplyAllConfig)
+class ApplyAll:
+    def __init__(self, steps: List[SoundEventTransform]):
+        self.steps = steps
+
+    def __call__(
+        self,
+        sound_event_annotation: data.SoundEventAnnotation,
+    ) -> data.SoundEventAnnotation:
+        for step in self.steps:
+            sound_event_annotation = step(sound_event_annotation)
+
+        return sound_event_annotation
+
+    @classmethod
+    def from_config(cls, config: ApplyAllConfig):
+        steps = [build_sound_event_transform(step) for step in config.steps]
+        return cls(steps)
+
+
+SoundEventTransformConfig = Annotated[
+    Union[
+        SetFrequencyBoundConfig,
+        ReplaceTagConfig,
+        MapTagValueConfig,
+        ApplyIfConfig,
+        ApplyAllConfig,
+    ],
+    Field(discriminator="name"),
+]
+
+
+def build_sound_event_transform(
+    config: SoundEventTransformConfig,
+) -> SoundEventTransform:
+    return _transforms.build(config)
+
+
+def transform_clip_annotation(
+    clip_annotation: data.ClipAnnotation,
+    transform: SoundEventTransform,
+) -> data.ClipAnnotation:
+    return clip_annotation.model_copy(
+        update=dict(
+            sound_events=[
+                transform(sound_event)
+                for sound_event in clip_annotation.sound_events
+            ]
+        )
+    )

src/batdetect2/evaluate/dataframe.py

@@ -0,0 +1,62 @@
+from typing import List
+
+import pandas as pd
+from soundevent.geometry import compute_bounds
+
+from batdetect2.typing.evaluate import MatchEvaluation
+
+
+def extract_matches_dataframe(matches: List[MatchEvaluation]) -> pd.DataFrame:
+    data = []
+
+    for match in matches:
+        gt_start_time = gt_low_freq = gt_end_time = gt_high_freq = None
+        pred_start_time = pred_low_freq = pred_end_time = pred_high_freq = None
+
+        sound_event_annotation = match.sound_event_annotation
+
+        if sound_event_annotation is not None:
+            geometry = sound_event_annotation.sound_event.geometry
+            assert geometry is not None
+            gt_start_time, gt_low_freq, gt_end_time, gt_high_freq = (
+                compute_bounds(geometry)
+            )
+
+        if match.pred_geometry is not None:
+            pred_start_time, pred_low_freq, pred_end_time, pred_high_freq = (
+                compute_bounds(match.pred_geometry)
+            )
+
+        data.append(
+            {
+                ("recording", "uuid"): match.clip.recording.uuid,
+                ("clip", "uuid"): match.clip.uuid,
+                ("clip", "start_time"): match.clip.start_time,
+                ("clip", "end_time"): match.clip.end_time,
+                ("gt", "uuid"): match.sound_event_annotation.uuid
+                if match.sound_event_annotation is not None
+                else None,
+                ("gt", "class"): match.gt_class,
+                ("gt", "det"): match.gt_det,
+                ("gt", "start_time"): gt_start_time,
+                ("gt", "end_time"): gt_end_time,
+                ("gt", "low_freq"): gt_low_freq,
+                ("gt", "high_freq"): gt_high_freq,
+                ("pred", "score"): match.pred_score,
+                ("pred", "class"): match.pred_class,
+                ("pred", "class_score"): match.pred_class_score,
+                ("pred", "start_time"): pred_start_time,
+                ("pred", "end_time"): pred_end_time,
+                ("pred", "low_freq"): pred_low_freq,
+                ("pred", "high_freq"): pred_high_freq,
+                ("match", "affinity"): match.affinity,
+                **{
+                    ("pred_class_score", key): value
+                    for key, value in match.pred_class_scores.items()
+                },
+            }
+        )
+
+    df = pd.DataFrame(data)
+    df.columns = pd.MultiIndex.from_tuples(df.columns)  # type: ignore
+    return df

src/batdetect2/evaluate/evaluate.py

@@ -0,0 +1,100 @@
+from typing import List, Optional, Tuple
+
+import pandas as pd
+from soundevent import data
+
+from batdetect2.evaluate.dataframe import extract_matches_dataframe
+from batdetect2.evaluate.match import match_all_predictions
+from batdetect2.evaluate.metrics import (
+    ClassificationAccuracy,
+    ClassificationMeanAveragePrecision,
+    DetectionAveragePrecision,
+)
+from batdetect2.models import Model
+from batdetect2.plotting.clips import build_audio_loader
+from batdetect2.postprocess import get_raw_predictions
+from batdetect2.preprocess import build_preprocessor
+from batdetect2.targets import build_targets
+from batdetect2.train.config import FullTrainingConfig
+from batdetect2.train.dataset import ValidationDataset
+from batdetect2.train.labels import build_clip_labeler
+from batdetect2.train.train import build_val_loader
+
+
+def evaluate(
+    model: Model,
+    test_annotations: List[data.ClipAnnotation],
+    config: Optional[FullTrainingConfig] = None,
+    num_workers: Optional[int] = None,
+) -> Tuple[pd.DataFrame, dict]:
+    config = config or FullTrainingConfig()
+
+    audio_loader = build_audio_loader(config.preprocess.audio)
+
+    preprocessor = build_preprocessor(config.preprocess)
+
+    targets = build_targets(config.targets)
+
+    labeller = build_clip_labeler(
+        targets,
+        min_freq=preprocessor.min_freq,
+        max_freq=preprocessor.max_freq,
+        config=config.train.labels,
+    )
+
+    loader = build_val_loader(
+        test_annotations,
+        audio_loader=audio_loader,
+        labeller=labeller,
+        preprocessor=preprocessor,
+        config=config.train,
+        num_workers=num_workers,
+    )
+
+    dataset: ValidationDataset = loader.dataset  # type: ignore
+
+    clip_annotations = []
+    predictions = []
+
+    for batch in loader:
+        outputs = model.detector(batch.spec)
+
+        clip_annotations = [
+            dataset.clip_annotations[int(example_idx)]
+            for example_idx in batch.idx
+        ]
+
+        predictions = get_raw_predictions(
+            outputs,
+            clips=[
+                clip_annotation.clip for clip_annotation in clip_annotations
+            ],
+            targets=targets,
+            postprocessor=model.postprocessor,
+        )
+
+        clip_annotations.extend(clip_annotations)
+        predictions.extend(predictions)
+
+    matches = match_all_predictions(
+        clip_annotations,
+        predictions,
+        targets=targets,
+        config=config.evaluation.match,
+    )
+
+    df = extract_matches_dataframe(matches)
+
+    metrics = [
+        DetectionAveragePrecision(),
+        ClassificationMeanAveragePrecision(class_names=targets.class_names),
+        ClassificationAccuracy(class_names=targets.class_names),
+    ]
+
+    results = {
+        name: value
+        for metric in metrics
+        for name, value in metric(matches).items()
+    }
+
+    return df, results

src/batdetect2/evaluate/match.py

@@ -1,6 +1,5 @@
 from collections.abc import Callable, Iterable, Mapping
 from dataclasses import dataclass, field
-from functools import partial
 from typing import List, Literal, Optional, Protocol, Tuple
 
 import numpy as np
@@ -9,7 +8,6 @@ from soundevent import data
 from soundevent.evaluation import compute_affinity
 from soundevent.evaluation import match_geometries as optimal_match
 from soundevent.geometry import compute_bounds
-from torch.multiprocessing import Pool
 
 from batdetect2.configs import BaseConfig
 from batdetect2.typing import (
@@ -284,7 +282,7 @@ def match_sound_events_and_raw_predictions(
     config = config or MatchConfig()
 
     target_sound_events = [
-        targets.transform(sound_event_annotation)
+        sound_event_annotation
         for sound_event_annotation in clip_annotation.sound_events
         if targets.filter(sound_event_annotation)
         and sound_event_annotation.sound_event.geometry is not None
@@ -430,17 +428,19 @@ def match_all_predictions(
     config: Optional[MatchConfig] = None,
 ) -> List[MatchEvaluation]:
     logger.info("Matching all annotations and predictions...")
-    with Pool() as p:
-        all_matches = p.starmap(
-            partial(
-                match_sound_events_and_raw_predictions,
-                targets=targets,
-                config=config,
-            ),
-            zip(clip_annotations, predictions),
+    return [
+        match
+        for clip_annotation, raw_predictions in zip(
+            clip_annotations,
+            predictions,
         )
-
-    return [match for matches in all_matches for match in matches]
+        for match in match_sound_events_and_raw_predictions(
+            clip_annotation,
+            raw_predictions,
+            targets=targets,
+            config=config,
+        )
+    ]
 
 
 @dataclass

src/batdetect2/models/__init__.py

@@ -29,7 +29,6 @@ provided here.
 from typing import List, Optional
 
 import torch
-from lightning import LightningModule
 from pydantic import Field
 from soundevent.data import PathLike
 
@@ -68,7 +67,10 @@ from batdetect2.postprocess import PostprocessConfig, build_postprocessor
 from batdetect2.preprocess import PreprocessingConfig, build_preprocessor
 from batdetect2.targets import TargetConfig, build_targets
 from batdetect2.typing.models import DetectionModel
-from batdetect2.typing.postprocess import DetectionsArray, PostprocessorProtocol
+from batdetect2.typing.postprocess import (
+    DetectionsTensor,
+    PostprocessorProtocol,
+)
 from batdetect2.typing.preprocess import PreprocessorProtocol
 from batdetect2.typing.targets import TargetProtocol
 
@@ -102,7 +104,16 @@ __all__ = [
 ]
 
 
-class Model(LightningModule):
+class ModelConfig(BaseConfig):
+    model: BackboneConfig = Field(default_factory=BackboneConfig)
+    preprocess: PreprocessingConfig = Field(
+        default_factory=PreprocessingConfig
+    )
+    postprocess: PostprocessConfig = Field(default_factory=PostprocessConfig)
+    targets: TargetConfig = Field(default_factory=TargetConfig)
+
+
+class Model(torch.nn.Module):
     detector: DetectionModel
     preprocessor: PreprocessorProtocol
     postprocessor: PostprocessorProtocol
@@ -114,43 +125,39 @@ class Model(LightningModule):
         preprocessor: PreprocessorProtocol,
         postprocessor: PostprocessorProtocol,
         targets: TargetProtocol,
+        config: ModelConfig,
     ):
         super().__init__()
         self.detector = detector
         self.preprocessor = preprocessor
         self.postprocessor = postprocessor
         self.targets = targets
-        self.save_hyperparameters()
+        self.config = config
 
-    def forward(self, wav: torch.Tensor) -> List[DetectionsArray]:
+    def forward(self, wav: torch.Tensor) -> List[DetectionsTensor]:
         spec = self.preprocessor(wav)
         outputs = self.detector(spec)
         return self.postprocessor(outputs)
 
 
-class ModelConfig(BaseConfig):
-    model: BackboneConfig = Field(default_factory=BackboneConfig)
-    preprocess: PreprocessingConfig = Field(
-        default_factory=PreprocessingConfig
-    )
-    postprocess: PostprocessConfig = Field(default_factory=PostprocessConfig)
-    targets: TargetConfig = Field(default_factory=TargetConfig)
-
-
 def build_model(config: Optional[ModelConfig] = None):
     config = config or ModelConfig()
 
     targets = build_targets(config=config.targets)
+
     preprocessor = build_preprocessor(config=config.preprocess)
+
     postprocessor = build_postprocessor(
         preprocessor=preprocessor,
         config=config.postprocess,
     )
+
     detector = build_detector(
         num_classes=len(targets.class_names),
         config=config.model,
     )
     return Model(
+        config=config,
         detector=detector,
         postprocessor=postprocessor,
         preprocessor=preprocessor,

src/batdetect2/models/blocks.py

@@ -56,7 +56,7 @@ __all__ = [
 
 
 class SelfAttentionConfig(BaseConfig):
-    block_type: Literal["SelfAttention"] = "SelfAttention"
+    name: Literal["SelfAttention"] = "SelfAttention"
     attention_channels: int
     temperature: float = 1
 
@@ -178,7 +178,7 @@ class SelfAttention(nn.Module):
 class ConvConfig(BaseConfig):
     """Configuration for a basic ConvBlock."""
 
-    block_type: Literal["ConvBlock"] = "ConvBlock"
+    name: Literal["ConvBlock"] = "ConvBlock"
     """Discriminator field indicating the block type."""
 
     out_channels: int
@@ -300,7 +300,7 @@ class VerticalConv(nn.Module):
 class FreqCoordConvDownConfig(BaseConfig):
     """Configuration for a FreqCoordConvDownBlock."""
 
-    block_type: Literal["FreqCoordConvDown"] = "FreqCoordConvDown"
+    name: Literal["FreqCoordConvDown"] = "FreqCoordConvDown"
     """Discriminator field indicating the block type."""
 
     out_channels: int
@@ -390,7 +390,7 @@ class FreqCoordConvDownBlock(nn.Module):
 class StandardConvDownConfig(BaseConfig):
     """Configuration for a StandardConvDownBlock."""
 
-    block_type: Literal["StandardConvDown"] = "StandardConvDown"
+    name: Literal["StandardConvDown"] = "StandardConvDown"
     """Discriminator field indicating the block type."""
 
     out_channels: int
@@ -460,7 +460,7 @@ class StandardConvDownBlock(nn.Module):
 class FreqCoordConvUpConfig(BaseConfig):
     """Configuration for a FreqCoordConvUpBlock."""
 
-    block_type: Literal["FreqCoordConvUp"] = "FreqCoordConvUp"
+    name: Literal["FreqCoordConvUp"] = "FreqCoordConvUp"
     """Discriminator field indicating the block type."""
 
     out_channels: int
@@ -569,7 +569,7 @@ class FreqCoordConvUpBlock(nn.Module):
 class StandardConvUpConfig(BaseConfig):
     """Configuration for a StandardConvUpBlock."""
 
-    block_type: Literal["StandardConvUp"] = "StandardConvUp"
+    name: Literal["StandardConvUp"] = "StandardConvUp"
     """Discriminator field indicating the block type."""
 
     out_channels: int
@@ -664,13 +664,13 @@ LayerConfig = Annotated[
         SelfAttentionConfig,
         "LayerGroupConfig",
     ],
-    Field(discriminator="block_type"),
+    Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configuration models."""
 
 
 class LayerGroupConfig(BaseConfig):
-    block_type: Literal["LayerGroup"] = "LayerGroup"
+    name: Literal["LayerGroup"] = "LayerGroup"
     layers: List[LayerConfig]
 
 
@@ -686,7 +686,7 @@ def build_layer_from_config(
     parameters derived from the config and the current pipeline state
     (`input_height`, `in_channels`).
 
-    It uses the `block_type` field within the `config` object to determine
+    It uses the `name` field within the `config` object to determine
     which block class to instantiate.
 
     Parameters
@@ -698,7 +698,7 @@ def build_layer_from_config(
     config : LayerConfig
         A Pydantic configuration object for the desired block (e.g., an
         instance of `ConvConfig`, `FreqCoordConvDownConfig`, etc.), identified
-        by its `block_type` field.
+        by its `name` field.
 
     Returns
     -------
@@ -711,11 +711,11 @@ def build_layer_from_config(
     Raises
     ------
     NotImplementedError
-        If the `config.block_type` does not correspond to a known block type.
+        If the `config.name` does not correspond to a known block type.
     ValueError
         If parameters derived from the config are invalid for the block.
     """
-    if config.block_type == "ConvBlock":
+    if config.name == "ConvBlock":
         return (
             ConvBlock(
                 in_channels=in_channels,
@@ -727,7 +727,7 @@ def build_layer_from_config(
             input_height,
         )
 
-    if config.block_type == "FreqCoordConvDown":
+    if config.name == "FreqCoordConvDown":
         return (
             FreqCoordConvDownBlock(
                 in_channels=in_channels,
@@ -740,7 +740,7 @@ def build_layer_from_config(
             input_height // 2,
         )
 
-    if config.block_type == "StandardConvDown":
+    if config.name == "StandardConvDown":
         return (
             StandardConvDownBlock(
                 in_channels=in_channels,
@@ -752,7 +752,7 @@ def build_layer_from_config(
             input_height // 2,
         )
 
-    if config.block_type == "FreqCoordConvUp":
+    if config.name == "FreqCoordConvUp":
         return (
             FreqCoordConvUpBlock(
                 in_channels=in_channels,
@@ -765,7 +765,7 @@ def build_layer_from_config(
             input_height * 2,
         )
 
-    if config.block_type == "StandardConvUp":
+    if config.name == "StandardConvUp":
         return (
             StandardConvUpBlock(
                 in_channels=in_channels,
@@ -777,7 +777,7 @@ def build_layer_from_config(
             input_height * 2,
         )
 
-    if config.block_type == "SelfAttention":
+    if config.name == "SelfAttention":
         return (
             SelfAttention(
                 in_channels=in_channels,
@@ -788,7 +788,7 @@ def build_layer_from_config(
             input_height,
         )
 
-    if config.block_type == "LayerGroup":
+    if config.name == "LayerGroup":
         current_channels = in_channels
         current_height = input_height
 
@@ -804,4 +804,4 @@ def build_layer_from_config(
 
         return nn.Sequential(*blocks), current_channels, current_height
 
-    raise NotImplementedError(f"Unknown block type {config.block_type}")
+    raise NotImplementedError(f"Unknown block type {config.name}")

src/batdetect2/models/bottleneck.py

@@ -128,7 +128,7 @@ class Bottleneck(nn.Module):
 
 BottleneckLayerConfig = Annotated[
     Union[SelfAttentionConfig,],
-    Field(discriminator="block_type"),
+    Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configs usable in Decoder."""
 

src/batdetect2/models/decoder.py

@@ -47,7 +47,7 @@ DecoderLayerConfig = Annotated[
         StandardConvUpConfig,
         LayerGroupConfig,
     ],
-    Field(discriminator="block_type"),
+    Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configs usable in Decoder."""
 
@@ -63,7 +63,7 @@ class DecoderConfig(BaseConfig):
     layers : List[DecoderLayerConfig]
         An ordered list of configuration objects, each defining one layer or
         block in the decoder sequence. Each item must be a valid block
-        config including a `block_type` field and necessary parameters like
+        config including a `name` field and necessary parameters like
         `out_channels`. Input channels for each layer are inferred sequentially.
         The list must contain at least one layer.
     """
@@ -249,9 +249,9 @@ def build_decoder(
     ------
     ValueError
         If `in_channels` or `input_height` are not positive, or if the layer
-        configuration is invalid (e.g., empty list, unknown `block_type`).
+        configuration is invalid (e.g., empty list, unknown `name`).
     NotImplementedError
-        If `build_layer_from_config` encounters an unknown `block_type`.
+        If `build_layer_from_config` encounters an unknown `name`.
     """
     config = config or DEFAULT_DECODER_CONFIG
 

src/batdetect2/models/encoder.py

@@ -49,7 +49,7 @@ EncoderLayerConfig = Annotated[
         StandardConvDownConfig,
         LayerGroupConfig,
     ],
-    Field(discriminator="block_type"),
+    Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configs usable in Encoder."""
 
@@ -66,7 +66,7 @@ class EncoderConfig(BaseConfig):
         An ordered list of configuration objects, each defining one layer or
         block in the encoder sequence. Each item must be a valid block config
         (e.g., `ConvConfig`, `FreqCoordConvDownConfig`,
-        `StandardConvDownConfig`) including a `block_type` field and necessary
+        `StandardConvDownConfig`) including a `name` field and necessary
         parameters like `out_channels`. Input channels for each layer are
         inferred sequentially. The list must contain at least one layer.
     """
@@ -287,9 +287,9 @@ def build_encoder(
     ------
     ValueError
         If `in_channels` or `input_height` are not positive, or if the layer
-        configuration is invalid (e.g., empty list, unknown `block_type`).
+        configuration is invalid (e.g., empty list, unknown `name`).
     NotImplementedError
-        If `build_layer_from_config` encounters an unknown `block_type`.
+        If `build_layer_from_config` encounters an unknown `name`.
     """
     if in_channels <= 0 or input_height <= 0:
         raise ValueError("in_channels and input_height must be positive.")

src/batdetect2/preprocess/spectrogram.py

@@ -1,6 +1,14 @@
 """Computes spectrograms from audio waveforms with configurable parameters."""
 
-from typing import Annotated, Callable, List, Literal, Optional, Union
+from typing import (
+    Annotated,
+    Callable,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Union,
+)
 
 import numpy as np
 import torch
@@ -306,7 +314,7 @@ class SpectrogramConfig(BaseConfig):
     stft: STFTConfig = Field(default_factory=STFTConfig)
     frequencies: FrequencyConfig = Field(default_factory=FrequencyConfig)
     size: ResizeConfig = Field(default_factory=ResizeConfig)
-    transforms: List[SpectrogramTransform] = Field(
+    transforms: Sequence[SpectrogramTransform] = Field(
         default_factory=lambda: [
             PcenConfig(),
             SpectralMeanSubstractionConfig(),

src/batdetect2/targets/__init__.py

@@ -1,53 +1,26 @@
-"""Main entry point for the BatDetect2 Target Definition subsystem.
-
-This package (`batdetect2.targets`) provides the tools and configurations
-necessary to define precisely what the BatDetect2 model should learn to detect,
-classify, and localize from audio data. It involves several conceptual steps,
-managed through configuration files and culminating in an executable pipeline:
-
-1.  **Terms (`.terms`)**: Defining vocabulary for annotation tags.
-2.  **Filtering (`.filtering`)**: Selecting relevant sound event annotations.
-3.  **Transformation (`.transform`)**: Modifying tags (standardization,
-    derivation).
-4.  **ROI Mapping (`.roi`)**: Defining how annotation geometry (ROIs) maps to
-    target position and size representations, and back.
-5.  **Class Definition (`.classes`)**: Mapping tags to target class names
-    (encoding) and mapping predicted names back to tags (decoding).
-
-This module exposes the key components for users to configure and utilize this
-target definition pipeline, primarily through the `TargetConfig` data structure
-and the `Targets` class (implementing `TargetProtocol`), which encapsulates the
-configured processing steps. The main way to create a functional `Targets`
-object is via the `build_targets` or `load_targets` functions.
-"""
+"""BatDetect2 Target Definition system."""
 
+from collections import Counter
 from typing import Iterable, List, Optional, Tuple
 
 from loguru import logger
-from pydantic import Field
+from pydantic import Field, field_validator
 from soundevent import data
 
 from batdetect2.configs import BaseConfig, load_config
+from batdetect2.data.conditions import (
+    SoundEventCondition,
+    build_sound_event_condition,
+)
 from batdetect2.targets.classes import (
-    ClassesConfig,
+    DEFAULT_CLASSES,
+    DEFAULT_GENERIC_CLASS,
     SoundEventDecoder,
     SoundEventEncoder,
-    TargetClass,
-    build_generic_class_tags,
+    TargetClassConfig,
     build_sound_event_decoder,
     build_sound_event_encoder,
     get_class_names_from_config,
-    load_classes_config,
-    load_decoder_from_config,
-    load_encoder_from_config,
-)
-from batdetect2.targets.filtering import (
-    FilterConfig,
-    FilterRule,
-    SoundEventFilter,
-    build_sound_event_filter,
-    load_filter_config,
-    load_filter_from_config,
 )
 from batdetect2.targets.rois import (
     AnchorBBoxMapperConfig,
@@ -55,114 +28,53 @@ from batdetect2.targets.rois import (
     ROITargetMapper,
     build_roi_mapper,
 )
-from batdetect2.targets.terms import (
-    TagInfo,
-    TermInfo,
-    TermRegistry,
-    call_type,
-    default_term_registry,
-    get_tag_from_info,
-    get_term_from_key,
-    individual,
-    register_term,
-)
-from batdetect2.targets.transform import (
-    DerivationRegistry,
-    DeriveTagRule,
-    MapValueRule,
-    ReplaceRule,
-    SoundEventTransformation,
-    TransformConfig,
-    build_transformation_from_config,
-    default_derivation_registry,
-    get_derivation,
-    load_transformation_config,
-    load_transformation_from_config,
-    register_derivation,
-)
+from batdetect2.targets.terms import call_type, individual
 from batdetect2.typing.targets import Position, Size, TargetProtocol
 
 __all__ = [
-    "ClassesConfig",
     "DEFAULT_TARGET_CONFIG",
-    "DeriveTagRule",
-    "FilterConfig",
-    "FilterRule",
-    "MapValueRule",
     "AnchorBBoxMapperConfig",
     "ROITargetMapper",
-    "ReplaceRule",
     "SoundEventDecoder",
     "SoundEventEncoder",
-    "SoundEventFilter",
-    "SoundEventTransformation",
-    "TagInfo",
-    "TargetClass",
+    "TargetClassConfig",
     "TargetConfig",
     "Targets",
-    "TermInfo",
-    "TransformConfig",
-    "build_generic_class_tags",
     "build_roi_mapper",
     "build_sound_event_decoder",
     "build_sound_event_encoder",
-    "build_sound_event_filter",
-    "build_transformation_from_config",
     "call_type",
     "get_class_names_from_config",
-    "get_derivation",
-    "get_tag_from_info",
-    "get_term_from_key",
     "individual",
-    "load_classes_config",
-    "load_decoder_from_config",
-    "load_encoder_from_config",
-    "load_filter_config",
-    "load_filter_from_config",
     "load_target_config",
-    "load_transformation_config",
-    "load_transformation_from_config",
-    "register_derivation",
-    "register_term",
 ]
 
 
 class TargetConfig(BaseConfig):
-    """Unified configuration for the entire target definition pipeline.
+    detection_target: TargetClassConfig = Field(default=DEFAULT_GENERIC_CLASS)
 
-    This model aggregates the configurations for semantic processing (filtering,
-    transformation, class definition) and geometric processing (ROI mapping).
-    It serves as the primary input for building a complete `Targets` object
-    via `build_targets` or `load_targets`.
-
-    Attributes
-    ----------
-    filtering : FilterConfig, optional
-        Configuration for filtering sound event annotations based on tags.
-        If None or omitted, no filtering is applied.
-    transforms : TransformConfig, optional
-        Configuration for transforming annotation tags
-        (mapping, derivation, etc.). If None or omitted, no tag transformations
-        are applied.
-    classes : ClassesConfig
-        Configuration defining the specific target classes, their tag matching
-        rules for encoding, their representative tags for decoding
-        (`output_tags`), and the definition of the generic class tags.
-        This section is mandatory.
-    roi : ROIConfig, optional
-        Configuration defining how geometric ROIs (e.g., bounding boxes) are
-        mapped to target representations (reference point, scaled size).
-        Controls `position`, `time_scale`, `frequency_scale`. If None or
-        omitted, default ROI mapping settings are used.
-    """
-
-    filtering: FilterConfig = Field(default_factory=FilterConfig)
-    transforms: TransformConfig = Field(default_factory=TransformConfig)
-    classes: ClassesConfig = Field(
-        default_factory=lambda: DEFAULT_CLASSES_CONFIG
+    classification_targets: List[TargetClassConfig] = Field(
+        default_factory=lambda: DEFAULT_CLASSES
     )
+
     roi: ROIMapperConfig = Field(default_factory=AnchorBBoxMapperConfig)
 
+    @field_validator("classification_targets")
+    def check_unique_class_names(cls, v: List[TargetClassConfig]):
+        """Ensure all defined class names are unique."""
+        names = [c.name for c in v]
+
+        if len(names) != len(set(names)):
+            name_counts = Counter(names)
+            duplicates = [
+                name for name, count in name_counts.items() if count > 1
+            ]
+            raise ValueError(
+                "Class names must be unique. Found duplicates: "
+                f"{', '.join(duplicates)}"
+            )
+        return v
+
 
 def load_target_config(
     path: data.PathLike,
@@ -238,8 +150,7 @@ class Targets(TargetProtocol):
         roi_mapper: ROITargetMapper,
         class_names: list[str],
         generic_class_tags: List[data.Tag],
-        filter_fn: Optional[SoundEventFilter] = None,
-        transform_fn: Optional[SoundEventTransformation] = None,
+        filter_fn: Optional[SoundEventCondition] = None,
         roi_mapper_overrides: Optional[dict[str, ROITargetMapper]] = None,
     ):
         """Initialize the Targets object.
@@ -272,7 +183,6 @@ class Targets(TargetProtocol):
         self._filter_fn = filter_fn
         self._encode_fn = encode_fn
         self._decode_fn = decode_fn
-        self._transform_fn = transform_fn
         self._roi_mapper_overrides = roi_mapper_overrides or {}
 
         for class_name in self._roi_mapper_overrides:
@@ -344,27 +254,6 @@ class Targets(TargetProtocol):
         """
         return self._decode_fn(class_label)
 
-    def transform(
-        self, sound_event: data.SoundEventAnnotation
-    ) -> data.SoundEventAnnotation:
-        """Apply the configured tag transformations to an annotation.
-
-        Parameters
-        ----------
-        sound_event : data.SoundEventAnnotation
-            The annotation whose tags should be transformed.
-
-        Returns
-        -------
-        data.SoundEventAnnotation
-            A new annotation object with the transformed tags. If no
-            transformations were configured, the original annotation object is
-            returned.
-        """
-        if self._transform_fn:
-            return self._transform_fn(sound_event)
-        return sound_event
-
     def encode_roi(
         self, sound_event: data.SoundEventAnnotation
     ) -> tuple[Position, Size]:
@@ -430,113 +319,14 @@ class Targets(TargetProtocol):
         return self._roi_mapper.decode(position, size)
 
 
-DEFAULT_CLASSES = [
-    TargetClass(
-        tags=[TagInfo(value="Myotis mystacinus")],
-        name="myomys",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Myotis alcathoe")],
-        name="myoalc",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Eptesicus serotinus")],
-        name="eptser",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Pipistrellus nathusii")],
-        name="pipnat",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Barbastellus barbastellus")],
-        name="barbar",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Myotis nattereri")],
-        name="myonat",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Myotis daubentonii")],
-        name="myodau",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Myotis brandtii")],
-        name="myobra",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Pipistrellus pipistrellus")],
-        name="pippip",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Myotis bechsteinii")],
-        name="myobec",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Pipistrellus pygmaeus")],
-        name="pippyg",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Rhinolophus hipposideros")],
-        name="rhihip",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Nyctalus leisleri")],
-        name="nyclei",
-        roi=AnchorBBoxMapperConfig(anchor="top-left"),
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Rhinolophus ferrumequinum")],
-        name="rhifer",
-        roi=AnchorBBoxMapperConfig(anchor="top-left"),
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Plecotus auritus")],
-        name="pleaur",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Nyctalus noctula")],
-        name="nycnoc",
-    ),
-    TargetClass(
-        tags=[TagInfo(value="Plecotus austriacus")],
-        name="pleaus",
-    ),
-]
-
-
-DEFAULT_CLASSES_CONFIG: ClassesConfig = ClassesConfig(
-    classes=DEFAULT_CLASSES,
-    generic_class=[TagInfo(value="Bat")],
-)
-
-
 DEFAULT_TARGET_CONFIG: TargetConfig = TargetConfig(
-    filtering=FilterConfig(
-        rules=[
-            FilterRule(
-                match_type="all",
-                tags=[TagInfo(key="event", value="Echolocation")],
-            ),
-            FilterRule(
-                match_type="exclude",
-                tags=[
-                    TagInfo(key="event", value="Feeding"),
-                    TagInfo(key="event", value="Unknown"),
-                    TagInfo(key="event", value="Not Bat"),
-                ],
-            ),
-        ]
-    ),
-    classes=DEFAULT_CLASSES_CONFIG,
+    classification_targets=DEFAULT_CLASSES,
+    detection_target=DEFAULT_GENERIC_CLASS,
     roi=AnchorBBoxMapperConfig(),
 )
 
 
-def build_targets(
-    config: Optional[TargetConfig] = None,
-    term_registry: TermRegistry = default_term_registry,
-    derivation_registry: DerivationRegistry = default_derivation_registry,
-) -> Targets:
+def build_targets(config: Optional[TargetConfig] = None) -> Targets:
     """Build a Targets object from a loaded TargetConfig.
 
     This factory function takes the unified `TargetConfig` and constructs all
@@ -550,13 +340,6 @@ def build_targets(
     ----------
     config : TargetConfig
         The loaded and validated unified target configuration object.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance to use for resolving term keys. Defaults
-        to the global `batdetect2.targets.terms.term_registry`.
-    derivation_registry : DerivationRegistry, optional
-        The DerivationRegistry instance to use for resolving derivation
-        function names. Defaults to the global
-        `batdetect2.targets.transform.derivation_registry`.
 
     Returns
     -------
@@ -577,40 +360,18 @@ def build_targets(
         lambda: config.to_yaml_string(),
     )
 
-    filter_fn = (
-        build_sound_event_filter(
-            config.filtering,
-            term_registry=term_registry,
-        )
-        if config.filtering
-        else None
-    )
-    encode_fn = build_sound_event_encoder(
-        config.classes,
-        term_registry=term_registry,
-    )
-    decode_fn = build_sound_event_decoder(
-        config.classes,
-        term_registry=term_registry,
-    )
-    transform_fn = (
-        build_transformation_from_config(
-            config.transforms,
-            term_registry=term_registry,
-            derivation_registry=derivation_registry,
-        )
-        if config.transforms
-        else None
-    )
+    filter_fn = build_sound_event_condition(config.detection_target.match_if)
+    encode_fn = build_sound_event_encoder(config.classification_targets)
+    decode_fn = build_sound_event_decoder(config.classification_targets)
+
     roi_mapper = build_roi_mapper(config.roi)
-    class_names = get_class_names_from_config(config.classes)
-    generic_class_tags = build_generic_class_tags(
-        config.classes,
-        term_registry=term_registry,
-    )
+    class_names = get_class_names_from_config(config.classification_targets)
+
+    generic_class_tags = config.detection_target.assign_tags
+
     roi_overrides = {
         class_config.name: build_roi_mapper(class_config.roi)
-        for class_config in config.classes.classes
+        for class_config in config.classification_targets
         if class_config.roi is not None
     }
 
@@ -621,7 +382,6 @@ def build_targets(
         class_names=class_names,
         roi_mapper=roi_mapper,
         generic_class_tags=generic_class_tags,
-        transform_fn=transform_fn,
         roi_mapper_overrides=roi_overrides,
     )
 
@@ -629,8 +389,6 @@ def build_targets(
 def load_targets(
     config_path: data.PathLike,
     field: Optional[str] = None,
-    term_registry: TermRegistry = default_term_registry,
-    derivation_registry: DerivationRegistry = default_derivation_registry,
 ) -> Targets:
     """Load a Targets object directly from a configuration file.
 
@@ -645,11 +403,6 @@ def load_targets(
     field : str, optional
         Dot-separated path to a nested section within the file containing
         the target configuration. If None, the entire file content is used.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance to use. Defaults to the global default.
-    derivation_registry : DerivationRegistry, optional
-        The DerivationRegistry instance to use. Defaults to the global
-        default.
 
     Returns
     -------
@@ -670,11 +423,7 @@ def load_targets(
         config_path,
         field=field,
     )
-    return build_targets(
-        config,
-        term_registry=term_registry,
-        derivation_registry=derivation_registry,
-    )
+    return build_targets(config)
 
 
 def iterate_encoded_sound_events(
@@ -690,8 +439,6 @@ def iterate_encoded_sound_events(
         if geometry is None:
             continue
 
-        sound_event = targets.transform(sound_event)
-
         class_name = targets.encode_class(sound_event)
         position, size = targets.encode_roi(sound_event)
 

src/batdetect2/targets/classes.py

@@ -1,253 +1,172 @@
-from collections import Counter
-from functools import partial
-from typing import Callable, Dict, List, Literal, Optional, Set, Tuple
+from typing import Dict, List, Optional
 
-from pydantic import Field, field_validator
+from pydantic import Field, PrivateAttr, computed_field, model_validator
 from soundevent import data
 
-from batdetect2.configs import BaseConfig, load_config
-from batdetect2.targets.rois import ROIMapperConfig
-from batdetect2.targets.terms import (
-    GENERIC_CLASS_KEY,
-    TagInfo,
-    TermRegistry,
-    default_term_registry,
-    get_tag_from_info,
+from batdetect2.configs import BaseConfig
+from batdetect2.data.conditions import (
+    AllOfConfig,
+    HasAllTagsConfig,
+    HasAnyTagConfig,
+    HasTagConfig,
+    NotConfig,
+    SoundEventCondition,
+    SoundEventConditionConfig,
+    build_sound_event_condition,
 )
+from batdetect2.targets.rois import AnchorBBoxMapperConfig, ROIMapperConfig
 from batdetect2.typing.targets import SoundEventDecoder, SoundEventEncoder
 
 __all__ = [
-    "DEFAULT_SPECIES_LIST",
-    "build_generic_class_tags",
     "build_sound_event_decoder",
     "build_sound_event_encoder",
     "get_class_names_from_config",
-    "load_classes_config",
-    "load_decoder_from_config",
-    "load_encoder_from_config",
 ]
 
 
-DEFAULT_SPECIES_LIST = [
-    "Barbastella barbastellus",
-    "Eptesicus serotinus",
-    "Myotis alcathoe",
-    "Myotis bechsteinii",
-    "Myotis brandtii",
-    "Myotis daubentonii",
-    "Myotis mystacinus",
-    "Myotis nattereri",
-    "Nyctalus leisleri",
-    "Nyctalus noctula",
-    "Pipistrellus nathusii",
-    "Pipistrellus pipistrellus",
-    "Pipistrellus pygmaeus",
-    "Plecotus auritus",
-    "Plecotus austriacus",
-    "Rhinolophus ferrumequinum",
-    "Rhinolophus hipposideros",
-]
-"""A default list of common bat species names found in the UK."""
-
-
-class TargetClass(BaseConfig):
-    """Defines criteria for encoding annotations and decoding predictions.
-
-    Each instance represents one potential output class for the classification
-    model. It specifies:
-    1. A unique `name` for the class.
-    2. The tag conditions (`tags` and `match_type`) an annotation must meet to
-       be assigned this class name during training data preparation (encoding).
-    3. An optional, alternative set of tags (`output_tags`) to be used when
-       converting a model's prediction of this class name back into annotation
-       tags (decoding).
-
-    Attributes
-    ----------
-    name : str
-        The unique name assigned to this target class (e.g., 'pippip',
-        'myodau', 'noise'). This name is used as the label during model
-        training and is the expected output from the model's prediction.
-        Should be unique across all TargetClass definitions in a configuration.
-    tags : List[TagInfo]
-        A list of one or more tags (defined using `TagInfo`) used to identify
-        if an existing annotation belongs to this class during encoding (data
-        preparation for training). The `match_type` attribute determines how
-        these tags are evaluated.
-    match_type : Literal["all", "any"], default="all"
-        Determines how the `tags` list is evaluated during encoding:
-        - "all": The annotation must have *all* the tags listed to match.
-        - "any": The annotation must have *at least one* of the tags listed
-                 to match.
-    output_tags: Optional[List[TagInfo]], default=None
-        An optional list of tags (defined using `TagInfo`) to be assigned to a
-        new annotation when the model predicts this class `name`. If `None`
-        (default), the tags listed in the `tags` field will be used for
-        decoding. If provided, this list overrides the `tags` field for the
-        purpose of decoding predictions back into meaningful annotation tags.
-        This allows, for example, training on broader categories but decoding
-        to more specific representative tags.
-    """
+class TargetClassConfig(BaseConfig):
+    """Defines a target class of sound events."""
 
     name: str
-    tags: List[TagInfo] = Field(min_length=1)
-    match_type: Literal["all", "any"] = Field(default="all")
-    output_tags: Optional[List[TagInfo]] = None
-    roi: Optional[ROIMapperConfig] = None
 
-
-def _get_default_classes() -> List[TargetClass]:
-    """Generate a list of default target classes.
-
-    Returns
-    -------
-    List[TargetClass]
-        A list of TargetClass objects, one for each species in
-        DEFAULT_SPECIES_LIST. The class names are simplified versions of the
-        species names.
-    """
-    return [
-        TargetClass(
-            name=_get_default_class_name(value),
-            tags=[TagInfo(key=GENERIC_CLASS_KEY, value=value)],
-        )
-        for value in DEFAULT_SPECIES_LIST
-    ]
-
-
-def _get_default_class_name(species: str) -> str:
-    """Generate a default class name from a species name.
-
-    Parameters
-    ----------
-    species : str
-        The species name (e.g., "Myotis daubentonii").
-
-    Returns
-    -------
-    str
-        A simplified class name (e.g., "myodau").
-        The genus and species names are converted to lowercase,
-        the first three letters of each are taken, and concatenated.
-    """
-    genus, species = species.strip().split(" ")
-    return f"{genus.lower()[:3]}{species.lower()[:3]}"
-
-
-def _get_default_generic_class() -> List[TagInfo]:
-    """Generate the default list of TagInfo objects for the generic class.
-
-    Provides a default set of tags used to represent the generic "Bat" category
-    when decoding predictions that didn't match a specific class.
-
-    Returns
-    -------
-    List[TagInfo]
-        A list containing default TagInfo objects, typically representing
-        `call_type: Echolocation` and `order: Chiroptera`.
-    """
-    return [
-        TagInfo(key="call_type", value="Echolocation"),
-        TagInfo(key="order", value="Chiroptera"),
-    ]
-
-
-class ClassesConfig(BaseConfig):
-    """Configuration defining target classes and the generic fallback category.
-
-    Holds the ordered list of specific target class definitions (`TargetClass`)
-    and defines the tags representing the generic category for sounds that pass
-    filtering but do not match any specific class.
-
-    The order of `TargetClass` objects in the `classes` list defines the
-    priority for classification during encoding. The system checks annotations
-    against these definitions sequentially and assigns the name of the *first*
-    matching class.
-
-    Attributes
-    ----------
-    classes : List[TargetClass]
-        An ordered list of specific target class definitions. The order
-        determines matching priority (first match wins). Defaults to a
-        standard set of classes via `get_default_classes`.
-    generic_class : List[TagInfo]
-        A list of tags defining the "generic" or "unclassified but relevant"
-        category (e.g., representing a generic 'Bat' call that wasn't
-        assigned to a specific species). These tags are typically assigned
-        during decoding when a sound event was detected and passed filtering
-        but did not match any specific class rule defined in the `classes` list.
-        Defaults to a standard set of tags via `get_default_generic_class`.
-
-    Raises
-    ------
-    ValueError
-        If validation fails (e.g., non-unique class names in the `classes`
-        list).
-
-    Notes
-    -----
-    - It is crucial that the `name` attribute of each `TargetClass` in the
-      `classes` list is unique. This configuration includes a validator to
-      enforce this uniqueness.
-    - The `generic_class` tags provide a baseline identity for relevant sounds
-      that don't fit into more specific defined categories.
-    """
-
-    classes: List[TargetClass] = Field(default_factory=_get_default_classes)
-
-    generic_class: List[TagInfo] = Field(
-        default_factory=_get_default_generic_class
+    condition_input: Optional[SoundEventConditionConfig] = Field(
+        alias="match_if",
+        default=None,
     )
 
-    @field_validator("classes")
-    def check_unique_class_names(cls, v: List[TargetClass]):
-        """Ensure all defined class names are unique."""
-        names = [c.name for c in v]
+    tags: Optional[List[data.Tag]] = Field(default=None, exclude=True)
 
-        if len(names) != len(set(names)):
-            name_counts = Counter(names)
-            duplicates = [
-                name for name, count in name_counts.items() if count > 1
-            ]
+    assign_tags: List[data.Tag] = Field(default_factory=list)
+
+    roi: Optional[ROIMapperConfig] = None
+
+    _match_if: SoundEventConditionConfig = PrivateAttr()
+
+    @model_validator(mode="after")
+    def _process_tags(self) -> "TargetClassConfig":
+        if self.tags and self.condition_input:
+            raise ValueError("Use either 'tags' or 'match_if', not both.")
+
+        if self.condition_input is not None:
+            self._match_if = self.condition_input
+            return self
+
+        if self.tags is None:
             raise ValueError(
-                "Class names must be unique. Found duplicates: "
-                f"{', '.join(duplicates)}"
+                f"Class '{self.name}' must have a 'tags' or 'match_if' rule."
             )
-        return v
+
+        self._match_if = HasAllTagsConfig(tags=self.tags)
+
+        if not self.assign_tags:
+            self.assign_tags = self.tags
+
+        return self
+
+    @computed_field
+    @property
+    def match_if(self) -> SoundEventConditionConfig:
+        return self._match_if
 
 
-def is_target_class(
-    sound_event_annotation: data.SoundEventAnnotation,
-    tags: Set[data.Tag],
-    match_all: bool = True,
-) -> bool:
-    """Check if a sound event annotation matches a set of required tags.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to check.
-    required_tags : Set[data.Tag]
-        A set of `soundevent.data.Tag` objects that define the class criteria.
-    match_all : bool, default=True
-        If True, checks if *all* `required_tags` are present in the
-        annotation's tags (subset check). If False, checks if *at least one*
-        of the `required_tags` is present (intersection check).
-
-    Returns
-    -------
-    bool
-        True if the annotation meets the tag criteria, False otherwise.
-    """
-    annotation_tags = set(sound_event_annotation.tags)
-
-    if match_all:
-        return tags <= annotation_tags
-
-    return bool(tags & annotation_tags)
+DEFAULT_GENERIC_CLASS = TargetClassConfig(
+    name="bat",
+    match_if=AllOfConfig(
+        conditions=[
+            HasTagConfig(tag=data.Tag(key="event", value="Echolocation")),
+            NotConfig(
+                condition=HasAnyTagConfig(
+                    tags=[
+                        data.Tag(key="event", value="Feeding"),
+                        data.Tag(key="event", value="Unknown"),
+                        data.Tag(key="event", value="Not Bat"),
+                    ]
+                )
+            ),
+        ]
+    ),
+    assign_tags=[
+        data.Tag(key="call_type", value="Echolocation"),
+        data.Tag(key="order", value="Chiroptera"),
+    ],
+)
 
 
-def get_class_names_from_config(config: ClassesConfig) -> List[str]:
+DEFAULT_CLASSES = [
+    TargetClassConfig(
+        name="myomys",
+        tags=[data.Tag(key="class", value="Myotis mystacinus")],
+    ),
+    TargetClassConfig(
+        name="myoalc",
+        tags=[data.Tag(key="class", value="Myotis alcathoe")],
+    ),
+    TargetClassConfig(
+        name="eptser",
+        tags=[data.Tag(key="class", value="Eptesicus serotinus")],
+    ),
+    TargetClassConfig(
+        name="pipnat",
+        tags=[data.Tag(key="class", value="Pipistrellus nathusii")],
+    ),
+    TargetClassConfig(
+        name="barbar",
+        tags=[data.Tag(key="class", value="Barbastellus barbastellus")],
+    ),
+    TargetClassConfig(
+        name="myonat",
+        tags=[data.Tag(key="class", value="Myotis nattereri")],
+    ),
+    TargetClassConfig(
+        name="myodau",
+        tags=[data.Tag(key="class", value="Myotis daubentonii")],
+    ),
+    TargetClassConfig(
+        name="myobra",
+        tags=[data.Tag(key="class", value="Myotis brandtii")],
+    ),
+    TargetClassConfig(
+        name="pippip",
+        tags=[data.Tag(key="class", value="Pipistrellus pipistrellus")],
+    ),
+    TargetClassConfig(
+        name="myobec",
+        tags=[data.Tag(key="class", value="Myotis bechsteinii")],
+    ),
+    TargetClassConfig(
+        name="pippyg",
+        tags=[data.Tag(key="class", value="Pipistrellus pygmaeus")],
+    ),
+    TargetClassConfig(
+        name="rhihip",
+        tags=[data.Tag(key="class", value="Rhinolophus hipposideros")],
+        roi=AnchorBBoxMapperConfig(anchor="top-left"),
+    ),
+    TargetClassConfig(
+        name="nyclei",
+        tags=[data.Tag(key="class", value="Nyctalus leisleri")],
+    ),
+    TargetClassConfig(
+        name="rhifer",
+        tags=[data.Tag(key="class", value="Rhinolophus ferrumequinum")],
+        roi=AnchorBBoxMapperConfig(anchor="top-left"),
+    ),
+    TargetClassConfig(
+        name="pleaur",
+        tags=[data.Tag(key="class", value="Plecotus auritus")],
+    ),
+    TargetClassConfig(
+        name="nycnoc",
+        tags=[data.Tag(key="class", value="Nyctalus noctula")],
+    ),
+    TargetClassConfig(
+        name="pleaus",
+        tags=[data.Tag(key="class", value="Plecotus austriacus")],
+    ),
+]
+
+
+def get_class_names_from_config(configs: List[TargetClassConfig]) -> List[str]:
     """Extract the list of class names from a ClassesConfig object.
 
     Parameters
@@ -260,340 +179,60 @@ def get_class_names_from_config(config: ClassesConfig) -> List[str]:
     List[str]
         An ordered list of unique class names defined in the configuration.
     """
-    return [class_info.name for class_info in config.classes]
-
-
-def _encode_with_multiple_classifiers(
-    sound_event_annotation: data.SoundEventAnnotation,
-    classifiers: List[Tuple[str, Callable[[data.SoundEventAnnotation], bool]]],
-) -> Optional[str]:
-    """Encode an annotation by checking against a list of classifiers.
-
-    Internal helper function used by the `SoundEventEncoder`. It iterates
-    through the provided list of (class_name, classifier_function) pairs.
-    Returns the name associated with the first classifier function that
-    returns True for the given annotation.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to encode.
-    classifiers : List[Tuple[str, Callable[[data.SoundEventAnnotation], bool]]]
-        An ordered list where each tuple contains a class name and a function
-        that returns True if the annotation matches that class. The order
-        determines priority.
-
-    Returns
-    -------
-    str or None
-        The name of the first matching class, or None if no classifier matches.
-    """
-    for class_name, classifier in classifiers:
-        if classifier(sound_event_annotation):
-            return class_name
-
-    return None
+    return [class_info.name for class_info in configs]
 
 
 def build_sound_event_encoder(
-    config: ClassesConfig,
-    term_registry: TermRegistry = default_term_registry,
+    configs: List[TargetClassConfig],
 ) -> SoundEventEncoder:
-    """Build a sound event encoder function from the classes configuration.
+    """Build a sound event encoder function from the classes configuration."""
+    conditions = {
+        class_config.name: build_sound_event_condition(class_config.match_if)
+        for class_config in configs
+    }
 
-    The returned encoder function iterates through the class definitions in the
-    order specified in the config. It assigns an annotation the name of the
-    first class definition it matches.
-
-    Parameters
-    ----------
-    config : ClassesConfig
-        The loaded and validated classes configuration object.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance used to look up term keys specified in the
-        `TagInfo` objects within the configuration. Defaults to the global
-        `batdetect2.targets.terms.registry`.
-
-    Returns
-    -------
-    SoundEventEncoder
-        A callable function that takes a `SoundEventAnnotation` and returns
-        an optional string representing the matched class name, or None if no
-        class matches.
-
-    Raises
-    ------
-    KeyError
-        If a term key specified in the configuration is not found in the
-        provided `term_registry`.
-    """
-    binary_classifiers = [
-        (
-            class_info.name,
-            partial(
-                is_target_class,
-                tags={
-                    get_tag_from_info(tag_info, term_registry=term_registry)
-                    for tag_info in class_info.tags
-                },
-                match_all=class_info.match_type == "all",
-            ),
-        )
-        for class_info in config.classes
-    ]
-
-    return partial(
-        _encode_with_multiple_classifiers,
-        classifiers=binary_classifiers,
-    )
+    return SoundEventClassifier(conditions)
 
 
-def _decode_class(
-    name: str,
-    mapping: Dict[str, List[data.Tag]],
-    raise_on_error: bool = True,
-) -> List[data.Tag]:
-    """Decode a class name into a list of representative tags using a mapping.
+class SoundEventClassifier:
+    def __init__(self, mapping: Dict[str, SoundEventCondition]):
+        self.mapping = mapping
 
-    Internal helper function used by the `SoundEventDecoder`. Looks up the
-    provided class `name` in the `mapping` dictionary.
-
-    Parameters
-    ----------
-    name : str
-        The class name to decode.
-    mapping : Dict[str, List[data.Tag]]
-        A dictionary mapping class names to lists of `soundevent.data.Tag`
-        objects.
-    raise_on_error : bool, default=True
-        If True, raises a ValueError if the `name` is not found in the
-        `mapping`. If False, returns an empty list if the `name` is not found.
-
-    Returns
-    -------
-    List[data.Tag]
-        The list of tags associated with the class name, or an empty list if
-        not found and `raise_on_error` is False.
-
-    Raises
-    ------
-    ValueError
-        If `name` is not found in `mapping` and `raise_on_error` is True.
-    """
-    if name not in mapping and raise_on_error:
-        raise ValueError(f"Class {name} not found in mapping.")
-
-    if name not in mapping:
-        return []
-
-    return mapping[name]
+    def __call__(
+        self, sound_event_annotation: data.SoundEventAnnotation
+    ) -> Optional[str]:
+        for name, condition in self.mapping.items():
+            if condition(sound_event_annotation):
+                return name
 
 
 def build_sound_event_decoder(
-    config: ClassesConfig,
-    term_registry: TermRegistry = default_term_registry,
+    configs: List[TargetClassConfig],
     raise_on_unmapped: bool = False,
 ) -> SoundEventDecoder:
-    """Build a sound event decoder function from the classes configuration.
-
-    Creates a callable `SoundEventDecoder` that maps a class name string
-    back to a list of representative `soundevent.data.Tag` objects based on
-    the `ClassesConfig`. It uses the `output_tags` field if provided in a
-    `TargetClass`, otherwise falls back to the `tags` field.
-
-    Parameters
-    ----------
-    config : ClassesConfig
-        The loaded and validated classes configuration object.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance used to look up term keys. Defaults to the
-        global `batdetect2.targets.terms.registry`.
-    raise_on_unmapped : bool, default=False
-        If True, the returned decoder function will raise a ValueError if asked
-        to decode a class name that is not in the configuration. If False, it
-        will return an empty list for unmapped names.
-
-    Returns
-    -------
-    SoundEventDecoder
-        A callable function that takes a class name string and returns a list
-        of `soundevent.data.Tag` objects.
-
-    Raises
-    ------
-    KeyError
-        If a term key specified in the configuration (`output_tags`, `tags`, or
-        `generic_class`) is not found in the provided `term_registry`.
-    """
-    mapping = {}
-    for class_info in config.classes:
-        tags_to_use = (
-            class_info.output_tags
-            if class_info.output_tags is not None
-            else class_info.tags
-        )
-        mapping[class_info.name] = [
-            get_tag_from_info(tag_info, term_registry=term_registry)
-            for tag_info in tags_to_use
-        ]
-
-    return partial(
-        _decode_class,
-        mapping=mapping,
-        raise_on_error=raise_on_unmapped,
-    )
+    """Build a sound event decoder function from the classes configuration."""
+    mapping = {
+        class_config.name: class_config.assign_tags for class_config in configs
+    }
+    return TagDecoder(mapping, raise_on_unknown=raise_on_unmapped)
 
 
-def build_generic_class_tags(
-    config: ClassesConfig,
-    term_registry: TermRegistry = default_term_registry,
-) -> List[data.Tag]:
-    """Extract and build the list of tags for the generic class from config.
+class TagDecoder:
+    def __init__(
+        self,
+        mapping: Dict[str, List[data.Tag]],
+        raise_on_unknown: bool = True,
+    ):
+        self.mapping = mapping
+        self.raise_on_unknown = raise_on_unknown
 
-    Converts the list of `TagInfo` objects defined in `config.generic_class`
-    into a list of `soundevent.data.Tag` objects using the term registry.
+    def __call__(self, class_name: str) -> List[data.Tag]:
+        tags = self.mapping.get(class_name)
 
-    Parameters
-    ----------
-    config : ClassesConfig
-        The loaded classes configuration object.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance for term lookups. Defaults to the global
-        `batdetect2.targets.terms.registry`.
+        if tags is None:
+            if self.raise_on_unknown:
+                raise ValueError("Invalid class name")
 
-    Returns
-    -------
-    List[data.Tag]
-        The list of fully constructed tags representing the generic class.
+            tags = []
 
-    Raises
-    ------
-    KeyError
-        If a term key specified in `config.generic_class` is not found in the
-        provided `term_registry`.
-    """
-    return [
-        get_tag_from_info(tag_info, term_registry=term_registry)
-        for tag_info in config.generic_class
-    ]
-
-
-def load_classes_config(
-    path: data.PathLike,
-    field: Optional[str] = None,
-) -> ClassesConfig:
-    """Load the target classes configuration from a file.
-
-    Parameters
-    ----------
-    path : data.PathLike
-        Path to the configuration file (YAML).
-    field : str, optional
-        If the classes configuration is nested under a specific key in the
-        file, specify the key here. Defaults to None.
-
-    Returns
-    -------
-    ClassesConfig
-        The loaded and validated classes configuration object.
-
-    Raises
-    ------
-    FileNotFoundError
-        If the config file path does not exist.
-    pydantic.ValidationError
-        If the config file structure does not match the ClassesConfig schema
-        or if class names are not unique.
-    """
-    return load_config(path, schema=ClassesConfig, field=field)
-
-
-def load_encoder_from_config(
-    path: data.PathLike,
-    field: Optional[str] = None,
-    term_registry: TermRegistry = default_term_registry,
-) -> SoundEventEncoder:
-    """Load a class encoder function directly from a configuration file.
-
-    This is a convenience function that combines loading the `ClassesConfig`
-    from a file and building the final `SoundEventEncoder` function.
-
-    Parameters
-    ----------
-    path : data.PathLike
-        Path to the configuration file (e.g., YAML).
-    field : str, optional
-        If the classes configuration is nested under a specific key in the
-        file, specify the key here. Defaults to None.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance used for term lookups. Defaults to the
-        global `batdetect2.targets.terms.registry`.
-
-    Returns
-    -------
-    SoundEventEncoder
-        The final encoder function ready to classify annotations.
-
-    Raises
-    ------
-    FileNotFoundError
-        If the config file path does not exist.
-    pydantic.ValidationError
-        If the config file structure does not match the ClassesConfig schema
-        or if class names are not unique.
-    KeyError
-        If a term key specified in the configuration is not found in the
-        provided `term_registry` during the build process.
-    """
-    config = load_classes_config(path, field=field)
-    return build_sound_event_encoder(config, term_registry=term_registry)
-
-
-def load_decoder_from_config(
-    path: data.PathLike,
-    field: Optional[str] = None,
-    term_registry: TermRegistry = default_term_registry,
-    raise_on_unmapped: bool = False,
-) -> SoundEventDecoder:
-    """Load a class decoder function directly from a configuration file.
-
-    This is a convenience function that combines loading the `ClassesConfig`
-    from a file and building the final `SoundEventDecoder` function.
-
-    Parameters
-    ----------
-    path : data.PathLike
-        Path to the configuration file (e.g., YAML).
-    field : str, optional
-        If the classes configuration is nested under a specific key in the
-        file, specify the key here. Defaults to None.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance used for term lookups. Defaults to the
-        global `batdetect2.targets.terms.registry`.
-    raise_on_unmapped : bool, default=False
-        If True, the returned decoder function will raise a ValueError if asked
-        to decode a class name that is not in the configuration. If False, it
-        will return an empty list for unmapped names.
-
-    Returns
-    -------
-    SoundEventDecoder
-        The final decoder function ready to convert class names back into tags.
-
-    Raises
-    ------
-    FileNotFoundError
-        If the config file path does not exist.
-    pydantic.ValidationError
-        If the config file structure does not match the ClassesConfig schema
-        or if class names are not unique.
-    KeyError
-        If a term key specified in the configuration is not found in the
-        provided `term_registry` during the build process.
-    """
-    config = load_classes_config(path, field=field)
-    return build_sound_event_decoder(
-        config,
-        term_registry=term_registry,
-        raise_on_unmapped=raise_on_unmapped,
-    )
+        return tags

src/batdetect2/targets/filtering.py

@@ -1,307 +0,0 @@
-import logging
-from functools import partial
-from typing import List, Literal, Optional, Set
-
-from pydantic import Field
-from soundevent import data
-
-from batdetect2.configs import BaseConfig, load_config
-from batdetect2.targets.terms import (
-    TagInfo,
-    TermRegistry,
-    default_term_registry,
-    get_tag_from_info,
-)
-from batdetect2.typing.targets import SoundEventFilter
-
-__all__ = [
-    "FilterConfig",
-    "FilterRule",
-    "build_sound_event_filter",
-    "build_filter_from_rule",
-    "load_filter_config",
-    "load_filter_from_config",
-]
-
-
-logger = logging.getLogger(__name__)
-
-
-class FilterRule(BaseConfig):
-    """Defines a single rule for filtering sound event annotations.
-
-    Based on the `match_type`, this rule checks if the tags associated with a
-    sound event annotation meet certain criteria relative to the `tags` list
-    defined in this rule.
-
-    Attributes
-    ----------
-    match_type : Literal["any", "all", "exclude", "equal"]
-        Determines how the `tags` list is used:
-        - "any": Pass if the annotation has at least one tag from the list.
-        - "all": Pass if the annotation has all tags from the list (it can
-                 have others too).
-        - "exclude": Pass if the annotation has none of the tags from the list.
-        - "equal": Pass if the annotation's tags are exactly the same set as
-                   provided in the list.
-    tags : List[TagInfo]
-        A list of tags (defined using TagInfo for configuration) that this
-        rule operates on.
-    """
-
-    match_type: Literal["any", "all", "exclude", "equal"]
-    tags: List[TagInfo]
-
-
-def has_any_tag(
-    sound_event_annotation: data.SoundEventAnnotation,
-    tags: Set[data.Tag],
-) -> bool:
-    """Check if the annotation has at least one of the specified tags.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to check.
-    tags : Set[data.Tag]
-        The set of tags to look for.
-
-    Returns
-    -------
-    bool
-        True if the annotation has one or more tags from the specified set,
-        False otherwise.
-    """
-    sound_event_tags = set(sound_event_annotation.tags)
-    return bool(tags & sound_event_tags)
-
-
-def contains_tags(
-    sound_event_annotation: data.SoundEventAnnotation,
-    tags: Set[data.Tag],
-) -> bool:
-    """Check if the annotation contains all of the specified tags.
-
-    The annotation may have additional tags beyond those specified.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to check.
-    tags : Set[data.Tag]
-        The set of tags that must all be present in the annotation.
-
-    Returns
-    -------
-    bool
-        True if the annotation's tags are a superset of the specified tags,
-        False otherwise.
-    """
-    sound_event_tags = set(sound_event_annotation.tags)
-    return tags <= sound_event_tags
-
-
-def does_not_have_tags(
-    sound_event_annotation: data.SoundEventAnnotation,
-    tags: Set[data.Tag],
-):
-    """Check if the annotation has none of the specified tags.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to check.
-    tags : Set[data.Tag]
-        The set of tags that must *not* be present in the annotation.
-
-    Returns
-    -------
-    bool
-        True if the annotation has zero tags in common with the specified set,
-        False otherwise.
-    """
-    return not has_any_tag(sound_event_annotation, tags)
-
-
-def equal_tags(
-    sound_event_annotation: data.SoundEventAnnotation,
-    tags: Set[data.Tag],
-) -> bool:
-    """Check if the annotation's tags are exactly equal to the specified set.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to check.
-    tags : Set[data.Tag]
-        The exact set of tags the annotation must have.
-
-    Returns
-    -------
-    bool
-        True if the annotation's tags set is identical to the specified set,
-        False otherwise.
-    """
-    sound_event_tags = set(sound_event_annotation.tags)
-    return tags == sound_event_tags
-
-
-def build_filter_from_rule(
-    rule: FilterRule,
-    term_registry: TermRegistry = default_term_registry,
-) -> SoundEventFilter:
-    """Creates a callable filter function from a single FilterRule.
-
-    Parameters
-    ----------
-    rule : FilterRule
-        The filter rule configuration object.
-
-    Returns
-    -------
-    SoundEventFilter
-        A function that takes a SoundEventAnnotation and returns True if it
-        passes the rule, False otherwise.
-
-    Raises
-    ------
-    ValueError
-        If the rule contains an invalid `match_type`.
-    """
-    tag_set = {
-        get_tag_from_info(tag_info, term_registry=term_registry)
-        for tag_info in rule.tags
-    }
-
-    if rule.match_type == "any":
-        return partial(has_any_tag, tags=tag_set)
-
-    if rule.match_type == "all":
-        return partial(contains_tags, tags=tag_set)
-
-    if rule.match_type == "exclude":
-        return partial(does_not_have_tags, tags=tag_set)
-
-    if rule.match_type == "equal":
-        return partial(equal_tags, tags=tag_set)
-
-    raise ValueError(
-        f"Invalid match type {rule.match_type}. Valid types "
-        "are: 'any', 'all', 'exclude' and 'equal'"
-    )
-
-
-def _passes_all_filters(
-    sound_event_annotation: data.SoundEventAnnotation,
-    filters: List[SoundEventFilter],
-) -> bool:
-    """Check if the annotation passes all provided filters.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to check.
-    filters : List[SoundEventFilter]
-        A list of filter functions to apply.
-
-    Returns
-    -------
-    bool
-        True if the annotation passes all filters, False otherwise.
-    """
-    for filter_fn in filters:
-        if not filter_fn(sound_event_annotation):
-            logging.debug(
-                f"Sound event annotation {sound_event_annotation.uuid} "
-                f"excluded due to rule {filter_fn}",
-            )
-            return False
-
-    return True
-
-
-class FilterConfig(BaseConfig):
-    """Configuration model for defining a list of filter rules.
-
-    Attributes
-    ----------
-    rules : List[FilterRule]
-        A list of FilterRule objects. An annotation must pass all rules in
-        this list to be considered valid by the filter built from this config.
-    """
-
-    rules: List[FilterRule] = Field(default_factory=list)
-
-
-def build_sound_event_filter(
-    config: FilterConfig,
-    term_registry: TermRegistry = default_term_registry,
-) -> SoundEventFilter:
-    """Builds a merged filter function from a FilterConfig object.
-
-    Creates individual filter functions for each rule in the configuration
-    and merges them using AND logic.
-
-    Parameters
-    ----------
-    config : FilterConfig
-        The configuration object containing the list of filter rules.
-
-    Returns
-    -------
-    SoundEventFilter
-        A single callable filter function that applies all defined rules.
-    """
-    filters = [
-        build_filter_from_rule(rule, term_registry=term_registry)
-        for rule in config.rules
-    ]
-    return partial(_passes_all_filters, filters=filters)
-
-
-def load_filter_config(
-    path: data.PathLike, field: Optional[str] = None
-) -> FilterConfig:
-    """Loads the filter configuration from a file.
-
-    Parameters
-    ----------
-    path : data.PathLike
-        Path to the configuration file (YAML).
-    field : Optional[str], optional
-        If the filter configuration is nested under a specific key in the
-        file, specify the key here. Defaults to None.
-
-    Returns
-    -------
-    FilterConfig
-        The loaded and validated filter configuration object.
-    """
-    return load_config(path, schema=FilterConfig, field=field)
-
-
-def load_filter_from_config(
-    path: data.PathLike,
-    field: Optional[str] = None,
-    term_registry: TermRegistry = default_term_registry,
-) -> SoundEventFilter:
-    """Loads filter configuration from a file and builds the filter function.
-
-    This is a convenience function that combines loading the configuration
-    and building the final callable filter function.
-
-    Parameters
-    ----------
-    path : data.PathLike
-        Path to the configuration file (YAML).
-    field : Optional[str], optional
-        If the filter configuration is nested under a specific key in the
-        file, specify the key here. Defaults to None.
-
-    Returns
-    -------
-    SoundEventFilter
-        The final merged filter function ready to be used.
-    """
-    config = load_filter_config(path=path, field=field)
-    return build_sound_event_filter(config, term_registry=term_registry)

src/batdetect2/targets/rois.py

@@ -20,7 +20,7 @@ selecting and configuring the desired mapper. This module separates the
 *geometric* aspect of target definition from *semantic* classification.
 """
 
-from typing import Annotated, List, Literal, Optional, Protocol, Tuple, Union
+from typing import Annotated, Literal, Optional, Tuple, Union
 
 import numpy as np
 from pydantic import Field
@@ -30,7 +30,7 @@ from batdetect2.configs import BaseConfig
 from batdetect2.preprocess import PreprocessingConfig, build_preprocessor
 from batdetect2.preprocess.audio import build_audio_loader
 from batdetect2.typing.preprocess import AudioLoader, PreprocessorProtocol
-from batdetect2.typing.targets import Position, Size
+from batdetect2.typing.targets import Position, ROITargetMapper, Size
 from batdetect2.utils.arrays import spec_to_xarray
 
 __all__ = [
@@ -83,73 +83,6 @@ DEFAULT_ANCHOR = "bottom-left"
 """Default reference position within the geometry ('bottom-left' corner)."""
 
 
-class ROITargetMapper(Protocol):
-    """Protocol defining the interface for ROI-to-target mapping.
-
-    Specifies the `encode` and `decode` methods required for converting a
-    `soundevent.data.SoundEvent` into a target representation (a reference
-    position and a size vector) and for recovering an approximate ROI from that
-    representation.
-
-    Attributes
-    ----------
-    dimension_names : List[str]
-        A list containing the names of the dimensions in the `Size` array
-        returned by `encode` and expected by `decode`.
-    """
-
-    dimension_names: List[str]
-
-    def encode(self, sound_event: data.SoundEvent) -> tuple[Position, Size]:
-        """Encode a SoundEvent's geometry into a position and size.
-
-        Parameters
-        ----------
-        sound_event : data.SoundEvent
-            The input sound event, which must have a geometry attribute.
-
-        Returns
-        -------
-        Tuple[Position, Size]
-            A tuple containing:
-            - The reference position as (time, frequency) coordinates.
-            - A NumPy array with the calculated size dimensions.
-
-        Raises
-        ------
-        ValueError
-            If the sound event does not have a geometry.
-        """
-        ...
-
-    def decode(self, position: Position, size: Size) -> data.Geometry:
-        """Decode a position and size back into a geometric ROI.
-
-        Performs the inverse mapping: takes a reference position and size
-        dimensions and reconstructs a geometric representation.
-
-        Parameters
-        ----------
-        position : Position
-            The reference position (time, frequency).
-        size : Size
-            NumPy array containing the size dimensions, matching the order
-            and meaning specified by `dimension_names`.
-
-        Returns
-        -------
-        soundevent.data.Geometry
-            The reconstructed geometry, typically a `BoundingBox`.
-
-        Raises
-        ------
-        ValueError
-            If the `size` array has an unexpected shape or if reconstruction
-            fails.
-        """
-        ...
-
-
 class AnchorBBoxMapperConfig(BaseConfig):
     """Configuration for `AnchorBBoxMapper`.
 
@@ -475,7 +408,10 @@ class PeakEnergyBBoxMapper(ROITargetMapper):
 
 
 ROIMapperConfig = Annotated[
-    Union[AnchorBBoxMapperConfig, PeakEnergyBBoxMapperConfig],
+    Union[
+        AnchorBBoxMapperConfig,
+        PeakEnergyBBoxMapperConfig,
+    ],
     Field(discriminator="name"),
 ]
 """A discriminated union of all supported ROI mapper configurations.
@@ -553,7 +489,7 @@ def _build_bounding_box(
 ) -> data.BoundingBox:
     """Construct a BoundingBox from a reference point, size, and position type.
 
-    Internal helper for `BBoxEncoder.recover_roi`. Calculates the box
+    Internal helper for `BBoxEncoder.decode`. Calculates the box
     coordinates [start_time, low_freq, end_time, high_freq] based on where
     the input `pos` (time, freq) is located relative to the box (e.g.,
     center, corner).

src/batdetect2/targets/terms.py

@@ -1,34 +1,11 @@
-"""Manages the vocabulary (Terms and Tags) for defining training targets.
+"""Manages the vocabulary for defining training targets."""
 
-This module provides the necessary tools to declare, register, and manage the
-set of `soundevent.data.Term` objects used throughout the `batdetect2.targets`
-sub-package. It establishes a consistent vocabulary for filtering,
-transforming, and classifying sound events based on their annotations (Tags).
-
-The core component is the `TermRegistry`, which maps unique string keys
-(aliases) to specific `Term` definitions. This allows users to refer to complex
-terms using simple, consistent keys in configuration files and code.
-
-Terms can be pre-defined, loaded from the `soundevent.terms` library, defined
-programmatically, or loaded from external configuration files (e.g., YAML).
-"""
-
-from collections.abc import Mapping
-from inspect import getmembers
-from typing import Dict, List, Optional
-
-from pydantic import BaseModel, Field
 from soundevent import data, terms
 
-from batdetect2.configs import load_config
-
 __all__ = [
     "call_type",
     "individual",
     "data_source",
-    "get_tag_from_info",
-    "TermInfo",
-    "TagInfo",
 ]
 
 # The default key used to reference the 'generic_class' term.
@@ -96,430 +73,3 @@ terms.register_term_set(
     ),
     override_existing=True,
 )
-
-
-class TermRegistry(Mapping[str, data.Term]):
-    """Manages a registry mapping unique keys to Term definitions.
-
-    This class acts as the central repository for the vocabulary of terms
-    used within the target definition process. It allows registering terms
-    with simple string keys and retrieving them consistently.
-    """
-
-    def __init__(self, terms: Optional[Dict[str, data.Term]] = None):
-        """Initializes the TermRegistry.
-
-        Parameters
-        ----------
-        terms : dict[str, soundevent.data.Term], optional
-            An optional dictionary of initial key-to-Term mappings
-            to populate the registry with. Defaults to an empty registry.
-        """
-        self._terms: Dict[str, data.Term] = terms or {}
-
-    def __getitem__(self, key: str) -> data.Term:
-        return self._terms[key]
-
-    def __len__(self) -> int:
-        return len(self._terms)
-
-    def __iter__(self):
-        return iter(self._terms)
-
-    def add_term(self, key: str, term: data.Term) -> None:
-        """Adds a Term object to the registry with the specified key.
-
-        Parameters
-        ----------
-        key : str
-            The unique string key to associate with the term.
-        term : soundevent.data.Term
-            The soundevent.data.Term object to register.
-
-        Raises
-        ------
-        KeyError
-            If a term with the provided key already exists in the
-            registry.
-        """
-        if key in self._terms:
-            raise KeyError("A term with the provided key already exists.")
-
-        self._terms[key] = term
-
-    def get_term(self, key: str) -> data.Term:
-        """Retrieves a registered term by its unique key.
-
-        Parameters
-        ----------
-        key : str
-            The unique string key of the term to retrieve.
-
-        Returns
-        -------
-        soundevent.data.Term
-            The corresponding soundevent.data.Term object.
-
-        Raises
-        ------
-        KeyError
-            If no term with the specified key is found, with a
-            helpful message suggesting listing available keys.
-        """
-        try:
-            return self._terms[key]
-        except KeyError as err:
-            raise KeyError(
-                "No term found for key "
-                f"'{key}'. Ensure it is registered or loaded. "
-                f"Available keys: {', '.join(self.get_keys())}"
-            ) from err
-
-    def add_custom_term(
-        self,
-        key: str,
-        name: Optional[str] = None,
-        uri: Optional[str] = None,
-        label: Optional[str] = None,
-        definition: Optional[str] = None,
-    ) -> data.Term:
-        """Creates a new Term from attributes and adds it to the registry.
-
-        This is useful for defining terms directly in code or when loading
-        from configuration files where only attributes are provided.
-
-        If optional fields (`name`, `label`, `definition`) are not provided,
-        reasonable defaults are used (`key` for name/label, "Unknown" for
-        definition).
-
-        Parameters
-        ----------
-        key : str
-            The unique string key for the new term.
-        name : str, optional
-            The name for the new term (defaults to `key`).
-        uri : str, optional
-            The URI for the new term (optional).
-        label : str, optional
-            The display label for the new term (defaults to `key`).
-        definition : str, optional
-            The definition for the new term (defaults to "Unknown").
-
-        Returns
-        -------
-        soundevent.data.Term
-            The newly created and registered soundevent.data.Term object.
-
-        Raises
-        ------
-        KeyError
-            If a term with the provided key already exists.
-        """
-        term = data.Term(
-            name=name or key,
-            label=label or key,
-            uri=uri,
-            definition=definition or "Unknown",
-        )
-        self.add_term(key, term)
-        return term
-
-    def get_keys(self) -> List[str]:
-        """Returns a list of all keys currently registered.
-
-        Returns
-        -------
-        list[str]
-            A list of strings representing the keys of all registered terms.
-        """
-        return list(self._terms.keys())
-
-    def get_terms(self) -> List[data.Term]:
-        """Returns a list of all registered terms.
-
-        Returns
-        -------
-        list[soundevent.data.Term]
-            A list containing all registered Term objects.
-        """
-        return list(self._terms.values())
-
-    def remove_key(self, key: str) -> None:
-        del self._terms[key]
-
-
-default_term_registry = TermRegistry(
-    terms=dict(
-        [
-            *getmembers(terms, lambda x: isinstance(x, data.Term)),
-            ("event", call_type),
-            ("species", terms.scientific_name),
-            ("individual", individual),
-            ("data_source", data_source),
-            (GENERIC_CLASS_KEY, generic_class),
-        ]
-    )
-)
-"""The default, globally accessible TermRegistry instance.
-
-It is pre-populated with standard terms from `soundevent.terms` and common
-terms defined in this module (`call_type`, `individual`, `generic_class`).
-Functions in this module use this registry by default unless another instance
-is explicitly passed.
-"""
-
-
-def get_term_from_key(
-    key: str,
-    term_registry: Optional[TermRegistry] = None,
-) -> data.Term:
-    """Convenience function to retrieve a term by key from a registry.
-
-    Uses the global default registry unless a specific `term_registry`
-    instance is provided.
-
-    Parameters
-    ----------
-    key : str
-        The unique key of the term to retrieve.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance to search in. Defaults to the global
-        `registry`.
-
-    Returns
-    -------
-    soundevent.data.Term
-        The corresponding soundevent.data.Term object.
-
-    Raises
-    ------
-    KeyError
-        If the key is not found in the specified registry.
-    """
-    term = terms.get_term(key)
-
-    if term:
-        return term
-
-    term_registry = term_registry or default_term_registry
-    return term_registry.get_term(key)
-
-
-def get_term_keys(
-    term_registry: TermRegistry = default_term_registry,
-) -> List[str]:
-    """Convenience function to get all registered keys from a registry.
-
-    Uses the global default registry unless a specific `term_registry`
-    instance is provided.
-
-    Parameters
-    ----------
-    term_registry : TermRegistry, optional
-        The TermRegistry instance to query. Defaults to the global `registry`.
-
-    Returns
-    -------
-    list[str]
-        A list of strings representing the keys of all registered terms.
-    """
-    return term_registry.get_keys()
-
-
-def get_terms(
-    term_registry: TermRegistry = default_term_registry,
-) -> List[data.Term]:
-    """Convenience function to get all registered terms from a registry.
-
-    Uses the global default registry unless a specific `term_registry`
-    instance is provided.
-
-    Parameters
-    ----------
-    term_registry : TermRegistry, optional
-        The TermRegistry instance to query. Defaults to the global `registry`.
-
-    Returns
-    -------
-    list[soundevent.data.Term]
-        A list containing all registered Term objects.
-    """
-    return term_registry.get_terms()
-
-
-class TagInfo(BaseModel):
-    """Represents information needed to define a specific Tag.
-
-    This model is typically used in configuration files (e.g., YAML) to
-    specify tags used for filtering, target class definition, or associating
-    tags with output classes. It links a tag value to a term definition
-    via the term's registry key.
-
-    Attributes
-    ----------
-    value : str
-        The value of the tag (e.g., "Myotis myotis", "Echolocation").
-    key : str, default="class"
-        The key (alias) of the term associated with this tag, as
-        registered in the TermRegistry. Defaults to "class", implying
-        it represents a classification target label by default.
-    """
-
-    value: str
-    key: str = GENERIC_CLASS_KEY
-
-
-def get_tag_from_info(
-    tag_info: TagInfo,
-    term_registry: Optional[TermRegistry] = None,
-) -> data.Tag:
-    """Creates a soundevent.data.Tag object from TagInfo data.
-
-    Looks up the term using the key in the provided `tag_info` from the
-    specified registry and constructs a Tag object.
-
-    Parameters
-    ----------
-    tag_info : TagInfo
-        The TagInfo object containing the value and term key.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance to use for term lookup. Defaults to the
-        global `registry`.
-
-    Returns
-    -------
-    soundevent.data.Tag
-        A soundevent.data.Tag object corresponding to the input info.
-
-    Raises
-    ------
-    KeyError
-        If the term key specified in `tag_info.key` is not found
-        in the registry.
-    """
-    term_registry = term_registry or default_term_registry
-    term = get_term_from_key(tag_info.key, term_registry=term_registry)
-    return data.Tag(term=term, value=tag_info.value)
-
-
-class TermInfo(BaseModel):
-    """Represents the definition of a Term within a configuration file.
-
-    This model allows users to define custom terms directly in configuration
-    files (e.g., YAML) which can then be loaded into the TermRegistry.
-    It mirrors the parameters of `TermRegistry.add_custom_term`.
-
-    Attributes
-    ----------
-    key : str
-        The unique key (alias) that will be used to register and
-        reference this term.
-    label : str, optional
-        The optional display label for the term. Defaults to `key`
-        if not provided during registration.
-    name : str, optional
-        The optional formal name for the term. Defaults to `key`
-        if not provided during registration.
-    uri : str, optional
-        The optional URI identifying the term (e.g., from a standard
-        vocabulary).
-    definition : str, optional
-        The optional textual definition of the term. Defaults to
-        "Unknown" if not provided during registration.
-    """
-
-    key: str
-    label: Optional[str] = None
-    name: Optional[str] = None
-    uri: Optional[str] = None
-    definition: Optional[str] = None
-
-
-class TermConfig(BaseModel):
-    """Pydantic schema for loading a list of term definitions from config.
-
-    This model typically corresponds to a section in a configuration file
-    (e.g., YAML) containing a list of term definitions to be registered.
-
-    Attributes
-    ----------
-    terms : list[TermInfo]
-        A list of TermInfo objects, each defining a term to be
-        registered. Defaults to an empty list.
-
-    Examples
-    --------
-    Example YAML structure:
-
-    ```yaml
-    terms:
-      - key: species
-        uri: dwc:scientificName
-        label: Scientific Name
-      - key: my_custom_term
-        name: My Custom Term
-        definition: Describes a specific project attribute.
-      # ... more TermInfo definitions
-    ```
-    """
-
-    terms: List[TermInfo] = Field(default_factory=list)
-
-
-def load_terms_from_config(
-    path: data.PathLike,
-    field: Optional[str] = None,
-    term_registry: TermRegistry = default_term_registry,
-) -> Dict[str, data.Term]:
-    """Loads term definitions from a configuration file and registers them.
-
-    Parses a configuration file (e.g., YAML) using the TermConfig schema,
-    extracts the list of TermInfo definitions, and adds each one as a
-    custom term to the specified TermRegistry instance.
-
-    Parameters
-    ----------
-    path : data.PathLike
-        The path to the configuration file.
-    field : str, optional
-        Optional key indicating a specific section within the config
-        file where the 'terms' list is located. If None, expects the
-        list directly at the top level or within a structure matching
-        TermConfig schema.
-    term_registry : TermRegistry, optional
-        The TermRegistry instance to add the loaded terms to. Defaults to
-        the global `registry`.
-
-    Returns
-    -------
-    dict[str, soundevent.data.Term]
-        A dictionary mapping the keys of the newly added terms to their
-        corresponding Term objects.
-
-    Raises
-    ------
-    FileNotFoundError
-        If the config file path does not exist.
-    pydantic.ValidationError
-        If the config file structure does not match the TermConfig schema.
-    KeyError
-        If a term key loaded from the config conflicts with a key
-        already present in the registry.
-    """
-    data = load_config(path, schema=TermConfig, field=field)
-    return {
-        info.key: term_registry.add_custom_term(
-            info.key,
-            name=info.name,
-            uri=info.uri,
-            label=info.label,
-            definition=info.definition,
-        )
-        for info in data.terms
-    }
-
-
-def register_term(
-    key: str, term: data.Term, registry: TermRegistry = default_term_registry
-) -> None:
-    registry.add_term(key, term)

src/batdetect2/targets/transform.py

@@ -1,708 +0,0 @@
-import importlib
-from functools import partial
-from typing import (
-    Annotated,
-    Callable,
-    Dict,
-    List,
-    Literal,
-    Mapping,
-    Optional,
-    Union,
-)
-
-from pydantic import Field
-from soundevent import data
-
-from batdetect2.configs import BaseConfig, load_config
-from batdetect2.targets.terms import (
-    TagInfo,
-    TermRegistry,
-    get_tag_from_info,
-    get_term_from_key,
-)
-
-__all__ = [
-    "DerivationRegistry",
-    "DeriveTagRule",
-    "MapValueRule",
-    "ReplaceRule",
-    "SoundEventTransformation",
-    "TransformConfig",
-    "build_transform_from_rule",
-    "build_transformation_from_config",
-    "default_derivation_registry",
-    "get_derivation",
-    "load_transformation_config",
-    "load_transformation_from_config",
-    "register_derivation",
-]
-
-SoundEventTransformation = Callable[
-    [data.SoundEventAnnotation], data.SoundEventAnnotation
-]
-"""Type alias for a sound event transformation function.
-
-A function that accepts a sound event annotation object and returns a
-(potentially) modified sound event annotation object. Transformations
-should generally return a copy of the annotation rather than modifying
-it in place.
-"""
-
-
-Derivation = Callable[[str], str]
-"""Type alias for a derivation function.
-
-A function that accepts a single string (typically a tag value) and returns
-a new string (the derived value).
-"""
-
-
-class MapValueRule(BaseConfig):
-    """Configuration for mapping specific values of a source term.
-
-    This rule replaces tags matching a specific term and one of the
-    original values with a new tag (potentially having a different term)
-    containing the corresponding replacement value. Useful for standardizing
-    or grouping tag values.
-
-    Attributes
-    ----------
-    rule_type : Literal["map_value"]
-        Discriminator field identifying this rule type.
-    source_term_key : str
-        The key (registered in `TermRegistry`) of the term whose tags' values
-        should be checked against the `value_mapping`.
-    value_mapping : Dict[str, str]
-        A dictionary mapping original string values to replacement string
-        values. Only tags whose value is a key in this dictionary will be
-        affected.
-    target_term_key : str, optional
-        The key (registered in `TermRegistry`) for the term of the *output*
-        tag. If None (default), the output tag uses the same term as the
-        source (`source_term_key`). If provided, the term of the affected
-        tag is changed to this target term upon replacement.
-    """
-
-    rule_type: Literal["map_value"] = "map_value"
-    source_term_key: str
-    value_mapping: Dict[str, str]
-    target_term_key: Optional[str] = None
-
-
-def map_value_transform(
-    sound_event_annotation: data.SoundEventAnnotation,
-    source_term: data.Term,
-    target_term: data.Term,
-    mapping: Dict[str, str],
-) -> data.SoundEventAnnotation:
-    """Apply a value mapping transformation to an annotation's tags.
-
-    Iterates through the annotation's tags. If a tag matches the `source_term`
-    and its value is found in the `mapping`, it is replaced by a new tag with
-    the `target_term` and the mapped value. Other tags are kept unchanged.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to transform.
-    source_term : data.Term
-        The term of tags whose values should be mapped.
-    target_term : data.Term
-        The term to use for the newly created tags after mapping.
-    mapping : Dict[str, str]
-        The dictionary mapping original values to new values.
-
-    Returns
-    -------
-    data.SoundEventAnnotation
-        A new annotation object with the transformed tags.
-    """
-    tags = []
-
-    for tag in sound_event_annotation.tags:
-        if tag.term != source_term or tag.value not in mapping:
-            tags.append(tag)
-            continue
-
-        new_value = mapping[tag.value]
-        tags.append(data.Tag(term=target_term, value=new_value))
-
-    return sound_event_annotation.model_copy(update=dict(tags=tags))
-
-
-class DeriveTagRule(BaseConfig):
-    """Configuration for deriving a new tag from an existing tag's value.
-
-    This rule applies a specified function (`derivation_function`) to the
-    value of tags matching the `source_term_key`. It then adds a new tag
-    with the `target_term_key` and the derived value.
-
-    Attributes
-    ----------
-    rule_type : Literal["derive_tag"]
-        Discriminator field identifying this rule type.
-    source_term_key : str
-        The key (registered in `TermRegistry`) of the term whose tag values
-        will be used as input to the derivation function.
-    derivation_function : str
-        The name/key identifying the derivation function to use. This can be
-        a key registered in the `DerivationRegistry` or, if
-        `import_derivation` is True, a full Python path like
-        `'my_module.my_submodule.my_function'`.
-    target_term_key : str, optional
-        The key (registered in `TermRegistry`) for the term of the new tag
-        that will be created with the derived value. If None (default), the
-        derived tag uses the same term as the source (`source_term_key`),
-        effectively performing an in-place value transformation.
-    import_derivation : bool, default=False
-        If True, treat `derivation_function` as a Python import path and
-        attempt to dynamically import it if not found in the registry.
-        Requires the function to be accessible in the Python environment.
-    keep_source : bool, default=True
-        If True, the original source tag (whose value was used for derivation)
-        is kept in the annotation's tag list alongside the newly derived tag.
-        If False, the original source tag is removed.
-    """
-
-    rule_type: Literal["derive_tag"] = "derive_tag"
-    source_term_key: str
-    derivation_function: str
-    target_term_key: Optional[str] = None
-    import_derivation: bool = False
-    keep_source: bool = True
-
-
-def derivation_tag_transform(
-    sound_event_annotation: data.SoundEventAnnotation,
-    source_term: data.Term,
-    target_term: data.Term,
-    derivation: Derivation,
-    keep_source: bool = True,
-) -> data.SoundEventAnnotation:
-    """Apply a derivation transformation to an annotation's tags.
-
-    Iterates through the annotation's tags. For each tag matching the
-    `source_term`, its value is passed to the `derivation` function.
-    A new tag is created with the `target_term` and the derived value,
-    and added to the output tag list. The original source tag is kept
-    or discarded based on `keep_source`. Other tags are kept unchanged.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to transform.
-    source_term : data.Term
-        The term of tags whose values serve as input for the derivation.
-    target_term : data.Term
-        The term to use for the newly created derived tags.
-    derivation : Derivation
-        The function to apply to the source tag's value.
-    keep_source : bool, default=True
-        Whether to keep the original source tag in the output.
-
-    Returns
-    -------
-    data.SoundEventAnnotation
-        A new annotation object with the transformed tags (including derived
-                                                           ones).
-    """
-    tags = []
-
-    for tag in sound_event_annotation.tags:
-        if tag.term != source_term:
-            tags.append(tag)
-            continue
-
-        if keep_source:
-            tags.append(tag)
-
-        new_value = derivation(tag.value)
-        tags.append(data.Tag(term=target_term, value=new_value))
-
-    return sound_event_annotation.model_copy(update=dict(tags=tags))
-
-
-class ReplaceRule(BaseConfig):
-    """Configuration for exactly replacing one specific tag with another.
-
-    This rule looks for an exact match of the `original` tag (both term and
-    value) and replaces it with the specified `replacement` tag.
-
-    Attributes
-    ----------
-    rule_type : Literal["replace"]
-        Discriminator field identifying this rule type.
-    original : TagInfo
-        The exact tag to search for, defined using its value and term key.
-    replacement : TagInfo
-        The tag to substitute in place of the original tag, defined using
-        its value and term key.
-    """
-
-    rule_type: Literal["replace"] = "replace"
-    original: TagInfo
-    replacement: TagInfo
-
-
-def replace_tag_transform(
-    sound_event_annotation: data.SoundEventAnnotation,
-    source: data.Tag,
-    target: data.Tag,
-) -> data.SoundEventAnnotation:
-    """Apply an exact tag replacement transformation.
-
-    Iterates through the annotation's tags. If a tag exactly matches the
-    `source` tag, it is replaced by the `target` tag. Other tags are kept
-    unchanged.
-
-    Parameters
-    ----------
-    sound_event_annotation : data.SoundEventAnnotation
-        The annotation to transform.
-    source : data.Tag
-        The exact tag to find and replace.
-    target : data.Tag
-        The tag to replace the source tag with.
-
-    Returns
-    -------
-    data.SoundEventAnnotation
-        A new annotation object with the replaced tag (if found).
-    """
-    tags = []
-
-    for tag in sound_event_annotation.tags:
-        if tag == source:
-            tags.append(target)
-        else:
-            tags.append(tag)
-
-    return sound_event_annotation.model_copy(update=dict(tags=tags))
-
-
-class TransformConfig(BaseConfig):
-    """Configuration model for defining a sequence of transformation rules.
-
-    Attributes
-    ----------
-    rules : List[Union[ReplaceRule, MapValueRule, DeriveTagRule]]
-        A list of transformation rules to apply. The rules are applied
-        sequentially in the order they appear in the list. The output of
-        one rule becomes the input for the next. The `rule_type` field
-        discriminates between the different rule models.
-    """
-
-    rules: List[
-        Annotated[
-            Union[ReplaceRule, MapValueRule, DeriveTagRule],
-            Field(discriminator="rule_type"),
-        ]
-    ] = Field(
-        default_factory=list,
-    )
-
-
-class DerivationRegistry(Mapping[str, Derivation]):
-    """A registry for managing named derivation functions.
-
-    Derivation functions are callables that take a string value and return
-    a transformed string value, used by `DeriveTagRule`. This registry
-    allows functions to be registered with a key and retrieved later.
-    """
-
-    def __init__(self):
-        """Initialize an empty DerivationRegistry."""
-        self._derivations: Dict[str, Derivation] = {}
-
-    def __getitem__(self, key: str) -> Derivation:
-        """Retrieve a derivation function by key."""
-        return self._derivations[key]
-
-    def __len__(self) -> int:
-        """Return the number of registered derivation functions."""
-        return len(self._derivations)
-
-    def __iter__(self):
-        """Return an iterator over the keys of registered functions."""
-        return iter(self._derivations)
-
-    def register(self, key: str, derivation: Derivation) -> None:
-        """Register a derivation function with a unique key.
-
-        Parameters
-        ----------
-        key : str
-            The unique key to associate with the derivation function.
-        derivation : Derivation
-            The callable derivation function (takes str, returns str).
-
-        Raises
-        ------
-        KeyError
-            If a derivation function with the same key is already registered.
-        """
-        if key in self._derivations:
-            raise KeyError(
-                f"A derivation with the provided key {key} already exists"
-            )
-
-        self._derivations[key] = derivation
-
-    def get_derivation(self, key: str) -> Derivation:
-        """Retrieve a derivation function by its registered key.
-
-        Parameters
-        ----------
-        key : str
-            The key of the derivation function to retrieve.
-
-        Returns
-        -------
-        Derivation
-            The requested derivation function.
-
-        Raises
-        ------
-        KeyError
-            If no derivation function with the specified key is registered.
-        """
-        try:
-            return self._derivations[key]
-        except KeyError as err:
-            raise KeyError(
-                f"No derivation with key {key} is registered."
-            ) from err
-
-    def get_keys(self) -> List[str]:
-        """Get a list of all registered derivation function keys.
-
-        Returns
-        -------
-        List[str]
-            The keys of all registered functions.
-        """
-        return list(self._derivations.keys())
-
-    def get_derivations(self) -> List[Derivation]:
-        """Get a list of all registered derivation functions.
-
-        Returns
-        -------
-        List[Derivation]
-            The registered derivation function objects.
-        """
-        return list(self._derivations.values())
-
-
-default_derivation_registry = DerivationRegistry()
-"""Global instance of the DerivationRegistry.
-
-Register custom derivation functions here to make them available by key
-in `DeriveTagRule` configuration.
-"""
-
-
-def get_derivation(
-    key: str,
-    import_derivation: bool = False,
-    registry: Optional[DerivationRegistry] = None,
-):
-    """Retrieve a derivation function by key, optionally importing it.
-
-    First attempts to find the function in the provided `registry`.
-    If not found and `import_derivation` is True, attempts to dynamically
-    import the function using the `key` as a full Python path
-    (e.g., 'my_module.submodule.my_func').
-
-    Parameters
-    ----------
-    key : str
-        The key or Python path of the derivation function.
-    import_derivation : bool, default=False
-        If True, attempt dynamic import if key is not in the registry.
-    registry : DerivationRegistry, optional
-        The registry instance to check first. Defaults to the global
-        `derivation_registry`.
-
-    Returns
-    -------
-    Derivation
-        The requested derivation function.
-
-    Raises
-    ------
-    KeyError
-        If the key is not found in the registry and either
-        `import_derivation` is False or the dynamic import fails.
-    ImportError
-        If dynamic import fails specifically due to module not found.
-    AttributeError
-        If dynamic import fails because the function name isn't in the module.
-    """
-    registry = registry or default_derivation_registry
-
-    if not import_derivation or key in registry:
-        return registry.get_derivation(key)
-
-    try:
-        module_path, func_name = key.rsplit(".", 1)
-        module = importlib.import_module(module_path)
-        func = getattr(module, func_name)
-        return func
-    except ImportError as err:
-        raise KeyError(
-            f"Unable to load derivation '{key}'. Check the path and ensure "
-            "it points to a valid callable function in an importable module."
-        ) from err
-
-
-TranformationRule = Annotated[
-    Union[ReplaceRule, MapValueRule, DeriveTagRule],
-    Field(discriminator="rule_type"),
-]
-
-
-def build_transform_from_rule(
-    rule: TranformationRule,
-    derivation_registry: Optional[DerivationRegistry] = None,
-    term_registry: Optional[TermRegistry] = None,
-) -> SoundEventTransformation:
-    """Build a specific SoundEventTransformation function from a rule config.
-
-    Selects the appropriate transformation logic based on the rule's
-    `rule_type`, fetches necessary terms and derivation functions, and
-    returns a partially applied function ready to transform an annotation.
-
-    Parameters
-    ----------
-    rule : Union[ReplaceRule, MapValueRule, DeriveTagRule]
-        The configuration object for a single transformation rule.
-    registry : DerivationRegistry, optional
-        The derivation registry to use for `DeriveTagRule`. Defaults to the
-        global `derivation_registry`.
-
-    Returns
-    -------
-    SoundEventTransformation
-        A callable that applies the specified rule to a SoundEventAnnotation.
-
-    Raises
-    ------
-    KeyError
-        If required term keys or derivation keys are not found.
-    ValueError
-        If the rule has an unknown `rule_type`.
-    ImportError, AttributeError, TypeError
-        If dynamic import of a derivation function fails.
-    """
-    if rule.rule_type == "replace":
-        source = get_tag_from_info(
-            rule.original,
-            term_registry=term_registry,
-        )
-        target = get_tag_from_info(
-            rule.replacement,
-            term_registry=term_registry,
-        )
-        return partial(replace_tag_transform, source=source, target=target)
-
-    if rule.rule_type == "derive_tag":
-        source_term = get_term_from_key(
-            rule.source_term_key,
-            term_registry=term_registry,
-        )
-        target_term = (
-            get_term_from_key(
-                rule.target_term_key,
-                term_registry=term_registry,
-            )
-            if rule.target_term_key
-            else source_term
-        )
-        derivation = get_derivation(
-            key=rule.derivation_function,
-            import_derivation=rule.import_derivation,
-            registry=derivation_registry,
-        )
-        return partial(
-            derivation_tag_transform,
-            source_term=source_term,
-            target_term=target_term,
-            derivation=derivation,
-            keep_source=rule.keep_source,
-        )
-
-    if rule.rule_type == "map_value":
-        source_term = get_term_from_key(
-            rule.source_term_key,
-            term_registry=term_registry,
-        )
-        target_term = (
-            get_term_from_key(
-                rule.target_term_key,
-                term_registry=term_registry,
-            )
-            if rule.target_term_key
-            else source_term
-        )
-        return partial(
-            map_value_transform,
-            source_term=source_term,
-            target_term=target_term,
-            mapping=rule.value_mapping,
-        )
-
-    # Handle unknown rule type
-    valid_options = ["replace", "derive_tag", "map_value"]
-    # Should be caught by Pydantic validation, but good practice
-    raise ValueError(
-        f"Invalid transform rule type '{getattr(rule, 'rule_type', 'N/A')}'. "
-        f"Valid options are: {valid_options}"
-    )
-
-
-def build_transformation_from_config(
-    config: TransformConfig,
-    derivation_registry: Optional[DerivationRegistry] = None,
-    term_registry: Optional[TermRegistry] = None,
-) -> SoundEventTransformation:
-    """Build a composite transformation function from a TransformConfig.
-
-    Creates a sequence of individual transformation functions based on the
-    rules defined in the configuration. Returns a single function that
-    applies these transformations sequentially to an annotation.
-
-    Parameters
-    ----------
-    config : TransformConfig
-        The configuration object containing the list of transformation rules.
-    derivation_reg : DerivationRegistry, optional
-        The derivation registry to use when building `DeriveTagRule`
-        transformations. Defaults to the global `derivation_registry`.
-
-    Returns
-    -------
-    SoundEventTransformation
-        A single function that applies all configured transformations in order.
-    """
-
-    transforms = [
-        build_transform_from_rule(
-            rule,
-            derivation_registry=derivation_registry,
-            term_registry=term_registry,
-        )
-        for rule in config.rules
-    ]
-
-    return partial(apply_sequence_of_transforms, transforms=transforms)
-
-
-def apply_sequence_of_transforms(
-    sound_event_annotation: data.SoundEventAnnotation,
-    transforms: list[SoundEventTransformation],
-) -> data.SoundEventAnnotation:
-    for transform in transforms:
-        sound_event_annotation = transform(sound_event_annotation)
-    return sound_event_annotation
-
-
-def load_transformation_config(
-    path: data.PathLike, field: Optional[str] = None
-) -> TransformConfig:
-    """Load the transformation configuration from a file.
-
-    Parameters
-    ----------
-    path : data.PathLike
-        Path to the configuration file (YAML).
-    field : str, optional
-        If the transformation configuration is nested under a specific key
-        in the file, specify the key here. Defaults to None.
-
-    Returns
-    -------
-    TransformConfig
-        The loaded and validated transformation configuration object.
-
-    Raises
-    ------
-    FileNotFoundError
-        If the config file path does not exist.
-    pydantic.ValidationError
-        If the config file structure does not match the TransformConfig schema.
-    """
-    return load_config(path=path, schema=TransformConfig, field=field)
-
-
-def load_transformation_from_config(
-    path: data.PathLike,
-    field: Optional[str] = None,
-    derivation_registry: Optional[DerivationRegistry] = None,
-    term_registry: Optional[TermRegistry] = None,
-) -> SoundEventTransformation:
-    """Load transformation config from a file and build the final function.
-
-    This is a convenience function that combines loading the configuration
-    and building the final callable transformation function that applies
-    all rules sequentially.
-
-    Parameters
-    ----------
-    path : data.PathLike
-        Path to the configuration file (YAML).
-    field : str, optional
-        If the transformation configuration is nested under a specific key
-        in the file, specify the key here. Defaults to None.
-
-    Returns
-    -------
-    SoundEventTransformation
-        The final composite transformation function ready to be used.
-
-    Raises
-    ------
-    FileNotFoundError
-        If the config file path does not exist.
-    pydantic.ValidationError
-        If the config file structure does not match the TransformConfig schema.
-    KeyError
-        If required term keys or derivation keys specified in the config
-        are not found during the build process.
-    ImportError, AttributeError, TypeError
-        If dynamic import of a derivation function specified in the config
-        fails.
-    """
-    config = load_transformation_config(path=path, field=field)
-    return build_transformation_from_config(
-        config,
-        derivation_registry=derivation_registry,
-        term_registry=term_registry,
-    )
-
-
-def register_derivation(
-    key: str,
-    derivation: Derivation,
-    derivation_registry: Optional[DerivationRegistry] = None,
-) -> None:
-    """Register a new derivation function in the global registry.
-
-    Parameters
-    ----------
-    key : str
-        The unique key to associate with the derivation function.
-    derivation : Derivation
-        The callable derivation function (takes str, returns str).
-    derivation_registry : DerivationRegistry, optional
-        The registry instance to register the derivation function with.
-        Defaults to the global `derivation_registry`.
-
-    Raises
-    ------
-    KeyError
-        If a derivation function with the same key is already registered.
-    """
-    derivation_registry = derivation_registry or default_derivation_registry
-    derivation_registry.register(key, derivation)

src/batdetect2/train/__init__.py

@@ -33,10 +33,6 @@ from batdetect2.train.losses import (
     SizeLossConfig,
     build_loss,
 )
-from batdetect2.train.preprocess import (
-    generate_train_example,
-    preprocess_annotations,
-)
 from batdetect2.train.train import (
     build_train_dataset,
     build_train_loader,
@@ -74,14 +70,12 @@ __all__ = [
     "build_trainer",
     "build_val_dataset",
     "build_val_loader",
-    "generate_train_example",
     "load_full_training_config",
     "load_label_config",
     "load_train_config",
     "mask_frequency",
     "mask_time",
     "mix_audio",
-    "preprocess_annotations",
     "scale_volume",
     "select_subclip",
     "train",

src/batdetect2/train/augmentations.py

@@ -44,7 +44,7 @@ AudioSource = Callable[[float], tuple[torch.Tensor, data.ClipAnnotation]]
 class MixAugmentationConfig(BaseConfig):
     """Configuration for MixUp augmentation (mixing two examples)."""
 
-    augmentation_type: Literal["mix_audio"] = "mix_audio"
+    name: Literal["mix_audio"] = "mix_audio"
 
     probability: float = 0.2
     """Probability of applying this augmentation to an example."""
@@ -140,7 +140,7 @@ def combine_clip_annotations(
 class EchoAugmentationConfig(BaseConfig):
     """Configuration for adding synthetic echo/reverb."""
 
-    augmentation_type: Literal["add_echo"] = "add_echo"
+    name: Literal["add_echo"] = "add_echo"
     probability: float = 0.2
     max_delay: float = 0.005
     min_weight: float = 0.0
@@ -187,7 +187,7 @@ def add_echo(
 class VolumeAugmentationConfig(BaseConfig):
     """Configuration for random volume scaling of the spectrogram."""
 
-    augmentation_type: Literal["scale_volume"] = "scale_volume"
+    name: Literal["scale_volume"] = "scale_volume"
     probability: float = 0.2
     min_scaling: float = 0.0
     max_scaling: float = 2.0
@@ -214,7 +214,7 @@ def scale_volume(spec: torch.Tensor, factor: float) -> torch.Tensor:
 
 
 class WarpAugmentationConfig(BaseConfig):
-    augmentation_type: Literal["warp"] = "warp"
+    name: Literal["warp"] = "warp"
     probability: float = 0.2
     delta: float = 0.04
 
@@ -296,7 +296,7 @@ def warp_spectrogram(
 
 
 class TimeMaskAugmentationConfig(BaseConfig):
-    augmentation_type: Literal["mask_time"] = "mask_time"
+    name: Literal["mask_time"] = "mask_time"
     probability: float = 0.2
     max_perc: float = 0.05
     max_masks: int = 3
@@ -353,7 +353,7 @@ def mask_time(
 
 
 class FrequencyMaskAugmentationConfig(BaseConfig):
-    augmentation_type: Literal["mask_freq"] = "mask_freq"
+    name: Literal["mask_freq"] = "mask_freq"
     probability: float = 0.2
     max_perc: float = 0.10
     max_masks: int = 3
@@ -414,7 +414,7 @@ AudioAugmentationConfig = Annotated[
         MixAugmentationConfig,
         EchoAugmentationConfig,
     ],
-    Field(discriminator="augmentation_type"),
+    Field(discriminator="name"),
 ]
 
 
@@ -425,7 +425,7 @@ SpectrogramAugmentationConfig = Annotated[
         FrequencyMaskAugmentationConfig,
         TimeMaskAugmentationConfig,
     ],
-    Field(discriminator="augmentation_type"),
+    Field(discriminator="name"),
 ]
 
 AugmentationConfig = Annotated[
@@ -437,7 +437,7 @@ AugmentationConfig = Annotated[
         FrequencyMaskAugmentationConfig,
         TimeMaskAugmentationConfig,
     ],
-    Field(discriminator="augmentation_type"),
+    Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of individual augmentation config."""
 
@@ -485,7 +485,7 @@ def build_augmentation_from_config(
     audio_source: Optional[AudioSource] = None,
 ) -> Optional[Augmentation]:
     """Factory function to build a single augmentation from its config."""
-    if config.augmentation_type == "mix_audio":
+    if config.name == "mix_audio":
         if audio_source is None:
             warnings.warn(
                 "Mix audio augmentation ('mix_audio') requires an "
@@ -500,31 +500,31 @@ def build_augmentation_from_config(
             max_weight=config.max_weight,
         )
 
-    if config.augmentation_type == "add_echo":
+    if config.name == "add_echo":
         return AddEcho(
             max_delay=int(config.max_delay * samplerate),
             min_weight=config.min_weight,
             max_weight=config.max_weight,
         )
 
-    if config.augmentation_type == "scale_volume":
+    if config.name == "scale_volume":
         return ScaleVolume(
             max_scaling=config.max_scaling,
             min_scaling=config.min_scaling,
         )
 
-    if config.augmentation_type == "warp":
+    if config.name == "warp":
         return WarpSpectrogram(
             delta=config.delta,
         )
 
-    if config.augmentation_type == "mask_time":
+    if config.name == "mask_time":
         return MaskTime(
             max_perc=config.max_perc,
             max_masks=config.max_masks,
         )
 
-    if config.augmentation_type == "mask_freq":
+    if config.name == "mask_freq":
         return MaskFrequency(
             max_perc=config.max_perc,
             max_masks=config.max_masks,

src/batdetect2/train/config.py

@@ -6,7 +6,6 @@ from soundevent import data
 from batdetect2.configs import BaseConfig, load_config
 from batdetect2.evaluate import EvaluationConfig
 from batdetect2.models import ModelConfig
-from batdetect2.targets import TargetConfig
 from batdetect2.train.augmentations import (
     DEFAULT_AUGMENTATION_CONFIG,
     AugmentationsConfig,
@@ -75,7 +74,6 @@ class TrainingConfig(BaseConfig):
     cliping: ClipingConfig = Field(default_factory=ClipingConfig)
     trainer: PLTrainerConfig = Field(default_factory=PLTrainerConfig)
     logger: LoggerConfig = Field(default_factory=CSVLoggerConfig)
-    targets: TargetConfig = Field(default_factory=TargetConfig)
     labels: LabelConfig = Field(default_factory=LabelConfig)
 
 

src/batdetect2/train/lightning.py

@@ -1,9 +1,14 @@
+from typing import Optional, Tuple
+
 import lightning as L
 import torch
+from soundevent.data import PathLike
 from torch.optim.adam import Adam
 from torch.optim.lr_scheduler import CosineAnnealingLR
 
-from batdetect2.models import Model
+from batdetect2.models import Model, build_model
+from batdetect2.train.config import FullTrainingConfig
+from batdetect2.train.losses import build_loss
 from batdetect2.typing import ModelOutput, TrainExample
 
 __all__ = [
@@ -16,22 +21,28 @@ class TrainingModule(L.LightningModule):
 
     def __init__(
         self,
-        model: Model,
-        loss: torch.nn.Module,
+        config: FullTrainingConfig,
         learning_rate: float = 0.001,
         t_max: int = 100,
+        model: Optional[Model] = None,
+        loss: Optional[torch.nn.Module] = None,
     ):
         super().__init__()
 
+        self.save_hyperparameters(logger=False)
+
+        self.config = config
         self.learning_rate = learning_rate
         self.t_max = t_max
 
+        if loss is None:
+            loss = build_loss(self.config.train.loss)
+
+        if model is None:
+            model = build_model(self.config)
+
         self.loss = loss
         self.model = model
-        self.save_hyperparameters(logger=False)
-
-    def forward(self, spec: torch.Tensor) -> ModelOutput:
-        return self.model(spec)
 
     def training_step(self, batch: TrainExample):
         outputs = self.model.detector(batch.spec)
@@ -59,3 +70,10 @@ class TrainingModule(L.LightningModule):
         optimizer = Adam(self.parameters(), lr=self.learning_rate)
         scheduler = CosineAnnealingLR(optimizer, T_max=self.t_max)
         return [optimizer], [scheduler]
+
+
+def load_model_from_checkpoint(
+    path: PathLike,
+) -> Tuple[Model, FullTrainingConfig]:
+    module = TrainingModule.load_from_checkpoint(path)  # type: ignore
+    return module.model, module.config

src/batdetect2/train/logging.py

@@ -5,10 +5,11 @@ import numpy as np
 from lightning.pytorch.loggers import Logger, MLFlowLogger, TensorBoardLogger
 from loguru import logger
 from pydantic import Field
+from soundevent import data
 
 from batdetect2.configs import BaseConfig
 
-DEFAULT_LOGS_DIR: str = "logs"
+DEFAULT_LOGS_DIR: str = "outputs"
 
 
 class DVCLiveConfig(BaseConfig):
@@ -31,7 +32,7 @@ class CSVLoggerConfig(BaseConfig):
 class TensorBoardLoggerConfig(BaseConfig):
     logger_type: Literal["tensorboard"] = "tensorboard"
     save_dir: str = DEFAULT_LOGS_DIR
-    name: Optional[str] = "default"
+    name: Optional[str] = "logs"
     version: Optional[str] = None
     log_graph: bool = False
 
@@ -57,7 +58,10 @@ LoggerConfig = Annotated[
 ]
 
 
-def create_dvclive_logger(config: DVCLiveConfig) -> Logger:
+def create_dvclive_logger(
+    config: DVCLiveConfig,
+    log_dir: Optional[data.PathLike] = None,
+) -> Logger:
     try:
         from dvclive.lightning import DVCLiveLogger  # type: ignore
     except ImportError as error:
@@ -68,7 +72,7 @@ def create_dvclive_logger(config: DVCLiveConfig) -> Logger:
         ) from error
 
     return DVCLiveLogger(
-        dir=config.dir,
+        dir=log_dir if log_dir is not None else config.dir,
         run_name=config.run_name,
         prefix=config.prefix,
         log_model=config.log_model,
@@ -76,29 +80,38 @@ def create_dvclive_logger(config: DVCLiveConfig) -> Logger:
     )
 
 
-def create_csv_logger(config: CSVLoggerConfig) -> Logger:
+def create_csv_logger(
+    config: CSVLoggerConfig,
+    log_dir: Optional[data.PathLike] = None,
+) -> Logger:
     from lightning.pytorch.loggers import CSVLogger
 
     return CSVLogger(
-        save_dir=config.save_dir,
+        save_dir=str(log_dir) if log_dir is not None else config.save_dir,
         name=config.name,
         version=config.version,
         flush_logs_every_n_steps=config.flush_logs_every_n_steps,
     )
 
 
-def create_tensorboard_logger(config: TensorBoardLoggerConfig) -> Logger:
+def create_tensorboard_logger(
+    config: TensorBoardLoggerConfig,
+    log_dir: Optional[data.PathLike] = None,
+) -> Logger:
     from lightning.pytorch.loggers import TensorBoardLogger
 
     return TensorBoardLogger(
-        save_dir=config.save_dir,
+        save_dir=str(log_dir) if log_dir is not None else config.save_dir,
         name=config.name,
         version=config.version,
         log_graph=config.log_graph,
     )
 
 
-def create_mlflow_logger(config: MLFlowLoggerConfig) -> Logger:
+def create_mlflow_logger(
+    config: MLFlowLoggerConfig,
+    log_dir: Optional[data.PathLike] = None,
+) -> Logger:
     try:
         from lightning.pytorch.loggers import MLFlowLogger
     except ImportError as error:
@@ -111,7 +124,7 @@ def create_mlflow_logger(config: MLFlowLoggerConfig) -> Logger:
     return MLFlowLogger(
         experiment_name=config.experiment_name,
         run_name=config.run_name,
-        save_dir=config.save_dir,
+        save_dir=str(log_dir) if log_dir is not None else config.save_dir,
         tracking_uri=config.tracking_uri,
         tags=config.tags,
         log_model=config.log_model,
@@ -126,7 +139,10 @@ LOGGER_FACTORY = {
 }
 
 
-def build_logger(config: LoggerConfig) -> Logger:
+def build_logger(
+    config: LoggerConfig,
+    log_dir: Optional[data.PathLike] = None,
+) -> Logger:
     """
     Creates a logger instance from a validated Pydantic config object.
     """
@@ -141,7 +157,7 @@ def build_logger(config: LoggerConfig) -> Logger:
 
     creation_func = LOGGER_FACTORY[logger_type]
 
-    return creation_func(config)
+    return creation_func(config, log_dir=log_dir)
 
 
 def get_image_plotter(logger: Logger):

src/batdetect2/train/preprocess.py

@@ -1,243 +0,0 @@
-"""Preprocesses datasets for BatDetect2 model training."""
-
-import os
-from pathlib import Path
-from typing import Callable, List, Optional, Sequence, TypedDict
-
-import numpy as np
-import torch
-import torch.utils.data
-from loguru import logger
-from pydantic import Field
-from soundevent import data
-from tqdm import tqdm
-
-from batdetect2.configs import BaseConfig, load_config
-from batdetect2.data.datasets import Dataset
-from batdetect2.preprocess import PreprocessingConfig, build_preprocessor
-from batdetect2.preprocess.audio import build_audio_loader
-from batdetect2.targets import TargetConfig, build_targets
-from batdetect2.train.labels import LabelConfig, build_clip_labeler
-from batdetect2.typing import ClipLabeller, PreprocessorProtocol
-from batdetect2.typing.preprocess import AudioLoader
-from batdetect2.typing.train import PreprocessedExample
-
-__all__ = [
-    "preprocess_annotations",
-    "generate_train_example",
-    "preprocess_dataset",
-    "TrainPreprocessConfig",
-    "load_train_preprocessing_config",
-    "save_preprocessed_example",
-    "load_preprocessed_example",
-]
-
-FilenameFn = Callable[[data.ClipAnnotation], str]
-"""Type alias for a function that generates an output filename."""
-
-
-class TrainPreprocessConfig(BaseConfig):
-    preprocess: PreprocessingConfig = Field(
-        default_factory=PreprocessingConfig
-    )
-    targets: TargetConfig = Field(default_factory=TargetConfig)
-    labels: LabelConfig = Field(default_factory=LabelConfig)
-
-
-def load_train_preprocessing_config(
-    path: data.PathLike,
-    field: Optional[str] = None,
-) -> TrainPreprocessConfig:
-    return load_config(path=path, schema=TrainPreprocessConfig, field=field)
-
-
-def preprocess_dataset(
-    dataset: Dataset,
-    config: TrainPreprocessConfig,
-    output: Path,
-    max_workers: Optional[int] = None,
-) -> None:
-    targets = build_targets(config=config.targets)
-    preprocessor = build_preprocessor(config=config.preprocess)
-    labeller = build_clip_labeler(
-        targets,
-        min_freq=preprocessor.min_freq,
-        max_freq=preprocessor.max_freq,
-        config=config.labels,
-    )
-    audio_loader = build_audio_loader(config=config.preprocess.audio)
-
-    if not output.exists():
-        logger.debug("Creating directory {directory}", directory=output)
-        output.mkdir(parents=True)
-
-    preprocess_annotations(
-        dataset,
-        output_dir=output,
-        audio_loader=audio_loader,
-        preprocessor=preprocessor,
-        labeller=labeller,
-        max_workers=max_workers,
-    )
-
-
-class Example(TypedDict):
-    audio: torch.Tensor
-    spectrogram: torch.Tensor
-    detection_heatmap: torch.Tensor
-    class_heatmap: torch.Tensor
-    size_heatmap: torch.Tensor
-
-
-def generate_train_example(
-    clip_annotation: data.ClipAnnotation,
-    audio_loader: AudioLoader,
-    preprocessor: PreprocessorProtocol,
-    labeller: ClipLabeller,
-) -> PreprocessedExample:
-    """Generate a complete training example for one annotation."""
-    wave = torch.tensor(
-        audio_loader.load_clip(clip_annotation.clip)
-    ).unsqueeze(0)
-    spectrogram = preprocessor(wave.unsqueeze(0)).squeeze(0)
-    heatmaps = labeller(clip_annotation, spectrogram)
-    return PreprocessedExample(
-        audio=wave,
-        spectrogram=spectrogram,
-        detection_heatmap=heatmaps.detection,
-        class_heatmap=heatmaps.classes,
-        size_heatmap=heatmaps.size,
-    )
-
-
-class PreprocessingDataset(torch.utils.data.Dataset):
-    def __init__(
-        self,
-        clips: Dataset,
-        audio_loader: AudioLoader,
-        preprocessor: PreprocessorProtocol,
-        labeller: ClipLabeller,
-        filename_fn: FilenameFn,
-        output_dir: Path,
-        force: bool = False,
-    ):
-        self.clips = clips
-        self.audio_loader = audio_loader
-        self.preprocessor = preprocessor
-        self.labeller = labeller
-        self.filename_fn = filename_fn
-        self.output_dir = output_dir
-        self.force = force
-
-    def __getitem__(self, idx) -> int:
-        clip_annotation = self.clips[idx]
-
-        filename = self.filename_fn(clip_annotation)
-
-        path = self.output_dir / filename
-
-        if path.exists() and not self.force:
-            return idx
-
-        if not path.parent.exists():
-            path.parent.mkdir()
-
-        example = generate_train_example(
-            clip_annotation,
-            audio_loader=self.audio_loader,
-            preprocessor=self.preprocessor,
-            labeller=self.labeller,
-        )
-
-        save_preprocessed_example(example, clip_annotation, path)
-
-        return idx
-
-    def __len__(self) -> int:
-        return len(self.clips)
-
-
-def save_preprocessed_example(
-    example: PreprocessedExample,
-    clip_annotation: data.ClipAnnotation,
-    path: data.PathLike,
-) -> None:
-    np.savez_compressed(
-        path,
-        audio=example.audio.numpy(),
-        spectrogram=example.spectrogram.numpy(),
-        detection_heatmap=example.detection_heatmap.numpy(),
-        class_heatmap=example.class_heatmap.numpy(),
-        size_heatmap=example.size_heatmap.numpy(),
-        clip_annotation=clip_annotation,
-    )
-
-
-def load_preprocessed_example(path: data.PathLike) -> PreprocessedExample:
-    item = np.load(path, mmap_mode="r+")
-    return PreprocessedExample(
-        audio=torch.tensor(item["audio"]),
-        spectrogram=torch.tensor(item["spectrogram"]),
-        size_heatmap=torch.tensor(item["size_heatmap"]),
-        detection_heatmap=torch.tensor(item["detection_heatmap"]),
-        class_heatmap=torch.tensor(item["class_heatmap"]),
-    )
-
-
-def list_preprocessed_files(
-    directory: data.PathLike, extension: str = ".npz"
-) -> List[Path]:
-    return list(Path(directory).glob(f"*{extension}"))
-
-
-def _get_filename(clip_annotation: data.ClipAnnotation) -> str:
-    """Generate a default output filename based on the annotation UUID."""
-    return f"{clip_annotation.uuid}"
-
-
-def preprocess_annotations(
-    clip_annotations: Sequence[data.ClipAnnotation],
-    output_dir: data.PathLike,
-    preprocessor: PreprocessorProtocol,
-    audio_loader: AudioLoader,
-    labeller: ClipLabeller,
-    filename_fn: FilenameFn = _get_filename,
-    max_workers: Optional[int] = None,
-) -> None:
-    """Preprocess a sequence of ClipAnnotations and save results to disk."""
-    output_dir = Path(output_dir)
-
-    if not output_dir.is_dir():
-        logger.info(
-            "Creating output directory: {output_dir}", output_dir=output_dir
-        )
-        output_dir.mkdir(parents=True)
-
-    logger.info(
-        "Starting preprocessing of {num_annotations} annotations with {max_workers} workers.",
-        num_annotations=len(clip_annotations),
-        max_workers=max_workers or "all available",
-    )
-
-    if max_workers is None:
-        max_workers = os.cpu_count() or 0
-
-    dataset = PreprocessingDataset(
-        clips=list(clip_annotations),
-        audio_loader=audio_loader,
-        preprocessor=preprocessor,
-        labeller=labeller,
-        output_dir=Path(output_dir),
-        filename_fn=filename_fn,
-    )
-
-    loader = torch.utils.data.DataLoader(
-        dataset,
-        batch_size=1,
-        shuffle=False,
-        num_workers=max_workers,
-        prefetch_factor=16,
-    )
-
-    for _ in tqdm(loader, total=len(dataset)):
-        pass

src/batdetect2/train/train.py

@@ -14,9 +14,9 @@ from batdetect2.evaluate.metrics import (
     ClassificationMeanAveragePrecision,
     DetectionAveragePrecision,
 )
-from batdetect2.models import Model, build_model
 from batdetect2.plotting.clips import AudioLoader, build_audio_loader
 from batdetect2.preprocess import build_preprocessor
+from batdetect2.targets import build_targets
 from batdetect2.train.augmentations import (
     RandomAudioSource,
     build_augmentations,
@@ -28,7 +28,6 @@ from batdetect2.train.dataset import TrainingDataset, ValidationDataset
 from batdetect2.train.labels import build_clip_labeler
 from batdetect2.train.lightning import TrainingModule
 from batdetect2.train.logging import build_logger
-from batdetect2.train.losses import build_loss
 from batdetect2.typing import (
     PreprocessorProtocol,
     TargetProtocol,
@@ -54,19 +53,21 @@ def train(
     model_path: Optional[data.PathLike] = None,
     train_workers: Optional[int] = None,
     val_workers: Optional[int] = None,
+    checkpoint_dir: Optional[data.PathLike] = None,
+    log_dir: Optional[data.PathLike] = None,
 ):
     config = config or FullTrainingConfig()
 
-    model = build_model(config=config)
+    targets = build_targets(config.targets)
 
-    trainer = build_trainer(config, targets=model.targets)
+    preprocessor = build_preprocessor(config.preprocess)
 
     audio_loader = build_audio_loader(config=config.preprocess.audio)
 
     labeller = build_clip_labeler(
-        model.targets,
-        min_freq=model.preprocessor.min_freq,
-        max_freq=model.preprocessor.max_freq,
+        targets,
+        min_freq=preprocessor.min_freq,
+        max_freq=preprocessor.max_freq,
         config=config.train.labels,
     )
 
@@ -74,7 +75,7 @@ def train(
         train_annotations,
         audio_loader=audio_loader,
         labeller=labeller,
-        preprocessor=build_preprocessor(config.preprocess),
+        preprocessor=preprocessor,
         config=config.train,
         num_workers=train_workers,
     )
@@ -84,7 +85,7 @@ def train(
             val_annotations,
             audio_loader=audio_loader,
             labeller=labeller,
-            preprocessor=build_preprocessor(config.preprocess),
+            preprocessor=preprocessor,
             config=config.train,
             num_workers=val_workers,
         )
@@ -97,11 +98,17 @@ def train(
         module = TrainingModule.load_from_checkpoint(model_path)  # type: ignore
     else:
         module = build_training_module(
-            model,
             config,
-            batches_per_epoch=len(train_dataloader),
+            t_max=config.train.t_max * len(train_dataloader),
         )
 
+    trainer = build_trainer(
+        config,
+        targets=targets,
+        checkpoint_dir=checkpoint_dir,
+        log_dir=log_dir,
+    )
+
     logger.info("Starting main training loop...")
     trainer.fit(
         module,
@@ -112,16 +119,14 @@ def train(
 
 
 def build_training_module(
-    model: Model,
-    config: FullTrainingConfig,
-    batches_per_epoch: int,
+    config: Optional[FullTrainingConfig] = None,
+    t_max: int = 200,
 ) -> TrainingModule:
-    loss = build_loss(config=config.train.loss)
+    config = config or FullTrainingConfig()
     return TrainingModule(
-        model=model,
-        loss=loss,
+        config=config,
         learning_rate=config.train.learning_rate,
-        t_max=config.train.t_max * batches_per_epoch,
+        t_max=t_max,
     )
 
 
@@ -129,10 +134,14 @@ def build_trainer_callbacks(
     targets: TargetProtocol,
     preprocessor: PreprocessorProtocol,
     config: EvaluationConfig,
+    checkpoint_dir: Optional[data.PathLike] = None,
 ) -> List[Callback]:
+    if checkpoint_dir is None:
+        checkpoint_dir = "outputs/checkpoints"
+
     return [
         ModelCheckpoint(
-            dirpath="outputs/checkpoints",
+            dirpath=str(checkpoint_dir),
             save_top_k=1,
             monitor="total_loss/val",
         ),
@@ -153,15 +162,22 @@ def build_trainer_callbacks(
 def build_trainer(
     conf: FullTrainingConfig,
     targets: TargetProtocol,
+    checkpoint_dir: Optional[data.PathLike] = None,
+    log_dir: Optional[data.PathLike] = None,
 ) -> Trainer:
     trainer_conf = conf.train.trainer
     logger.opt(lazy=True).debug(
         "Building trainer with config: \n{config}",
         config=lambda: trainer_conf.to_yaml_string(exclude_none=True),
     )
-    train_logger = build_logger(conf.train.logger)
+    train_logger = build_logger(conf.train.logger, log_dir=log_dir)
 
-    train_logger.log_hyperparams(conf.model_dump(mode="json"))
+    train_logger.log_hyperparams(
+        conf.model_dump(
+            mode="json",
+            exclude_none=True,
+        )
+    )
 
     return Trainer(
         **trainer_conf.model_dump(exclude_none=True),
@@ -170,6 +186,7 @@ def build_trainer(
             targets,
             config=conf.evaluation,
             preprocessor=build_preprocessor(conf.preprocess),
+            checkpoint_dir=checkpoint_dir,
         ),
     )
 

src/batdetect2/typing/targets.py

@@ -67,8 +67,7 @@ class TargetProtocol(Protocol):
     This protocol outlines the standard attributes and methods for an object
     that encapsulates the complete, configured process for handling sound event
     annotations (both tags and geometry). It defines how to:
-    - Filter relevant annotations.
-    - Transform annotation tags.
+    - Select relevant annotations.
     - Encode an annotation into a specific target class name.
     - Decode a class name back into representative tags.
     - Extract a target reference position from an annotation's geometry (ROI).
@@ -121,26 +120,6 @@ class TargetProtocol(Protocol):
         """
         ...
 
-    def transform(
-        self,
-        sound_event: data.SoundEventAnnotation,
-    ) -> data.SoundEventAnnotation:
-        """Apply tag transformations to an annotation.
-
-        Parameters
-        ----------
-        sound_event : data.SoundEventAnnotation
-            The annotation whose tags should be transformed.
-
-        Returns
-        -------
-        data.SoundEventAnnotation
-            A new annotation object with the transformed tags. Implementations
-            should return the original annotation object if no transformations
-            were configured.
-        """
-        ...
-
     def encode_class(
         self,
         sound_event: data.SoundEventAnnotation,
@@ -248,3 +227,70 @@ class TargetProtocol(Protocol):
             if reconstruction fails based on the configured position type.
         """
         ...
+
+
+class ROITargetMapper(Protocol):
+    """Protocol defining the interface for ROI-to-target mapping.
+
+    Specifies the `encode` and `decode` methods required for converting a
+    `soundevent.data.SoundEvent` into a target representation (a reference
+    position and a size vector) and for recovering an approximate ROI from that
+    representation.
+
+    Attributes
+    ----------
+    dimension_names : List[str]
+        A list containing the names of the dimensions in the `Size` array
+        returned by `encode` and expected by `decode`.
+    """
+
+    dimension_names: List[str]
+
+    def encode(self, sound_event: data.SoundEvent) -> tuple[Position, Size]:
+        """Encode a SoundEvent's geometry into a position and size.
+
+        Parameters
+        ----------
+        sound_event : data.SoundEvent
+            The input sound event, which must have a geometry attribute.
+
+        Returns
+        -------
+        Tuple[Position, Size]
+            A tuple containing:
+            - The reference position as (time, frequency) coordinates.
+            - A NumPy array with the calculated size dimensions.
+
+        Raises
+        ------
+        ValueError
+            If the sound event does not have a geometry.
+        """
+        ...
+
+    def decode(self, position: Position, size: Size) -> data.Geometry:
+        """Decode a position and size back into a geometric ROI.
+
+        Performs the inverse mapping: takes a reference position and size
+        dimensions and reconstructs a geometric representation.
+
+        Parameters
+        ----------
+        position : Position
+            The reference position (time, frequency).
+        size : Size
+            NumPy array containing the size dimensions, matching the order
+            and meaning specified by `dimension_names`.
+
+        Returns
+        -------
+        soundevent.data.Geometry
+            The reconstructed geometry, typically a `BoundingBox`.
+
+        Raises
+        ------
+        ValueError
+            If the `size` array has an unexpected shape or if reconstruction
+            fails.
+        """
+        ...

tests/conftest.py

@@ -15,13 +15,10 @@ from batdetect2.preprocess import build_preprocessor
 from batdetect2.preprocess.audio import build_audio_loader
 from batdetect2.targets import (
     TargetConfig,
-    TermRegistry,
     build_targets,
     call_type,
 )
-from batdetect2.targets.classes import ClassesConfig, TargetClass
-from batdetect2.targets.filtering import FilterConfig, FilterRule
-from batdetect2.targets.terms import TagInfo
+from batdetect2.targets.classes import TargetClassConfig
 from batdetect2.train.clips import build_clipper
 from batdetect2.train.labels import build_clip_labeler
 from batdetect2.typing import (
@@ -355,18 +352,6 @@ def create_annotation_project():
     return factory
 
 
-@pytest.fixture
-def sample_term_registry() -> TermRegistry:
-    """Fixture for a sample TermRegistry."""
-    registry = TermRegistry()
-    registry.add_custom_term("class")
-    registry.add_custom_term("order")
-    registry.add_custom_term("species")
-    registry.add_custom_term("call_type")
-    registry.add_custom_term("quality")
-    return registry
-
-
 @pytest.fixture
 def sample_preprocessor() -> PreprocessorProtocol:
     return build_preprocessor()
@@ -378,56 +363,45 @@ def sample_audio_loader() -> AudioLoader:
 
 
 @pytest.fixture
-def bat_tag() -> TagInfo:
-    return TagInfo(key="class", value="bat")
+def bat_tag() -> data.Tag:
+    return data.Tag(key="class", value="bat")
 
 
 @pytest.fixture
-def noise_tag() -> TagInfo:
-    return TagInfo(key="class", value="noise")
+def noise_tag() -> data.Tag:
+    return data.Tag(key="class", value="noise")
 
 
 @pytest.fixture
-def myomyo_tag() -> TagInfo:
-    return TagInfo(key="species", value="Myotis myotis")
+def myomyo_tag() -> data.Tag:
+    return data.Tag(key="species", value="Myotis myotis")
 
 
 @pytest.fixture
-def pippip_tag() -> TagInfo:
-    return TagInfo(key="species", value="Pipistrellus pipistrellus")
+def pippip_tag() -> data.Tag:
+    return data.Tag(key="species", value="Pipistrellus pipistrellus")
 
 
 @pytest.fixture
 def sample_target_config(
-    sample_term_registry: TermRegistry,
-    bat_tag: TagInfo,
-    noise_tag: TagInfo,
-    myomyo_tag: TagInfo,
-    pippip_tag: TagInfo,
+    bat_tag: data.Tag,
+    myomyo_tag: data.Tag,
+    pippip_tag: data.Tag,
 ) -> TargetConfig:
     return TargetConfig(
-        filtering=FilterConfig(
-            rules=[FilterRule(match_type="exclude", tags=[noise_tag])]
-        ),
-        classes=ClassesConfig(
-            classes=[
-                TargetClass(name="pippip", tags=[pippip_tag]),
-                TargetClass(name="myomyo", tags=[myomyo_tag]),
-            ],
-            generic_class=[bat_tag],
-        ),
+        detection_target=TargetClassConfig(name="bat", tags=[bat_tag]),
+        classification_targets=[
+            TargetClassConfig(name="pippip", tags=[pippip_tag]),
+            TargetClassConfig(name="myomyo", tags=[myomyo_tag]),
+        ],
     )
 
 
 @pytest.fixture
 def sample_targets(
     sample_target_config: TargetConfig,
-    sample_term_registry: TermRegistry,
 ) -> TargetProtocol:
-    return build_targets(
-        sample_target_config,
-        term_registry=sample_term_registry,
-    )
+    return build_targets(sample_target_config)
 
 
 @pytest.fixture
@@ -443,10 +417,8 @@ def sample_labeller(
 
 
 @pytest.fixture
-def sample_clipper(
-    sample_preprocessor: PreprocessorProtocol,
-) -> ClipperProtocol:
-    return build_clipper(preprocessor=sample_preprocessor)
+def sample_clipper() -> ClipperProtocol:
+    return build_clipper()
 
 
 @pytest.fixture

tests/test_data/test_transforms/test_conditions.py

@@ -0,0 +1,516 @@
+import textwrap
+
+import pytest
+import yaml
+from pydantic import TypeAdapter
+from soundevent import data
+
+from batdetect2.data.conditions import (
+    SoundEventConditionConfig,
+    build_sound_event_condition,
+)
+
+
+def build_condition_from_str(content):
+    content = textwrap.dedent(content)
+    content = yaml.safe_load(content)
+    config = TypeAdapter(SoundEventConditionConfig).validate_python(content)
+    return build_sound_event_condition(config)
+
+
+def test_has_tag(sound_event: data.SoundEvent):
+    condition = build_condition_from_str("""
+    name: has_tag
+    tag:
+        key: species
+        value: Myotis myotis
+    """)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
+    )
+    assert condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[data.Tag(key="species", value="Eptesicus fuscus")],  # type: ignore
+    )
+    assert not condition(sound_event_annotation)
+
+
+def test_has_all_tags(sound_event: data.SoundEvent):
+    condition = build_condition_from_str("""
+    name: has_all_tags
+    tags:
+        - key: species
+          value: Myotis myotis
+        - key: event
+          value: Echolocation
+    """)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
+    )
+    assert not condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[
+            data.Tag(key="species", value="Eptesicus fuscus"),  # type: ignore
+            data.Tag(key="event", value="Echolocation"),  # type: ignore
+        ],
+    )
+    assert not condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[
+            data.Tag(key="species", value="Myotis myotis"),  # type: ignore
+            data.Tag(key="event", value="Echolocation"),  # type: ignore
+        ],
+    )
+    assert condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[
+            data.Tag(key="species", value="Myotis myotis"),  # type: ignore
+            data.Tag(key="event", value="Echolocation"),  # type: ignore
+            data.Tag(key="sex", value="Female"),  # type: ignore
+        ],
+    )
+    assert condition(sound_event_annotation)
+
+
+def test_has_any_tags(sound_event: data.SoundEvent):
+    condition = build_condition_from_str("""
+    name: has_any_tag
+    tags:
+        - key: species
+          value: Myotis myotis
+        - key: event
+          value: Echolocation
+    """)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
+    )
+    assert condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[
+            data.Tag(key="species", value="Eptesicus fuscus"),  # type: ignore
+            data.Tag(key="event", value="Echolocation"),  # type: ignore
+        ],
+    )
+    assert condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[
+            data.Tag(key="species", value="Myotis myotis"),  # type: ignore
+            data.Tag(key="event", value="Echolocation"),  # type: ignore
+        ],
+    )
+    assert condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[
+            data.Tag(key="species", value="Eptesicus fuscus"),  # type: ignore
+            data.Tag(key="event", value="Social"),  # type: ignore
+        ],
+    )
+    assert not condition(sound_event_annotation)
+
+
+def test_not(sound_event: data.SoundEvent):
+    condition = build_condition_from_str("""
+    name: not
+    condition:
+        name: has_tag
+        tag:
+            key: species
+            value: Myotis myotis
+    """)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
+    )
+    assert not condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[data.Tag(key="species", value="Eptesicus fuscus")],  # type: ignore
+    )
+    assert condition(sound_event_annotation)
+
+    sound_event_annotation = data.SoundEventAnnotation(
+        sound_event=sound_event,
+        tags=[
+            data.Tag(key="species", value="Myotis myotis"),  # type: ignore
+            data.Tag(key="event", value="Echolocation"),  # type: ignore
+        ],
+    )
+    assert not condition(sound_event_annotation)
+
+
+def test_duration(recording: data.Recording):
+    se1 = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            recording=recording, geometry=data.TimeInterval(coordinates=[0, 1])
+        ),
+    )
+    se2 = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            recording=recording, geometry=data.TimeInterval(coordinates=[0, 2])
+        ),
+    )
+    se3 = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            recording=recording, geometry=data.TimeInterval(coordinates=[0, 3])
+        ),
+    )
+
+    condition = build_condition_from_str("""
+    name: duration
+    operator: lt
+    seconds: 2
+    """)
+    assert condition(se1)
+    assert not condition(se2)
+    assert not condition(se3)
+
+    condition = build_condition_from_str("""
+    name: duration
+    operator: lte
+    seconds: 2
+    """)
+
+    assert condition(se1)
+    assert condition(se2)
+    assert not condition(se3)
+
+    condition = build_condition_from_str("""
+    name: duration
+    operator: gt
+    seconds: 2
+    """)
+
+    assert not condition(se1)
+    assert not condition(se2)
+    assert condition(se3)
+
+    condition = build_condition_from_str("""
+    name: duration
+    operator: gte
+    seconds: 2
+    """)
+
+    assert not condition(se1)
+    assert condition(se2)
+    assert condition(se3)
+
+    condition = build_condition_from_str("""
+    name: duration
+    operator: eq
+    seconds: 2
+    """)
+
+    assert not condition(se1)
+    assert condition(se2)
+    assert not condition(se3)
+
+
+def test_frequency(recording: data.Recording):
+    se12 = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            recording=recording,
+            geometry=data.BoundingBox(coordinates=[0, 100, 1, 200]),
+        ),
+    )
+    se13 = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            recording=recording,
+            geometry=data.BoundingBox(coordinates=[0, 100, 2, 300]),
+        ),
+    )
+    se14 = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            recording=recording,
+            geometry=data.BoundingBox(coordinates=[0, 100, 3, 400]),
+        ),
+    )
+    se24 = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            recording=recording,
+            geometry=data.BoundingBox(coordinates=[0, 200, 3, 400]),
+        ),
+    )
+    se34 = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            recording=recording,
+            geometry=data.BoundingBox(coordinates=[0, 300, 3, 400]),
+        ),
+    )
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: high
+    operator: lt
+    hertz: 300
+    """)
+    assert condition(se12)
+    assert not condition(se13)
+    assert not condition(se14)
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: high
+    operator: lte
+    hertz: 300
+    """)
+
+    assert condition(se12)
+    assert condition(se13)
+    assert not condition(se14)
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: high
+    operator: gt
+    hertz: 300
+    """)
+
+    assert not condition(se12)
+    assert not condition(se13)
+    assert condition(se14)
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: high
+    operator: gte
+    hertz: 300
+    """)
+
+    assert not condition(se12)
+    assert condition(se13)
+    assert condition(se14)
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: high
+    operator: eq
+    hertz: 300
+    """)
+
+    assert not condition(se12)
+    assert condition(se13)
+    assert not condition(se14)
+
+    # LOW
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: low
+    operator: lt
+    hertz: 200
+    """)
+    assert condition(se14)
+    assert not condition(se24)
+    assert not condition(se34)
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: low
+    operator: lte
+    hertz: 200
+    """)
+
+    assert condition(se14)
+    assert condition(se24)
+    assert not condition(se34)
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: low
+    operator: gt
+    hertz: 200
+    """)
+
+    assert not condition(se14)
+    assert not condition(se24)
+    assert condition(se34)
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: low
+    operator: gte
+    hertz: 200
+    """)
+
+    assert not condition(se14)
+    assert condition(se24)
+    assert condition(se34)
+
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: low
+    operator: eq
+    hertz: 200
+    """)
+
+    assert not condition(se14)
+    assert condition(se24)
+    assert not condition(se34)
+
+
+def test_frequency_is_false_for_temporal_geometries(recording: data.Recording):
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: low
+    operator: eq
+    hertz: 200
+    """)
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeInterval(coordinates=[0, 3]),
+            recording=recording,
+        )
+    )
+    assert not condition(se)
+
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeStamp(coordinates=3),
+            recording=recording,
+        )
+    )
+    assert not condition(se)
+
+
+def test_has_tags_fails_if_empty():
+    with pytest.raises(ValueError):
+        build_condition_from_str("""
+        name: has_tags
+        tags: []
+        """)
+
+
+def test_frequency_is_false_if_no_geometry(recording: data.Recording):
+    condition = build_condition_from_str("""
+    name: frequency
+    boundary: low
+    operator: eq
+    hertz: 200
+    """)
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(geometry=None, recording=recording)
+    )
+    assert not condition(se)
+
+
+def test_duration_is_false_if_no_geometry(recording: data.Recording):
+    condition = build_condition_from_str("""
+    name: duration
+    operator: eq
+    seconds: 1
+    """)
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(geometry=None, recording=recording)
+    )
+    assert not condition(se)
+
+
+def test_all_of(recording: data.Recording):
+    condition = build_condition_from_str("""
+    name: all_of
+    conditions:
+        - name: has_tag
+          tag:
+            key: species
+            value: Myotis myotis
+        - name: duration
+          operator: lt
+          seconds: 1
+    """)
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeInterval(coordinates=[0, 0.5]),
+            recording=recording,
+        ),
+        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
+    )
+    assert condition(se)
+
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeInterval(coordinates=[0, 2]),
+            recording=recording,
+        ),
+        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
+    )
+    assert not condition(se)
+
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeInterval(coordinates=[0, 0.5]),
+            recording=recording,
+        ),
+        tags=[data.Tag(key="species", value="Eptesicus fuscus")],  # type: ignore
+    )
+    assert not condition(se)
+
+
+def test_any_of(recording: data.Recording):
+    condition = build_condition_from_str("""
+    name: any_of
+    conditions:
+        - name: has_tag
+          tag:
+            key: species
+            value: Myotis myotis
+        - name: duration
+          operator: lt
+          seconds: 1
+    """)
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeInterval(coordinates=[0, 2]),
+            recording=recording,
+        ),
+        tags=[data.Tag(key="species", value="Eptesicus fuscus")],  # type: ignore
+    )
+    assert not condition(se)
+
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeInterval(coordinates=[0, 0.5]),
+            recording=recording,
+        ),
+        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
+    )
+    assert condition(se)
+
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeInterval(coordinates=[0, 2]),
+            recording=recording,
+        ),
+        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
+    )
+    assert condition(se)
+
+    se = data.SoundEventAnnotation(
+        sound_event=data.SoundEvent(
+            geometry=data.TimeInterval(coordinates=[0, 0.5]),
+            recording=recording,
+        ),
+        tags=[data.Tag(key="species", value="Eptesicus fuscus")],  # type: ignore
+    )
+    assert condition(se)

tests/test_targets/test_classes.py

@@ -3,26 +3,15 @@ from typing import Callable
 from uuid import uuid4
 
 import pytest
-from pydantic import ValidationError
 from soundevent import data
 from soundevent.terms import get_term
 
 from batdetect2.targets.classes import (
-    DEFAULT_SPECIES_LIST,
-    ClassesConfig,
-    TargetClass,
-    _get_default_class_name,
-    _get_default_classes,
-    build_generic_class_tags,
+    TargetClassConfig,
     build_sound_event_decoder,
     build_sound_event_encoder,
     get_class_names_from_config,
-    is_target_class,
-    load_classes_config,
-    load_decoder_from_config,
-    load_encoder_from_config,
 )
-from batdetect2.targets.terms import TagInfo
 
 
 @pytest.fixture
@@ -33,8 +22,8 @@ def sample_annotation(
     return data.SoundEventAnnotation(
         sound_event=sound_event,
         tags=[
-            data.Tag(key="species", value="Pipistrellus pipistrellus"),  # type: ignore
-            data.Tag(key="quality", value="Good"),  # type: ignore
+            data.Tag(key="species", value="Pipistrellus pipistrellus"),
+            data.Tag(key="quality", value="Good"),
         ],
     )
 
@@ -51,291 +40,71 @@ def create_temp_yaml(tmp_path: Path) -> Callable[[str], Path]:
     return factory
 
 
-def test_target_class_creation():
-    target_class = TargetClass(
-        name="pippip",
-        tags=[TagInfo(key="species", value="Pipistrellus pipistrellus")],
-    )
-    assert target_class.name == "pippip"
-    assert target_class.tags[0].key == "species"
-    assert target_class.tags[0].value == "Pipistrellus pipistrellus"
-    assert target_class.match_type == "all"
-
-
-def test_classes_config_creation():
-    target_class = TargetClass(
-        name="pippip",
-        tags=[TagInfo(key="species", value="Pipistrellus pipistrellus")],
-    )
-    config = ClassesConfig(classes=[target_class])
-    assert len(config.classes) == 1
-    assert config.classes[0].name == "pippip"
-
-
-def test_classes_config_unique_names():
-    target_class1 = TargetClass(
-        name="pippip",
-        tags=[TagInfo(key="species", value="Pipistrellus pipistrellus")],
-    )
-    target_class2 = TargetClass(
-        name="myodau",
-        tags=[TagInfo(key="species", value="Myotis daubentonii")],
-    )
-    ClassesConfig(classes=[target_class1, target_class2])  # No error
-
-
-def test_classes_config_non_unique_names():
-    target_class1 = TargetClass(
-        name="pippip",
-        tags=[TagInfo(key="species", value="Pipistrellus pipistrellus")],
-    )
-    target_class2 = TargetClass(
-        name="pippip",
-        tags=[TagInfo(key="species", value="Myotis daubentonii")],
-    )
-    with pytest.raises(ValidationError):
-        ClassesConfig(classes=[target_class1, target_class2])
-
-
-def test_load_classes_config_valid(create_temp_yaml: Callable[[str], Path]):
-    yaml_content = """
-    classes:
-      - name: pippip
-        tags:
-          - key: species
-            value: Pipistrellus pipistrellus
-    """
-    temp_yaml_path = create_temp_yaml(yaml_content)
-    config = load_classes_config(temp_yaml_path)
-    assert len(config.classes) == 1
-    assert config.classes[0].name == "pippip"
-
-
-def test_load_classes_config_invalid(create_temp_yaml: Callable[[str], Path]):
-    yaml_content = """
-    classes:
-      - name: pippip
-        tags:
-          - key: species
-            value: Pipistrellus pipistrellus
-      - name: pippip
-        tags:
-          - key: species
-            value: Myotis daubentonii
-    """
-    temp_yaml_path = create_temp_yaml(yaml_content)
-    with pytest.raises(ValidationError):
-        load_classes_config(temp_yaml_path)
-
-
-def test_is_target_class_match_all(
-    sample_annotation: data.SoundEventAnnotation,
-):
-    tags = {
-        data.Tag(key="species", value="Pipistrellus pipistrellus"),  # type: ignore
-        data.Tag(key="quality", value="Good"),  # type: ignore
-    }
-    assert is_target_class(sample_annotation, tags, match_all=True) is True
-
-    tags = {data.Tag(key="species", value="Pipistrellus pipistrellus")}  # type: ignore
-    assert is_target_class(sample_annotation, tags, match_all=True) is True
-
-    tags = {data.Tag(key="species", value="Myotis daubentonii")}  # type: ignore
-    assert is_target_class(sample_annotation, tags, match_all=True) is False
-
-
-def test_is_target_class_match_any(
-    sample_annotation: data.SoundEventAnnotation,
-):
-    tags = {
-        data.Tag(key="species", value="Pipistrellus pipistrellus"),  # type: ignore
-        data.Tag(key="quality", value="Good"),  # type: ignore
-    }
-    assert is_target_class(sample_annotation, tags, match_all=False) is True
-
-    tags = {data.Tag(key="species", value="Pipistrellus pipistrellus")}  # type: ignore
-    assert is_target_class(sample_annotation, tags, match_all=False) is True
-
-    tags = {data.Tag(key="species", value="Myotis daubentonii")}  # type: ignore
-    assert is_target_class(sample_annotation, tags, match_all=False) is False
-
-
 def test_get_class_names_from_config():
-    target_class1 = TargetClass(
+    target_class1 = TargetClassConfig(
         name="pippip",
-        tags=[TagInfo(key="species", value="Pipistrellus pipistrellus")],
+        tags=[data.Tag(key="species", value="Pipistrellus pipistrellus")],
     )
-    target_class2 = TargetClass(
+    target_class2 = TargetClassConfig(
         name="myodau",
-        tags=[TagInfo(key="species", value="Myotis daubentonii")],
+        tags=[data.Tag(key="species", value="Myotis daubentonii")],
     )
-    config = ClassesConfig(classes=[target_class1, target_class2])
-    names = get_class_names_from_config(config)
+    names = get_class_names_from_config([target_class1, target_class2])
     assert names == ["pippip", "myodau"]
 
 
 def test_build_encoder_from_config(
     sample_annotation: data.SoundEventAnnotation,
 ):
-    config = ClassesConfig(
-        classes=[
-            TargetClass(
-                name="pippip",
-                tags=[
-                    TagInfo(key="species", value="Pipistrellus pipistrellus")
-                ],
-            )
-        ]
-    )
-    encoder = build_sound_event_encoder(config)
+    classes = [
+        TargetClassConfig(
+            name="pippip",
+            tags=[data.Tag(key="species", value="Pipistrellus pipistrellus")],
+        )
+    ]
+    encoder = build_sound_event_encoder(classes)
     result = encoder(sample_annotation)
     assert result == "pippip"
 
-    config = ClassesConfig(classes=[])
-    encoder = build_sound_event_encoder(config)
+    classes = []
+    encoder = build_sound_event_encoder(classes)
     result = encoder(sample_annotation)
     assert result is None
 
 
-def test_load_encoder_from_config_valid(
-    sample_annotation: data.SoundEventAnnotation,
-    create_temp_yaml: Callable[[str], Path],
-):
-    yaml_content = """
-    classes:
-      - name: pippip
-        tags:
-          - key: species
-            value: Pipistrellus pipistrellus
-    """
-    temp_yaml_path = create_temp_yaml(yaml_content)
-    encoder = load_encoder_from_config(temp_yaml_path)
-    # We cannot directly compare the function, so we test it.
-    result = encoder(sample_annotation)  # type: ignore
-    assert result == "pippip"
-
-
-def test_load_encoder_from_config_invalid(
-    create_temp_yaml: Callable[[str], Path],
-):
-    yaml_content = """
-    classes:
-      - name: pippip
-        tags:
-          - key: invalid_key
-            value: Pipistrellus pipistrellus
-    """
-    temp_yaml_path = create_temp_yaml(yaml_content)
-    with pytest.raises(KeyError):
-        load_encoder_from_config(temp_yaml_path)
-
-
-def test_get_default_class_name():
-    assert _get_default_class_name("Myotis daubentonii") == "myodau"
-
-
-def test_get_default_classes():
-    default_classes = _get_default_classes()
-    assert len(default_classes) == len(DEFAULT_SPECIES_LIST)
-    first_class = default_classes[0]
-    assert isinstance(first_class, TargetClass)
-    assert first_class.name == _get_default_class_name(DEFAULT_SPECIES_LIST[0])
-    assert first_class.tags[0].key == "class"
-    assert first_class.tags[0].value == DEFAULT_SPECIES_LIST[0]
-
-
 def test_build_decoder_from_config():
-    config = ClassesConfig(
-        classes=[
-            TargetClass(
-                name="pippip",
-                tags=[
-                    TagInfo(key="species", value="Pipistrellus pipistrellus")
-                ],
-                output_tags=[TagInfo(key="call_type", value="Echolocation")],
-            )
-        ],
-        generic_class=[TagInfo(key="order", value="Chiroptera")],
-    )
-    decoder = build_sound_event_decoder(config)
+    classes = [
+        TargetClassConfig(
+            name="pippip",
+            tags=[data.Tag(key="species", value="Pipistrellus pipistrellus")],
+            assign_tags=[data.Tag(key="call_type", value="Echolocation")],
+        )
+    ]
+    decoder = build_sound_event_decoder(classes)
     tags = decoder("pippip")
     assert len(tags) == 1
     assert tags[0].term == get_term("event")
     assert tags[0].value == "Echolocation"
 
     # Test when output_tags is None, should fall back to tags
-    config = ClassesConfig(
-        classes=[
-            TargetClass(
-                name="pippip",
-                tags=[
-                    TagInfo(key="species", value="Pipistrellus pipistrellus")
-                ],
-            )
-        ],
-        generic_class=[TagInfo(key="order", value="Chiroptera")],
-    )
-    decoder = build_sound_event_decoder(config)
+    classes = [
+        TargetClassConfig(
+            name="pippip",
+            tags=[data.Tag(key="species", value="Pipistrellus pipistrellus")],
+        )
+    ]
+    decoder = build_sound_event_decoder(classes)
     tags = decoder("pippip")
     assert len(tags) == 1
     assert tags[0].term == get_term("species")
     assert tags[0].value == "Pipistrellus pipistrellus"
 
     # Test raise_on_unmapped=True
-    decoder = build_sound_event_decoder(config, raise_on_unmapped=True)
+    decoder = build_sound_event_decoder(classes, raise_on_unmapped=True)
     with pytest.raises(ValueError):
         decoder("unknown_class")
 
     # Test raise_on_unmapped=False
-    decoder = build_sound_event_decoder(config, raise_on_unmapped=False)
+    decoder = build_sound_event_decoder(classes, raise_on_unmapped=False)
     tags = decoder("unknown_class")
     assert len(tags) == 0
-
-
-def test_load_decoder_from_config_valid(
-    create_temp_yaml: Callable[[str], Path],
-):
-    yaml_content = """
-    classes:
-      - name: pippip
-        tags:
-          - key: species
-            value: Pipistrellus pipistrellus
-        output_tags:
-          - key: call_type
-            value: Echolocation
-    generic_class:
-      - key: order
-        value: Chiroptera
-    """
-    temp_yaml_path = create_temp_yaml(yaml_content)
-    decoder = load_decoder_from_config(
-        temp_yaml_path,
-    )
-    tags = decoder("pippip")
-    assert len(tags) == 1
-    assert tags[0].term == get_term("call_type")
-    assert tags[0].value == "Echolocation"
-
-
-def test_build_generic_class_tags_from_config():
-    config = ClassesConfig(
-        classes=[
-            TargetClass(
-                name="pippip",
-                tags=[
-                    TagInfo(key="species", value="Pipistrellus pipistrellus")
-                ],
-            )
-        ],
-        generic_class=[
-            TagInfo(key="order", value="Chiroptera"),
-            TagInfo(key="call_type", value="Echolocation"),
-        ],
-    )
-    generic_tags = build_generic_class_tags(config)
-    assert len(generic_tags) == 2
-    assert generic_tags[0].term == get_term("order")
-    assert generic_tags[0].value == "Chiroptera"
-    assert generic_tags[1].term == get_term("call_type")
-    assert generic_tags[1].value == "Echolocation"

tests/test_targets/test_filtering.py

@@ -1,210 +0,0 @@
-from pathlib import Path
-from typing import Callable, List, Set
-
-import pytest
-from soundevent import data
-
-from batdetect2.targets import build_targets
-from batdetect2.targets.filtering import (
-    FilterConfig,
-    FilterRule,
-    build_filter_from_rule,
-    build_sound_event_filter,
-    contains_tags,
-    does_not_have_tags,
-    equal_tags,
-    has_any_tag,
-    load_filter_config,
-    load_filter_from_config,
-)
-from batdetect2.targets.terms import TagInfo, generic_class
-
-
-@pytest.fixture
-def create_annotation(
-    sound_event: data.SoundEvent,
-) -> Callable[[List[str]], data.SoundEventAnnotation]:
-    """Helper function to create a SoundEventAnnotation with given tags."""
-
-    def factory(tags: List[str]) -> data.SoundEventAnnotation:
-        return data.SoundEventAnnotation(
-            sound_event=sound_event,
-            tags=[
-                data.Tag(
-                    term=generic_class,
-                    value=tag,
-                )
-                for tag in tags
-            ],
-        )
-
-    return factory
-
-
-def create_tag_set(tags: List[str]) -> Set[data.Tag]:
-    """Helper function to create a set of data.Tag objects from a list of strings."""
-    return {
-        data.Tag(
-            term=generic_class,
-            value=tag,
-        )
-        for tag in tags
-    }
-
-
-def test_has_any_tag(create_annotation):
-    annotation = create_annotation(["tag1", "tag2"])
-    tags = create_tag_set(["tag1", "tag3"])
-    assert has_any_tag(annotation, tags) is True
-
-    annotation = create_annotation(["tag2", "tag4"])
-    tags = create_tag_set(["tag1", "tag3"])
-    assert has_any_tag(annotation, tags) is False
-
-
-def test_contains_tags(create_annotation):
-    annotation = create_annotation(["tag1", "tag2", "tag3"])
-    tags = create_tag_set(["tag1", "tag2"])
-    assert contains_tags(annotation, tags) is True
-
-    annotation = create_annotation(["tag1", "tag2"])
-    tags = create_tag_set(["tag1", "tag2", "tag3"])
-    assert contains_tags(annotation, tags) is False
-
-
-def test_does_not_have_tags(create_annotation):
-    annotation = create_annotation(["tag1", "tag2"])
-    tags = create_tag_set(["tag3", "tag4"])
-    assert does_not_have_tags(annotation, tags) is True
-
-    annotation = create_annotation(["tag1", "tag2"])
-    tags = create_tag_set(["tag1", "tag3"])
-    assert does_not_have_tags(annotation, tags) is False
-
-
-def test_equal_tags(create_annotation):
-    annotation = create_annotation(["tag1", "tag2"])
-    tags = create_tag_set(["tag1", "tag2"])
-    assert equal_tags(annotation, tags) is True
-
-    annotation = create_annotation(["tag1", "tag2", "tag3"])
-    tags = create_tag_set(["tag1", "tag2"])
-    assert equal_tags(annotation, tags) is False
-
-
-def test_build_filter_from_rule():
-    rule_any = FilterRule(match_type="any", tags=[TagInfo(value="tag1")])
-    build_filter_from_rule(rule_any)
-
-    rule_all = FilterRule(match_type="all", tags=[TagInfo(value="tag1")])
-    build_filter_from_rule(rule_all)
-
-    rule_exclude = FilterRule(
-        match_type="exclude", tags=[TagInfo(value="tag1")]
-    )
-    build_filter_from_rule(rule_exclude)
-
-    rule_equal = FilterRule(match_type="equal", tags=[TagInfo(value="tag1")])
-    build_filter_from_rule(rule_equal)
-
-    with pytest.raises(ValueError):
-        FilterRule(match_type="invalid", tags=[TagInfo(value="tag1")])  # type: ignore
-        build_filter_from_rule(
-            FilterRule(match_type="invalid", tags=[TagInfo(value="tag1")])  # type: ignore
-        )
-
-
-def test_build_filter_from_config(create_annotation):
-    config = FilterConfig(
-        rules=[
-            FilterRule(match_type="any", tags=[TagInfo(value="tag1")]),
-            FilterRule(match_type="any", tags=[TagInfo(value="tag2")]),
-        ]
-    )
-    filter_from_config = build_sound_event_filter(config)
-
-    annotation_pass = create_annotation(["tag1", "tag2"])
-    assert filter_from_config(annotation_pass)
-
-    annotation_fail = create_annotation(["tag1"])
-    assert not filter_from_config(annotation_fail)
-
-
-def test_load_filter_config(tmp_path: Path):
-    test_config_path = tmp_path / "filtering.yaml"
-    test_config_path.write_text(
-        """
-rules:
-    - match_type: any
-      tags:
-        - value: tag1
-    """
-    )
-    config = load_filter_config(test_config_path)
-    assert isinstance(config, FilterConfig)
-    assert len(config.rules) == 1
-    rule = config.rules[0]
-    assert rule.match_type == "any"
-    assert len(rule.tags) == 1
-    assert rule.tags[0].value == "tag1"
-
-
-def test_load_filter_from_config(tmp_path: Path, create_annotation):
-    test_config_path = tmp_path / "filtering.yaml"
-    test_config_path.write_text(
-        """
-rules:
-    - match_type: any
-      tags:
-        - value: tag1
-    """
-    )
-
-    filter_result = load_filter_from_config(test_config_path)
-    annotation = create_annotation(["tag1", "tag3"])
-    assert filter_result(annotation)
-
-    test_config_path = tmp_path / "filtering.yaml"
-    test_config_path.write_text(
-        """
-rules:
-    - match_type: any
-      tags:
-        - value: tag2
-    """
-    )
-
-    filter_result = load_filter_from_config(test_config_path)
-    annotation = create_annotation(["tag1", "tag3"])
-    assert filter_result(annotation) is False
-
-
-def test_default_filtering_over_example_dataset(
-    example_annotations: List[data.ClipAnnotation],
-):
-    targets = build_targets()
-
-    clip1 = example_annotations[0]
-    clip2 = example_annotations[1]
-    clip3 = example_annotations[2]
-
-    assert (
-        sum(
-            [targets.filter(sound_event) for sound_event in clip1.sound_events]
-        )
-        == 9
-    )
-
-    assert (
-        sum(
-            [targets.filter(sound_event) for sound_event in clip2.sound_events]
-        )
-        == 15
-    )
-
-    assert (
-        sum(
-            [targets.filter(sound_event) for sound_event in clip3.sound_events]
-        )
-        == 20
-    )

tests/test_targets/test_rois.py

@@ -8,6 +8,11 @@ from batdetect2.preprocess import (
     build_preprocessor,
 )
 from batdetect2.preprocess.audio import build_audio_loader
+from batdetect2.preprocess.spectrogram import (
+    ScaleAmplitudeConfig,
+    SpectralMeanSubstractionConfig,
+    SpectrogramConfig,
+)
 from batdetect2.targets.rois import (
     DEFAULT_ANCHOR,
     DEFAULT_FREQUENCY_SCALE,
@@ -548,14 +553,7 @@ def test_peak_energy_bbox_mapper_encode_decode_roundtrip(generate_whistle):
 
     # Instantiate the mapper.
     preprocessor = build_preprocessor(
-        PreprocessingConfig.model_validate(
-            {
-                "spectrogram": {
-                    "pcen": None,
-                    "spectral_mean_substraction": False,
-                }
-            }
-        )
+        PreprocessingConfig(spectrogram=SpectrogramConfig(transforms=[]))
     )
     audio_loader = build_audio_loader()
     mapper = PeakEnergyBBoxMapper(
@@ -597,14 +595,13 @@ def test_build_roi_mapper_for_anchor_bbox():
 
 def test_build_roi_mapper_for_peak_energy_bbox():
     # Given
-    preproc_config = PreprocessingConfig.model_validate(
-        {
-            "spectrogram": {
-                "pcen": None,
-                "spectral_mean_substraction": True,
-                "scale": "dB",
-            }
-        }
+    preproc_config = PreprocessingConfig(
+        spectrogram=SpectrogramConfig(
+            transforms=[
+                ScaleAmplitudeConfig(scale="db"),
+                SpectralMeanSubstractionConfig(),
+            ]
+        ),
     )
     config = PeakEnergyBBoxMapperConfig(
         loading_buffer=0.99,

tests/test_targets/test_targets.py

@@ -1,46 +1,55 @@
 from collections.abc import Callable
 from pathlib import Path
 
-from soundevent import data
+from soundevent import data, terms
 
 from batdetect2.targets import build_targets, load_target_config
-from batdetect2.targets.terms import get_term_from_key
 
 
 def test_can_override_default_roi_mapper_per_class(
     create_temp_yaml: Callable[..., Path],
     recording: data.Recording,
-    sample_term_registry,
 ):
     yaml_content = """
+    detection_target:
+        name: bat
+        match_if:
+            name: has_tag
+            tag:
+                key: order
+                value: Chiroptera
+        assign_tags:
+            - key: order
+              value: Chiroptera
+
+    classification_targets:
+      - name: pippip
+        tags:
+            - key: species
+              value: Pipistrellus pipistrellus
+
+      - name: myomyo
+        tags:
+            - key: species
+              value: Myotis myotis
+        roi:
+            name: anchor_bbox
+            anchor: top-left
+
     roi:
         name: anchor_bbox
         anchor: bottom-left
-    classes:
-        classes:
-          - name: pippip
-            tags:
-              - key: species
-                value: Pipistrellus pipistrellus
-          - name: myomyo
-            tags:
-              - key: species
-                value: Myotis myotis
-            roi:
-                name: anchor_bbox
-                anchor: top-left
-        generic_class:
-          - key: order
-            value: Chiroptera
     """
     config_path = create_temp_yaml(yaml_content)
 
     config = load_target_config(config_path)
-    targets = build_targets(config, term_registry=sample_term_registry)
+    targets = build_targets(config)
 
     geometry = data.BoundingBox(coordinates=[0.1, 12_000, 0.2, 18_000])
 
-    species = get_term_from_key("species", term_registry=sample_term_registry)
+    species = terms.get_term("species")
+    assert species is not None
+
     se1 = data.SoundEventAnnotation(
         sound_event=data.SoundEvent(recording=recording, geometry=geometry),
         tags=[data.Tag(term=species, value="Pipistrellus pipistrellus")],
@@ -62,38 +71,47 @@ def test_can_override_default_roi_mapper_per_class(
 # TODO: rename this test function
 def test_roi_is_recovered_roundtrip_even_with_overriders(
     create_temp_yaml,
-    sample_term_registry,
     recording,
 ):
     yaml_content = """
+    detection_target:
+        name: bat
+        match_if:
+            name: has_tag
+            tag:
+                key: order
+                value: Chiroptera
+        assign_tags:
+            - key: order
+              value: Chiroptera
+
+    classification_targets:
+      - name: pippip
+        tags:
+            - key: species
+              value: Pipistrellus pipistrellus
+
+      - name: myomyo
+        tags:
+            - key: species
+              value: Myotis myotis
+        roi:
+            name: anchor_bbox
+            anchor: top-left
+
     roi:
         name: anchor_bbox
         anchor: bottom-left
-    classes:
-        classes:
-          - name: pippip
-            tags:
-              - key: species
-                value: Pipistrellus pipistrellus
-          - name: myomyo
-            tags:
-              - key: species
-                value: Myotis myotis
-            roi:
-                name: anchor_bbox
-                anchor: top-left
-        generic_class:
-          - key: order
-            value: Chiroptera
     """
     config_path = create_temp_yaml(yaml_content)
 
     config = load_target_config(config_path)
-    targets = build_targets(config, term_registry=sample_term_registry)
+    targets = build_targets(config)
 
     geometry = data.BoundingBox(coordinates=[0.1, 12_000, 0.2, 18_000])
 
-    species = get_term_from_key("species", term_registry=sample_term_registry)
+    species = terms.get_term("species")
+    assert species is not None
     se1 = data.SoundEventAnnotation(
         sound_event=data.SoundEvent(recording=recording, geometry=geometry),
         tags=[data.Tag(term=species, value="Pipistrellus pipistrellus")],

tests/test_targets/test_terms.py

@@ -1,179 +0,0 @@
-import pytest
-import yaml
-from soundevent import data
-
-from batdetect2.targets import terms
-from batdetect2.targets.terms import (
-    TagInfo,
-    TermRegistry,
-    load_terms_from_config,
-)
-
-
-def test_term_registry_initialization():
-    registry = TermRegistry()
-    assert registry._terms == {}
-
-    initial_terms = {
-        "test_term": data.Term(name="test", label="Test", definition="test")
-    }
-    registry = TermRegistry(terms=initial_terms)
-    assert registry._terms == initial_terms
-
-
-def test_term_registry_add_term():
-    registry = TermRegistry()
-    term = data.Term(name="test", label="Test", definition="test")
-    registry.add_term("test_key", term)
-    assert registry._terms["test_key"] == term
-
-
-def test_term_registry_get_term():
-    registry = TermRegistry()
-    term = data.Term(name="test", label="Test", definition="test")
-    registry.add_term("test_key", term)
-    retrieved_term = registry.get_term("test_key")
-    assert retrieved_term == term
-
-
-def test_term_registry_add_custom_term():
-    registry = TermRegistry()
-    term = registry.add_custom_term(
-        "custom_key", name="custom", label="Custom", definition="A custom term"
-    )
-    assert registry._terms["custom_key"] == term
-    assert term.name == "custom"
-    assert term.label == "Custom"
-    assert term.definition == "A custom term"
-
-
-def test_term_registry_add_duplicate_term():
-    registry = TermRegistry()
-    term = data.Term(name="test", label="Test", definition="test")
-    registry.add_term("test_key", term)
-    with pytest.raises(KeyError):
-        registry.add_term("test_key", term)
-
-
-def test_term_registry_get_term_not_found():
-    registry = TermRegistry()
-    with pytest.raises(KeyError):
-        registry.get_term("non_existent_key")
-
-
-def test_term_registry_get_keys():
-    registry = TermRegistry()
-    term1 = data.Term(name="test1", label="Test1", definition="test")
-    term2 = data.Term(name="test2", label="Test2", definition="test")
-    registry.add_term("key1", term1)
-    registry.add_term("key2", term2)
-    keys = registry.get_keys()
-    assert set(keys) == {"key1", "key2"}
-
-
-def test_get_term_from_key():
-    term = terms.get_term_from_key("event")
-    assert term == terms.call_type
-
-    custom_registry = TermRegistry()
-    custom_term = data.Term(name="custom", label="Custom", definition="test")
-    custom_registry.add_term("custom_key", custom_term)
-    term = terms.get_term_from_key("custom_key", term_registry=custom_registry)
-    assert term == custom_term
-
-
-def test_get_term_keys():
-    keys = terms.get_term_keys()
-    assert "event" in keys
-    assert "individual" in keys
-    assert terms.GENERIC_CLASS_KEY in keys
-
-    custom_registry = TermRegistry()
-    custom_term = data.Term(name="custom", label="Custom", definition="test")
-    custom_registry.add_term("custom_key", custom_term)
-    keys = terms.get_term_keys(term_registry=custom_registry)
-    assert "custom_key" in keys
-
-
-def test_tag_info_and_get_tag_from_info():
-    tag_info = TagInfo(value="Myotis myotis", key="event")
-    tag = terms.get_tag_from_info(tag_info)
-    assert tag.value == "Myotis myotis"
-    assert tag.term == terms.call_type
-
-
-def test_get_tag_from_info_key_not_found():
-    tag_info = TagInfo(value="test", key="non_existent_key")
-    with pytest.raises(KeyError):
-        terms.get_tag_from_info(tag_info)
-
-
-def test_load_terms_from_config(tmp_path):
-    term_registry = TermRegistry()
-    config_data = {
-        "terms": [
-            {
-                "key": "species",
-                "name": "dwc:scientificName",
-                "label": "Scientific Name",
-            },
-            {
-                "key": "my_custom_term",
-                "name": "soundevent:custom_term",
-                "definition": "Describes a specific project attribute",
-            },
-        ]
-    }
-    config_file = tmp_path / "config.yaml"
-    with open(config_file, "w") as f:
-        yaml.dump(config_data, f)
-
-    loaded_terms = load_terms_from_config(
-        config_file,
-        term_registry=term_registry,
-    )
-    assert "species" in loaded_terms
-    assert "my_custom_term" in loaded_terms
-    assert loaded_terms["species"].name == "dwc:scientificName"
-    assert loaded_terms["my_custom_term"].name == "soundevent:custom_term"
-
-
-def test_load_terms_from_config_file_not_found():
-    with pytest.raises(FileNotFoundError):
-        load_terms_from_config("non_existent_file.yaml")
-
-
-def test_load_terms_from_config_validation_error(tmp_path):
-    config_data = {
-        "terms": [
-            {
-                "key": "species",
-                "uri": "dwc:scientificName",
-                "label": 123,
-            },  # Invalid label type
-        ]
-    }
-    config_file = tmp_path / "config.yaml"
-    with open(config_file, "w") as f:
-        yaml.dump(config_data, f)
-
-    with pytest.raises(ValueError):
-        load_terms_from_config(config_file)
-
-
-def test_load_terms_from_config_key_already_exists(tmp_path):
-    config_data = {
-        "terms": [
-            {
-                "key": "event",
-                "uri": "dwc:scientificName",
-                "label": "Scientific Name",
-            },  # Duplicate key
-        ]
-    }
-    config_file = tmp_path / "config.yaml"
-    with open(config_file, "w") as f:
-        yaml.dump(config_data, f)
-
-    with pytest.raises(KeyError):
-        load_terms_from_config(config_file)

tests/test_targets/test_transform.py

@@ -1,363 +0,0 @@
-from pathlib import Path
-
-import pytest
-from soundevent import data
-
-from batdetect2.targets import (
-    DeriveTagRule,
-    MapValueRule,
-    ReplaceRule,
-    TagInfo,
-    TransformConfig,
-    build_transformation_from_config,
-)
-from batdetect2.targets.terms import TermRegistry
-from batdetect2.targets.transform import (
-    DerivationRegistry,
-    build_transform_from_rule,
-)
-
-
-@pytest.fixture
-def term_registry():
-    return TermRegistry()
-
-
-@pytest.fixture
-def derivation_registry():
-    return DerivationRegistry()
-
-
-@pytest.fixture
-def term1(term_registry: TermRegistry) -> data.Term:
-    return term_registry.add_custom_term(key="term1")
-
-
-@pytest.fixture
-def term2(term_registry: TermRegistry) -> data.Term:
-    return term_registry.add_custom_term(key="term2")
-
-
-@pytest.fixture
-def annotation(
-    sound_event: data.SoundEvent,
-    term1: data.Term,
-) -> data.SoundEventAnnotation:
-    return data.SoundEventAnnotation(
-        sound_event=sound_event, tags=[data.Tag(term=term1, value="value1")]
-    )
-
-
-def test_map_value_rule(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-):
-    rule = MapValueRule(
-        rule_type="map_value",
-        source_term_key="term1",
-        value_mapping={"value1": "value2"},
-    )
-    transform_fn = build_transform_from_rule(rule, term_registry=term_registry)
-    transformed_annotation = transform_fn(annotation)
-    assert transformed_annotation.tags[0].value == "value2"
-
-
-def test_map_value_rule_no_match(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-):
-    rule = MapValueRule(
-        rule_type="map_value",
-        source_term_key="term1",
-        value_mapping={"other_value": "value2"},
-    )
-    transform_fn = build_transform_from_rule(rule, term_registry=term_registry)
-    transformed_annotation = transform_fn(annotation)
-    assert transformed_annotation.tags[0].value == "value1"
-
-
-def test_replace_rule(
-    annotation: data.SoundEventAnnotation,
-    term2: data.Term,
-    term_registry: TermRegistry,
-):
-    rule = ReplaceRule(
-        rule_type="replace",
-        original=TagInfo(key="term1", value="value1"),
-        replacement=TagInfo(key="term2", value="value2"),
-    )
-    transform_fn = build_transform_from_rule(rule, term_registry=term_registry)
-    transformed_annotation = transform_fn(annotation)
-    assert transformed_annotation.tags[0].term == term2
-    assert transformed_annotation.tags[0].value == "value2"
-
-
-def test_replace_rule_no_match(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-    term2: data.Term,
-):
-    rule = ReplaceRule(
-        rule_type="replace",
-        original=TagInfo(key="term1", value="wrong_value"),
-        replacement=TagInfo(key="term2", value="value2"),
-    )
-    transform_fn = build_transform_from_rule(rule, term_registry=term_registry)
-    transformed_annotation = transform_fn(annotation)
-    assert transformed_annotation.tags[0].key == "term1"
-    assert transformed_annotation.tags[0].term != term2
-    assert transformed_annotation.tags[0].value == "value1"
-
-
-def test_build_transformation_from_config(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-):
-    config = TransformConfig(
-        rules=[
-            MapValueRule(
-                rule_type="map_value",
-                source_term_key="term1",
-                value_mapping={"value1": "value2"},
-            ),
-            ReplaceRule(
-                rule_type="replace",
-                original=TagInfo(key="term2", value="value2"),
-                replacement=TagInfo(key="term3", value="value3"),
-            ),
-        ]
-    )
-    term_registry.add_custom_term("term2")
-    term_registry.add_custom_term("term3")
-    transform = build_transformation_from_config(
-        config,
-        term_registry=term_registry,
-    )
-    transformed_annotation = transform(annotation)
-    assert transformed_annotation.tags[0].key == "term1"
-    assert transformed_annotation.tags[0].value == "value2"
-
-
-def test_derive_tag_rule(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-    derivation_registry: DerivationRegistry,
-    term1: data.Term,
-):
-    def derivation_func(x: str) -> str:
-        return x + "_derived"
-
-    derivation_registry.register("my_derivation", derivation_func)
-
-    rule = DeriveTagRule(
-        rule_type="derive_tag",
-        source_term_key="term1",
-        derivation_function="my_derivation",
-    )
-    transform_fn = build_transform_from_rule(
-        rule,
-        term_registry=term_registry,
-        derivation_registry=derivation_registry,
-    )
-    transformed_annotation = transform_fn(annotation)
-
-    assert len(transformed_annotation.tags) == 2
-    assert transformed_annotation.tags[0].term == term1
-    assert transformed_annotation.tags[0].value == "value1"
-    assert transformed_annotation.tags[1].term == term1
-    assert transformed_annotation.tags[1].value == "value1_derived"
-
-
-def test_derive_tag_rule_keep_source_false(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-    derivation_registry: DerivationRegistry,
-    term1: data.Term,
-):
-    def derivation_func(x: str) -> str:
-        return x + "_derived"
-
-    derivation_registry.register("my_derivation", derivation_func)
-
-    rule = DeriveTagRule(
-        rule_type="derive_tag",
-        source_term_key="term1",
-        derivation_function="my_derivation",
-        keep_source=False,
-    )
-    transform_fn = build_transform_from_rule(
-        rule,
-        term_registry=term_registry,
-        derivation_registry=derivation_registry,
-    )
-    transformed_annotation = transform_fn(annotation)
-
-    assert len(transformed_annotation.tags) == 1
-    assert transformed_annotation.tags[0].term == term1
-    assert transformed_annotation.tags[0].value == "value1_derived"
-
-
-def test_derive_tag_rule_target_term(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-    derivation_registry: DerivationRegistry,
-    term1: data.Term,
-    term2: data.Term,
-):
-    def derivation_func(x: str) -> str:
-        return x + "_derived"
-
-    derivation_registry.register("my_derivation", derivation_func)
-
-    rule = DeriveTagRule(
-        rule_type="derive_tag",
-        source_term_key="term1",
-        derivation_function="my_derivation",
-        target_term_key="term2",
-    )
-    transform_fn = build_transform_from_rule(
-        rule,
-        term_registry=term_registry,
-        derivation_registry=derivation_registry,
-    )
-    transformed_annotation = transform_fn(annotation)
-
-    assert len(transformed_annotation.tags) == 2
-    assert transformed_annotation.tags[0].term == term1
-    assert transformed_annotation.tags[0].value == "value1"
-    assert transformed_annotation.tags[1].term == term2
-    assert transformed_annotation.tags[1].value == "value1_derived"
-
-
-def test_derive_tag_rule_import_derivation(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-    term1: data.Term,
-    tmp_path: Path,
-):
-    # Create a dummy derivation function in a temporary file
-    derivation_module_path = (
-        tmp_path / "temp_derivation.py"
-    )  # Changed to /tmp since /home/santiago is not writable
-    derivation_module_path.write_text(
-        """
-def my_imported_derivation(x: str) -> str:
-    return x + "_imported"
-"""
-    )
-    # Ensure the temporary file is importable by adding its directory to sys.path
-    import sys
-
-    sys.path.insert(0, str(tmp_path))
-
-    rule = DeriveTagRule(
-        rule_type="derive_tag",
-        source_term_key="term1",
-        derivation_function="temp_derivation.my_imported_derivation",
-        import_derivation=True,
-    )
-    transform_fn = build_transform_from_rule(rule, term_registry=term_registry)
-    transformed_annotation = transform_fn(annotation)
-
-    assert len(transformed_annotation.tags) == 2
-    assert transformed_annotation.tags[0].term == term1
-    assert transformed_annotation.tags[0].value == "value1"
-    assert transformed_annotation.tags[1].term == term1
-    assert transformed_annotation.tags[1].value == "value1_imported"
-
-    # Clean up the temporary file and sys.path
-    sys.path.remove(str(tmp_path))
-
-
-def test_derive_tag_rule_invalid_derivation(term_registry: TermRegistry):
-    rule = DeriveTagRule(
-        rule_type="derive_tag",
-        source_term_key="term1",
-        derivation_function="nonexistent_derivation",
-    )
-    with pytest.raises(KeyError):
-        build_transform_from_rule(rule, term_registry=term_registry)
-
-
-def test_build_transform_from_rule_invalid_rule_type():
-    class InvalidRule:
-        rule_type = "invalid"
-
-    rule = InvalidRule()  # type: ignore
-
-    with pytest.raises(ValueError):
-        build_transform_from_rule(rule)  # type: ignore
-
-
-def test_map_value_rule_target_term(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-    term2: data.Term,
-):
-    rule = MapValueRule(
-        rule_type="map_value",
-        source_term_key="term1",
-        value_mapping={"value1": "value2"},
-        target_term_key="term2",
-    )
-    transform_fn = build_transform_from_rule(rule, term_registry=term_registry)
-    transformed_annotation = transform_fn(annotation)
-    assert transformed_annotation.tags[0].term == term2
-    assert transformed_annotation.tags[0].value == "value2"
-
-
-def test_map_value_rule_target_term_none(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-    term1: data.Term,
-):
-    rule = MapValueRule(
-        rule_type="map_value",
-        source_term_key="term1",
-        value_mapping={"value1": "value2"},
-        target_term_key=None,
-    )
-    transform_fn = build_transform_from_rule(rule, term_registry=term_registry)
-    transformed_annotation = transform_fn(annotation)
-    assert transformed_annotation.tags[0].term == term1
-    assert transformed_annotation.tags[0].value == "value2"
-
-
-def test_derive_tag_rule_target_term_none(
-    annotation: data.SoundEventAnnotation,
-    term_registry: TermRegistry,
-    derivation_registry: DerivationRegistry,
-    term1: data.Term,
-):
-    def derivation_func(x: str) -> str:
-        return x + "_derived"
-
-    derivation_registry.register("my_derivation", derivation_func)
-
-    rule = DeriveTagRule(
-        rule_type="derive_tag",
-        source_term_key="term1",
-        derivation_function="my_derivation",
-        target_term_key=None,
-    )
-    transform_fn = build_transform_from_rule(
-        rule,
-        term_registry=term_registry,
-        derivation_registry=derivation_registry,
-    )
-    transformed_annotation = transform_fn(annotation)
-
-    assert len(transformed_annotation.tags) == 2
-    assert transformed_annotation.tags[0].term == term1
-    assert transformed_annotation.tags[0].value == "value1"
-    assert transformed_annotation.tags[1].term == term1
-    assert transformed_annotation.tags[1].value == "value1_derived"
-
-
-def test_build_transformation_from_config_empty(
-    annotation: data.SoundEventAnnotation,
-):
-    config = TransformConfig(rules=[])
-    transform = build_transformation_from_config(config)
-    transformed_annotation = transform(annotation)
-    assert transformed_annotation == annotation

tests/test_train/test_augmentations.py

@@ -1,170 +0,0 @@
-from collections.abc import Callable
-
-import pytest
-import torch
-from soundevent import data
-
-from batdetect2.train.augmentations import (
-    add_echo,
-    mix_audio,
-)
-from batdetect2.train.clips import select_subclip
-from batdetect2.train.preprocess import generate_train_example
-from batdetect2.typing import AudioLoader, ClipLabeller, PreprocessorProtocol
-
-
-def test_mix_examples(
-    sample_preprocessor: PreprocessorProtocol,
-    sample_audio_loader: AudioLoader,
-    sample_labeller: ClipLabeller,
-    create_recording: Callable[..., data.Recording],
-):
-    recording1 = create_recording()
-    recording2 = create_recording()
-
-    clip1 = data.Clip(recording=recording1, start_time=0.2, end_time=0.7)
-    clip2 = data.Clip(recording=recording2, start_time=0.3, end_time=0.8)
-
-    clip_annotation_1 = data.ClipAnnotation(clip=clip1)
-    clip_annotation_2 = data.ClipAnnotation(clip=clip2)
-
-    example1 = generate_train_example(
-        clip_annotation_1,
-        audio_loader=sample_audio_loader,
-        preprocessor=sample_preprocessor,
-        labeller=sample_labeller,
-    )
-    example2 = generate_train_example(
-        clip_annotation_2,
-        audio_loader=sample_audio_loader,
-        preprocessor=sample_preprocessor,
-        labeller=sample_labeller,
-    )
-
-    mixed = mix_audio(
-        example1,
-        example2,
-        weight=0.3,
-        preprocessor=sample_preprocessor,
-    )
-
-    assert mixed.spectrogram.shape == example1.spectrogram.shape
-    assert mixed.detection_heatmap.shape == example1.detection_heatmap.shape
-    assert mixed.size_heatmap.shape == example1.size_heatmap.shape
-    assert mixed.class_heatmap.shape == example1.class_heatmap.shape
-
-
-@pytest.mark.parametrize("duration1", [0.1, 0.4, 0.7])
-@pytest.mark.parametrize("duration2", [0.1, 0.4, 0.7])
-def test_mix_examples_of_different_durations(
-    sample_preprocessor: PreprocessorProtocol,
-    sample_audio_loader: AudioLoader,
-    sample_labeller: ClipLabeller,
-    create_recording: Callable[..., data.Recording],
-    duration1: float,
-    duration2: float,
-):
-    recording1 = create_recording()
-    recording2 = create_recording()
-
-    clip1 = data.Clip(recording=recording1, start_time=0, end_time=duration1)
-    clip2 = data.Clip(recording=recording2, start_time=0, end_time=duration2)
-
-    clip_annotation_1 = data.ClipAnnotation(clip=clip1)
-    clip_annotation_2 = data.ClipAnnotation(clip=clip2)
-
-    example1 = generate_train_example(
-        clip_annotation_1,
-        audio_loader=sample_audio_loader,
-        preprocessor=sample_preprocessor,
-        labeller=sample_labeller,
-    )
-    example2 = generate_train_example(
-        clip_annotation_2,
-        audio_loader=sample_audio_loader,
-        preprocessor=sample_preprocessor,
-        labeller=sample_labeller,
-    )
-
-    mixed = mix_audio(
-        example1,
-        example2,
-        weight=0.3,
-        preprocessor=sample_preprocessor,
-    )
-
-    assert mixed.spectrogram.shape == example1.spectrogram.shape
-    assert mixed.detection_heatmap.shape == example1.detection_heatmap.shape
-    assert mixed.size_heatmap.shape == example1.size_heatmap.shape
-    assert mixed.class_heatmap.shape == example1.class_heatmap.shape
-
-
-def test_add_echo(
-    sample_preprocessor: PreprocessorProtocol,
-    sample_audio_loader: AudioLoader,
-    sample_labeller: ClipLabeller,
-    create_recording: Callable[..., data.Recording],
-):
-    recording1 = create_recording()
-    clip1 = data.Clip(recording=recording1, start_time=0.2, end_time=0.7)
-    clip_annotation_1 = data.ClipAnnotation(clip=clip1)
-
-    original = generate_train_example(
-        clip_annotation_1,
-        audio_loader=sample_audio_loader,
-        preprocessor=sample_preprocessor,
-        labeller=sample_labeller,
-    )
-    with_echo = add_echo(
-        original,
-        preprocessor=sample_preprocessor,
-        delay=0.1,
-        weight=0.3,
-    )
-
-    assert with_echo.spectrogram.shape == original.spectrogram.shape
-    torch.testing.assert_close(
-        with_echo.size_heatmap,
-        original.size_heatmap,
-        atol=0,
-        rtol=0,
-    )
-    torch.testing.assert_close(
-        with_echo.class_heatmap,
-        original.class_heatmap,
-        atol=0,
-        rtol=0,
-    )
-    torch.testing.assert_close(
-        with_echo.detection_heatmap,
-        original.detection_heatmap,
-        atol=0,
-        rtol=0,
-    )
-
-
-def test_selected_random_subclip_has_the_correct_width(
-    sample_preprocessor: PreprocessorProtocol,
-    sample_audio_loader: AudioLoader,
-    sample_labeller: ClipLabeller,
-    create_recording: Callable[..., data.Recording],
-):
-    recording1 = create_recording()
-    clip1 = data.Clip(recording=recording1, start_time=0.2, end_time=0.7)
-    clip_annotation_1 = data.ClipAnnotation(clip=clip1)
-
-    original = generate_train_example(
-        clip_annotation_1,
-        audio_loader=sample_audio_loader,
-        preprocessor=sample_preprocessor,
-        labeller=sample_labeller,
-    )
-
-    subclip = select_subclip(
-        original,
-        input_samplerate=256_000,
-        output_samplerate=1000,
-        start=0,
-        duration=0.512,
-    )
-    assert subclip.spectrogram.shape[1] == 512

tests/test_train/test_clips.py

@@ -1,27 +0,0 @@
-from soundevent import data
-
-from batdetect2.train import generate_train_example
-from batdetect2.typing import (
-    AudioLoader,
-    ClipLabeller,
-    ClipperProtocol,
-    PreprocessorProtocol,
-)
-
-
-def test_default_clip_size_is_correct(
-    sample_clipper: ClipperProtocol,
-    sample_labeller: ClipLabeller,
-    sample_audio_loader: AudioLoader,
-    clip_annotation: data.ClipAnnotation,
-    sample_preprocessor: PreprocessorProtocol,
-):
-    example = generate_train_example(
-        clip_annotation=clip_annotation,
-        audio_loader=sample_audio_loader,
-        preprocessor=sample_preprocessor,
-        labeller=sample_labeller,
-    )
-
-    clip, _, _ = sample_clipper(example)
-    assert clip.spectrogram.shape == (1, 128, 256)

tests/test_train/test_labels.py

@@ -5,7 +5,6 @@ from soundevent import data
 
 from batdetect2.targets import TargetConfig, build_targets
 from batdetect2.targets.rois import AnchorBBoxMapperConfig
-from batdetect2.targets.terms import TagInfo
 from batdetect2.train.labels import generate_heatmaps
 
 recording = data.Recording(
@@ -26,7 +25,8 @@ clip = data.Clip(
 
 def test_generated_heatmap_are_non_zero_at_correct_positions(
     sample_target_config: TargetConfig,
-    pippip_tag: TagInfo,
+    pippip_tag: data.Tag,
+    bat_tag: data.Tag,
 ):
     config = sample_target_config.model_copy(
         update=dict(
@@ -49,14 +49,14 @@ def test_generated_heatmap_are_non_zero_at_correct_positions(
                         coordinates=[10, 10, 20, 30],
                     ),
                 ),
-                tags=[data.Tag(key=pippip_tag.key, value=pippip_tag.value)],  # type: ignore
+                tags=[pippip_tag, bat_tag],
             )
         ],
     )
 
     detection_heatmap, class_heatmap, size_heatmap = generate_heatmaps(
         clip_annotation,
-        torch.rand([100, 100]),
+        torch.rand([1, 100, 100]),
         min_freq=0,
         max_freq=100,
         targets=targets,
@@ -67,4 +67,4 @@ def test_generated_heatmap_are_non_zero_at_correct_positions(
     assert size_heatmap[1, 10, 10] == 20
     assert class_heatmap[pippip_index, 10, 10] == 1.0
     assert class_heatmap[myomyo_index, 10, 10] == 0.0
-    assert detection_heatmap[10, 10] == 1.0
+    assert detection_heatmap[0, 10, 10] == 1.0

tests/test_train/test_lightning.py

@@ -4,14 +4,16 @@ import lightning as L
 import torch
 from soundevent import data
 
+from batdetect2.models import build_model
 from batdetect2.train import FullTrainingConfig, TrainingModule
 from batdetect2.train.train import build_training_module
 from batdetect2.typing.preprocess import AudioLoader
 
 
 def build_default_module():
+    model = build_model()
     config = FullTrainingConfig()
-    return build_training_module(config)
+    return build_training_module(model, config=config)
 
 
 def test_can_initialize_default_module():
@@ -32,14 +34,14 @@ def test_can_save_checkpoint(
 
     recovered = TrainingModule.load_from_checkpoint(path)
 
-    wav = torch.tensor(sample_audio_loader.load_clip(clip))
+    wav = torch.tensor(sample_audio_loader.load_clip(clip)).unsqueeze(0)
 
     spec1 = module.model.preprocessor(wav)
     spec2 = recovered.model.preprocessor(wav)
 
     torch.testing.assert_close(spec1, spec2, rtol=0, atol=0)
 
-    output1 = module(spec1.unsqueeze(0).unsqueeze(0))
-    output2 = recovered(spec2.unsqueeze(0).unsqueeze(0))
+    output1 = module(spec1.unsqueeze(0))
+    output2 = recovered(spec2.unsqueeze(0))
 
     torch.testing.assert_close(output1, output2, rtol=0, atol=0)

tests/test_train/test_preprocessing.py

@@ -1,230 +0,0 @@
-import pytest
-from soundevent import data
-from soundevent.terms import get_term
-
-from batdetect2.postprocess import build_postprocessor, load_postprocess_config
-from batdetect2.preprocess import build_preprocessor, load_preprocessing_config
-from batdetect2.targets import build_targets, load_target_config
-from batdetect2.train.labels import build_clip_labeler, load_label_config
-from batdetect2.train.preprocess import generate_train_example
-from batdetect2.typing import ModelOutput
-from batdetect2.typing.preprocess import AudioLoader
-
-
-@pytest.fixture
-def build_from_config(
-    create_temp_yaml,
-):
-    def build(yaml_content):
-        config_path = create_temp_yaml(yaml_content)
-
-        targets_config = load_target_config(config_path, field="targets")
-        preprocessing_config = load_preprocessing_config(
-            config_path,
-            field="preprocessing",
-        )
-        labels_config = load_label_config(config_path, field="labels")
-        postprocessing_config = load_postprocess_config(
-            config_path,
-            field="postprocessing",
-        )
-
-        targets = build_targets(targets_config)
-        preprocessor = build_preprocessor(preprocessing_config)
-        labeller = build_clip_labeler(
-            targets=targets,
-            config=labels_config,
-            min_freq=preprocessor.min_freq,
-            max_freq=preprocessor.max_freq,
-        )
-        postprocessor = build_postprocessor(
-            preprocessor=preprocessor,
-            config=postprocessing_config,
-        )
-
-        return targets, preprocessor, labeller, postprocessor
-
-    return build
-
-
-def test_encoding_decoding_roundtrip_recovers_object(
-    sample_audio_loader: AudioLoader,
-    build_from_config,
-    recording,
-):
-    yaml_content = """
-    labels:
-    targets:
-        roi:
-            name: anchor_bbox
-            anchor: bottom-left
-        classes:
-            classes:
-              - name: pippip
-                tags:
-                  - key: species
-                    value: Pipistrellus pipistrellus
-            generic_class:
-              - key: order
-                value: Chiroptera
-    preprocessing:
-    """
-    _, preprocessor, labeller, postprocessor = build_from_config(yaml_content)
-
-    geometry = data.BoundingBox(coordinates=[0.1, 40_000, 0.2, 80_000])
-    se1 = data.SoundEventAnnotation(
-        sound_event=data.SoundEvent(recording=recording, geometry=geometry),
-        tags=[
-            data.Tag(key="species", value="Pipistrellus pipistrellus"),  # type: ignore
-        ],
-    )
-    clip = data.Clip(start_time=0, end_time=0.5, recording=recording)
-    clip_annotation = data.ClipAnnotation(clip=clip, sound_events=[se1])
-
-    encoded = generate_train_example(
-        clip_annotation,
-        sample_audio_loader,
-        preprocessor,
-        labeller,
-    )
-    predictions = postprocessor.get_predictions(
-        ModelOutput(
-            detection_probs=encoded.detection_heatmap.unsqueeze(0).unsqueeze(
-                0
-            ),
-            size_preds=encoded.size_heatmap.unsqueeze(0),
-            class_probs=encoded.class_heatmap.unsqueeze(0),
-            features=encoded.spectrogram.unsqueeze(0).unsqueeze(0),
-        ),
-        [clip],
-    )[0]
-
-    assert isinstance(predictions, data.ClipPrediction)
-    assert len(predictions.sound_events) == 1
-
-    recovered = predictions.sound_events[0]
-    assert recovered.sound_event.geometry is not None
-    assert isinstance(recovered.sound_event.geometry, data.BoundingBox)
-    start_time_rec, low_freq_rec, end_time_rec, high_freq_rec = (
-        recovered.sound_event.geometry.coordinates
-    )
-    start_time_or, low_freq_or, end_time_or, high_freq_or = (
-        geometry.coordinates
-    )
-
-    assert start_time_rec == pytest.approx(start_time_or, abs=0.01)
-    assert low_freq_rec == pytest.approx(low_freq_or, abs=1_000)
-    assert end_time_rec == pytest.approx(end_time_or, abs=0.01)
-    assert high_freq_rec == pytest.approx(high_freq_or, abs=1_000)
-
-    assert len(recovered.tags) == 2
-
-    predicted_species_tag = next(
-        iter(t for t in recovered.tags if t.tag.term == get_term("species")),
-        None,
-    )
-    assert predicted_species_tag is not None
-    assert predicted_species_tag.score == 1
-    assert predicted_species_tag.tag.value == "Pipistrellus pipistrellus"
-
-    predicted_order_tag = next(
-        iter(t for t in recovered.tags if t.tag.term == get_term("order")),
-        None,
-    )
-    assert predicted_order_tag is not None
-    assert predicted_order_tag.score == 1
-    assert predicted_order_tag.tag.value == "Chiroptera"
-
-
-def test_encoding_decoding_roundtrip_recovers_object_with_roi_override(
-    sample_audio_loader: AudioLoader,
-    build_from_config,
-    recording,
-):
-    yaml_content = """
-    labels:
-    targets:
-        roi:
-            name: anchor_bbox
-            anchor: bottom-left
-        classes:
-            classes:
-              - name: pippip
-                tags:
-                  - key: species
-                    value: Pipistrellus pipistrellus
-              - name: myomyo
-                tags:
-                  - key: species
-                    value: Myotis myotis
-                roi:
-                    name: anchor_bbox
-                    anchor: top-left
-            generic_class:
-              - key: order
-                value: Chiroptera
-    preprocessing:
-    """
-    _, preprocessor, labeller, postprocessor = build_from_config(yaml_content)
-
-    geometry = data.BoundingBox(coordinates=[0.1, 40_000, 0.2, 80_000])
-    se1 = data.SoundEventAnnotation(
-        sound_event=data.SoundEvent(recording=recording, geometry=geometry),
-        tags=[data.Tag(key="species", value="Myotis myotis")],  # type: ignore
-    )
-    clip = data.Clip(start_time=0, end_time=0.5, recording=recording)
-    clip_annotation = data.ClipAnnotation(clip=clip, sound_events=[se1])
-
-    encoded = generate_train_example(
-        clip_annotation,
-        sample_audio_loader,
-        preprocessor,
-        labeller,
-    )
-    predictions = postprocessor.get_predictions(
-        ModelOutput(
-            detection_probs=encoded.detection_heatmap.unsqueeze(0).unsqueeze(
-                0
-            ),
-            size_preds=encoded.size_heatmap.unsqueeze(0),
-            class_probs=encoded.class_heatmap.unsqueeze(0),
-            features=encoded.spectrogram.unsqueeze(0).unsqueeze(0),
-        ),
-        [clip],
-    )[0]
-
-    assert isinstance(predictions, data.ClipPrediction)
-    assert len(predictions.sound_events) == 1
-
-    recovered = predictions.sound_events[0]
-    assert recovered.sound_event.geometry is not None
-    assert isinstance(recovered.sound_event.geometry, data.BoundingBox)
-    start_time_rec, low_freq_rec, end_time_rec, high_freq_rec = (
-        recovered.sound_event.geometry.coordinates
-    )
-    start_time_or, low_freq_or, end_time_or, high_freq_or = (
-        geometry.coordinates
-    )
-
-    assert start_time_rec == pytest.approx(start_time_or, abs=0.01)
-    assert low_freq_rec == pytest.approx(low_freq_or, abs=1_000)
-    assert end_time_rec == pytest.approx(end_time_or, abs=0.01)
-    assert high_freq_rec == pytest.approx(high_freq_or, abs=1_000)
-
-    assert len(recovered.tags) == 2
-
-    predicted_species_tag = next(
-        iter(t for t in recovered.tags if t.tag.term == get_term("species")),
-        None,
-    )
-    assert predicted_species_tag is not None
-    assert predicted_species_tag.score == 1
-    assert predicted_species_tag.tag.value == "Myotis myotis"
-
-    predicted_order_tag = next(
-        iter(t for t in recovered.tags if t.tag.term == get_term("order")),
-        None,
-    )
-    assert predicted_order_tag is not None
-    assert predicted_order_tag.score == 1
-    assert predicted_order_tag.tag.value == "Chiroptera"