mirror of
https://github.com/macaodha/batdetect2.git
synced 2025-06-29 22:51:58 +02:00
Separated the protocols to separate types module
This commit is contained in:
mbsantiago
parent
3417c496db
commit
19febf2216
@ -1,87 +1,193 @@
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"""Module containing functions for preprocessing audio clips."""
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from functools import partial
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from typing import Callable, Optional, Protocol
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from typing import Optional, Union
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import numpy as np
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import xarray as xr
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from pydantic import Field
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from soundevent import data
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from batdetect2.configs import BaseConfig, load_config
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from batdetect2.preprocess.audio import (
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DEFAULT_DURATION,
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SCALE_RAW_AUDIO,
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TARGET_SAMPLERATE_HZ,
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AudioConfig,
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ResampleConfig,
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adjust_audio_duration,
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build_audio_loader,
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convert_to_xr,
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load_clip_audio,
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)
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from batdetect2.preprocess.config import (
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PreprocessingConfig,
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load_preprocessing_config,
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load_file_audio,
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load_recording_audio,
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resample_audio,
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)
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from batdetect2.preprocess.spectrogram import (
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MAX_FREQ,
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MIN_FREQ,
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AmplitudeScaleConfig,
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ConfigurableSpectrogramBuilder,
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FrequencyConfig,
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LogScaleConfig,
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PcenScaleConfig,
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Scales,
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PcenConfig,
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SpecSizeConfig,
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SpectrogramConfig,
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STFTConfig,
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build_spectrogram_builder,
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compute_spectrogram,
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get_spectrogram_resolution,
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)
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from batdetect2.preprocess.types import (
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AudioLoader,
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Preprocessor,
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SpectrogramBuilder,
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)
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__all__ = [
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"AmplitudeScaleConfig",
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"AudioConfig",
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"AudioLoader",
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"ConfigurableSpectrogramBuilder",
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"DEFAULT_DURATION",
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"FrequencyConfig",
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"FrequencyConfig",
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"LogScaleConfig",
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"MAX_FREQ",
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"MIN_FREQ",
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"PcenScaleConfig",
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"PcenConfig",
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"PcenConfig",
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"PreprocessingConfig",
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"ResampleConfig",
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"SCALE_RAW_AUDIO",
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"STFTConfig",
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"STFTConfig",
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"Scales",
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"SpecSizeConfig",
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"SpecSizeConfig",
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"SpectrogramBuilder",
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"SpectrogramConfig",
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"SpectrogramConfig",
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"TARGET_SAMPLERATE_HZ",
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"adjust_audio_duration",
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"build_audio_loader",
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"build_spectrogram_builder",
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"compute_spectrogram",
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"convert_to_xr",
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"get_spectrogram_resolution",
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"load_clip_audio",
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"load_file_audio",
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"load_preprocessing_config",
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"preprocess_audio_clip",
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"load_recording_audio",
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"resample_audio",
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]
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class AudioPreprocessor(Protocol):
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def __call__(
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class PreprocessingConfig(BaseConfig):
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"""Configuration for preprocessing data."""
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audio: AudioConfig = Field(default_factory=AudioConfig)
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spectrogram: SpectrogramConfig = Field(default_factory=SpectrogramConfig)
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class StandardPreprocessor(Preprocessor):
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audio_loader: AudioLoader
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spectrogram_builder: SpectrogramBuilder
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default_samplerate: int
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def __init__(
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self,
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audio_loader: AudioLoader,
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spectrogram_builder: SpectrogramBuilder,
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default_samplerate: int,
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) -> None:
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self.audio_loader = audio_loader
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self.spectrogram_builder = spectrogram_builder
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self.default_samplerate = default_samplerate
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def load_file_audio(
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self,
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path: data.PathLike,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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return self.audio_loader.load_file(
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path,
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audio_dir=audio_dir,
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)
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def load_recording_audio(
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self,
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recording: data.Recording,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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return self.audio_loader.load_recording(
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recording,
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audio_dir=audio_dir,
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)
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def load_clip_audio(
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self,
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clip: data.Clip,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray: ...
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) -> xr.DataArray:
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return self.audio_loader.load_clip(
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clip,
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audio_dir=audio_dir,
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)
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def preprocess_file(
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self,
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path: data.PathLike,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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wav = self.load_file_audio(path, audio_dir=audio_dir)
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return self.spectrogram_builder(
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wav,
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samplerate=self.default_samplerate,
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)
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def preprocess_recording(
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self,
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recording: data.Recording,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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wav = self.load_recording_audio(recording, audio_dir=audio_dir)
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return self.spectrogram_builder(
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wav,
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samplerate=self.default_samplerate,
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)
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def preprocess_clip(
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self,
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clip: data.Clip,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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wav = self.load_clip_audio(clip, audio_dir=audio_dir)
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return self.spectrogram_builder(
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wav,
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samplerate=self.default_samplerate,
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)
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def compute_spectrogram(
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self, wav: Union[xr.DataArray, np.ndarray]
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) -> xr.DataArray:
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return self.spectrogram_builder(
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wav,
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samplerate=self.default_samplerate,
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)
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def load_preprocessing_config(
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path: data.PathLike,
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field: Optional[str] = None,
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) -> PreprocessingConfig:
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return load_config(path, schema=PreprocessingConfig, field=field)
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def build_preprocessor_from_config(
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config: PreprocessingConfig,
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) -> AudioPreprocessor:
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return partial(preprocess_audio_clip, config=config)
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def preprocess_audio_clip(
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clip: data.Clip,
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config: Optional[PreprocessingConfig] = None,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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"""Preprocesses audio clip to generate spectrogram.
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Parameters
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----------
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clip
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The audio clip to preprocess.
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config
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Configuration for preprocessing.
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Returns
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-------
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xr.DataArray
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Preprocessed spectrogram.
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"""
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config = config or PreprocessingConfig()
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wav = load_clip_audio(clip, config=config.audio, audio_dir=audio_dir)
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return compute_spectrogram(wav, config=config.spectrogram)
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) -> Preprocessor:
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default_samplerate = (
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config.audio.resample.samplerate
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if config.audio.resample
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else TARGET_SAMPLERATE_HZ
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)
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return StandardPreprocessor(
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audio_loader=build_audio_loader(config.audio),
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spectrogram_builder=build_spectrogram_builder(config.spectrogram),
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default_samplerate=default_samplerate,
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)
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@ -20,7 +20,7 @@ The primary interface is the `AudioLoader` protocol, with
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`AudioConfig`.
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"""
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from typing import Optional, Protocol
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from typing import Optional
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import numpy as np
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import xarray as xr
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@ -32,9 +32,9 @@ from soundevent.arrays import operations as ops
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from soundfile import LibsndfileError
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from batdetect2.configs import BaseConfig
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from batdetect2.preprocess.types import AudioLoader
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__all__ = [
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"AudioLoader",
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"ResampleConfig",
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"AudioConfig",
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"ConfigurableAudioLoader",
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@ -60,106 +60,6 @@ DEFAULT_DURATION = None
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"""Default setting for target audio duration in seconds."""
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class AudioLoader(Protocol):
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"""Defines the interface for an audio loading and processing component.
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An AudioLoader is responsible for retrieving audio data corresponding to
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different soundevent objects (files, Recordings, Clips) and applying a
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configured set of initial preprocessing steps. Adhering to this protocol
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allows for different loading strategies or implementations.
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"""
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def load_file(
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self,
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path: data.PathLike,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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"""Load and preprocess audio directly from a file path.
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Parameters
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----------
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path : PathLike
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Path to the audio file.
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audio_dir : PathLike, optional
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A directory prefix to prepend to the path if `path` is relative.
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Returns
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-------
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xr.DataArray
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The loaded and preprocessed audio waveform as an xarray DataArray
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with time coordinates. Typically loads only the first channel.
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Raises
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------
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FileNotFoundError
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If the audio file cannot be found.
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Exception
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If the audio file cannot be loaded or processed.
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"""
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...
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def load_recording(
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self,
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recording: data.Recording,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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"""Load and preprocess the entire audio for a Recording object.
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Parameters
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----------
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recording : data.Recording
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The Recording object containing metadata about the audio file.
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audio_dir : PathLike, optional
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A directory where the audio file associated with the recording
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can be found, especially if the path in the recording is relative.
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Returns
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-------
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xr.DataArray
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The loaded and preprocessed audio waveform. Typically loads only
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the first channel.
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Raises
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------
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FileNotFoundError
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If the audio file associated with the recording cannot be found.
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Exception
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If the audio file cannot be loaded or processed.
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"""
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...
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def load_clip(
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self,
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clip: data.Clip,
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audio_dir: Optional[data.PathLike] = None,
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) -> xr.DataArray:
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"""Load and preprocess the audio segment defined by a Clip object.
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Parameters
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----------
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clip : data.Clip
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The Clip object specifying the recording and the start/end times
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of the segment to load.
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audio_dir : PathLike, optional
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A directory where the audio file associated with the clip's
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recording can be found.
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Returns
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-------
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xr.DataArray
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The loaded and preprocessed audio waveform for the specified clip
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duration. Typically loads only the first channel.
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Raises
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------
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FileNotFoundError
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If the audio file associated with the clip cannot be found.
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Exception
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If the audio file cannot be loaded or processed.
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"""
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...
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class ResampleConfig(BaseConfig):
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"""Configuration for audio resampling.
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@ -167,7 +67,7 @@ class ResampleConfig(BaseConfig):
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----------
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samplerate : int, default=256000
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The target sample rate in Hz to resample the audio to. Must be > 0.
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mode : str, default="poly"
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method : str, default="poly"
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The resampling algorithm to use. Options:
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- "poly": Polyphase resampling using `scipy.signal.resample_poly`.
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Generally fast.
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@ -177,7 +77,7 @@ class ResampleConfig(BaseConfig):
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"""
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samplerate: int = Field(default=TARGET_SAMPLERATE_HZ, gt=0)
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mode: str = "poly"
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method: str = "poly"
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class AudioConfig(BaseConfig):
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@ -191,8 +91,8 @@ class AudioConfig(BaseConfig):
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----------
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resample : ResampleConfig, optional
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Configuration for resampling. If provided (or defaulted), audio will
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be resampled to the specified `samplerate` using the specified `mode`.
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If set to `None` in the config file, resampling is skipped.
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be resampled to the specified `samplerate` using the specified
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`method`. If set to `None` in the config file, resampling is skipped.
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Defaults to a ResampleConfig instance with standard settings.
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scale : bool, default=False
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If True, scales the audio waveform using peak normalization so that
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@ -579,14 +479,14 @@ def adjust_audio_duration(
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def resample_audio(
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wav: xr.DataArray,
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samplerate: int = TARGET_SAMPLERATE_HZ,
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mode: str = "poly",
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method: str = "poly",
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dtype: DTypeLike = np.float32, # type: ignore
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) -> xr.DataArray:
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"""Resample an audio waveform DataArray to a target sample rate.
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Updates the 'time' coordinate axis according to the new sample rate and
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number of samples. Uses either polyphase (`scipy.signal.resample_poly`)
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or Fourier method (`scipy.signal.resample`) based on the `mode`.
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or Fourier method (`scipy.signal.resample`) based on the `method`.
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Parameters
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----------
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@ -594,7 +494,7 @@ def resample_audio(
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Input audio waveform with 'time' dimension and coordinates.
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samplerate : int, default=TARGET_SAMPLERATE_HZ
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Target sample rate in Hz.
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mode : str, default="poly"
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method : str, default="poly"
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Resampling algorithm: "poly" or "fourier".
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dtype : DTypeLike, default=np.float32
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Target data type for the resampled array.
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@ -610,7 +510,7 @@ def resample_audio(
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------
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ValueError
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If `wav` lacks a 'time' dimension, the original sample rate cannot
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be determined, `samplerate` is non-positive, or `mode` is invalid.
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be determined, `samplerate` is non-positive, or `method` is invalid.
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"""
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if "time" not in wav.dims:
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raise ValueError("Audio must have a time dimension")
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@ -622,14 +522,14 @@ def resample_audio(
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if original_samplerate == samplerate:
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return wav.astype(dtype)
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if mode == "poly":
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if method == "poly":
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resampled = resample_audio_poly(
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wav,
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sr_orig=original_samplerate,
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sr_new=samplerate,
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axis=time_axis,
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)
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elif mode == "fourier":
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elif method == "fourier":
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resampled = resample_audio_fourier(
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wav,
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sr_orig=original_samplerate,
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@ -637,7 +537,9 @@ def resample_audio(
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axis=time_axis,
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)
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else:
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raise NotImplementedError(f"Resampling mode '{mode}' not implemented")
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raise NotImplementedError(
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f"Resampling method '{method}' not implemented"
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)
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start, stop = arrays.get_dim_range(wav, dim="time")
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times = np.linspace(
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@ -1,31 +0,0 @@
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from typing import Optional
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from pydantic import Field
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from soundevent.data import PathLike
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from batdetect2.configs import BaseConfig, load_config
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from batdetect2.preprocess.audio import (
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AudioConfig,
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)
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from batdetect2.preprocess.spectrogram import (
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SpectrogramConfig,
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)
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__all__ = [
|
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"PreprocessingConfig",
|
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"load_preprocessing_config",
|
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]
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class PreprocessingConfig(BaseConfig):
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"""Configuration for preprocessing data."""
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audio: AudioConfig = Field(default_factory=AudioConfig)
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spectrogram: SpectrogramConfig = Field(default_factory=SpectrogramConfig)
|
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def load_preprocessing_config(
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path: PathLike,
|
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field: Optional[str] = None,
|
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) -> PreprocessingConfig:
|
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return load_config(path, schema=PreprocessingConfig, field=field)
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@ -6,20 +6,20 @@ spectrogram representations suitable for input into deep learning models like
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BatDetect2.
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|
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It offers a configurable pipeline including:
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1. Short-Time Fourier Transform (STFT) calculation.
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1. Short-Time Fourier Transform (STFT) calculation to get magnitude.
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2. Frequency axis cropping to a relevant range.
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3. Amplitude scaling (e.g., Logarithmic, Per-Channel Energy Normalization -
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PCEN).
|
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4. Simple denoising (optional).
|
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5. Resizing to target dimensions (optional).
|
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6. Final peak normalization (optional).
|
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3. Per-Channel Energy Normalization (PCEN) (optional).
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4. Amplitude scaling/representation (dB, power, or linear amplitude).
|
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5. Simple spectral mean subtraction denoising (optional).
|
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6. Resizing to target dimensions (optional).
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7. Final peak normalization (optional).
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|
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Configuration is managed via the `SpectrogramConfig` class, allowing for
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reproducible spectrogram generation consistent between training and inference.
|
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The core computation is performed by `compute_spectrogram`.
|
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"""
|
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from typing import Literal, Optional, Protocol, Union
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from typing import Literal, Optional, Union
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import librosa
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import librosa.core.spectrum
|
||||
@ -32,65 +32,23 @@ from soundevent.arrays import operations as ops
|
||||
|
||||
from batdetect2.configs import BaseConfig
|
||||
from batdetect2.preprocess.audio import convert_to_xr
|
||||
from batdetect2.preprocess.types import SpectrogramBuilder
|
||||
|
||||
__all__ = [
|
||||
"SpectrogramBuilder",
|
||||
"STFTConfig",
|
||||
"FrequencyConfig",
|
||||
"SpecSizeConfig",
|
||||
"LogScaleConfig",
|
||||
"PcenScaleConfig",
|
||||
"AmplitudeScaleConfig",
|
||||
"Scales",
|
||||
"PcenConfig",
|
||||
"SpectrogramConfig",
|
||||
"ConfigurableSpectrogramBuilder",
|
||||
"build_spectrogram_builder",
|
||||
"compute_spectrogram",
|
||||
"get_spectrogram_resolution",
|
||||
"MIN_FREQ",
|
||||
"MAX_FREQ",
|
||||
]
|
||||
|
||||
|
||||
class SpectrogramBuilder(Protocol):
|
||||
"""Defines the interface for a spectrogram generation component.
|
||||
|
||||
A SpectrogramBuilder takes a waveform (as numpy array or xarray DataArray)
|
||||
and produces a spectrogram (as an xarray DataArray) based on its internal
|
||||
configuration or implementation.
|
||||
"""
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
wav: Union[np.ndarray, xr.DataArray],
|
||||
samplerate: Optional[int] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Generate a spectrogram from an audio waveform.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
wav : Union[np.ndarray, xr.DataArray]
|
||||
The input audio waveform. If a numpy array, `samplerate` must
|
||||
also be provided. If an xarray DataArray, it must have a 'time'
|
||||
coordinate from which the sample rate can be inferred.
|
||||
samplerate : int, optional
|
||||
The sample rate of the audio in Hz. Required if `wav` is a
|
||||
numpy array. If `wav` is an xarray DataArray, this parameter is
|
||||
ignored as the sample rate is derived from the coordinates.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The computed spectrogram as an xarray DataArray with 'time' and
|
||||
'frequency' coordinates.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If `wav` is a numpy array and `samplerate` is not provided, or
|
||||
if `wav` is an xarray DataArray without a valid 'time' coordinate.
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
MIN_FREQ = 10_000
|
||||
"""Default minimum frequency (Hz) for spectrogram frequency cropping."""
|
||||
|
||||
@ -151,109 +109,84 @@ class SpecSizeConfig(BaseConfig):
|
||||
resize_factor : float, optional
|
||||
Factor by which to resize the spectrogram along the time axis *after*
|
||||
STFT calculation. A value of 0.5 halves the number of time bins,
|
||||
2.0 doubles it. If None (default), no resizing along the time axis
|
||||
is performed relative to the STFT output width. Must be > 0 if provided.
|
||||
2.0 doubles it. If None (default), no resizing along the time axis is
|
||||
performed relative to the STFT output width. Must be > 0 if provided.
|
||||
"""
|
||||
|
||||
height: int = 128
|
||||
resize_factor: Optional[float] = 0.5
|
||||
|
||||
|
||||
class LogScaleConfig(BaseConfig):
|
||||
"""Configuration marker for using Logarithmic Amplitude Scaling."""
|
||||
class PcenConfig(BaseConfig):
|
||||
"""Configuration for Per-Channel Energy Normalization (PCEN).
|
||||
|
||||
name: Literal["log"] = "log"
|
||||
|
||||
|
||||
class PcenScaleConfig(BaseConfig):
|
||||
"""Configuration for Per-Channel Energy Normalization (PCEN) scaling.
|
||||
|
||||
PCEN is an adaptive gain control method often used for audio event
|
||||
detection.
|
||||
PCEN is an adaptive gain control method that can help emphasize transients
|
||||
and suppress stationary noise. Applied after STFT and frequency cropping,
|
||||
but before final amplitude scaling (dB, power, amplitude).
|
||||
|
||||
Attributes
|
||||
----------
|
||||
name : Literal["pcen"]
|
||||
Discriminator field identifying this scaling type.
|
||||
time_constant : float, default=0.4
|
||||
Time constant (in seconds) for the PCEN smoothing filter. Controls how
|
||||
quickly the normalization adapts to energy changes.
|
||||
Time constant (in seconds) for the PCEN smoothing filter. Controls
|
||||
how quickly the normalization adapts to energy changes.
|
||||
gain : float, default=0.98
|
||||
Gain factor (alpha in some formulations). Controls the AGC behavior.
|
||||
Gain factor (alpha). Controls the adaptive gain component.
|
||||
bias : float, default=2.0
|
||||
Bias factor (delta in some formulations). Added before the
|
||||
exponentiation.
|
||||
Bias factor (delta). Added before the exponentiation.
|
||||
power : float, default=0.5
|
||||
Exponent (r in some formulations). Controls the compression
|
||||
characteristic.
|
||||
Exponent (r). Controls the compression characteristic.
|
||||
"""
|
||||
|
||||
name: Literal["pcen"] = "pcen"
|
||||
time_constant: float = 0.4
|
||||
gain: float = 0.98
|
||||
bias: float = 2
|
||||
power: float = 0.5
|
||||
|
||||
|
||||
class AmplitudeScaleConfig(BaseConfig):
|
||||
"""Configuration marker for using Linear Amplitude (no scaling applied).
|
||||
|
||||
Note: The actual output is typically magnitude from STFT, not raw amplitude.
|
||||
This option essentially skips log or PCEN scaling.
|
||||
"""
|
||||
|
||||
name: Literal["amplitude"] = "amplitude"
|
||||
|
||||
|
||||
Scales = Union[LogScaleConfig, PcenScaleConfig, AmplitudeScaleConfig]
|
||||
"""Type alias for the different amplitude scaling configuration options."""
|
||||
|
||||
|
||||
class SpectrogramConfig(BaseConfig):
|
||||
"""Unified configuration for spectrogram generation.
|
||||
"""Unified configuration for spectrogram generation pipeline.
|
||||
|
||||
Aggregates settings for STFT, frequency selection, amplitude scaling,
|
||||
resizing, and optional post-processing steps like denoising and final
|
||||
normalization.
|
||||
Aggregates settings for all steps involved in converting a preprocessed
|
||||
audio waveform into a final spectrogram representation suitable for model input.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
stft : STFTConfig
|
||||
Configuration for the Short-Time Fourier Transform. Defaults to standard
|
||||
settings via `STFTConfig`.
|
||||
Configuration for the initial Short-Time Fourier Transform.
|
||||
Defaults to standard settings via `STFTConfig`.
|
||||
frequencies : FrequencyConfig
|
||||
Configuration for cropping the frequency range. Defaults to standard
|
||||
settings via `FrequencyConfig`.
|
||||
scale : Scales
|
||||
Configuration for amplitude scaling. Determines whether to apply
|
||||
log scaling, PCEN, or leave as linear magnitude. Defaults to PCEN
|
||||
via `PcenScaleConfig`. Use the `name` field ("log", "pcen", "amplitude")
|
||||
in config files to select the type and provide relevant parameters.
|
||||
Configuration for cropping the frequency range after STFT.
|
||||
Defaults to standard settings via `FrequencyConfig`.
|
||||
pcen : PcenConfig, optional
|
||||
Configuration for applying Per-Channel Energy Normalization (PCEN). If
|
||||
provided, PCEN is applied after frequency cropping. If None or omitted
|
||||
(default), PCEN is skipped.
|
||||
scale : Literal["dB", "amplitude", "power"], default="amplitude"
|
||||
Determines the final amplitude representation *after* optional PCEN.
|
||||
- "amplitude": Use linear magnitude values (output of STFT or PCEN).
|
||||
- "power": Use power values (magnitude squared).
|
||||
- "dB": Use logarithmic (decibel-like) scaling applied to the magnitude
|
||||
(or PCEN output if enabled). Calculated as `log1p(C * S)`.
|
||||
size : SpecSizeConfig, optional
|
||||
Configuration for resizing the final spectrogram dimensions (height in
|
||||
frequency bins, optional time resizing factor). If None or omitted,
|
||||
no resizing is performed after STFT and frequency cropping. Defaults
|
||||
to standard settings via `SpecSizeConfig`.
|
||||
denoise : bool, default=True
|
||||
If True (default), applies a simple spectral mean subtraction denoising
|
||||
step after amplitude scaling.
|
||||
max_scale : bool, default=False
|
||||
Configuration for resizing the spectrogram dimensions
|
||||
(frequency height, optional time width factor). Applied after PCEN and
|
||||
scaling. If None (default), no resizing is performed.
|
||||
spectral_mean_substraction : bool, default=True
|
||||
If True (default), applies simple spectral mean subtraction denoising
|
||||
*after* PCEN and amplitude scaling, but *before* resizing.
|
||||
peak_normalize : bool, default=False
|
||||
If True, applies a final peak normalization to the spectrogram *after*
|
||||
all other steps (including log/PCEN scaling and resizing), scaling the
|
||||
maximum value across the entire spectrogram to 1.0. If False (default),
|
||||
this final scaling is skipped. **Note:** Applying this after log or PCEN
|
||||
scaling will alter the characteristics of those scales.
|
||||
all other steps (including resizing), scaling the overall maximum value
|
||||
to 1.0. If False (default), this final normalization is skipped.
|
||||
"""
|
||||
|
||||
stft: STFTConfig = Field(default_factory=STFTConfig)
|
||||
frequencies: FrequencyConfig = Field(default_factory=FrequencyConfig)
|
||||
scale: Scales = Field(
|
||||
default_factory=PcenScaleConfig,
|
||||
discriminator="name",
|
||||
)
|
||||
pcen: Optional[PcenConfig] = Field(default_factory=PcenConfig)
|
||||
scale: Literal["dB", "amplitude", "power"] = "amplitude"
|
||||
size: Optional[SpecSizeConfig] = Field(default_factory=SpecSizeConfig)
|
||||
denoise: bool = True
|
||||
max_scale: bool = False
|
||||
spectral_mean_substraction: bool = True
|
||||
peak_normalize: bool = False
|
||||
|
||||
|
||||
class ConfigurableSpectrogramBuilder(SpectrogramBuilder):
|
||||
@ -362,13 +295,13 @@ def compute_spectrogram(
|
||||
"""Compute a spectrogram from a waveform using specified configurations.
|
||||
|
||||
Applies a sequence of operations based on the `config`:
|
||||
|
||||
1. Compute STFT magnitude (`stft`).
|
||||
2. Crop frequency axis (`crop_spectrogram_frequencies`).
|
||||
3. Apply amplitude scaling (log, PCEN, or none) (`scale_spectrogram`).
|
||||
4. Apply denoising if enabled (`denoise_spectrogram`).
|
||||
5. Resize dimensions if specified (`resize_spectrogram`).
|
||||
6. Apply final peak normalization if enabled (`max_scale`).
|
||||
3. Apply PCEN if configured (`apply_pcen`).
|
||||
4. Apply final amplitude scaling (dB, power, amplitude) (`scale_spectrogram`).
|
||||
5. Apply spectral mean subtraction denoising if enabled.
|
||||
6. Resize dimensions if specified (`resize_spectrogram`).
|
||||
7. Apply final peak normalization if enabled.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
@ -411,10 +344,19 @@ def compute_spectrogram(
|
||||
max_freq=config.frequencies.max_freq,
|
||||
)
|
||||
|
||||
if config.pcen:
|
||||
spec = apply_pcen(
|
||||
spec,
|
||||
time_constant=config.pcen.time_constant,
|
||||
gain=config.pcen.gain,
|
||||
power=config.pcen.power,
|
||||
bias=config.pcen.bias,
|
||||
)
|
||||
|
||||
spec = scale_spectrogram(spec, scale=config.scale)
|
||||
|
||||
if config.denoise:
|
||||
spec = denoise_spectrogram(spec)
|
||||
if config.spectral_mean_substraction:
|
||||
spec = remove_spectral_mean(spec)
|
||||
|
||||
if config.size:
|
||||
spec = resize_spectrogram(
|
||||
@ -423,7 +365,7 @@ def compute_spectrogram(
|
||||
resize_factor=config.size.resize_factor,
|
||||
)
|
||||
|
||||
if config.max_scale:
|
||||
if config.peak_normalize:
|
||||
spec = ops.scale(spec, 1 / (10e-6 + np.max(spec)))
|
||||
|
||||
return spec.astype(dtype)
|
||||
@ -550,7 +492,7 @@ def stft(
|
||||
)
|
||||
|
||||
|
||||
def denoise_spectrogram(spec: xr.DataArray) -> xr.DataArray:
|
||||
def remove_spectral_mean(spec: xr.DataArray) -> xr.DataArray:
|
||||
"""Apply simple spectral mean subtraction for denoising.
|
||||
|
||||
Subtracts the mean value of each frequency bin (calculated across time)
|
||||
@ -576,23 +518,22 @@ def denoise_spectrogram(spec: xr.DataArray) -> xr.DataArray:
|
||||
|
||||
def scale_spectrogram(
|
||||
spec: xr.DataArray,
|
||||
scale: Scales,
|
||||
scale: Literal["dB", "power", "amplitude"],
|
||||
dtype: DTypeLike = np.float32, # type: ignore
|
||||
) -> xr.DataArray:
|
||||
"""Apply configured amplitude scaling to the spectrogram.
|
||||
"""Apply final amplitude scaling/representation to the spectrogram.
|
||||
|
||||
Dispatches to the appropriate scaling function (log, PCEN) based on the
|
||||
`scale` configuration object's `name` field. If `scale.name` is
|
||||
"amplitude", the spectrogram is returned unchanged (as it's already
|
||||
magnitude/amplitude).
|
||||
Converts the input magnitude spectrogram based on the `scale` type:
|
||||
- "dB": Applies logarithmic scaling `log1p(C * S)`.
|
||||
- "power": Squares the magnitude values `S^2`.
|
||||
- "amplitude": Returns the input magnitude values `S` unchanged.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
spec : xr.DataArray
|
||||
Input magnitude spectrogram.
|
||||
scale : Scales
|
||||
The configuration object specifying the scaling method and parameters
|
||||
(instance of LogScaleConfig, PcenScaleConfig, or AmplitudeScaleConfig).
|
||||
Input magnitude spectrogram (potentially after PCEN).
|
||||
scale : Literal["dB", "power", "amplitude"]
|
||||
The target amplitude representation.
|
||||
dtype : DTypeLike, default=np.float32
|
||||
Target data type for the output scaled spectrogram.
|
||||
|
||||
@ -601,22 +542,16 @@ def scale_spectrogram(
|
||||
xr.DataArray
|
||||
Spectrogram with the specified amplitude scaling applied.
|
||||
"""
|
||||
if scale.name == "log":
|
||||
if scale == "dB":
|
||||
return scale_log(spec, dtype=dtype)
|
||||
|
||||
if scale.name == "pcen":
|
||||
return scale_pcen(
|
||||
spec,
|
||||
time_constant=scale.time_constant,
|
||||
gain=scale.gain,
|
||||
power=scale.power,
|
||||
bias=scale.bias,
|
||||
)
|
||||
if scale == "power":
|
||||
return spec**2
|
||||
|
||||
return spec
|
||||
|
||||
|
||||
def scale_pcen(
|
||||
def apply_pcen(
|
||||
spec: xr.DataArray,
|
||||
time_constant: float = 0.4,
|
||||
gain: float = 0.98,
|
||||
|
||||
377
batdetect2/preprocess/types.py
Normal file
377
batdetect2/preprocess/types.py
Normal file
@ -0,0 +1,377 @@
|
||||
"""Defines common interfaces (Protocols) for preprocessing components.
|
||||
|
||||
This module centralizes the Protocol definitions used throughout the
|
||||
`batdetect2.preprocess` package. Protocols define expected methods and
|
||||
signatures, allowing for flexible and interchangeable implementations of
|
||||
components like audio loaders and spectrogram builders.
|
||||
|
||||
Using these protocols ensures that different parts of the preprocessing
|
||||
pipeline can interact consistently, regardless of the specific underlying
|
||||
implementation (e.g., different libraries or custom configurations).
|
||||
"""
|
||||
|
||||
from typing import Optional, Protocol, Union
|
||||
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
from soundevent import data
|
||||
|
||||
|
||||
class AudioLoader(Protocol):
|
||||
"""Defines the interface for an audio loading and processing component.
|
||||
|
||||
An AudioLoader is responsible for retrieving audio data corresponding to
|
||||
different soundevent objects (files, Recordings, Clips) and applying a
|
||||
configured set of initial preprocessing steps. Adhering to this protocol
|
||||
allows for different loading strategies or implementations.
|
||||
"""
|
||||
|
||||
def load_file(
|
||||
self,
|
||||
path: data.PathLike,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load and preprocess audio directly from a file path.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : PathLike
|
||||
Path to the audio file.
|
||||
audio_dir : PathLike, optional
|
||||
A directory prefix to prepend to the path if `path` is relative.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The loaded and preprocessed audio waveform as an xarray DataArray
|
||||
with time coordinates. Typically loads only the first channel.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError
|
||||
If the audio file cannot be found.
|
||||
Exception
|
||||
If the audio file cannot be loaded or processed.
|
||||
"""
|
||||
...
|
||||
|
||||
def load_recording(
|
||||
self,
|
||||
recording: data.Recording,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load and preprocess the entire audio for a Recording object.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
recording : data.Recording
|
||||
The Recording object containing metadata about the audio file.
|
||||
audio_dir : PathLike, optional
|
||||
A directory where the audio file associated with the recording
|
||||
can be found, especially if the path in the recording is relative.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The loaded and preprocessed audio waveform. Typically loads only
|
||||
the first channel.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError
|
||||
If the audio file associated with the recording cannot be found.
|
||||
Exception
|
||||
If the audio file cannot be loaded or processed.
|
||||
"""
|
||||
...
|
||||
|
||||
def load_clip(
|
||||
self,
|
||||
clip: data.Clip,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load and preprocess the audio segment defined by a Clip object.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clip : data.Clip
|
||||
The Clip object specifying the recording and the start/end times
|
||||
of the segment to load.
|
||||
audio_dir : PathLike, optional
|
||||
A directory where the audio file associated with the clip's
|
||||
recording can be found.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The loaded and preprocessed audio waveform for the specified clip
|
||||
duration. Typically loads only the first channel.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError
|
||||
If the audio file associated with the clip cannot be found.
|
||||
Exception
|
||||
If the audio file cannot be loaded or processed.
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
class SpectrogramBuilder(Protocol):
|
||||
"""Defines the interface for a spectrogram generation component.
|
||||
|
||||
A SpectrogramBuilder takes a waveform (as numpy array or xarray DataArray)
|
||||
and produces a spectrogram (as an xarray DataArray) based on its internal
|
||||
configuration or implementation.
|
||||
"""
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
wav: Union[np.ndarray, xr.DataArray],
|
||||
samplerate: Optional[int] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Generate a spectrogram from an audio waveform.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
wav : Union[np.ndarray, xr.DataArray]
|
||||
The input audio waveform. If a numpy array, `samplerate` must
|
||||
also be provided. If an xarray DataArray, it must have a 'time'
|
||||
coordinate from which the sample rate can be inferred.
|
||||
samplerate : int, optional
|
||||
The sample rate of the audio in Hz. Required if `wav` is a
|
||||
numpy array. If `wav` is an xarray DataArray, this parameter is
|
||||
ignored as the sample rate is derived from the coordinates.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The computed spectrogram as an xarray DataArray with 'time' and
|
||||
'frequency' coordinates.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If `wav` is a numpy array and `samplerate` is not provided, or
|
||||
if `wav` is an xarray DataArray without a valid 'time' coordinate.
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
class Preprocessor(Protocol):
|
||||
"""Defines a high-level interface for the complete preprocessing pipeline.
|
||||
|
||||
A Preprocessor combines audio loading and spectrogram generation steps.
|
||||
It provides methods to go directly from source descriptions (file paths,
|
||||
Recording objects, Clip objects) to the final spectrogram representation
|
||||
needed by the model. It may also expose intermediate steps like audio
|
||||
loading or spectrogram computation from a waveform.
|
||||
"""
|
||||
|
||||
def preprocess_file(
|
||||
self,
|
||||
path: data.PathLike,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load audio from a file and compute the final processed spectrogram.
|
||||
|
||||
Performs the full pipeline:
|
||||
|
||||
Load -> Preprocess Audio -> Compute Spectrogram.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : PathLike
|
||||
Path to the audio file.
|
||||
audio_dir : PathLike, optional
|
||||
A directory prefix if `path` is relative.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The final processed spectrogram.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError
|
||||
If the audio file cannot be found.
|
||||
Exception
|
||||
If any step in the loading or preprocessing fails.
|
||||
"""
|
||||
...
|
||||
|
||||
def preprocess_recording(
|
||||
self,
|
||||
recording: data.Recording,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load audio for a Recording and compute the processed spectrogram.
|
||||
|
||||
Performs the full pipeline for the entire duration of the recording.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
recording : data.Recording
|
||||
The Recording object.
|
||||
audio_dir : PathLike, optional
|
||||
Directory containing the audio file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The final processed spectrogram.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError
|
||||
If the audio file cannot be found.
|
||||
Exception
|
||||
If any step in the loading or preprocessing fails.
|
||||
"""
|
||||
...
|
||||
|
||||
def preprocess_clip(
|
||||
self,
|
||||
clip: data.Clip,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load audio for a Clip and compute the final processed spectrogram.
|
||||
|
||||
Performs the full pipeline for the specified clip segment.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clip : data.Clip
|
||||
The Clip object defining the audio segment.
|
||||
audio_dir : PathLike, optional
|
||||
Directory containing the audio file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The final processed spectrogram.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError
|
||||
If the audio file cannot be found.
|
||||
Exception
|
||||
If any step in the loading or preprocessing fails.
|
||||
"""
|
||||
...
|
||||
|
||||
def load_file_audio(
|
||||
self,
|
||||
path: data.PathLike,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load and preprocess *only* the audio waveform from a file path.
|
||||
|
||||
Performs the initial audio loading and waveform processing steps
|
||||
(like resampling, scaling), but stops *before* spectrogram generation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : PathLike
|
||||
Path to the audio file.
|
||||
audio_dir : PathLike, optional
|
||||
A directory prefix if `path` is relative.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The loaded and preprocessed audio waveform.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError, Exception
|
||||
If audio loading/preprocessing fails.
|
||||
"""
|
||||
...
|
||||
|
||||
def load_recording_audio(
|
||||
self,
|
||||
recording: data.Recording,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load and preprocess *only* the audio waveform for a Recording.
|
||||
|
||||
Performs the initial audio loading and waveform processing steps
|
||||
for the entire recording duration.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
recording : data.Recording
|
||||
The Recording object.
|
||||
audio_dir : PathLike, optional
|
||||
Directory containing the audio file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The loaded and preprocessed audio waveform.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError, Exception
|
||||
If audio loading/preprocessing fails.
|
||||
"""
|
||||
...
|
||||
|
||||
def load_clip_audio(
|
||||
self,
|
||||
clip: data.Clip,
|
||||
audio_dir: Optional[data.PathLike] = None,
|
||||
) -> xr.DataArray:
|
||||
"""Load and preprocess *only* the audio waveform for a Clip.
|
||||
|
||||
Performs the initial audio loading and waveform processing steps
|
||||
for the specified clip segment.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clip : data.Clip
|
||||
The Clip object defining the segment.
|
||||
audio_dir : PathLike, optional
|
||||
Directory containing the audio file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The loaded and preprocessed audio waveform segment.
|
||||
|
||||
Raises
|
||||
------
|
||||
FileNotFoundError, Exception
|
||||
If audio loading/preprocessing fails.
|
||||
"""
|
||||
...
|
||||
|
||||
def compute_spectrogram(
|
||||
self,
|
||||
wav: Union[xr.DataArray, np.ndarray],
|
||||
) -> xr.DataArray:
|
||||
"""Compute the spectrogram from a pre-loaded audio waveform.
|
||||
|
||||
Applies the spectrogram generation steps (STFT, scaling, etc.) defined
|
||||
by the `SpectrogramBuilder` component of the preprocessor to an
|
||||
already loaded (and potentially preprocessed) waveform.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
wav : Union[xr.DataArray, np.ndarray]
|
||||
The input audio waveform. If numpy array, `samplerate` is required.
|
||||
samplerate : int, optional
|
||||
Sample rate in Hz (required if `wav` is np.ndarray).
|
||||
|
||||
Returns
|
||||
-------
|
||||
xr.DataArray
|
||||
The computed spectrogram.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError, Exception
|
||||
If waveform input is invalid or spectrogram computation fails.
|
||||
"""
|
||||
...
|
||||
Loading…
Reference in New Issue
Block a user