config.py

#################
# Misc settings #
#################

# Random seed for gaussian noise
RANDOM_SEED: int = 42

##########################
# Model paths and config #
##########################

MODEL_VERSION: str = "V2.4"
PB_MODEL: str = "checkpoints/V2.4/BirdNET_GLOBAL_6K_V2.4_Model"
# MODEL_PATH = PB_MODEL # This will load the protobuf model
MODEL_PATH: str = "checkpoints/V2.4/BirdNET_GLOBAL_6K_V2.4_Model_FP32.tflite"
MDATA_MODEL_PATH: str = "checkpoints/V2.4/BirdNET_GLOBAL_6K_V2.4_MData_Model_V2_FP16.tflite"
LABELS_FILE: str = "checkpoints/V2.4/BirdNET_GLOBAL_6K_V2.4_Labels.txt"
TRANSLATED_LABELS_PATH: str = "labels/V2.4"

# Path to custom trained classifier
# If None, no custom classifier will be used
# Make sure to set the LABELS_FILE above accordingly
CUSTOM_CLASSIFIER = None

##################
# Audio settings #
##################

# We use a sample rate of 48kHz, so the model input size is
# (batch size, 48000 kHz * 3 seconds) = (1, 144000)
# Recordings will be resampled automatically.
SAMPLE_RATE: int = 48000

# We're using 3-second chunks
SIG_LENGTH: float = 3.0

# Define overlap between consecutive chunks <3.0; 0 = no overlap
SIG_OVERLAP: float = 0

# Define minimum length of audio chunk for prediction,
# chunks shorter than 3 seconds will be padded with zeros
SIG_MINLEN: float = 1.0

# Frequency range. This is model specific and should not be changed.
SIG_FMIN: int = 0
SIG_FMAX: int = 15000

# Settings for bandpass filter
BANDPASS_FMIN: int = 0
BANDPASS_FMAX: int = 15000

#####################
# Metadata settings #
#####################

LATITUDE: float = -1
LONGITUDE: float = -1
WEEK: int = -1
LOCATION_FILTER_THRESHOLD: float = 0.03

######################
# Inference settings #
######################

# If None or empty file, no custom species list will be used
# Note: Entries in this list have to match entries from the LABELS_FILE
# We use the 2021 eBird taxonomy for species names (Clements list)
CODES_FILE: str = "eBird_taxonomy_codes_2021E.json"
SPECIES_LIST_FILE: str = "example/species_list.txt"

# File input path and output path for selection tables
INPUT_PATH: str = "example/"
OUTPUT_PATH: str = "example/"

# Supported file types
ALLOWED_FILETYPES: list[str] = ["wav", "flac", "mp3", "ogg", "m4a", "wma", "aiff", "aif"]

# Number of threads to use for inference.
# Can be as high as number of CPUs in your system
CPU_THREADS: int = 8
TFLITE_THREADS: int = 1

# False will output logits, True will convert to sigmoid activations
APPLY_SIGMOID: bool = True
SIGMOID_SENSITIVITY: float = 1.0

# Minimum confidence score to include in selection table
# (be aware: if APPLY_SIGMOID = False, this no longer represents
# probabilities and needs to be adjusted)
MIN_CONFIDENCE: float = 0.1

# Number of samples to process at the same time. Higher values can increase
# processing speed, but will also increase memory usage.
# Might only be useful for GPU inference.
BATCH_SIZE: int = 1


# Number of seconds to load from a file at a time
# Files will be loaded into memory in segments that are only as long as this value
# Lowering this value results in lower memory usage
FILE_SPLITTING_DURATION: int = 600

# Whether to use noise to pad the signal
# If set to False, the signal will be padded with zeros
USE_NOISE: bool = False

# Specifies the output format. 'table' denotes a Raven selection table,
# 'audacity' denotes a TXT file with the same format as Audacity timeline labels
# 'csv' denotes a generic CSV file with start, end, species and confidence.
RESULT_TYPES: set[str] | list[str] = {"table"}
OUTPUT_RAVEN_FILENAME: str = "BirdNET_SelectionTable.txt"  # this is for combined Raven selection tables only
OUTPUT_RTABLE_FILENAME: str = "BirdNET_RTable.csv"
OUTPUT_KALEIDOSCOPE_FILENAME: str = "BirdNET_Kaleidoscope.csv"
OUTPUT_CSV_FILENAME: str = "BirdNET_CombinedTable.csv"

# Whether to skip existing results in the output path
# If set to False, existing files will not be overwritten
SKIP_EXISTING_RESULTS: bool = False

COMBINE_RESULTS: bool = False
#####################
# Training settings #
#####################

# Training data path
TRAIN_DATA_PATH: str = "train_data/"

# Sample crop mode
SAMPLE_CROP_MODE: str = "center"

# List of non-event classes
NON_EVENT_CLASSES: list[str] = ["noise", "other", "background", "silence"]

# Upsampling settings
UPSAMPLING_RATIO: float = 0.0
UPSAMPLING_MODE = "repeat"

# Number of epochs to train for
TRAIN_EPOCHS: int = 50

# Batch size for training
TRAIN_BATCH_SIZE: int = 32

# Validation split (percentage)
TRAIN_VAL_SPLIT: float = 0.2

# Learning rate for training
TRAIN_LEARNING_RATE: float = 0.001

# Number of hidden units in custom classifier
# If >0, a two-layer classifier will be trained
TRAIN_HIDDEN_UNITS: int = 0

# Dropout rate for training
TRAIN_DROPOUT: float = 0.0

# Whether to use mixup for training
TRAIN_WITH_MIXUP: bool = False

# Whether to apply label smoothing for training
TRAIN_WITH_LABEL_SMOOTHING: bool = False

# Model output format
TRAINED_MODEL_OUTPUT_FORMAT: str = "tflite"

# Model save mode (replace or append new classifier)
TRAINED_MODEL_SAVE_MODE: str = "replace"

# Cache settings
TRAIN_CACHE_MODE: str = "none"
TRAIN_CACHE_FILE: str = "train_cache.npz"

# Use automatic Hyperparameter tuning
AUTOTUNE: bool = False

# How many trials are done for the hyperparameter tuning
AUTOTUNE_TRIALS: int = 50

# How many executions per trial are done for the hyperparameter tuning
# Mutliple executions will be averaged, so the evaluation is more consistent
AUTOTUNE_EXECUTIONS_PER_TRIAL: int = 1

# If a binary classification model is trained.
# This value will be detected automatically in the training script, if only one class and a non-event class is used.
BINARY_CLASSIFICATION: bool = False

# If a model for a multi-label setting is trained.
# This value will automatically be set, if subfolders in the input direcotry are named with multiple classes separated by commas.
MULTI_LABEL: bool = False

#####################
# Misc runtime vars #
#####################
CODES = {}
LABELS: list[str] = []
TRANSLATED_LABELS: list[str] = []
SPECIES_LIST: list[str] = []
ERROR_LOG_FILE: str = "error_log.txt"
FILE_LIST = []
FILE_STORAGE_PATH: str = ""

######################
# Get and set config #
######################


def getConfig():
    return {
        "RANDOM_SEED": RANDOM_SEED,
        "MODEL_VERSION": MODEL_VERSION,
        "PB_MODEL": PB_MODEL,
        "MODEL_PATH": MODEL_PATH,
        "MDATA_MODEL_PATH": MDATA_MODEL_PATH,
        "LABELS_FILE": LABELS_FILE,
        "TRANSLATED_LABELS_PATH": TRANSLATED_LABELS_PATH,
        "CUSTOM_CLASSIFIER": CUSTOM_CLASSIFIER,
        "SAMPLE_RATE": SAMPLE_RATE,
        "SIG_LENGTH": SIG_LENGTH,
        "SIG_OVERLAP": SIG_OVERLAP,
        "SIG_MINLEN": SIG_MINLEN,
        "SIG_FMIN": SIG_FMIN,
        "SIG_FMAX": SIG_FMAX,
        "BANDPASS_FMIN": BANDPASS_FMIN,
        "BANDPASS_FMAX": BANDPASS_FMAX,
        "LATITUDE": LATITUDE,
        "LONGITUDE": LONGITUDE,
        "WEEK": WEEK,
        "LOCATION_FILTER_THRESHOLD": LOCATION_FILTER_THRESHOLD,
        "CODES_FILE": CODES_FILE,
        "SPECIES_LIST_FILE": SPECIES_LIST_FILE,
        "ALLOWED_FILETYPES": ALLOWED_FILETYPES,
        "INPUT_PATH": INPUT_PATH,
        "OUTPUT_PATH": OUTPUT_PATH,
        "CPU_THREADS": CPU_THREADS,
        "TFLITE_THREADS": TFLITE_THREADS,
        "APPLY_SIGMOID": APPLY_SIGMOID,
        "SIGMOID_SENSITIVITY": SIGMOID_SENSITIVITY,
        "MIN_CONFIDENCE": MIN_CONFIDENCE,
        "BATCH_SIZE": BATCH_SIZE,
        "RESULT_TYPES": RESULT_TYPES,
        "OUTPUT_FILENAME": OUTPUT_RAVEN_FILENAME,
        "TRAIN_DATA_PATH": TRAIN_DATA_PATH,
        "SAMPLE_CROP_MODE": SAMPLE_CROP_MODE,
        "NON_EVENT_CLASSES": NON_EVENT_CLASSES,
        "UPSAMPLING_RATIO": UPSAMPLING_RATIO,
        "UPSAMPLING_MODE": UPSAMPLING_MODE,
        "TRAIN_EPOCHS": TRAIN_EPOCHS,
        "TRAIN_VAL_SPLIT": TRAIN_VAL_SPLIT,
        "TRAIN_BATCH_SIZE": TRAIN_BATCH_SIZE,
        "TRAIN_LEARNING_RATE": TRAIN_LEARNING_RATE,
        "TRAIN_HIDDEN_UNITS": TRAIN_HIDDEN_UNITS,
        "TRAIN_DROPOUT": TRAIN_DROPOUT,
        "TRAIN_WITH_MIXUP": TRAIN_WITH_MIXUP,
        "TRAIN_WITH_LABEL_SMOOTHING": TRAIN_WITH_LABEL_SMOOTHING,
        "TRAINED_MODEL_OUTPUT_FORMAT": TRAINED_MODEL_OUTPUT_FORMAT,
        "TRAINED_MODEL_SAVE_MODE": TRAINED_MODEL_SAVE_MODE,
        "TRAIN_CACHE_MODE": TRAIN_CACHE_MODE,
        "TRAIN_CACHE_FILE": TRAIN_CACHE_FILE,
        "CODES": CODES,
        "LABELS": LABELS,
        "TRANSLATED_LABELS": TRANSLATED_LABELS,
        "SPECIES_LIST": SPECIES_LIST,
        "ERROR_LOG_FILE": ERROR_LOG_FILE,
        "FILE_LIST": FILE_LIST,
        "FILE_STORAGE_PATH": FILE_STORAGE_PATH,
        "SKIP_EXISTING_RESULTS": SKIP_EXISTING_RESULTS,
        "USE_NOISE": USE_NOISE,
    }


def setConfig(c):

    global RANDOM_SEED
    global MODEL_VERSION
    global PB_MODEL
    global MODEL_PATH
    global MDATA_MODEL_PATH
    global LABELS_FILE
    global TRANSLATED_LABELS_PATH
    global CUSTOM_CLASSIFIER
    global SAMPLE_RATE
    global SIG_LENGTH
    global SIG_OVERLAP
    global SIG_MINLEN
    global SIG_FMIN
    global SIG_FMAX
    global BANDPASS_FMIN
    global BANDPASS_FMAX
    global LATITUDE
    global LONGITUDE
    global WEEK
    global LOCATION_FILTER_THRESHOLD
    global CODES_FILE
    global SPECIES_LIST_FILE
    global ALLOWED_FILETYPES
    global INPUT_PATH
    global OUTPUT_PATH
    global CPU_THREADS
    global TFLITE_THREADS
    global APPLY_SIGMOID
    global SIGMOID_SENSITIVITY
    global MIN_CONFIDENCE
    global BATCH_SIZE
    global RESULT_TYPES
    global OUTPUT_RAVEN_FILENAME
    global TRAIN_DATA_PATH
    global SAMPLE_CROP_MODE
    global NON_EVENT_CLASSES
    global UPSAMPLING_RATIO
    global UPSAMPLING_MODE
    global TRAIN_EPOCHS
    global TRAIN_VAL_SPLIT
    global TRAIN_BATCH_SIZE
    global TRAIN_LEARNING_RATE
    global TRAIN_HIDDEN_UNITS
    global TRAIN_DROPOUT
    global TRAIN_WITH_MIXUP
    global TRAIN_WITH_LABEL_SMOOTHING
    global TRAINED_MODEL_OUTPUT_FORMAT
    global TRAINED_MODEL_SAVE_MODE
    global TRAIN_CACHE_MODE
    global TRAIN_CACHE_FILE
    global CODES
    global LABELS
    global TRANSLATED_LABELS
    global SPECIES_LIST
    global ERROR_LOG_FILE
    global FILE_LIST
    global FILE_STORAGE_PATH
    global SKIP_EXISTING_RESULTS
    global USE_NOISE

    RANDOM_SEED = c["RANDOM_SEED"]
    MODEL_VERSION = c["MODEL_VERSION"]
    PB_MODEL = c["PB_MODEL"]
    MODEL_PATH = c["MODEL_PATH"]
    MDATA_MODEL_PATH = c["MDATA_MODEL_PATH"]
    LABELS_FILE = c["LABELS_FILE"]
    TRANSLATED_LABELS_PATH = c["TRANSLATED_LABELS_PATH"]
    CUSTOM_CLASSIFIER = c["CUSTOM_CLASSIFIER"]
    SAMPLE_RATE = c["SAMPLE_RATE"]
    SIG_LENGTH = c["SIG_LENGTH"]
    SIG_OVERLAP = c["SIG_OVERLAP"]
    SIG_MINLEN = c["SIG_MINLEN"]
    SIG_FMIN = c["SIG_FMIN"]
    SIG_FMAX = c["SIG_FMAX"]
    BANDPASS_FMIN = c["BANDPASS_FMIN"]
    BANDPASS_FMAX = c["BANDPASS_FMAX"]
    LATITUDE = c["LATITUDE"]
    LONGITUDE = c["LONGITUDE"]
    WEEK = c["WEEK"]
    LOCATION_FILTER_THRESHOLD = c["LOCATION_FILTER_THRESHOLD"]
    CODES_FILE = c["CODES_FILE"]
    SPECIES_LIST_FILE = c["SPECIES_LIST_FILE"]
    ALLOWED_FILETYPES = c["ALLOWED_FILETYPES"]
    INPUT_PATH = c["INPUT_PATH"]
    OUTPUT_PATH = c["OUTPUT_PATH"]
    CPU_THREADS = c["CPU_THREADS"]
    TFLITE_THREADS = c["TFLITE_THREADS"]
    APPLY_SIGMOID = c["APPLY_SIGMOID"]
    SIGMOID_SENSITIVITY = c["SIGMOID_SENSITIVITY"]
    MIN_CONFIDENCE = c["MIN_CONFIDENCE"]
    BATCH_SIZE = c["BATCH_SIZE"]
    RESULT_TYPES = c["RESULT_TYPES"]
    OUTPUT_RAVEN_FILENAME = c["OUTPUT_FILENAME"]
    TRAIN_DATA_PATH = c["TRAIN_DATA_PATH"]
    SAMPLE_CROP_MODE = c["SAMPLE_CROP_MODE"]
    NON_EVENT_CLASSES = c["NON_EVENT_CLASSES"]
    UPSAMPLING_RATIO = c["UPSAMPLING_RATIO"]
    UPSAMPLING_MODE = c["UPSAMPLING_MODE"]
    TRAIN_EPOCHS = c["TRAIN_EPOCHS"]
    TRAIN_VAL_SPLIT = c["TRAIN_VAL_SPLIT"]
    TRAIN_BATCH_SIZE = c["TRAIN_BATCH_SIZE"]
    TRAIN_LEARNING_RATE = c["TRAIN_LEARNING_RATE"]
    TRAIN_HIDDEN_UNITS = c["TRAIN_HIDDEN_UNITS"]
    TRAIN_DROPOUT = c["TRAIN_DROPOUT"]
    TRAIN_WITH_MIXUP = c["TRAIN_WITH_MIXUP"]
    TRAIN_WITH_LABEL_SMOOTHING = c["TRAIN_WITH_LABEL_SMOOTHING"]
    TRAINED_MODEL_OUTPUT_FORMAT = c["TRAINED_MODEL_OUTPUT_FORMAT"]
    TRAINED_MODEL_SAVE_MODE = c["TRAINED_MODEL_SAVE_MODE"]
    TRAIN_CACHE_MODE = c["TRAIN_CACHE_MODE"]
    TRAIN_CACHE_FILE = c["TRAIN_CACHE_FILE"]
    CODES = c["CODES"]
    LABELS = c["LABELS"]
    TRANSLATED_LABELS = c["TRANSLATED_LABELS"]
    SPECIES_LIST = c["SPECIES_LIST"]
    ERROR_LOG_FILE = c["ERROR_LOG_FILE"]
    FILE_LIST = c["FILE_LIST"]
    FILE_STORAGE_PATH = c["FILE_STORAGE_PATH"]
    SKIP_EXISTING_RESULTS = c["SKIP_EXISTING_RESULTS"]
    USE_NOISE = c["USE_NOISE"]