Below is an example of deploying a trained multi-class ResNet model over a longer sound file.
# Load the library
library(gibbonNetR)
# Load data
data("TempBinWav")
# Create a temp directory to save the file
dir.create(
file.path(tempdir(), "/MultiDir/Wav/", sep = ""),
recursive = T,
showWarnings = FALSE
)
# Write to temp directory
writeWave(TempBinWav, filename = paste(file.path(tempdir(), "/MultiDir/Wav/"), "TempBinWav.wav", sep = ""))
# Find model path
trained_models_dir <- system.file("extdata", "trainedresnetmulti/", package = "gibbonNetR")
# Specify model path
ModelPath <- list.files(trained_models_dir, full.names = T)
# Deploy trained model over sound files
deploy_CNN_multi(
clip_duration = 12,
architecture = "resnet18",
output_folder = file.path(tempdir(), "/MultiDir/Results/Images/", sep = ""),
output_folder_selections = file.path(tempdir(), "/MultiDir/Results/Selections/", sep = ""),
output_folder_wav = file.path(tempdir(), "/MultiDir/Results/Wavs/", sep = ""),
detect_pattern = NA,
top_model_path = ModelPath,
path_to_files = paste(tempdir(), "/MultiDir/Wav/", sep = ""),
downsample_rate = "NA",
save_wav = F,
class_names = c(
"female.gibbon",
"hornbill.helmeted",
"hornbill.rhino",
"long.argus",
"noise"
),
noise_category = "noise",
single_class = FALSE,
single_class_category = "female.gibbon",
threshold = .25,
max_freq_khz = 2
)We can then look at the resulting selection table
# List the files in the selection table folder (as specified above)
TempDir <- list.files(file.path(tempdir(), "/MultiDir/Results/Selections/", sep = ""),
full.names = TRUE)
# Read in the selection table
read.delim(TempDir[1])
# There is one gibbon detection
Selection View Channel Begin.Time..s. End.Time..s. Low.Freq..Hz.
1 1 Spectrogram 1 1 13 25 100
High.Freq..Hz. Probability Detections Class
1 2000 0.25 TempBinWav.wav_13 female.gibbon