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import gradio as gr
import os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import bat_detect.utils.detector_utils as du
import bat_detect.utils.audio_utils as au
import bat_detect.utils.plot_utils as viz
# setup the arguments
args = {}
args = du.get_default_bd_args()
args['detection_threshold'] = 0.3
args['time_expansion_factor'] = 1
args['model_path'] = 'models/Net2DFast_UK_same.pth.tar'
max_duration = 2.0
# load the model
model, params = du.load_model(args['model_path'])
df = gr.Dataframe(
headers=["species", "time", "detection_prob", "species_prob"],
datatype=["str", "str", "str", "str"],
row_count=1,
col_count=(4, "fixed"),
label='Predictions'
)
examples = [['example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav', 0.3],
['example_data/audio/20180530_213516-EPTSER-LR_0_0.5.wav', 0.3],
['example_data/audio/20180627_215323-RHIFER-LR_0_0.5.wav', 0.3]]
def make_prediction(file_name=None, detection_threshold=0.3):
if file_name is not None:
audio_file = file_name
else:
return "You must provide an input audio file."
if detection_threshold is not None and detection_threshold != '':
args['detection_threshold'] = float(detection_threshold)
# process the file to generate predictions
results = du.process_file(audio_file, model, params, args, max_duration=max_duration)
anns = [ann for ann in results['pred_dict']['annotation']]
clss = [aa['class'] for aa in anns]
st_time = [aa['start_time'] for aa in anns]
cls_prob = [aa['class_prob'] for aa in anns]
det_prob = [aa['det_prob'] for aa in anns]
data = {'species': clss, 'time': st_time, 'detection_prob': det_prob, 'species_prob': cls_prob}
df = pd.DataFrame(data=data)
im = generate_results_image(audio_file, anns)
return [df, im]
def generate_results_image(audio_file, anns):
# load audio
sampling_rate, audio = au.load_audio_file(audio_file, args['time_expansion_factor'],
params['target_samp_rate'], params['scale_raw_audio'], max_duration=max_duration)
duration = audio.shape[0] / sampling_rate
# generate spec
spec, spec_viz = au.generate_spectrogram(audio, sampling_rate, params, True, False)
# create fig
plt.close('all')
fig = plt.figure(1, figsize=(spec.shape[1]/100, spec.shape[0]/100), dpi=100, frameon=False)
spec_duration = au.x_coords_to_time(spec.shape[1], sampling_rate, params['fft_win_length'], params['fft_overlap'])
viz.create_box_image(spec, fig, anns, 0, spec_duration, spec_duration, params, spec.max()*1.1, False, True)
plt.ylabel('Freq - kHz')
plt.xlabel('Time - secs')
plt.tight_layout()
# convert fig to image
fig.canvas.draw()
data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
w, h = fig.canvas.get_width_height()
im = data.reshape((int(h), int(w), -1))
return im
descr_txt = "Demo of BatDetect2 deep learning-based bat echolocation call detection. " \
"<br>This model is only trained on bat species from the UK. If the input " \
"file is longer than 2 seconds, only the first 2 seconds will be processed." \
"<br>Check out the paper [here](https://www.biorxiv.org/content/10.1101/2022.12.14.520490v1)."
gr.Interface(
fn = make_prediction,
inputs = [gr.Audio(source="upload", type="filepath", optional=True),
gr.Dropdown([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])],
outputs = [df, gr.Image(label="Visualisation")],
theme = "huggingface",
title = "BatDetect2 Demo",
description = descr_txt,
examples = examples,
allow_flagging = 'never',
).launch()
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