mirror of
https://github.com/macaodha/batdetect2.git
synced 2025-06-29 14:41:58 +02:00
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
import gradio as gr
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import os
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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import bat_detect.utils.detector_utils as du
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import bat_detect.utils.audio_utils as au
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import bat_detect.utils.plot_utils as viz
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# setup the arguments
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args = {}
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args = du.get_default_bd_args()
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args['detection_threshold'] = 0.3
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args['time_expansion_factor'] = 1
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args['model_path'] = 'models/Net2DFast_UK_same.pth.tar'
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# load the model
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model, params = du.load_model(args['model_path'])
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"""
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# read the audio file
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sampling_rate, audio = au.load_audio_file(audio_file, args['time_expansion_factor'], params['target_samp_rate'], params['scale_raw_audio'])
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duration = audio.shape[0] / sampling_rate
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print('File duration: {} seconds'.format(duration))
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# generate spectrogram for visualization
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spec, spec_viz = au.generate_spectrogram(audio, sampling_rate, params, True, False)
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# display the detections on top of the spectrogram
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# note, if the audio file is very long, this image will be very large - best to crop the audio first
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start_time = 0.0
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detections = [ann for ann in results['pred_dict']['annotation']]
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fig = plt.figure(1, figsize=(spec.shape[1]/100, spec.shape[0]/100), dpi=100, frameon=False)
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spec_duration = au.x_coords_to_time(spec.shape[1], sampling_rate, params['fft_win_length'], params['fft_overlap'])
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viz.create_box_image(spec, fig, detections, start_time, start_time+spec_duration, spec_duration, params, spec.max()*1.1, False)
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plt.ylabel('Freq - kHz')
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plt.xlabel('Time - secs')
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plt.title(os.path.basename(audio_file))
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plt.show()
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"""
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df = gr.Dataframe(
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headers=["species", "time_in_file", "species_prob"],
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datatype=["str", "str", "str"],
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row_count=1,
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col_count=(3, "fixed"),
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)
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examples = [['example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav', 0.3],
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['example_data/audio/20180530_213516-EPTSER-LR_0_0.5.wav', 0.3],
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['example_data/audio/20180627_215323-RHIFER-LR_0_0.5.wav', 0.3]]
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def make_prediction(file_name=None, detection_threshold=0.3):
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if file_name is not None:
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audio_file = file_name
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else:
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return "You must provide an input audio file."
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if detection_threshold != '':
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args['detection_threshold'] = float(detection_threshold)
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results = du.process_file(audio_file, model, params, args, max_duration=5.0)
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clss = [aa['class'] for aa in results['pred_dict']['annotation']]
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st_time = [aa['start_time'] for aa in results['pred_dict']['annotation']]
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cls_prob = [aa['class_prob'] for aa in results['pred_dict']['annotation']]
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data = {'species': clss, 'time_in_file': st_time, 'species_prob': cls_prob}
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df = pd.DataFrame(data=data)
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return df
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descr_txt = "Demo of BatDetect2 deep learning-based bat echolocation call detection. " \
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"<br>This model is only trained on bat species from the UK. If the input " \
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"file is longer than 5 seconds, only the first 5 seconds will be processed." \
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"<br>Check out the paper [here](https://www.biorxiv.org/content/10.1101/2022.12.14.520490v1)."
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gr.Interface(
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fn = make_prediction,
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inputs = [gr.Audio(source="upload", type="filepath", optional=True),
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gr.Dropdown([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])],
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outputs = df,
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theme = "huggingface",
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title = "BatDetect2 Demo",
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description = descr_txt,
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examples = examples,
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allow_flagging = 'never',
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).launch()
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