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| import librosa | |
| import tensorflow as tf | |
| import soundfile | |
| import gradio as gr | |
| import pandas as pd | |
| import os | |
| import random | |
| import numpy as np | |
| # Set the environment variable | |
| os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" | |
| audio_files_path = 'examples' | |
| c_names = ['Bronchiectasis', 'COPD', 'Healthy', 'Pneumonia', 'URTI'] | |
| # Loading Audio Files | |
| audio_files = [] | |
| for file in os.listdir(audio_files_path): | |
| if file.endswith('.wav'): | |
| audio_files.append(file) | |
| # convert the list to a df | |
| audio_files_df = pd.DataFrame(audio_files, columns=['audio_paths']) | |
| # print(audio_files_df.iloc[0]['audio_file']) | |
| audio_files_to_show = audio_files_df['audio_paths'].tolist() | |
| # create a gradio interface | |
| # 0. Load models | |
| # 1. Audio File input | |
| # 2. clear and Submit button | |
| # 3. Upon submit , first preprocess the audio file using log mel and then run the outputs through the AI model | |
| # # 4. Output the prediction | |
| def load_model(): | |
| # Load the model | |
| return tf.keras.models.load_model("models/lung_disease_predictor_cnn_logmel_without_data_augmentation.keras") | |
| def preprocessing(audio_file, mode): | |
| # we want to resample audio to 16 kHz | |
| sr_new = 16000 # 16kHz sample rate | |
| x, sr = librosa.load(audio_file, sr=sr_new) | |
| # padding sound | |
| # because duration of sound is dominantly 20 s and all of sample rate is 22050 | |
| # we want to pad or truncated sound which is below or above 20 s respectively | |
| max_len = 5 * sr_new # length of sound array = time x sample rate | |
| if x.shape[0] < max_len: | |
| # padding with zero | |
| pad_width = max_len - x.shape[0] | |
| x = np.pad(x, (0, pad_width)) | |
| elif x.shape[0] > max_len: | |
| # truncated | |
| x = x[:max_len] | |
| if mode == 'mfcc': | |
| feature = librosa.feature.mfcc(y=x, sr=sr_new) | |
| elif mode == 'log_mel': | |
| feature = librosa.feature.melspectrogram(y=x, sr=sr_new, n_mels=128, fmax=8000) | |
| feature = librosa.power_to_db(feature, ref=np.max) | |
| return feature | |
| def predict_lung_disease(audio_data): | |
| # Create a temporary file | |
| filename = "temp/lungs_audio.wav" # Set your desired filename | |
| soundfile.write(filename, audio_data[1],samplerate=audio_data[0]) # Save audio to file | |
| # Process the temporary audio file | |
| processed_audio = preprocessing(filename, 'log_mel').reshape((-1, 128, 157, 1)) | |
| new_preds = model.predict(processed_audio) | |
| new_classpreds = np.argmax(new_preds, axis=1) | |
| print(str(c_names[new_classpreds[0]])) | |
| return str(c_names[new_classpreds[0]]) | |
| # Gradio Interface | |
| model = load_model() | |
| # have example audio files to test | |
| # Interface | |
| iface = gr.Interface( | |
| fn=predict_lung_disease, | |
| inputs=["audio"], | |
| outputs="text", | |
| title="VitalSenseAI", | |
| examples= | |
| [ | |
| [os.path.join(audio_files_path,audio_file)] for audio_file in audio_files_to_show | |
| ] | |
| ) | |
| iface.launch() | |