| import librosa | |
| import numpy as np | |
| import tensorflow as tf | |
| from pydub import AudioSegment | |
| from keras.models import load_model | |
| import os | |
| model_rep = load_model('/home/mansi/anaconda3/beproject/stutter_det/models/best_model_rep.h5') | |
| model_pro = load_model('/home/mansi/anaconda3/beproject/stutter_det/models/best_model_pro.h5') | |
| def detect_prolongation(mfcc): | |
| s = 0 | |
| for m in mfcc: | |
| y = model_pro.predict(m.reshape(1,2,44,1), batch_size=1) | |
| y = np.around(y,decimals=2) | |
| if y[0][0] > 0.5: | |
| s += y[0][0] | |
| p_sev = s/len(mfcc)*100 | |
| return p_sev | |
| def detect_repetition(mfcc): | |
| s = 0 | |
| for m in mfcc: | |
| y = model_rep.predict(m.reshape(1,13,44,1), batch_size=1) | |
| y = np.around(y,decimals=2) | |
| if y[0][0] > 0.5: | |
| s += y[0][0] | |
| r_sev = s/len(mfcc)*100 | |
| return r_sev | |
| def detect_stutter(audio): | |
| sound_file = AudioSegment.from_wav(audio) | |
| audio_chunks = sound_file[::1000] | |
| ps = 0 | |
| rs = 0 | |
| mfcc_arr_p = [] | |
| mfcc_arr_r = [] | |
| for i, chunk in enumerate(audio_chunks): | |
| chunkfile = "chunks_test/chunk{0}.wav".format(i) | |
| chunk.export(chunkfile, format="wav") | |
| y, sr = librosa.load(chunkfile) | |
| mfcc = np.array(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)) | |
| if mfcc.shape[0] == 13 and mfcc.shape[1] == 44: | |
| a = [] | |
| a.append(mfcc) | |
| mfcc_arr_r.append(a) | |
| b = [] | |
| b.append(mfcc[0]) | |
| b.append(mfcc[12]) | |
| mfcc_arr_p.append(b) | |
| mfcc_arr_r = np.array(mfcc_arr_r) | |
| mfcc_arr_p = np.array(mfcc_arr_p) | |
| mfcc_arr_r.reshape(mfcc_arr_r.shape[0], 13, 44, 1) | |
| mfcc_arr_p.reshape(mfcc_arr_p.shape[0], 2, 44, 1) | |
| p_sev = detect_prolongation(mfcc_arr_p) | |
| r_sev = detect_repetition(mfcc_arr_r) | |
| o_sev = (p_sev+r_sev)/2 | |
| return p_sev, r_sev, o_sev | |
| if __name__== "__main__": | |
| common = '/home/mansi/anaconda3/beproject/stutter_det/demo_audios' | |
| arr1 = os.listdir(common) | |
| for a in arr1: | |
| print('\n'+a) | |
| arr2 = os.listdir(common+'/'+a) | |
| for b in arr2: | |
| if b.endswith('.wav'): | |
| print('\n'+b) | |
| p_sev, r_sev, o_sev = detect_stutter(common+'/'+a+'/'+b) | |
| print('Prolongation % : '+str(p_sev)) | |
| print('Repetition % : '+str(r_sev)) | |
| print('Overall stutter % : '+str(o_sev)) | |