predict.py

#Developed By: Tonumoy Mukherjee


import pickle
import os
import numpy as np
from tqdm import tqdm
from scipy.io import wavfile
from python_speech_features import mfcc
from keras.models import load_model
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import confusion_matrix

def build_predictions(audio_dir):
    y_true = []
    y_pred = []
    fn_prob = {}
    
    print('Extacting features from audio')
    for fn in tqdm(os.listdir(audio_dir)):
        rate, wav = wavfile.read(os.path.join(audio_dir, fn))
        label = fn2class[fn]
        c = classes.index(label)
        y_prob = []
        
        for i in range(0, wav.shape[0]-config.step, config.step):
            sample = wav[i:i+config.step]
            x = mfcc(sample, rate, numcep=config.nfeat,
                     nfilt=config.nfilt, nfft=config.nfft)
            x = (x - config.min) / (config.max - config.min) 
            
            if config.mode == 'conv':
                x = x.reshape(1, x.shape[0], x.shape[1], 1)
            elif config.mode == 'time':
                x = np.expand_dims(x, axis=0)
            y_hat = model.predict(x)
            y_prob.append(y_hat)
            y_pred.append(np.argmax(y_hat))
            y_true.append(c)
            
        fn_prob[fn] = np.mean(y_prob, axis=0).flatten()
        
    return y_true, y_pred, fn_prob
    
    
df = pd.read_csv('test_new.csv') # test csv file input
classes = list(np.unique(df.label))
fn2class = dict(zip(df.fname, df.label))
p_path = os.path.join('pickles' , 'conv.p')

with open(p_path, 'rb') as handle:
    config = pickle.load(handle)
    
model = load_model(config.model_path)

y_true, y_pred, fn_prob = build_predictions('clean-test') #test data folder input
acc_score = accuracy_score(y_true = y_true, y_pred = y_pred)

kappa_score = cohen_kappa_score(y_true, y_pred, labels=None, weights=None, sample_weight=None)
cfn_mat = confusion_matrix(y_true, y_pred, labels=None, sample_weight=None) #confusion matrix
y_probs = []
for i, row in df.iterrows():
    y_prob = fn_prob[row.fname]
    y_probs.append(y_prob)
    for c, p in zip(classes, y_prob):
        df.at[i, c] = p
        
y_pred = [classes[np.argmax(y)] for y in y_probs]
df['y_pred'] = y_pred

df.to_csv('predictions_time_256_1000fs 0.2 sec win.csv', index = False) #prediction output