predict

predict.py

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

def build_predictions(audio_dir):
    y_true = []
    y_pred = []
    fn_prob = {}

    print('Extracting 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).T
            x = (x - config.min) / (config.max - config.min)

            x = x.reshape(1, x.shape[0], x.shape[1], 1)
            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()
    print(fn_prob)
    return y_true, y_pred, fn_prob

df = pd.read_csv('office_sounds.csv')
classes = list(np.unique(df.class_name))
fn2class = dict(zip(df.slice_file_name, df.class_name))
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')
acc_score = accuracy_score(y_true=y_true, y_pred=y_pred)

y_probs = []
for i, row in df.iterrows():
    y_prob = fn_prob[row.slice_file_name]
    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.csv', index=False)