predict.py

import os
from scipy.io import wavfile
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from keras.layers import Conv2D, MaxPool2D, Flatten, LSTM
from keras.layers import Dropout, Dense, TimeDistributed
from keras.models import load_model
from keras.utils import to_categorical
from sklearn.utils.class_weight import compute_class_weight
from tqdm import tqdm
from python_speech_features import mfcc
import datetime
import pickle
from keras.callbacks import ModelCheckpoint
import pandas as pd
import sys
import keras
from sklearn.metrics import accuracy_score


print(sys.version)
print(keras.__version__)
def build_predictions(audio_directory):
	y_true=[]
	y_pred=[]
	fn_prob={}
	print('Extracting')
	for fn in tqdm(os.listdir(audio_directory)):
		rate,wav=wavfile.read(os.path.join(audio_directory,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)
			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('instruments2.csv')
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')
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.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('prediction.csv',index=False)