# Author: Mozilla DeepSpeech v0.4.0
import numpy as np
import scipy.io.wavfile as wav
from python_speech_features import mfcc
def audiofile_to_input_vector(audio_filename, numcep, numcontext):
r"""
Given a WAV audio file at ``audio_filename``, calculates ``numcep`` MFCC features
at every 0.01s time step with a window length of 0.025s. Appends ``numcontext``
context frames to the left and right of each time step, and returns this data
in a numpy array.
"""
# Load wav files
fs, audio = wav.read(audio_filename)
# Get mfcc coefficients
features = mfcc(audio, samplerate=fs, numcep=numcep, winlen=0.032, winstep=0.02, winfunc=np.hamming)
# Add empty initial and final contexts
empty_context = np.zeros((numcontext, numcep), dtype=features.dtype)
features = np.concatenate((empty_context, features, empty_context))
return features
