timitphonemerec.py
def _mfcc_and_labels(audio, labels):
  """ Convert to MFCC features and corresponding (interpolated) labels.

  Returns:
    A tuple, `(mfcc_features, mfcc_labels)`. A 1-D float array and a 1-D int
      array, both with the same shape.
  """
  mfcc_sample_rate = 100.0
  winfunc = lambda x: np.hamming(x)
  mfcc_features = python_speech_features.mfcc(audio, samplerate=timit.SAMPLE_RATE, winlen=0.025,
                                              winstep=1.0/mfcc_sample_rate, lowfreq=85.0,
                                              highfreq=timit.SAMPLE_RATE/2, winfunc=winfunc)
  t_audio = np.linspace(0.0, audio.shape[0] * 1.0 / timit.SAMPLE_RATE, audio.size, endpoint=False)
  t_mfcc = np.linspace(0.0, mfcc_features.shape[0] * 1.0 / mfcc_sample_rate, mfcc_features.shape[0], endpoint=False)
  interp_func = scipy.interpolate.interp1d(t_audio, labels, kind='nearest')
  mfcc_labels = interp_func(t_mfcc)
  return mfcc_features, mfcc_labels
 Convert to MFCC features and corresponding (interpolated) labels.

  Returns:
    A tuple, `(mfcc_features, mfcc_labels)`. A 1-D float array and a 1-D int
      array, both with the same shape.
  
Convert to MFCC features and corresponding (interpolated) labels.
Convert to MFCC features and corresponding ( interpolated ) labels .
def _mfcc_and_labels(audio, labels):
  mfcc_sample_rate = 100.0
  winfunc = lambda x: np.hamming(x)
  mfcc_features = python_speech_features.mfcc(audio, samplerate=timit.SAMPLE_RATE, winlen=0.025,
                                              winstep=1.0/mfcc_sample_rate, lowfreq=85.0,
                                              highfreq=timit.SAMPLE_RATE/2, winfunc=winfunc)
  t_audio = np.linspace(0.0, audio.shape[0] * 1.0 / timit.SAMPLE_RATE, audio.size, endpoint=False)
  t_mfcc = np.linspace(0.0, mfcc_features.shape[0] * 1.0 / mfcc_sample_rate, mfcc_features.shape[0], endpoint=False)
  interp_func = scipy.interpolate.interp1d(t_audio, labels, kind='nearest')
  mfcc_labels = interp_func(t_mfcc)
  return mfcc_features, mfcc_labels
def _mfcc_and_labels ( audio , labels ) : mfcc_sample_rate = 100.0 winfunc = lambda x : np . hamming ( x ) mfcc_features = python_speech_features . mfcc ( audio , samplerate = timit . SAMPLE_RATE , winlen = 0.025 , winstep = 1.0 / mfcc_sample_rate , lowfreq = 85.0 , highfreq = timit . SAMPLE_RATE / 2 , winfunc = winfunc ) t_audio = np . linspace ( 0.0 , audio . shape [ 0 ] * 1.0 / timit . SAMPLE_RATE , audio . size , endpoint = False ) t_mfcc = np . linspace ( 0.0 , mfcc_features . shape [ 0 ] * 1.0 / mfcc_sample_rate , mfcc_features . shape [ 0 ] , endpoint = False ) interp_func = scipy . interpolate . interp1d ( t_audio , labels , kind = 'nearest' ) mfcc_labels = interp_func ( t_mfcc ) return mfcc_features , mfcc_labels
Convert to MFCC features and corresponding (interpolated) labels.
Convert to MFCC features and corresponding ( interpolated ) labels .
""" Convert to MFCC features and corresponding (interpolated) labels.

  Returns:
    A tuple, `(mfcc_features, mfcc_labels)`. A 1-D float array and a 1-D int
      array, both with the same shape.
  """
A tuple, `(mfcc_features, mfcc_labels)`. A 1-D float array and a 1-D int
array, both with the same shape.
A tuple ` ( mfcc_features mfcc_labels ) ` . A 1 - D float array and a 1 - D int array both with the same shape .
audio
labels