(data,
                        audio_sample_rate=8000,
                        log_offset=0.0,
                        window_length_secs=0.025,
                        hop_length_secs=0.010,
                        **kwargs)
return np.log(mel_spectrogram + log_offset)
Convert waveform to a log magnitude mel-frequency spectrogram.

  Args:
    data: 1D np.array of waveform data.
    audio_sample_rate: The sampling rate of data.
    log_offset: Add this to values when taking log to avoid -Infs.
    window_length_secs: Duration of each window to analyze.
    hop_length_secs: Advance between successive analysis windows.
    **kwargs: Additional arguments to pass to spectrogram_to_mel_matrix.

  Returns:
    2D np.array of (num_frames, num_mel_bins) consisting of log mel filterbank
    magnitudes for successive frames.
Convert waveform to a log magnitude mel-frequency spectrogram.
Convert waveform to a log magnitude mel - frequency spectrogram .
def log_mel_spectrogram(data,
                        audio_sample_rate=8000,
                        log_offset=0.0,
                        window_length_secs=0.025,
                        hop_length_secs=0.010,
                        **kwargs):
  """Convert waveform to a log magnitude mel-frequency spectrogram.

  Args:
    data: 1D np.array of waveform data.
    audio_sample_rate: The sampling rate of data.
    log_offset: Add this to values when taking log to avoid -Infs.
    window_length_secs: Duration of each window to analyze.
    hop_length_secs: Advance between successive analysis windows.
    **kwargs: Additional arguments to pass to spectrogram_to_mel_matrix.

  Returns:
    2D np.array of (num_frames, num_mel_bins) consisting of log mel filterbank
    magnitudes for successive frames.
  """
  window_length_samples = int(round(audio_sample_rate * window_length_secs))
  hop_length_samples = int(round(audio_sample_rate * hop_length_secs))
  fft_length = 2 ** int(np.ceil(np.log(window_length_samples) / np.log(2.0)))
  spectrogram = stft_magnitude(
      data,
      fft_length=fft_length,
      hop_length=hop_length_samples,
      window_length=window_length_samples)
  mel_spectrogram = np.dot(spectrogram, spectrogram_to_mel_matrix(
      num_spectrogram_bins=spectrogram.shape[1],
      audio_sample_rate=audio_sample_rate, **kwargs))
  return np.log(mel_spectrogram + log_offset)
def log_mel_spectrogram ( data , audio_sample_rate = 8000 , log_offset = 0.0 , window_length_secs = 0.025 , hop_length_secs = 0.010 , * * kwargs ) : window_length_samples = int ( round ( audio_sample_rate * window_length_secs ) ) hop_length_samples = int ( round ( audio_sample_rate * hop_length_secs ) ) fft_length = 2 ** int ( np . ceil ( np . log ( window_length_samples ) / np . log ( 2.0 ) ) ) spectrogram = stft_magnitude ( data , fft_length = fft_length , hop_length = hop_length_samples , window_length = window_length_samples ) mel_spectrogram = np . dot ( spectrogram , spectrogram_to_mel_matrix ( num_spectrogram_bins = spectrogram . shape [ 1 ] , audio_sample_rate = audio_sample_rate , * * kwargs ) ) return np . log ( mel_spectrogram + log_offset )