(waveforms,
                                    sample_rate=16000,
                                    preemphasis=0.97,
                                    frame_length=0.025,
                                    frame_step=0.010,
                                    fft_length=None,
                                    lower_edge_hertz=80.0,
                                    upper_edge_hertz=7600.0,
                                    num_mel_bins=80,
                                    log_noise_floor=1e-3,
                                    apply_mask=True)
return fbank
Implement mel-filterbank extraction using tf ops.
  Args:
    waveforms: float32 tensor with shape [max_len, nchannels]
    sample_rate: sampling rate of the waveform
    preemphasis: waveform high-pass filtering constant
    frame_length: frame length in ms
    frame_step: frame_Step in ms
    fft_length: number of fft bins
    lower_edge_hertz: lowest frequency of the filterbank
    upper_edge_hertz: highest frequency of the filterbank
    num_mel_bins: filterbank size
    log_noise_floor: clip small values to prevent numeric overflow in log
    apply_mask: When working on a batch of samples, set padding frames to zero
  Returns:
    filterbanks: a float32 tensor with shape [nchannles, max_len, num_bins]
Implement mel-filterbank extraction using tf ops.
  Args:
    waveforms: float32 tensor with shape [max_len, nchannels]
    sample_rate: sampling rate of the waveform
    preemphasis: waveform high-pass filtering constant
    frame_length: frame length in ms
    frame_step: frame_Step in ms
    fft_length: number of fft bins
    lower_edge_hertz: lowest frequency of the filterbank
    upper_edge_hertz: highest frequency of the filterbank
    num_mel_bins: filterbank size
    log_noise_floor: clip small values to prevent numeric overflow in log
    apply_mask: When working on a batch of samples, set padding frames to zero
  Returns:
    filterbanks: a float32 tensor with shape [nchannles, max_len, num_bins]
Implement mel - filterbank extraction using tf ops . Args : waveforms : float32 tensor with shape [ max_len nchannels ] sample_rate : sampling rate of the waveform preemphasis : waveform high - pass filtering constant frame_length : frame length in ms frame_step : frame_Step in ms fft_length : number of fft bins lower_edge_hertz : lowest frequency of the filterbank upper_edge_hertz : highest frequency of the filterbank num_mel_bins : filterbank size log_noise_floor : clip small values to prevent numeric overflow in log apply_mask : When working on a batch of samples set padding frames to zero Returns : filterbanks : a float32 tensor with shape [ nchannles max_len num_bins ]
def compute_mel_filterbank_features(waveforms,
                                    sample_rate=16000,
                                    preemphasis=0.97,
                                    frame_length=0.025,
                                    frame_step=0.010,
                                    fft_length=None,
                                    lower_edge_hertz=80.0,
                                    upper_edge_hertz=7600.0,
                                    num_mel_bins=80,
                                    log_noise_floor=1e-3,
                                    apply_mask=True):
  """Implement mel-filterbank extraction using tf ops.
  Args:
    waveforms: float32 tensor with shape [max_len, nchannels]
    sample_rate: sampling rate of the waveform
    preemphasis: waveform high-pass filtering constant
    frame_length: frame length in ms
    frame_step: frame_Step in ms
    fft_length: number of fft bins
    lower_edge_hertz: lowest frequency of the filterbank
    upper_edge_hertz: highest frequency of the filterbank
    num_mel_bins: filterbank size
    log_noise_floor: clip small values to prevent numeric overflow in log
    apply_mask: When working on a batch of samples, set padding frames to zero
  Returns:
    filterbanks: a float32 tensor with shape [nchannles, max_len, num_bins]
  """
  del log_noise_floor, apply_mask
  spectrogram = powspec_feat(
      waveforms,
      sr=sample_rate,
      nfft=512 if not fft_length else fft_length,
      winlen=frame_length,
      winstep=frame_step,
      lowfreq=lower_edge_hertz,
      highfreq=upper_edge_hertz,
      preemph=preemphasis)

  # [channels, time, feat_dim]
  fbank = fbank_feat(
      spectrogram,
      sr=sample_rate,
      feature_size=num_mel_bins,
      nfft=512 if not fft_length else fft_length,
      lowfreq=lower_edge_hertz,
      highfreq=upper_edge_hertz)

  # [time, feat_dim]
  fbank = tf.cond(
      tf.equal(tf.rank(fbank), 3),
      true_fn=lambda: fbank[0, :, :],
      false_fn=lambda: fbank)
  return fbank
def compute_mel_filterbank_features ( waveforms , sample_rate = 16000 , preemphasis = 0.97 , frame_length = 0.025 , frame_step = 0.010 , fft_length = None , lower_edge_hertz = 80.0 , upper_edge_hertz = 7600.0 , num_mel_bins = 80 , log_noise_floor = 1e-3 , apply_mask = True ) : del log_noise_floor , apply_mask spectrogram = powspec_feat ( waveforms , sr = sample_rate , nfft = 512 if not fft_length else fft_length , winlen = frame_length , winstep = frame_step , lowfreq = lower_edge_hertz , highfreq = upper_edge_hertz , preemph = preemphasis ) # [channels, time, feat_dim] fbank = fbank_feat ( spectrogram , sr = sample_rate , feature_size = num_mel_bins , nfft = 512 if not fft_length else fft_length , lowfreq = lower_edge_hertz , highfreq = upper_edge_hertz ) # [time, feat_dim] fbank = tf . cond ( tf . equal ( tf . rank ( fbank ) , 3 ) , true_fn = lambda : fbank [ 0 , : , : ] , false_fn = lambda : fbank ) return fbank