(data, sample_rate)
return log_mel_examples
Converts audio waveform into an array of examples for VGGish.

  Args:
    data: np.array of either one dimension (mono) or two dimensions
      (multi-channel, with the outer dimension representing channels).
      Each sample is generally expected to lie in the range [-1.0, +1.0],
      although this is not required.
    sample_rate: Sample rate of data.

  Returns:
    3-D np.array of shape [num_examples, num_frames, num_bands] which represents
    a sequence of examples, each of which contains a patch of log mel
    spectrogram, covering num_frames frames of audio and num_bands mel frequency
    bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.
Converts audio waveform into an array of examples for VGGish.
Converts audio waveform into an array of examples for VGGish .
def waveform_to_examples(data, sample_rate):
  """Converts audio waveform into an array of examples for VGGish.

  Args:
    data: np.array of either one dimension (mono) or two dimensions
      (multi-channel, with the outer dimension representing channels).
      Each sample is generally expected to lie in the range [-1.0, +1.0],
      although this is not required.
    sample_rate: Sample rate of data.

  Returns:
    3-D np.array of shape [num_examples, num_frames, num_bands] which represents
    a sequence of examples, each of which contains a patch of log mel
    spectrogram, covering num_frames frames of audio and num_bands mel frequency
    bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.
  """
  # Convert to mono.
  if len(data.shape) > 1:
    data = np.mean(data, axis=1)
  # Resample to the rate assumed by VGGish.
  if sample_rate != vggish_params.SAMPLE_RATE:
    data = resampy.resample(data, sample_rate, vggish_params.SAMPLE_RATE)

  # Compute log mel spectrogram features.
  log_mel = mel_features.log_mel_spectrogram(
      data,
      audio_sample_rate=vggish_params.SAMPLE_RATE,
      log_offset=vggish_params.LOG_OFFSET,
      window_length_secs=vggish_params.STFT_WINDOW_LENGTH_SECONDS,
      hop_length_secs=vggish_params.STFT_HOP_LENGTH_SECONDS,
      num_mel_bins=vggish_params.NUM_MEL_BINS,
      lower_edge_hertz=vggish_params.MEL_MIN_HZ,
      upper_edge_hertz=vggish_params.MEL_MAX_HZ)

  # Frame features into examples.
  features_sample_rate = 1.0 / vggish_params.STFT_HOP_LENGTH_SECONDS
  example_window_length = int(round(
      vggish_params.EXAMPLE_WINDOW_SECONDS * features_sample_rate))
  example_hop_length = int(round(
      vggish_params.EXAMPLE_HOP_SECONDS * features_sample_rate))
  log_mel_examples = mel_features.frame(
      log_mel,
      window_length=example_window_length,
      hop_length=example_hop_length)
  return log_mel_examples
def waveform_to_examples ( data , sample_rate ) : # Convert to mono. if len ( data . shape ) > 1 : data = np . mean ( data , axis = 1 ) # Resample to the rate assumed by VGGish. if sample_rate != vggish_params . SAMPLE_RATE : data = resampy . resample ( data , sample_rate , vggish_params . SAMPLE_RATE ) # Compute log mel spectrogram features. log_mel = mel_features . log_mel_spectrogram ( data , audio_sample_rate = vggish_params . SAMPLE_RATE , log_offset = vggish_params . LOG_OFFSET , window_length_secs = vggish_params . STFT_WINDOW_LENGTH_SECONDS , hop_length_secs = vggish_params . STFT_HOP_LENGTH_SECONDS , num_mel_bins = vggish_params . NUM_MEL_BINS , lower_edge_hertz = vggish_params . MEL_MIN_HZ , upper_edge_hertz = vggish_params . MEL_MAX_HZ ) # Frame features into examples. features_sample_rate = 1.0 / vggish_params . STFT_HOP_LENGTH_SECONDS example_window_length = int ( round ( vggish_params . EXAMPLE_WINDOW_SECONDS * features_sample_rate ) ) example_hop_length = int ( round ( vggish_params . EXAMPLE_HOP_SECONDS * features_sample_rate ) ) log_mel_examples = mel_features . frame ( log_mel , window_length = example_window_length , hop_length = example_hop_length ) return log_mel_examples