# MFCC Spectrogram conversion code from VGGish, Google Inc.
# https://github.com/tensorflow/models/tree/master/research/audioset

import numpy as np
import mel_features
import vggish_params

def waveform_to_examples(data, sample_rate):
  # Convert to mono.
  if len(data.shape) > 1:
    data = np.mean(data, axis=1)

  # 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
# MFCC Spectrogram conversion code from VGGish, Google Inc.
# https://github.com/tensorflow/models/tree/master/research/audioset

import numpy as np
import mel_features
import vggish_params

def waveform_to_examples(data, sample_rate):
  # Convert to mono.
  if len(data.shape) > 1:
    data = np.mean(data, axis=1)

  # 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
# MFCC Spectrogram conversion code from VGGish, Google Inc. # https://github.com/tensorflow/models/tree/master/research/audioset # Convert to mono. # Compute log mel spectrogram features. # Frame features into examples.