Python vggish_params.LOG_OFFSET属性代码示例

# 需要导入模块: import vggish_params [as 别名]
# 或者: from vggish_params import LOG_OFFSET [as 别名]
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 

# 需要导入模块: import vggish_params [as 别名]
# 或者: from vggish_params import LOG_OFFSET [as 别名]
def waveform_to_examples_subtract_bg(data, sample_rate, bg):

    # 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_subtract_bg(
        data,
        bg,
        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 

# 需要导入模块: import vggish_params [as 别名]
# 或者: from vggish_params import LOG_OFFSET [as 别名]
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 

# 需要导入模块: import vggish_params [as 别名]
# 或者: from vggish_params import LOG_OFFSET [as 别名]
def define_vggish_slim(training=False):
  """Defines the VGGish TensorFlow model.

  All ops are created in the current default graph, under the scope 'vggish/'.

  The input is a placeholder named 'vggish/input_features' of type float32 and
  shape [batch_size, num_frames, num_bands] where batch_size is variable and
  num_frames and num_bands are constants, and [num_frames, num_bands] represents
  a log-mel-scale spectrogram patch covering num_bands frequency bands and
  num_frames time frames (where each frame step is usually 10ms). This is
  produced by computing the stabilized log(mel-spectrogram + params.LOG_OFFSET).
  The output is an op named 'vggish/embedding' which produces the activations of
  a 128-D embedding layer, which is usually the penultimate layer when used as
  part of a full model with a final classifier layer.

  Args:
    training: If true, all parameters are marked trainable.

  Returns:
    The op 'vggish/embeddings'.
  """
  # Defaults:
  # - All weights are initialized to N(0, INIT_STDDEV).
  # - All biases are initialized to 0.
  # - All activations are ReLU.
  # - All convolutions are 3x3 with stride 1 and SAME padding.
  # - All max-pools are 2x2 with stride 2 and SAME padding.
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      weights_initializer=tf.truncated_normal_initializer(
                          stddev=params.INIT_STDDEV),
                      biases_initializer=tf.zeros_initializer(),
                      activation_fn=tf.nn.relu,
                      trainable=training), \
       slim.arg_scope([slim.conv2d],
                      kernel_size=[3, 3], stride=1, padding='SAME'), \
       slim.arg_scope([slim.max_pool2d],
                      kernel_size=[2, 2], stride=2, padding='SAME'), \
       tf.variable_scope('vggish'):
    # Input: a batch of 2-D log-mel-spectrogram patches.
    features = tf.placeholder(
        tf.float32, shape=(None, params.NUM_FRAMES, params.NUM_BANDS),
        name='input_features')
    # Reshape to 4-D so that we can convolve a batch with conv2d().
    net = tf.reshape(features, [-1, params.NUM_FRAMES, params.NUM_BANDS, 1])

    # The VGG stack of alternating convolutions and max-pools.
    net = slim.conv2d(net, 64, scope='conv1')
    net = slim.max_pool2d(net, scope='pool1')
    net = slim.conv2d(net, 128, scope='conv2')
    net = slim.max_pool2d(net, scope='pool2')
    net = slim.repeat(net, 2, slim.conv2d, 256, scope='conv3')
    net = slim.max_pool2d(net, scope='pool3')
    net = slim.repeat(net, 2, slim.conv2d, 512, scope='conv4')
    net = slim.max_pool2d(net, scope='pool4')

    # Flatten before entering fully-connected layers
    net = slim.flatten(net)
    net = slim.repeat(net, 2, slim.fully_connected, 4096, scope='fc1')
    # The embedding layer.
    net = slim.fully_connected(net, params.EMBEDDING_SIZE, scope='fc2')
    return tf.identity(net, name='embedding')