loader.py

from scipy.io.wavfile import read as wavread
import numpy as np

import tensorflow as tf

import sys


def decode_audio(fp, fs=None, num_channels=1, normalize=False, fast_wav=False):
  """Decodes audio file paths into 32-bit floating point vectors.

  Args:
    fp: Audio file path.
    fs: If specified, resamples decoded audio to this rate.
    mono: If true, averages channels to mono.
    fast_wav: Assume fp is a standard WAV file (PCM 16-bit or float 32-bit).

  Returns:
    A np.float32 array containing the audio samples at specified sample rate.
  """
  if fast_wav:
    # Read with scipy wavread (fast).
    _fs, _wav = wavread(fp)
    if fs is not None and fs != _fs:
      raise NotImplementedError('Scipy cannot resample audio.')
    if _wav.dtype == np.int16:
      _wav = _wav.astype(np.float32)
      _wav /= 32768.
    elif _wav.dtype == np.float32:
      _wav = np.copy(_wav)
    else:
      raise NotImplementedError('Scipy cannot process atypical WAV files.')
  else:
    # Decode with librosa load (slow but supports file formats like mp3).
    import librosa
    _wav, _fs = librosa.core.load(fp, sr=fs, mono=False)
    if _wav.ndim == 2:
      _wav = np.swapaxes(_wav, 0, 1)

  assert _wav.dtype == np.float32

  # At this point, _wav is np.float32 either [nsamps,] or [nsamps, nch].
  # We want [nsamps, 1, nch] to mimic 2D shape of spectral feats.
  if _wav.ndim == 1:
    nsamps = _wav.shape[0]
    nch = 1
  else:
    nsamps, nch = _wav.shape
  _wav = np.reshape(_wav, [nsamps, 1, nch])
 
  # Average (mono) or expand (stereo) channels
  if nch != num_channels:
    if num_channels == 1:
      _wav = np.mean(_wav, 2, keepdims=True)
    elif nch == 1 and num_channels == 2:
      _wav = np.concatenate([_wav, _wav], axis=2)
    else:
      raise ValueError('Number of audio channels not equal to num specified')
  if normalize:
    factor = np.max(np.abs(_wav))
    if factor > 0:
      _wav /= factor

  return _wav


def decode_extract_and_batch(
    fps,
    batch_size,
    slice_len,
    decode_fs,
    decode_num_channels,
    decode_normalize=True,
    decode_fast_wav=False,
    decode_parallel_calls=1,
    slice_randomize_offset=False,
    slice_first_only=False,
    slice_overlap_ratio=0,
    slice_pad_end=False,
    repeat=False,
    shuffle=False,
    shuffle_buffer_size=None,
    prefetch_size=None,
    prefetch_gpu_num=None):
  # tf.debugging.set_log_device_placement(True)
  """Decodes audio file paths into mini-batches of samples.

  Args:
    fps: List of audio file paths.
    batch_size: Number of items in the batch.
    slice_len: Length of the sliceuences in samples or feature timesteps.
    decode_fs: (Re-)sample rate for decoded audio files.
    decode_num_channels: Number of channels for decoded audio files.
    decode_normalize: If false, do not normalize audio waveforms.
    decode_fast_wav: If true, uses scipy to decode standard wav files.
    decode_parallel_calls: Number of parallel decoding threads.
    slice_randomize_offset: If true, randomize starting position for slice.
    slice_first_only: If true, only use first slice from each audio file.
    slice_overlap_ratio: Ratio of overlap between adjacent slices.
    slice_pad_end: If true, allows zero-padded examples from the end of each audio file.
    repeat: If true (for training), continuously iterate through the dataset.
    shuffle: If true (for training), buffer and shuffle the sliceuences.
    shuffle_buffer_size: Number of examples to queue up before grabbing a batch.
    prefetch_size: Number of examples to prefetch from the queue.
    prefetch_gpu_num: If specified, prefetch examples to GPU.

  Returns:
    A tuple of np.float32 tensors representing audio waveforms.
      audio: [batch_size, slice_len, 1, nch]
  """
  # Create dataset of filepaths
  dataset = tf.data.Dataset.from_tensor_slices(fps)

  # Shuffle all filepaths every epoch
  if shuffle:
    dataset = dataset.shuffle(buffer_size=len(fps))

  # Repeat
  if repeat:
    dataset = dataset.repeat()

  def _decode_audio_shaped(fp):
    _decode_audio_closure = lambda _fp: decode_audio(
      _fp,
      fs=decode_fs,
      num_channels=decode_num_channels,
      normalize=decode_normalize,
      fast_wav=decode_fast_wav)

    audio = tf.py_func(
        _decode_audio_closure,
        [fp],
        tf.float32,
        stateful=False)
    audio.set_shape([None, 1, decode_num_channels])

    return audio

  # Decode audio
  dataset = dataset.map(
      _decode_audio_shaped,
      num_parallel_calls=decode_parallel_calls)

  # Parallel
  def _slice(audio):
    # Calculate hop size
    if slice_overlap_ratio < 0:
      raise ValueError('Overlap ratio must be greater than 0')
    slice_hop = int(round(slice_len * (1. - slice_overlap_ratio)) + 1e-4)
    if slice_hop < 1:
      raise ValueError('Overlap ratio too high')

    # Randomize starting phase:
    if slice_randomize_offset:
      start = tf.random_uniform([], maxval=slice_len, dtype=tf.int32)
      audio = audio[start:]

    # Extract sliceuences
    audio_slices = tf.contrib.signal.frame(
        audio,
        slice_len,
        slice_hop,
        pad_end=slice_pad_end,
        pad_value=0,
        axis=0)

    # Only use first slice if requested
    if slice_first_only:
      audio_slices = audio_slices[:1]

    return audio_slices

  def _slice_dataset_wrapper(audio):
    audio_slices = _slice(audio)
    return tf.data.Dataset.from_tensor_slices(audio_slices)

  # Extract parallel sliceuences from both audio and features
  dataset = dataset.flat_map(_slice_dataset_wrapper)

  # Shuffle examples
  if shuffle:
    dataset = dataset.shuffle(buffer_size=shuffle_buffer_size)

  # Make batches
  dataset = dataset.batch(batch_size, drop_remainder=True)

  # Prefetch a number of batches
  if prefetch_size is not None:
    dataset = dataset.prefetch(prefetch_size)
    if prefetch_gpu_num is not None and prefetch_gpu_num >= 0:
      print('prefetch_gpu_num : ',prefetch_gpu_num)
      dataset = dataset.apply(
          tf.data.experimental.prefetch_to_device(
            '/device:GPU:{}'.format(prefetch_gpu_num)))

  # Get tensors
  iterator = dataset.make_one_shot_iterator()
  
  return iterator.get_next()