text: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Model definitions for simple speech recognition.
  
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import hashlib
  import math
  import os.path
  import random
  import re
  import sys
  import tarfile
  
  import numpy as np
  from scipy.io import wavfile
  from python_speech_features import mfcc
  from six.moves import urllib
  from six.moves import xrange  # pylint: disable=redefined-builtin
  import tensorflow as tf
  
  from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
  from tensorflow.python.ops import io_ops
  from tensorflow.python.platform import gfile
  from tensorflow.python.util import compat
  
  MAX_NUM_WAVS_PER_CLASS = 2**27 - 1  # ~134M
  SILENCE_LABEL = '_silence_'
  SILENCE_INDEX = 0
  UNKNOWN_WORD_LABEL = '_unknown_'
  UNKNOWN_WORD_INDEX = 1
  BACKGROUND_NOISE_DIR_NAME = '_background_noise_'
  RANDOM_SEED = 59185
  
  def psf_mfcc(wav_path):
    frequency_sampling, x = wavfile.read(wav_path)
    # print(frequency_sampling)
    # print(x.shape, x.dtype)
    
    x = np.pad(x, (0, 16000 - x.shape[0]), mode='constant')
  
    if x.dtype == 'int16':
        nb_bits = 16 # -> 16-bit wav files
    elif x.dtype == 'int32':
        nb_bits = 32 # -> 32-bit wav files
    max_nb_bit = float(2 ** (nb_bits - 1))
    audio_signal = x / max_nb_bit # samples is a numpy array of float representing the samples
    clamp_audio = audio_signal.clip(-1., 1.)
  
    features_mfcc = mfcc(clamp_audio, samplerate=16000, 
                        winlen=0.04, winstep=0.02, numcep=10, nfilt=40, lowfreq=20, highfreq=4000,
                        nfft=800, appendEnergy=False, preemph=0, ceplifter=0)
    # print(features_mfcc.shape)
  
    # outputs = features_mfcc.flatten()
    # print(outputs.shape)
    # print(outputs)
  
    return features_mfcc
  
  def prepare_words_list(wanted_words):
    """Prepends common tokens to the custom word list.
  
    Args:
      wanted_words: List of strings containing the custom words.
  
    Returns:
      List with the standard silence and unknown tokens added.
    """
    return [SILENCE_LABEL, UNKNOWN_WORD_LABEL] + wanted_words
  
  
  def which_set(filename, validation_percentage, testing_percentage):
    """Determines which data partition the file should belong to.
  
    We want to keep files in the same training, validation, or testing sets even
    if new ones are added over time. This makes it less likely that testing
    samples will accidentally be reused in training when long runs are restarted
    for example. To keep this stability, a hash of the filename is taken and used
    to determine which set it should belong to. This determination only depends on
    the name and the set proportions, so it won't change as other files are added.
  
    It's also useful to associate particular files as related (for example words
    spoken by the same person), so anything after '_nohash_' in a filename is
    ignored for set determination. This ensures that 'bobby_nohash_0.wav' and
    'bobby_nohash_1.wav' are always in the same set, for example.
  
    Args:
      filename: File path of the data sample.
      validation_percentage: How much of the data set to use for validation.
      testing_percentage: How much of the data set to use for testing.
  
    Returns:
      String, one of 'training', 'validation', or 'testing'.
    """
    base_name = os.path.basename(filename)
    # We want to ignore anything after '_nohash_' in the file name when
    # deciding which set to put a wav in, so the data set creator has a way of
    # grouping wavs that are close variations of each other.
    hash_name = re.sub(r'_nohash_.*$', '', base_name)
    # This looks a bit magical, but we need to decide whether this file should
    # go into the training, testing, or validation sets, and we want to keep
    # existing files in the same set even if more files are subsequently
    # added.
    # To do that, we need a stable way of deciding based on just the file name
    # itself, so we do a hash of that and then use that to generate a
    # probability value that we use to assign it.
    hash_name_hashed = hashlib.sha1(compat.as_bytes(hash_name)).hexdigest()
    percentage_hash = ((int(hash_name_hashed, 16) %
                        (MAX_NUM_WAVS_PER_CLASS + 1)) *
                       (100.0 / MAX_NUM_WAVS_PER_CLASS))
    if percentage_hash < validation_percentage:
      result = 'validation'
    elif percentage_hash < (testing_percentage + validation_percentage):
      result = 'testing'
    else:
      result = 'training'
    return result
  
  
  def load_wav_file(filename):
    """Loads an audio file and returns a float PCM-encoded array of samples.
  
    Args:
      filename: Path to the .wav file to load.
  
    Returns:
      Numpy array holding the sample data as floats between -1.0 and 1.0.
    """
    with tf.Session(graph=tf.Graph()) as sess:
      wav_filename_placeholder = tf.placeholder(tf.string, [])
      wav_loader = io_ops.read_file(wav_filename_placeholder)
      wav_decoder = contrib_audio.decode_wav(wav_loader, desired_channels=1)
      return sess.run(
          wav_decoder,
          feed_dict={wav_filename_placeholder: filename}).audio.flatten()
  
  
  def save_wav_file(filename, wav_data, sample_rate):
    """Saves audio sample data to a .wav audio file.
  
    Args:
      filename: Path to save the file to.
      wav_data: 2D array of float PCM-encoded audio data.
      sample_rate: Samples per second to encode in the file.
    """
    with tf.Session(graph=tf.Graph()) as sess:
      wav_filename_placeholder = tf.placeholder(tf.string, [])
      sample_rate_placeholder = tf.placeholder(tf.int32, [])
      wav_data_placeholder = tf.placeholder(tf.float32, [None, 1])
      wav_encoder = contrib_audio.encode_wav(wav_data_placeholder,
                                             sample_rate_placeholder)
      wav_saver = io_ops.write_file(wav_filename_placeholder, wav_encoder)
      sess.run(
          wav_saver,
          feed_dict={
              wav_filename_placeholder: filename,
              sample_rate_placeholder: sample_rate,
              wav_data_placeholder: np.reshape(wav_data, (-1, 1))
          })
  
  
  class AudioProcessor(object):
    """Handles loading, partitioning, and preparing audio training data."""
  
    def __init__(self, data_url, data_dir, silence_percentage, unknown_percentage,
                 wanted_words, validation_percentage, testing_percentage,
                 model_settings):
      self.data_dir = data_dir
      self.maybe_download_and_extract_dataset(data_url, data_dir)
      self.prepare_data_index(silence_percentage, unknown_percentage,
                              wanted_words, validation_percentage,
                              testing_percentage)
      self.prepare_background_data()
      self.prepare_processing_graph(model_settings)
  
    def maybe_download_and_extract_dataset(self, data_url, dest_directory):
      """Download and extract data set tar file.
  
      If the data set we're using doesn't already exist, this function
      downloads it from the TensorFlow.org website and unpacks it into a
      directory.
      If the data_url is none, don't download anything and expect the data
      directory to contain the correct files already.
  
      Args:
        data_url: Web location of the tar file containing the data set.
        dest_directory: File path to extract data to.
      """
      if not data_url:
        return
      if not os.path.exists(dest_directory):
        os.makedirs(dest_directory)
      filename = data_url.split('/')[-1]
      filepath = os.path.join(dest_directory, filename)
      if not os.path.exists(filepath):
  
        def _progress(count, block_size, total_size):
          sys.stdout.write(
              '\r>> Downloading %s %.1f%%' %
              (filename, float(count * block_size) / float(total_size) * 100.0))
          sys.stdout.flush()
  
        try:
          filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress)
        except:
          tf.logging.error('Failed to download URL: %s to folder: %s', data_url,
                           filepath)
          tf.logging.error('Please make sure you have enough free space and'
                           ' an internet connection')
          raise
        print()
        statinfo = os.stat(filepath)
        tf.logging.info('Successfully downloaded %s (%d bytes)', filename,
                        statinfo.st_size)
      tarfile.open(filepath, 'r:gz').extractall(dest_directory)
  
    def prepare_data_index(self, silence_percentage, unknown_percentage,
                           wanted_words, validation_percentage,
                           testing_percentage):
      """Prepares a list of the samples organized by set and label.
  
      The training loop needs a list of all the available data, organized by
      which partition it should belong to, and with ground truth labels attached.
      This function analyzes the folders below the `data_dir`, figures out the
      right
      labels for each file based on the name of the subdirectory it belongs to,
      and uses a stable hash to assign it to a data set partition.
  
      Args:
        silence_percentage: How much of the resulting data should be background.
        unknown_percentage: How much should be audio outside the wanted classes.
        wanted_words: Labels of the classes we want to be able to recognize.
        validation_percentage: How much of the data set to use for validation.
        testing_percentage: How much of the data set to use for testing.
  
      Returns:
        Dictionary containing a list of file information for each set partition,
        and a lookup map for each class to determine its numeric index.
  
      Raises:
        Exception: If expected files are not found.
      """
      # Make sure the shuffling and picking of unknowns is deterministic.
      random.seed(RANDOM_SEED)
      wanted_words_index = {}
      for index, wanted_word in enumerate(wanted_words):
        # 因为分类0是静音、分类1是未知
        wanted_words_index[wanted_word] = index + 2
      self.data_index = {'validation': [], 'testing': [], 'training': []}
      unknown_index = {'validation': [], 'testing': [], 'training': []}
      all_words = {}
      # Look through all the subfolders to find audio samples
      search_path = os.path.join(self.data_dir, '*', '*.wav')
      for wav_path in gfile.Glob(search_path):
        _, word = os.path.split(os.path.dirname(wav_path))
        word = word.lower()
        # Treat the '_background_noise_' folder as a special case, since we expect
        # it to contain long audio samples we mix in to improve training.
        if word == BACKGROUND_NOISE_DIR_NAME:
          continue
        all_words[word] = True
        set_index = which_set(wav_path, validation_percentage, testing_percentage)
        # If it's a known class, store its detail, otherwise add it to the list
        # we'll use to train the unknown label.
        if word in wanted_words_index:
          self.data_index[set_index].append({'label': word, 'file': wav_path})
        else:
          unknown_index[set_index].append({'label': word, 'file': wav_path})
      if not all_words:
        raise Exception('No .wavs found at ' + search_path)
      for index, wanted_word in enumerate(wanted_words):
        if wanted_word not in all_words:
          raise Exception('Expected to find ' + wanted_word +
                          ' in labels but only found ' +
                          ', '.join(all_words.keys()))
      # We need an arbitrary file to load as the input for the silence samples.
      # It's multiplied by zero later, so the content doesn't matter.
      silence_wav_path = self.data_index['training'][0]['file']
      for set_index in ['validation', 'testing', 'training']:
        set_size = len(self.data_index[set_index])
        silence_size = int(math.ceil(set_size * silence_percentage / 100))
        for _ in range(silence_size):
          self.data_index[set_index].append({
              'label': SILENCE_LABEL,
              'file': silence_wav_path
          })
        # Pick some unknowns to add to each partition of the data set.
        random.shuffle(unknown_index[set_index])
        unknown_size = int(math.ceil(set_size * unknown_percentage / 100))
        self.data_index[set_index].extend(unknown_index[set_index][:unknown_size])
      # Make sure the ordering is random.
      for set_index in ['validation', 'testing', 'training']:
        random.shuffle(self.data_index[set_index])
      # Prepare the rest of the result data structure.
      self.words_list = prepare_words_list(wanted_words)
      self.word_to_index = {}
      for word in all_words:
        if word in wanted_words_index:
          self.word_to_index[word] = wanted_words_index[word]
        else:
          self.word_to_index[word] = UNKNOWN_WORD_INDEX
      self.word_to_index[SILENCE_LABEL] = SILENCE_INDEX
  
    def prepare_background_data(self):
      """Searches a folder for background noise audio, and loads it into memory.
  
      It's expected that the background audio samples will be in a subdirectory
      named '_background_noise_' inside the 'data_dir' folder, as .wavs that match
      the sample rate of the training data, but can be much longer in duration.
  
      If the '_background_noise_' folder doesn't exist at all, this isn't an
      error, it's just taken to mean that no background noise augmentation should
      be used. If the folder does exist, but it's empty, that's treated as an
      error.
  
      Returns:
        List of raw PCM-encoded audio samples of background noise.
  
      Raises:
        Exception: If files aren't found in the folder.
      """
      self.background_data = []
      background_dir = os.path.join(self.data_dir, BACKGROUND_NOISE_DIR_NAME)
      if not os.path.exists(background_dir):
        return self.background_data
      with tf.Session(graph=tf.Graph()) as sess:
        wav_filename_placeholder = tf.placeholder(tf.string, [])
        wav_loader = io_ops.read_file(wav_filename_placeholder)
        wav_decoder = contrib_audio.decode_wav(wav_loader, desired_channels=1)
        search_path = os.path.join(self.data_dir, BACKGROUND_NOISE_DIR_NAME,
                                   '*.wav')
        for wav_path in gfile.Glob(search_path):
          wav_data = sess.run(
              wav_decoder,
              feed_dict={wav_filename_placeholder: wav_path}).audio.flatten()
          self.background_data.append(wav_data)
        if not self.background_data:
          raise Exception('No background wav files were found in ' + search_path)
  
    def prepare_processing_graph(self, model_settings):
      """Builds a TensorFlow graph to apply the input distortions.
  
      Creates a graph that loads a WAVE file, decodes it, scales the volume,
      shifts it in time, adds in background noise, calculates a spectrogram, and
      then builds an MFCC fingerprint from that.
  
      This must be called with an active TensorFlow session running, and it
      creates multiple placeholder inputs, and one output:
  
        - wav_filename_placeholder_: Filename of the WAV to load.
        - foreground_volume_placeholder_: How loud the main clip should be.
        - time_shift_padding_placeholder_: Where to pad the clip.
        - time_shift_offset_placeholder_: How much to move the clip in time.
        - background_data_placeholder_: PCM sample data for background noise.
        - background_volume_placeholder_: Loudness of mixed-in background.
        - mfcc_: Output 2D fingerprint of processed audio.
  
      Args:
        model_settings: Information about the current model being trained.
      """
      desired_samples = model_settings['desired_samples']
      self.wav_filename_placeholder_ = tf.placeholder(tf.string, [])
      wav_loader = io_ops.read_file(self.wav_filename_placeholder_)
      wav_decoder = contrib_audio.decode_wav(
          wav_loader, desired_channels=1, desired_samples=desired_samples)
      # Allow the audio sample's volume to be adjusted.
      self.foreground_volume_placeholder_ = tf.placeholder(tf.float32, [])
      scaled_foreground = tf.multiply(wav_decoder.audio,
                                      self.foreground_volume_placeholder_)
      # Shift the sample's start position, and pad any gaps with zeros.
      self.time_shift_padding_placeholder_ = tf.placeholder(tf.int32, [2, 2])
      self.time_shift_offset_placeholder_ = tf.placeholder(tf.int32, [2])
      padded_foreground = tf.pad(
          scaled_foreground,
          self.time_shift_padding_placeholder_,
          mode='CONSTANT')
      # tf.slice 从inputs中抽取部分内容
      sliced_foreground = tf.slice(padded_foreground,
                                   self.time_shift_offset_placeholder_,
                                   [desired_samples, -1])
      # Mix in background noise.
      self.background_data_placeholder_ = tf.placeholder(tf.float32,
                                                         [desired_samples, 1])
      self.background_volume_placeholder_ = tf.placeholder(tf.float32, [])
      background_mul = tf.multiply(self.background_data_placeholder_,
                                   self.background_volume_placeholder_)
      background_add = tf.add(background_mul, sliced_foreground)
      background_clamp = tf.clip_by_value(background_add, -1.0, 1.0)
      # Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio.
      spectrogram = contrib_audio.audio_spectrogram(
          background_clamp,
          window_size=model_settings['window_size_samples'],
          stride=model_settings['window_stride_samples'],
          magnitude_squared=True)
      self.mfcc_ = contrib_audio.mfcc(
          spectrogram,
          wav_decoder.sample_rate,
          dct_coefficient_count=model_settings['dct_coefficient_count'])
      
      # print(model_settings['desired_samples'], model_settings['window_size_samples'], 
      #       model_settings['window_stride_samples'], model_settings['dct_coefficient_count'])
      # print(spectrogram.shape)
      # print(self.mfcc_.shape)
      # print(wav_decoder.sample_rate)
      # sys.exit(0)
  
    def set_size(self, mode):
      """Calculates the number of samples in the dataset partition.
  
      Args:
        mode: Which partition, must be 'training', 'validation', or 'testing'.
  
      Returns:
        Number of samples in the partition.
      """
      return len(self.data_index[mode])
  
    def get_data(self, how_many, offset, model_settings, background_frequency,
                 background_volume_range, time_shift, mode, sess):
      """Gather samples from the data set, applying transformations as needed.
  
      When the mode is 'training', a random selection of samples will be returned,
      otherwise the first N clips in the partition will be used. This ensures that
      validation always uses the same samples, reducing noise in the metrics.
  
      Args:
        how_many: Desired number of samples to return. -1 means the entire
          contents of this partition.
        offset: Where to start when fetching deterministically.
        model_settings: Information about the current model being trained.
        background_frequency: How many clips will have background noise, 0.0 to
          1.0.
        background_volume_range: How loud the background noise will be.
        time_shift: How much to randomly shift the clips by in time.
        mode: Which partition to use, must be 'training', 'validation', or
          'testing'.
        sess: TensorFlow session that was active when processor was created.
  
      Returns:
        List of sample data for the transformed samples, and list of labels in
        one-hot form.
      """
      # Pick one of the partitions to choose samples from.
      candidates = self.data_index[mode]
      if how_many == -1:
        sample_count = len(candidates)
      else:
        sample_count = max(0, min(how_many, len(candidates) - offset))
      # Data and labels will be populated and returned.
      data = np.zeros((sample_count, model_settings['fingerprint_size']))
      labels = np.zeros((sample_count, model_settings['label_count']))
      desired_samples = model_settings['desired_samples']
      use_background = self.background_data and (mode == 'training')
      pick_deterministically = (mode != 'training')
      # Use the processing graph we created earlier to repeatedly to generate the
      # final output sample data we'll use in training.
      for i in xrange(offset, offset + sample_count):
        # Pick which audio sample to use.
        if how_many == -1 or pick_deterministically:
          sample_index = i
        else:
          sample_index = np.random.randint(len(candidates))
        sample = candidates[sample_index]
        # If we're time shifting, set up the offset for this sample.
        if time_shift > 0:
          time_shift_amount = np.random.randint(-time_shift, time_shift)
        else:
          time_shift_amount = 0
        if time_shift_amount > 0:
          time_shift_padding = [[time_shift_amount, 0], [0, 0]]
          time_shift_offset = [0, 0]
        else:
          time_shift_padding = [[0, -time_shift_amount], [0, 0]]
          time_shift_offset = [-time_shift_amount, 0]
        input_dict = {
            self.wav_filename_placeholder_: sample['file'],
            self.time_shift_padding_placeholder_: time_shift_padding,
            self.time_shift_offset_placeholder_: time_shift_offset,
        }
        # Choose a section of background noise to mix in.
        if use_background:
          background_index = np.random.randint(len(self.background_data))
          background_samples = self.background_data[background_index]
          background_offset = np.random.randint(
              0, len(background_samples) - model_settings['desired_samples'])
          background_clipped = background_samples[background_offset:(
              background_offset + desired_samples)]
          background_reshaped = background_clipped.reshape([desired_samples, 1])
          if np.random.uniform(0, 1) < background_frequency:
            background_volume = np.random.uniform(0, background_volume_range)
          else:
            background_volume = 0
        else:
          background_reshaped = np.zeros([desired_samples, 1])
          background_volume = 0
        input_dict[self.background_data_placeholder_] = background_reshaped
        input_dict[self.background_volume_placeholder_] = background_volume     # 这里的background_volume默认设置为0
        # If we want silence, mute out the main sample but leave the background.
        if sample['label'] == SILENCE_LABEL:
          input_dict[self.foreground_volume_placeholder_] = 0
        else:
          input_dict[self.foreground_volume_placeholder_] = 1
          
        # Run the graph to produce the output audio.
        # data[i - offset, :] = sess.run(self.mfcc_, feed_dict=input_dict).flatten()
        tmp = psf_mfcc(sample['file']).flatten()
        # print(tmp.shape, sample['file'], sample['label'], time_shift_padding, time_shift_offset)
        data[i - offset, :] = tmp
        label_index = self.word_to_index[sample['label']]
        labels[i - offset, label_index] = 1
      return data, labels
  
    def get_wav_files(self, how_many, offset, model_settings, mode):
      """Return wav_file names and labels from train/val/test sets.
      """
      # Pick one of the partitions to choose samples from.
      candidates = self.data_index[mode]
      if how_many == -1:
        sample_count = len(candidates)
      else:
        sample_count = max(0, min(how_many, len(candidates) - offset))
      pick_deterministically = (mode != 'training')
      wav_files = []
      labels = np.zeros((sample_count, model_settings['label_count']))
      for i in xrange(offset, offset + sample_count):
        # Pick which audio sample to use.
        if how_many == -1 or pick_deterministically:
          sample_index = i
        else:
          sample_index = np.random.randint(len(candidates))
        sample = candidates[sample_index]
        if sample['label'] == SILENCE_LABEL:
          wav_files.append('silence.wav')
        else:
          wav_files.append(sample['file'])
        label_index = self.word_to_index[sample['label']]
        labels[i - offset, label_index] = 1
      return wav_files, labels
  
  
    def get_unprocessed_data(self, how_many, model_settings, mode):
      """Retrieve sample data for the given partition, with no transformations.
  
      Args:
        how_many: Desired number of samples to return. -1 means the entire
          contents of this partition.
        model_settings: Information about the current model being trained.
        mode: Which partition to use, must be 'training', 'validation', or
          'testing'.
  
      Returns:
        List of sample data for the samples, and list of labels in one-hot form.
      """
      candidates = self.data_index[mode]
      if how_many == -1:
        sample_count = len(candidates)
      else:
        sample_count = how_many
      desired_samples = model_settings['desired_samples']
      words_list = self.words_list
      data = np.zeros((sample_count, desired_samples))
      labels = []
      with tf.Session(graph=tf.Graph()) as sess:
        wav_filename_placeholder = tf.placeholder(tf.string, [])
        wav_loader = io_ops.read_file(wav_filename_placeholder)
        wav_decoder = contrib_audio.decode_wav(
            wav_loader, desired_channels=1, desired_samples=desired_samples)
        foreground_volume_placeholder = tf.placeholder(tf.float32, [])
        scaled_foreground = tf.multiply(wav_decoder.audio,
                                        foreground_volume_placeholder)
        for i in range(sample_count):
          if how_many == -1:
            sample_index = i
          else:
            sample_index = np.random.randint(len(candidates))
          sample = candidates[sample_index]
          input_dict = {wav_filename_placeholder: sample['file']}
          if sample['label'] == SILENCE_LABEL:
            input_dict[foreground_volume_placeholder] = 0
          else:
            input_dict[foreground_volume_placeholder] = 1
          data[i, :] = sess.run(scaled_foreground, feed_dict=input_dict).flatten()
          label_index = self.word_to_index[sample['label']]
          labels.append(words_list[label_index])
      return data, labels
  