Add audio data provider and a simplified DeepSpeech2 model configuration.

deep_speech_2/README.md

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+# Deep Speech 2 on PaddlePaddle
+
+```
+sh requirements.sh
+python librispeech.py
+python train.py
+```
+
+Please add warp-ctc library path (usually $PADDLE_INSTALL_DIR/Paddle/third_party/install/warpctc/lib) to LD_LIBRARY_PATH.

deep_speech_2/audio_data_utils.py

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+"""
+    Providing basic audio data preprocessing pipeline, and offering
+    both instance-level and batch-level data reader interfaces.
+"""
+import paddle.v2 as paddle
+import logging
+import json
+import random
+import soundfile
+import numpy as np
+import os
+
+RANDOM_SEED = 0
+logger = logging.getLogger(__name__)
+
+
+class DataGenerator(object):
+    """
+    DataGenerator provides basic audio data preprocessing pipeline, and offer
+    both instance-level and batch-level data reader interfaces.
+    Normalized FFT are used as audio features here.
+
+    :param vocab_filepath: Vocabulary file path for indexing tokenized
+                           transcriptions.
+    :type vocab_filepath: basestring
+    :param normalizer_manifest_path: Manifest filepath for collecting feature
+                                     normalization statistics, e.g. mean, std.
+    :type normalizer_manifest_path: basestring
+    :param normalizer_num_samples: Number of instances sampled for collecting
+                                   feature normalization statistics.
+                                   Default is 100.
+    :type normalizer_num_samples: int
+    :param max_duration: Audio clips with duration (in seconds) greater than
+                         this will be discarded. Default is 20.0.
+    :type max_duration: float
+    :param min_duration: Audio clips with duration (in seconds) smaller than
+                         this will be discarded. Default is 0.0.
+    :type min_duration: float
+    :param stride_ms: Striding size (in milliseconds) for generating frames.
+                      Default is 10.0. 
+    :type stride_ms: float
+    :param window_ms: Window size (in milliseconds) for frames. Default is 20.0.
+    :type window_ms: float
+    :param max_frequency: Maximun frequency for FFT features. FFT features of
+                          frequency larger than this will be discarded.
+                          If set None, all features will be kept.
+                          Default is None.
+    :type max_frequency: float
+    """
+
+    def __init__(self,
+                 vocab_filepath,
+                 normalizer_manifest_path,
+                 normalizer_num_samples=100,
+                 max_duration=20.0,
+                 min_duration=0.0,
+                 stride_ms=10.0,
+                 window_ms=20.0,
+                 max_frequency=None):
+        self.__max_duration__ = max_duration
+        self.__min_duration__ = min_duration
+        self.__stride_ms__ = stride_ms
+        self.__window_ms__ = window_ms
+        self.__max_frequency__ = max_frequency
+        self.__random__ = random.Random(RANDOM_SEED)
+        # load vocabulary (dictionary)
+        self.__vocab_dict__, self.__vocab_list__ = \
+            self.__load_vocabulary_from_file__(vocab_filepath)
+        # collect normalizer statistics
+        self.__mean__, self.__std__ = self.__collect_normalizer_statistics__(
+            manifest_path=normalizer_manifest_path,
+            num_samples=normalizer_num_samples)
+
+    def __audio_featurize__(self, audio_filename):
+        """
+        Preprocess audio data, including feature extraction, normalization etc..
+        """
+        features = self.__audio_basic_featurize__(audio_filename)
+        return self.__normalize__(features)
+
+    def __text_featurize__(self, text):
+        """
+        Preprocess text data, including tokenizing and token indexing etc..
+        """
+        return self.__convert_text_to_char_index__(
+            text=text, vocabulary=self.__vocab_dict__)
+
+    def __audio_basic_featurize__(self, audio_filename):
+        """
+        Compute basic (without normalization etc.) features for audio data.
+        """
+        return self.__spectrogram_from_file__(
+            filename=audio_filename,
+            stride_ms=self.__stride_ms__,
+            window_ms=self.__window_ms__,
+            max_freq=self.__max_frequency__)
+
+    def __collect_normalizer_statistics__(self, manifest_path, num_samples=100):
+        """
+        Compute feature normalization statistics, i.e. mean and stddev.
+        """
+        # read manifest
+        manifest = self.__read_manifest__(
+            manifest_path=manifest_path,
+            max_duration=self.__max_duration__,
+            min_duration=self.__min_duration__)
+        # sample for statistics
+        sampled_manifest = self.__random__.sample(manifest, num_samples)
+        # extract spectrogram feature
+        features = []
+        for instance in sampled_manifest:
+            spectrogram = self.__audio_basic_featurize__(
+                instance["audio_filepath"])
+            features.append(spectrogram)
+        features = np.hstack(features)
+        mean = np.mean(features, axis=1).reshape([-1, 1])
+        std = np.std(features, axis=1).reshape([-1, 1])
+        return mean, std
+
+    def __normalize__(self, features, eps=1e-14):
+        """
+        Normalize features to be of zero mean and unit stddev.
+        """
+        return (features - self.__mean__) / (self.__std__ + eps)
+
+    def __spectrogram_from_file__(self,
+                                  filename,
+                                  stride_ms=10.0,
+                                  window_ms=20.0,
+                                  max_freq=None,
+                                  eps=1e-14):
+        """
+        Laod audio data and calculate the log of spectrogram by FFT.
+        Refer to utils.py in https://github.com/baidu-research/ba-dls-deepspeech
+        """
+        audio, sample_rate = soundfile.read(filename)
+        if audio.ndim >= 2:
+            audio = np.mean(audio, 1)
+        if max_freq is None:
+            max_freq = sample_rate / 2
+        if max_freq > sample_rate / 2:
+            raise ValueError("max_freq must be greater than half of "
+                             "sample rate.")
+        if stride_ms > window_ms:
+            raise ValueError("Stride size must not be greater than "
+                             "window size.")
+        stride_size = int(0.001 * sample_rate * stride_ms)
+        window_size = int(0.001 * sample_rate * window_ms)
+        spectrogram, freqs = self.__extract_spectrogram__(
+            audio,
+            window_size=window_size,
+            stride_size=stride_size,
+            sample_rate=sample_rate)
+        ind = np.where(freqs <= max_freq)[0][-1] + 1
+        return np.log(spectrogram[:ind, :] + eps)
+
+    def __extract_spectrogram__(self, samples, window_size, stride_size,
+                                sample_rate):
+        """
+        Compute the spectrogram by FFT for a discrete real signal.
+        Refer to utils.py in https://github.com/baidu-research/ba-dls-deepspeech
+        """
+        # extract strided windows
+        truncate_size = (len(samples) - window_size) % stride_size
+        samples = samples[:len(samples) - truncate_size]
+        nshape = (window_size, (len(samples) - window_size) // stride_size + 1)
+        nstrides = (samples.strides[0], samples.strides[0] * stride_size)
+        windows = np.lib.stride_tricks.as_strided(
+            samples, shape=nshape, strides=nstrides)
+        assert np.all(
+            windows[:, 1] == samples[stride_size:(stride_size + window_size)])
+        # window weighting, squared Fast Fourier Transform (fft), scaling
+        weighting = np.hanning(window_size)[:, None]
+        fft = np.fft.rfft(windows * weighting, axis=0)
+        fft = np.absolute(fft)**2
+        scale = np.sum(weighting**2) * sample_rate
+        fft[1:-1, :] *= (2.0 / scale)
+        fft[(0, -1), :] /= scale
+        # prepare fft frequency list
+        freqs = float(sample_rate) / window_size * np.arange(fft.shape[0])
+        return fft, freqs
+
+    def __load_vocabulary_from_file__(self, vocabulary_path):
+        """
+        Load vocabulary from file.
+        """
+        if not os.path.exists(vocabulary_path):
+            raise ValueError("Vocabulary file %s not found.", vocabulary_path)
+        vocab_lines = []
+        with open(vocabulary_path, 'r') as file:
+            vocab_lines.extend(file.readlines())
+        vocab_list = [line[:-1] for line in vocab_lines]
+        vocab_dict = dict(
+            [(token, id) for (id, token) in enumerate(vocab_list)])
+        return vocab_dict, vocab_list
+
+    def __convert_text_to_char_index__(self, text, vocabulary):
+        """
+        Convert text string to a list of character index integers.
+        """
+        return [vocabulary[w] for w in text]
+
+    def __read_manifest__(self, manifest_path, max_duration, min_duration):
+        """
+        Load and parse manifest file.
+        """
+        manifest = []
+        for json_line in open(manifest_path):
+            try:
+                json_data = json.loads(json_line)
+            except Exception as e:
+                raise ValueError("Error reading manifest: %s" % str(e))
+            if (json_data["duration"] <= max_duration and
+                    json_data["duration"] >= min_duration):
+                manifest.append(json_data)
+        return manifest
+
+    def __padding_batch__(self, batch, padding_to=-1, flatten=False):
+        """
+        Padding audio part of features (only in the time axis -- column axis)
+        with zeros, to make each instance in the batch share the same
+        audio feature shape.
+
+        If `padding_to` is set -1, the maximun column numbers in the batch will
+        be used as the target size. Otherwise, `padding_to` will be the target
+        size. Default is -1.
+
+        If `flatten` is set True, audio data will be flatten to be a 1-dim
+        ndarray. Default is False.
+        """
+        new_batch = []
+        # get target shape
+        max_length = max([audio.shape[1] for audio, text in batch])
+        if padding_to != -1:
+            if padding_to < max_length:
+                raise ValueError("If padding_to is not -1, it should be greater"
+                                 " or equal to the original instance length.")
+            max_length = padding_to
+        # padding
+        for audio, text in batch:
+            padded_audio = np.zeros([audio.shape[0], max_length])
+            padded_audio[:, :audio.shape[1]] = audio
+            if flatten:
+                padded_audio = padded_audio.flatten()
+            new_batch.append((padded_audio, text))
+        return new_batch
+
+    def instance_reader_creator(self,
+                                manifest_path,
+                                sort_by_duration=True,
+                                shuffle=False):
+        """
+        Instance reader creator for audio data. Creat a callable function to
+        produce instances of data.
+
+        Instance: a tuple of a numpy ndarray of audio spectrogram and a list of
+        tokenized and indexed transcription text.
+
+        :param manifest_path: Filepath of manifest for audio clip files.
+        :type manifest_path: basestring
+        :param sort_by_duration: Sort the audio clips by duration if set True
+                                 (for SortaGrad).
+        :type sort_by_duration: bool
+        :param shuffle: Shuffle the audio clips if set True.
+        :type shuffle: bool
+        :return: Data reader function.
+        :rtype: callable
+        """
+        if sort_by_duration and shuffle:
+            sort_by_duration = False
+            logger.warn("When shuffle set to true, "
+                        "sort_by_duration is forced to set False.")
+
+        def reader():
+            # read manifest
+            manifest = self.__read_manifest__(
+                manifest_path=manifest_path,
+                max_duration=self.__max_duration__,
+                min_duration=self.__min_duration__)
+            # sort (by duration) or shuffle manifest
+            if sort_by_duration:
+                manifest.sort(key=lambda x: x["duration"])
+            if shuffle:
+                self.__random__.shuffle(manifest)
+            # extract spectrogram feature
+            for instance in manifest:
+                spectrogram = self.__audio_featurize__(
+                    instance["audio_filepath"])
+                transcript = self.__text_featurize__(instance["text"])
+                yield (spectrogram, transcript)
+
+        return reader
+
+    def batch_reader_creator(self,
+                             manifest_path,
+                             batch_size,
+                             padding_to=-1,
+                             flatten=False,
+                             sort_by_duration=True,
+                             shuffle=False):
+        """
+        Batch data reader creator for audio data. Creat a callable function to
+        produce batches of data.
+
+        Audio features will be padded with zeros to make each instance in the
+        batch to share the same audio feature shape.
+
+        :param manifest_path: Filepath of manifest for audio clip files.
+        :type manifest_path: basestring
+        :param batch_size: Instance number in a batch.
+        :type batch_size: int
+        :param padding_to:  If set -1, the maximun column numbers in the batch
+                            will be used as the target size for padding.
+                            Otherwise, `padding_to` will be the target size.
+                            Default is -1.
+        :type padding_to: int
+        :param flatten: If set True, audio data will be flatten to be a 1-dim
+                        ndarray. Otherwise, 2-dim ndarray. Default is False.
+        :type flatten: bool
+        :param sort_by_duration: Sort the audio clips by duration if set True
+                                 (for SortaGrad).
+        :type sort_by_duration: bool
+        :param shuffle: Shuffle the audio clips if set True.
+        :type shuffle: bool
+        :return: Batch reader function, producing batches of data when called.
+        :rtype: callable
+        """
+
+        def batch_reader():
+            instance_reader = self.instance_reader_creator(
+                manifest_path=manifest_path,
+                sort_by_duration=sort_by_duration,
+                shuffle=shuffle)
+            batch = []
+            for instance in instance_reader():
+                batch.append(instance)
+                if len(batch) == batch_size:
+                    yield self.__padding_batch__(batch, padding_to, flatten)
+                    batch = []
+            if len(batch) > 0:
+                yield self.__padding_batch__(batch, padding_to, flatten)
+
+        return batch_reader
+
+    def vocabulary_size(self):
+        """
+        Get vocabulary size.
+
+        :return: Vocabulary size.
+        :rtype: int
+        """
+        return len(self.__vocab_list__)
+
+    def vocabulary_dict(self):
+        """
+        Get vocabulary in dict.
+
+        :return: Vocabulary in dict.
+        :rtype: dict
+        """
+        return self.__vocab_dict__
+
+    def vocabulary_list(self):
+        """
+        Get vocabulary in list.
+
+        :return: Vocabulary in list
+        :rtype: list
+        """
+        return self.__vocab_list__
+
+    def data_name_feeding(self):
+        """
+        Get feeddings (data field name and corresponding field id).
+
+        :return: Feeding dict.
+        :rtype: dict
+        """
+        feeding = {
+            "audio_spectrogram": 0,
+            "transcript_text": 1,
+        }
+        return feeding