Refactor the whole data preprocessor part for DeepSpeech2. #91
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…ize dir, add augmentaion interfaces etc.). 1. Refactor data preprocessor with new added class AudioSegment, SpeechSegment, TextFeaturizer, AudioFeaturizer, SpeechFeaturizer. 2. Add data augmentation interfaces and class AugmentorBase, AugmentationPipeline, VolumnPerturbAugmentor etc.. 3. Seperate normalizer's mean and std computing from training, by adding FeatureNormalizer and a seperate tool compute_mean_std.py. 4. Re-organize directory.
xinghai-sun
requested review from
qingqing01,
kuke,
pkuyym,
lcy-seso and
chrisxu2016
qingqing01
reviewed
Jun 14, 2017
| @@ -86,6 +83,12 @@ | |||
| help="If set None, the training will start from scratch. " | |||
| "Otherwise, the training will resume from " | |||
| "the existing model of this path. (default: %(default)s)") | |||
| parser.add_argument( | |||
| "--augmentation_config", | |||
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真实运行的时候需要提供augmentation_config配置吗？只看到code里注释的json格式，没看到json文件，如果运行的时候需要，可否提供一个json文件，用户用时配置就可以
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这个建议很好，当前augmentation_config为str格式（由于目前augmentation仅留置了接口，所以默认augmentation_config='{}'，即augmentation不生效)，配置json string确实不方便。
因为模型参数较多，后续可以统一提供一个config file。
chrisxu2016
reviewed
Jun 14, 2017
| :rtype: AudioSegment | ||
| """ | ||
| samples, sample_rate = soundfile.read(file, dtype='float32') | ||
| return cls(samples, sample_rate) |
| :param gain: Gain in decibels to apply to samples. | ||
| :type gain: float | ||
| """ | ||
| self._samples *= 10.**(gain / 20.) |
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建议这里返回一个新建一个audio对象，方便后面添加add_noise时，复用这个方法
return type(self)(10.**(gain / 20.) * self._samples, self._sample_rate)
chrisxu2016
reviewed
Jun 15, 2017
| :return: Number of samples. | ||
| :rtype: int | ||
| """ | ||
| return self._samples.shape(0) |
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应该是 self._samples.shape[0], ()改为[]
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resolve #90
AudioSegment,SpeechSegment,TextFeaturizer,AudioFeaturizer,SpeechFeaturizeretc.AugmentorBase,AugmentationPipeline,VolumePerturbAugmentoretc., to make it easier to add more data augmentation models.DataGenerator. AddFeatureNormalizer. -compute_mean_std.pyfor users to create mean_std file before training.datadirectory intodatasetsanddata_utils.