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@ -9,169 +9,103 @@ import multiprocessing
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import paddle.v2 as paddle
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from model import DeepSpeech2Model
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from data_utils.data import DataGenerator
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import utils
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NUM_CPU = multiprocessing.cpu_count() // 2
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--batch_size", default=256, type=int, help="Minibatch size.")
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parser.add_argument(
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"--num_passes",
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default=200,
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type=int,
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help="Training pass number. (default: %(default)s)")
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parser.add_argument(
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"--num_iterations_print",
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default=100,
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type=int,
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help="Number of iterations for every train cost printing. "
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"(default: %(default)s)")
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parser.add_argument(
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"--num_conv_layers",
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default=2,
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type=int,
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help="Convolution layer number. (default: %(default)s)")
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parser.add_argument(
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"--num_rnn_layers",
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default=3,
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type=int,
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help="RNN layer number. (default: %(default)s)")
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parser.add_argument(
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"--rnn_layer_size",
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default=2048,
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type=int,
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help="RNN layer cell number. (default: %(default)s)")
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parser.add_argument(
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"--share_rnn_weights",
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default=True,
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type=distutils.util.strtobool,
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help="Whether to share input-hidden weights between forword and backward "
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"directional simple RNNs. Only available when use_gru=False. "
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"(default: %(default)s)")
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parser.add_argument(
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"--use_gru",
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default=False,
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type=distutils.util.strtobool,
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help="Use GRU or simple RNN. (default: %(default)s)")
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parser.add_argument(
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"--adam_learning_rate",
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default=5e-4,
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type=float,
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help="Learning rate for ADAM Optimizer. (default: %(default)s)")
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parser.add_argument(
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"--use_gpu",
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default=True,
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type=distutils.util.strtobool,
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help="Use gpu or not. (default: %(default)s)")
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parser.add_argument(
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"--use_sortagrad",
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default=True,
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type=distutils.util.strtobool,
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help="Use sortagrad or not. (default: %(default)s)")
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parser.add_argument(
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"--specgram_type",
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default='linear',
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type=str,
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help="Feature type of audio data: 'linear' (power spectrum)"
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" or 'mfcc'. (default: %(default)s)")
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parser.add_argument(
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"--max_duration",
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default=27.0,
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type=float,
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help="Audios with duration larger than this will be discarded. "
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"(default: %(default)s)")
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parser.add_argument(
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"--min_duration",
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default=0.0,
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type=float,
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help="Audios with duration smaller than this will be discarded. "
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"(default: %(default)s)")
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parser.add_argument(
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"--shuffle_method",
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default='batch_shuffle_clipped',
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type=str,
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help="Shuffle method: 'instance_shuffle', 'batch_shuffle', "
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"'batch_shuffle_batch'. (default: %(default)s)")
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parser.add_argument(
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"--trainer_count",
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default=8,
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type=int,
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help="Trainer number. (default: %(default)s)")
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parser.add_argument(
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"--num_threads_data",
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default=multiprocessing.cpu_count() // 2,
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type=int,
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help="Number of cpu threads for preprocessing data. (default: %(default)s)")
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parser.add_argument(
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"--mean_std_filepath",
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default='mean_std.npz',
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type=str,
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help="Manifest path for normalizer. (default: %(default)s)")
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parser.add_argument(
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"--train_manifest_path",
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default='datasets/manifest.train',
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type=str,
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help="Manifest path for training. (default: %(default)s)")
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parser.add_argument(
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"--dev_manifest_path",
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default='datasets/manifest.dev',
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type=str,
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help="Manifest path for validation. (default: %(default)s)")
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parser.add_argument(
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"--vocab_filepath",
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default='datasets/vocab/eng_vocab.txt',
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type=str,
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help="Vocabulary filepath. (default: %(default)s)")
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parser.add_argument(
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"--init_model_path",
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default=None,
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type=str,
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help="If set None, the training will start from scratch. "
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"Otherwise, the training will resume from "
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"the existing model of this path. (default: %(default)s)")
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parser.add_argument(
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"--output_model_dir",
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default="./checkpoints",
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type=str,
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help="Directory for saving models. (default: %(default)s)")
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parser.add_argument(
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"--augmentation_config",
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default=open('conf/augmentation.config', 'r').read(),
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type=str,
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help="Augmentation configuration in json-format. "
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"(default: %(default)s)")
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parser.add_argument(
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"--is_local",
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default=True,
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type=distutils.util.strtobool,
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help="Set to false if running with pserver in paddlecloud. "
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"(default: %(default)s)")
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def add_arg(argname, type, default, help, **kwargs):
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type = distutils.util.strtobool if type == bool else type
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parser.add_argument(
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"--" + argname,
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default=default,
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type=type,
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help=help + ' Default: %(default)s.',
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**kwargs)
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# yapf: disable
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# configurations of optimization
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add_arg('batch_size', int, 256, "Minibatch size.")
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add_arg('learning_rate', float, 5e-4, "Learning rate.")
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add_arg('use_sortagrad', bool, True, "Use SortaGrad or not.")
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add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
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add_arg('use_gpu', bool, True, "Use GPU or not.")
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add_arg('num_passes', int, 200, "# of training epochs.")
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add_arg('is_local', bool, True, "Use pserver or not.")
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add_arg('num_iter_print', int, 100, "Every # iterations for printing "
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"train cost.")
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# configurations of data preprocess
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add_arg('max_duration', float, 27.0, "Longest audio duration allowed.")
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add_arg('min_duration', float, 0.0, "Shortest audio duration allowed.")
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add_arg('parallels_data', int, NUM_CPU,"# of CPUs for data preprocessing.")
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add_arg('specgram_type', str,
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'linear',
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"Audio feature type. Options: linear, mfcc.",
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choices=['linear', 'mfcc'])
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add_arg('augment_conf_path',str,
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'conf/augmentation.config',
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"Filepath of augmentation configuration file (json-format).")
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add_arg('shuffle_method', str,
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'batch_shuffle_clipped',
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"Shuffle method.",
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choices=['instance_shuffle', 'batch_shuffle', 'batch_shuffle_clipped'])
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# configurations of model structure
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add_arg('num_conv_layers', int, 2, "# of convolution layers.")
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add_arg('num_rnn_layers', int, 3, "# of recurrent layers.")
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add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
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add_arg('use_gru', bool, False, "Use GRUs instead of Simple RNNs.")
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add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
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"bi-directional RNNs. Not for GRU.")
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# configurations of data io
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add_arg('train_manifest', str,
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'datasets/manifest.train',
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"Filepath of train manifest.")
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add_arg('dev_manifest', str,
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'datasets/manifest.dev',
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"Filepath of validation manifest.")
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add_arg('mean_std_path', str,
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'mean_std.npz',
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"Filepath of normalizer's mean & std.")
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add_arg('vocab_path', str,
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'datasets/vocab/eng_vocab.txt',
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"Filepath of vocabulary.")
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# configurations of model io
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add_arg('init_model_path', str,
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None,
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"If None, the training starts from scratch, "
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"otherwise, it resumes from the pre-trained model.")
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add_arg('output_model_dir', str,
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"./checkpoints",
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"Directory for saving checkpoints.")
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args = parser.parse_args()
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# yapf: disable
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def train():
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"""DeepSpeech2 training."""
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train_generator = DataGenerator(
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vocab_filepath=args.vocab_filepath,
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mean_std_filepath=args.mean_std_filepath,
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augmentation_config=args.augmentation_config,
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vocab_filepath=args.vocab_path,
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mean_std_filepath=args.mean_std_path,
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augmentation_config=open(args.augment_conf_path, 'r').read(),
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max_duration=args.max_duration,
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min_duration=args.min_duration,
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specgram_type=args.specgram_type,
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num_threads=args.num_threads_data)
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num_threads=args.parallels_data)
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dev_generator = DataGenerator(
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vocab_filepath=args.vocab_filepath,
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mean_std_filepath=args.mean_std_filepath,
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vocab_filepath=args.vocab_path,
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mean_std_filepath=args.mean_std_path,
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augmentation_config="{}",
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specgram_type=args.specgram_type,
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num_threads=args.num_threads_data)
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num_threads=args.parallels_data)
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train_batch_reader = train_generator.batch_reader_creator(
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manifest_path=args.train_manifest_path,
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manifest_path=args.train_manifest,
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batch_size=args.batch_size,
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min_batch_size=args.trainer_count,
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sortagrad=args.use_sortagrad if args.init_model_path is None else False,
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shuffle_method=args.shuffle_method)
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dev_batch_reader = dev_generator.batch_reader_creator(
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manifest_path=args.dev_manifest_path,
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manifest_path=args.dev_manifest,
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batch_size=args.batch_size,
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min_batch_size=1, # must be 1, but will have errors.
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sortagrad=False,
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@ -184,21 +118,28 @@ def train():
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rnn_layer_size=args.rnn_layer_size,
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use_gru=args.use_gru,
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pretrained_model_path=args.init_model_path,
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share_rnn_weights=args.share_rnn_weights)
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share_rnn_weights=args.share_weights)
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ds2_model.train(
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train_batch_reader=train_batch_reader,
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dev_batch_reader=dev_batch_reader,
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feeding_dict=train_generator.feeding,
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learning_rate=args.adam_learning_rate,
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learning_rate=args.learning_rate,
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gradient_clipping=400,
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num_passes=args.num_passes,
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num_iterations_print=args.num_iterations_print,
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num_iterations_print=args.num_iter_print,
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output_model_dir=args.output_model_dir,
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is_local=args.is_local)
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def print_arguments(args):
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print("----------- Configuration Arguments -----------")
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for arg, value in sorted(vars(args).iteritems()):
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print("%s: %s" % (arg, value))
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print("------------------------------------------------")
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def main():
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utils.print_arguments(args)
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print_arguments(args)
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paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
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train()
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Xinghai Sun
7 years ago