add deploy.py

deep_speech_2/deploy.py

+"""Deployment for DeepSpeech2 model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import gzip
+import distutils.util
+import multiprocessing
+import paddle.v2 as paddle
+from data_utils.data import DataGenerator
+from model import deep_speech2
+from swig_ctc_beam_search_decoder import *
+from swig_scorer import Scorer
+from error_rate import wer
+import utils
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument(
+    "--num_samples",
+    default=100,
+    type=int,
+    help="Number of samples for inference. (default: %(default)s)")
+parser.add_argument(
+    "--num_conv_layers",
+    default=2,
+    type=int,
+    help="Convolution layer number. (default: %(default)s)")
+parser.add_argument(
+    "--num_rnn_layers",
+    default=3,
+    type=int,
+    help="RNN layer number. (default: %(default)s)")
+parser.add_argument(
+    "--rnn_layer_size",
+    default=512,
+    type=int,
+    help="RNN layer cell number. (default: %(default)s)")
+parser.add_argument(
+    "--use_gpu",
+    default=True,
+    type=distutils.util.strtobool,
+    help="Use gpu or not. (default: %(default)s)")
+parser.add_argument(
+    "--num_threads_data",
+    default=multiprocessing.cpu_count(),
+    type=int,
+    help="Number of cpu threads for preprocessing data. (default: %(default)s)")
+parser.add_argument(
+    "--mean_std_filepath",
+    default='mean_std.npz',
+    type=str,
+    help="Manifest path for normalizer. (default: %(default)s)")
+parser.add_argument(
+    "--decode_manifest_path",
+    default='datasets/manifest.test',
+    type=str,
+    help="Manifest path for decoding. (default: %(default)s)")
+parser.add_argument(
+    "--model_filepath",
+    default='ds2_new_models_0628/params.pass-51.tar.gz',
+    type=str,
+    help="Model filepath. (default: %(default)s)")
+parser.add_argument(
+    "--vocab_filepath",
+    default='datasets/vocab/eng_vocab.txt',
+    type=str,
+    help="Vocabulary filepath. (default: %(default)s)")
+parser.add_argument(
+    "--decode_method",
+    default='beam_search',
+    type=str,
+    help="Method for ctc decoding: best_path or beam_search. (default: %(default)s)"
+)
+parser.add_argument(
+    "--beam_size",
+    default=500,
+    type=int,
+    help="Width for beam search decoding. (default: %(default)d)")
+parser.add_argument(
+    "--num_results_per_sample",
+    default=1,
+    type=int,
+    help="Number of output per sample in beam search. (default: %(default)d)")
+parser.add_argument(
+    "--language_model_path",
+    default="lm/data/en.00.UNKNOWN.klm",
+    type=str,
+    help="Path for language model. (default: %(default)s)")
+parser.add_argument(
+    "--alpha",
+    default=0.26,
+    type=float,
+    help="Parameter associated with language model. (default: %(default)f)")
+parser.add_argument(
+    "--beta",
+    default=0.1,
+    type=float,
+    help="Parameter associated with word count. (default: %(default)f)")
+parser.add_argument(
+    "--cutoff_prob",
+    default=0.99,
+    type=float,
+    help="The cutoff probability of pruning"
+    "in beam search. (default: %(default)f)")
+args = parser.parse_args()
+
+
+def infer():
+    """Deployment for DeepSpeech2."""
+    # initialize data generator
+    data_generator = DataGenerator(
+        vocab_filepath=args.vocab_filepath,
+        mean_std_filepath=args.mean_std_filepath,
+        augmentation_config='{}',
+        num_threads=args.num_threads_data)
+
+    # create network config
+    # paddle.data_type.dense_array is used for variable batch input.
+    # The size 161 * 161 is only an placeholder value and the real shape
+    # of input batch data will be induced during training.
+    audio_data = paddle.layer.data(
+        name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
+    text_data = paddle.layer.data(
+        name="transcript_text",
+        type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
+    output_probs = deep_speech2(
+        audio_data=audio_data,
+        text_data=text_data,
+        dict_size=data_generator.vocab_size,
+        num_conv_layers=args.num_conv_layers,
+        num_rnn_layers=args.num_rnn_layers,
+        rnn_size=args.rnn_layer_size,
+        is_inference=True)
+
+    # load parameters
+    parameters = paddle.parameters.Parameters.from_tar(
+        gzip.open(args.model_filepath))
+
+    # prepare infer data
+    batch_reader = data_generator.batch_reader_creator(
+        manifest_path=args.decode_manifest_path,
+        batch_size=args.num_samples,
+        min_batch_size=1,
+        sortagrad=False,
+        shuffle_method=None)
+    infer_data = batch_reader().next()
+
+    # run inference
+    infer_results = paddle.infer(
+        output_layer=output_probs, parameters=parameters, input=infer_data)
+    num_steps = len(infer_results) // len(infer_data)
+    probs_split = [
+        infer_results[i * num_steps:(i + 1) * num_steps]
+        for i in xrange(len(infer_data))
+    ]
+
+    # targe transcription
+    target_transcription = [
+        ''.join(
+            [data_generator.vocab_list[index] for index in infer_data[i][1]])
+        for i, probs in enumerate(probs_split)
+    ]
+
+    ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
+    ## decode and print
+
+    wer_sum, wer_counter = 0, 0
+    for i, probs in enumerate(probs_split):
+        beam_result = ctc_beam_search_decoder(
+            probs.tolist(),
+            args.beam_size,
+            data_generator.vocab_list,
+            len(data_generator.vocab_list),
+            args.cutoff_prob,
+            ext_scorer, )
+
+        print("\nTarget Transcription:\t%s" % target_transcription[i])
+        print("Beam %d: %f \t%s" % (0, beam_result[0][0], beam_result[0][1]))
+        wer_cur = wer(target_transcription[i], beam_result[0][1])
+        wer_sum += wer_cur
+        wer_counter += 1
+        print("cur wer = %f , average wer = %f" %
+              (wer_cur, wer_sum / wer_counter))
+
+
+def main():
+    utils.print_arguments(args)
+    paddle.init(use_gpu=args.use_gpu, trainer_count=1)
+    infer()
+
+
+if __name__ == '__main__':
+    main()