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286 lines
12 KiB
286 lines
12 KiB
"""Contains DeepSpeech2 model."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import sys
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import os
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import time
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import logging
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import gzip
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from distutils.dir_util import mkpath
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import paddle.v2 as paddle
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from decoders.swig_wrapper import Scorer
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from decoders.swig_wrapper import ctc_greedy_decoder
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from decoders.swig_wrapper import ctc_beam_search_decoder_batch
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from model_utils.network import deep_speech_v2_network
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logging.basicConfig(
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format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s')
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class DeepSpeech2Model(object):
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"""DeepSpeech2Model class.
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:param vocab_size: Decoding vocabulary size.
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:type vocab_size: int
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:param num_conv_layers: Number of stacking convolution layers.
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:type num_conv_layers: int
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:param num_rnn_layers: Number of stacking RNN layers.
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:type num_rnn_layers: int
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:param rnn_layer_size: RNN layer size (number of RNN cells).
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:type rnn_layer_size: int
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:param pretrained_model_path: Pretrained model path. If None, will train
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from stratch.
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:type pretrained_model_path: basestring|None
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:param share_rnn_weights: Whether to share input-hidden weights between
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forward and backward directional RNNs.Notice that
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for GRU, weight sharing is not supported.
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:type share_rnn_weights: bool
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"""
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def __init__(self, vocab_size, num_conv_layers, num_rnn_layers,
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rnn_layer_size, use_gru, pretrained_model_path,
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share_rnn_weights):
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self._create_network(vocab_size, num_conv_layers, num_rnn_layers,
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rnn_layer_size, use_gru, share_rnn_weights)
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self._create_parameters(pretrained_model_path)
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self._inferer = None
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self._loss_inferer = None
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self._ext_scorer = None
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self.logger = logging.getLogger("")
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self.logger.setLevel(level=logging.INFO)
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def train(self,
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train_batch_reader,
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dev_batch_reader,
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feeding_dict,
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learning_rate,
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gradient_clipping,
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num_passes,
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output_model_dir,
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is_local=True,
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num_iterations_print=100,
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test_off=False):
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"""Train the model.
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:param train_batch_reader: Train data reader.
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:type train_batch_reader: callable
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:param dev_batch_reader: Validation data reader.
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:type dev_batch_reader: callable
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:param feeding_dict: Feeding is a map of field name and tuple index
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of the data that reader returns.
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:type feeding_dict: dict|list
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:param learning_rate: Learning rate for ADAM optimizer.
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:type learning_rate: float
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:param gradient_clipping: Gradient clipping threshold.
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:type gradient_clipping: float
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:param num_passes: Number of training epochs.
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:type num_passes: int
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:param num_iterations_print: Number of training iterations for printing
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a training loss.
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:type rnn_iteratons_print: int
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:param is_local: Set to False if running with pserver with multi-nodes.
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:type is_local: bool
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:param output_model_dir: Directory for saving the model (every pass).
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:type output_model_dir: basestring
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:param test_off: Turn off testing.
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:type test_off: bool
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"""
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# prepare model output directory
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if not os.path.exists(output_model_dir):
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mkpath(output_model_dir)
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# prepare optimizer and trainer
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optimizer = paddle.optimizer.Adam(
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learning_rate=learning_rate,
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gradient_clipping_threshold=gradient_clipping)
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trainer = paddle.trainer.SGD(
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cost=self._loss,
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parameters=self._parameters,
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update_equation=optimizer,
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is_local=is_local)
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# create event handler
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def event_handler(event):
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global start_time, cost_sum, cost_counter
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if isinstance(event, paddle.event.EndIteration):
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cost_sum += event.cost
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cost_counter += 1
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if (event.batch_id + 1) % num_iterations_print == 0:
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output_model_path = os.path.join(output_model_dir,
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"params.latest.tar.gz")
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with gzip.open(output_model_path, 'w') as f:
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trainer.save_parameter_to_tar(f)
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print("\nPass: %d, Batch: %d, TrainCost: %f" %
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(event.pass_id, event.batch_id + 1,
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cost_sum / cost_counter))
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cost_sum, cost_counter = 0.0, 0
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else:
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sys.stdout.write('.')
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sys.stdout.flush()
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if isinstance(event, paddle.event.BeginPass):
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start_time = time.time()
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cost_sum, cost_counter = 0.0, 0
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if isinstance(event, paddle.event.EndPass):
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if test_off:
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print("\n------- Time: %d sec, Pass: %d" %
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(time.time() - start_time, event.pass_id))
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else:
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result = trainer.test(
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reader=dev_batch_reader, feeding=feeding_dict)
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print(
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"\n------- Time: %d sec, Pass: %d, "
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"ValidationCost: %s" %
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(time.time() - start_time, event.pass_id, result.cost))
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output_model_path = os.path.join(
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output_model_dir, "params.pass-%d.tar.gz" % event.pass_id)
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with gzip.open(output_model_path, 'w') as f:
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trainer.save_parameter_to_tar(f)
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# run train
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trainer.train(
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reader=train_batch_reader,
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event_handler=event_handler,
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num_passes=num_passes,
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feeding=feeding_dict)
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def infer_loss_batch(self, infer_data):
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"""Model inference. Infer the ctc loss for a batch of speech
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utterances.
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:param infer_data: List of utterances to infer, with each utterance a
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tuple of audio features and transcription text (empty
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string).
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:type infer_data: list
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:return: List of ctc loss.
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:rtype: List of float
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"""
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# define inferer
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if self._loss_inferer == None:
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self._loss_inferer = paddle.inference.Inference(
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output_layer=self._loss, parameters=self._parameters)
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# run inference
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return self._loss_inferer.infer(input=infer_data)
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def infer_batch(self, infer_data, decoding_method, beam_alpha, beam_beta,
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beam_size, cutoff_prob, cutoff_top_n, vocab_list,
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language_model_path, num_processes):
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"""Model inference. Infer the transcription for a batch of speech
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utterances.
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:param infer_data: List of utterances to infer, with each utterance
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consisting of a tuple of audio features and
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transcription text (empty string).
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:type infer_data: list
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:param decoding_method: Decoding method name, 'ctc_greedy' or
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'ctc_beam_search'.
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:param decoding_method: string
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:param beam_alpha: Parameter associated with language model.
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:type beam_alpha: float
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:param beam_beta: Parameter associated with word count.
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:type beam_beta: float
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:param beam_size: Width for Beam search.
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:type beam_size: int
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:param cutoff_prob: Cutoff probability in pruning,
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default 1.0, no pruning.
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:type cutoff_prob: float
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:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
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characters with highest probs in vocabulary will be
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used in beam search, default 40.
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:type cutoff_top_n: int
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:param vocab_list: List of tokens in the vocabulary, for decoding.
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:type vocab_list: list
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:param language_model_path: Filepath for language model.
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:type language_model_path: basestring|None
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:param num_processes: Number of processes (CPU) for decoder.
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:type num_processes: int
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:return: List of transcription texts.
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:rtype: List of basestring
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"""
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# define inferer
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if self._inferer == None:
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self._inferer = paddle.inference.Inference(
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output_layer=self._log_probs, parameters=self._parameters)
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# run inference
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infer_results = self._inferer.infer(input=infer_data)
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num_steps = len(infer_results) // len(infer_data)
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probs_split = [
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infer_results[i * num_steps:(i + 1) * num_steps]
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for i in xrange(0, len(infer_data))
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]
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# run decoder
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results = []
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if decoding_method == "ctc_greedy":
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# best path decode
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for i, probs in enumerate(probs_split):
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output_transcription = ctc_greedy_decoder(
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probs_seq=probs, vocabulary=vocab_list)
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results.append(output_transcription)
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elif decoding_method == "ctc_beam_search":
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# initialize external scorer
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if self._ext_scorer == None:
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self._loaded_lm_path = language_model_path
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self.logger.info("begin to initialize the external scorer "
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"for decoding")
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self._ext_scorer = Scorer(beam_alpha, beam_beta,
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language_model_path, vocab_list)
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lm_char_based = self._ext_scorer.is_character_based()
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lm_max_order = self._ext_scorer.get_max_order()
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lm_dict_size = self._ext_scorer.get_dict_size()
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self.logger.info("language model: "
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"is_character_based = %d," % lm_char_based +
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" max_order = %d," % lm_max_order +
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" dict_size = %d" % lm_dict_size)
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self.logger.info("end initializing scorer. Start decoding ...")
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else:
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self._ext_scorer.reset_params(beam_alpha, beam_beta)
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assert self._loaded_lm_path == language_model_path
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# beam search decode
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num_processes = min(num_processes, len(probs_split))
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beam_search_results = ctc_beam_search_decoder_batch(
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probs_split=probs_split,
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vocabulary=vocab_list,
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beam_size=beam_size,
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num_processes=num_processes,
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ext_scoring_func=self._ext_scorer,
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cutoff_prob=cutoff_prob,
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cutoff_top_n=cutoff_top_n)
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results = [result[0][1] for result in beam_search_results]
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else:
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raise ValueError("Decoding method [%s] is not supported." %
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decoding_method)
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return results
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def _create_parameters(self, model_path=None):
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"""Load or create model parameters."""
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if model_path is None:
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self._parameters = paddle.parameters.create(self._loss)
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else:
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self._parameters = paddle.parameters.Parameters.from_tar(
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gzip.open(model_path))
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def _create_network(self, vocab_size, num_conv_layers, num_rnn_layers,
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rnn_layer_size, use_gru, share_rnn_weights):
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"""Create data layers and model network."""
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# paddle.data_type.dense_array is used for variable batch input.
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# The size 161 * 161 is only an placeholder value and the real shape
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# of input batch data will be induced during training.
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audio_data = paddle.layer.data(
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name="audio_spectrogram",
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type=paddle.data_type.dense_array(161 * 161))
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text_data = paddle.layer.data(
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name="transcript_text",
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type=paddle.data_type.integer_value_sequence(vocab_size))
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self._log_probs, self._loss = deep_speech_v2_network(
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audio_data=audio_data,
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text_data=text_data,
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dict_size=vocab_size,
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num_conv_layers=num_conv_layers,
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num_rnn_layers=num_rnn_layers,
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rnn_size=rnn_layer_size,
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use_gru=use_gru,
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share_rnn_weights=share_rnn_weights)
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