def DecodeWithTheta(self, theta, input_batch):
"""Constructs the inference graph."""
p = self.params
with tf.name_scope('decode'), tf.name_scope(p.name):
with tf.name_scope('encoder'):
encoder_outputs = self._FrontendAndEncoderFProp(theta, input_batch.src)
with tf.name_scope('beam_search'):
decoder_outs = self.decoder.BeamSearchDecodeWithTheta(
theta.decoder, encoder_outputs)
if py_utils.use_tpu():
# Decoder metric computation contains arbitrary execution
# that may not run on TPU.
dec_metrics = py_utils.RunOnTpuHost(self._ComputeDecoderMetrics,
decoder_outs, input_batch)
else:
dec_metrics = self._ComputeDecoderMetrics(decoder_outs, input_batch)
return dec_metrics
def _CalculateErrorRates(self, dec_outs_dict, input_batch):
return {'stuff': dec_outs_dict.sparse_ids.values}
gt_seq_lens = py_utils.LengthsFromBitMask(input_batch.tgt.paddings, 1)
gt_transcripts = py_utils.RunOnTpuHost(
self.input_generator.IdsToStrings, input_batch.tgt.labels,
gt_seq_lens)
# token error rate
char_dist = tf.edit_distance(tf.string_split(dec_outs_dict.transcripts,
sep=''),
tf.string_split(gt_transcripts, sep=''),
normalize=False)
ref_chars = tf.strings.length(gt_transcripts)
num_wrong_chars = tf.reduce_sum(char_dist)
num_ref_chars = tf.cast(tf.reduce_sum(ref_chars), tf.float32)
cer = num_wrong_chars / num_ref_chars
# word error rate
word_dist = decoder_utils.ComputeWer(dec_outs_dict.transcripts,
gt_transcripts) # (B, 2)
num_wrong_words = tf.reduce_sum(word_dist[:, 0])
num_ref_words = tf.reduce_sum(word_dist[:, 1])
wer = num_wrong_words / num_ref_words
ret_dict = {
'target_ids': input_batch.tgt.ids,
'target_labels': input_batch.tgt.labels,
'target_weights': input_batch.tgt.weights,
'target_paddings': input_batch.tgt.paddings,
'target_transcripts': gt_transcripts,
'decoded_transcripts': dec_outs_dict.transcripts,
'wer': wer,
'cer': cer,
'num_wrong_words': num_wrong_words,
'num_ref_words': num_ref_words,
'num_wrong_chars': num_wrong_chars,
'num_ref_chars': num_ref_chars
}
if not py_utils.use_tpu():
ret_dict['utt_id'] = input_batch.sample_ids
return ret_dict
def DecodeWithTheta(self, theta, input_batch):
"""Constructs the inference graph."""
p = self.params
# from IPython import embed; embed()
with tf.name_scope('decode'), tf.name_scope(p.name):
with tf.name_scope('encoder'):
encoder_outputs = self._FrontendAndEncoderFProp(
theta, input_batch.src)
if p.inference_compute_only_log_softmax:
global_step = tf.train.get_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
with tf.control_dependencies([increment_global_step]):
log_probabilities = tf.transpose(tf.nn.log_softmax(
encoder_outputs.encoded, axis=2),
perm=(1, 0, 2))
with tf.name_scope('decoder'):
decoder_outs = self._DecodeCTC(encoder_outputs)
# encoder_outputs's shape is [T,B,F]
return {
'log_probabilities':
log_probabilities,
'log_probabilities_lengths':
py_utils.LengthsFromBitMask(encoder_outputs.padding, 0),
'int64_uttid':
input_batch.sample_ids,
'int64_audio_document_id':
input_batch.audio_document_ids,
'num_utterances_in_audio_document':
input_batch.num_utterances_in_audio_document,
'transcripts':
decoder_outs.transcripts,
}
with tf.name_scope('decoder'):
decoder_outs = self._DecodeCTC(encoder_outputs)
decoder_metrics = py_utils.RunOnTpuHost(self._CalculateErrorRates,
decoder_outs, input_batch)
return decoder_metrics
