Merge pull request #3395 from CatalinVoss/patch-1
Minor Training Variable Consistency fix
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commit
b72e2643c4
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@ -730,7 +730,7 @@ def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
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logits = tf.squeeze(logits, [1])
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# Apply softmax for CTC decoder
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logits = tf.nn.softmax(logits, name='logits')
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probs = tf.nn.softmax(logits, name='logits')
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if batch_size <= 0:
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if tflite:
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@ -743,7 +743,7 @@ def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
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'input_lengths': seq_length,
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},
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{
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'outputs': logits,
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'outputs': probs,
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},
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layers
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)
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@ -763,7 +763,7 @@ def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
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inputs['input_lengths'] = seq_length
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outputs = {
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'outputs': logits,
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'outputs': probs,
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'new_state_c': new_state_c,
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'new_state_h': new_state_h,
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'mfccs': mfccs,
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@ -900,21 +900,21 @@ def do_single_file_inference(input_file_path):
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features = create_overlapping_windows(features).eval(session=session)
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features_len = features_len.eval(session=session)
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logits = outputs['outputs'].eval(feed_dict={
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probs = outputs['outputs'].eval(feed_dict={
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inputs['input']: features,
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inputs['input_lengths']: features_len,
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inputs['previous_state_c']: previous_state_c,
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inputs['previous_state_h']: previous_state_h,
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}, session=session)
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logits = np.squeeze(logits)
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probs = np.squeeze(probs)
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if FLAGS.scorer_path:
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scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta,
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FLAGS.scorer_path, Config.alphabet)
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else:
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scorer = None
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decoded = ctc_beam_search_decoder(logits, Config.alphabet, FLAGS.beam_width,
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decoded = ctc_beam_search_decoder(probs, Config.alphabet, FLAGS.beam_width,
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scorer=scorer, cutoff_prob=FLAGS.cutoff_prob,
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cutoff_top_n=FLAGS.cutoff_top_n)
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# Print highest probability result
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