py-ctc-decode's Introduction

py-ctc-decode

Decoding output of CTC trained models. Implementation was adapted from https://github.com/PaddlePaddle/DeepSpeech, but in python for easier modifications. If you need the speed, use the Paddle implementation.

Best Path
Beam Search
Beam Search with LM

Usage

Following examples show how to use the different decoding strategies. It is assumed that _ is the blank symbol. Probabilites are expected to be in natural log.

Best Path

logits = [] # TxV
vocabulary = [' ', 'a', 'b', 'c', '_'] # V
decoder = ctcdecode.BestPathDecoder(
    vocabulary,
    num_workers=4
)

predictions = decoder.decode_batch(logits)
prediction = decoder.decode(logits[0])

Beam Search

logits = [] # TxV
vocabulary = [' ', 'a', 'b', 'c', '_'] # V
decoder = ctcdecode.BeamSearchDecoder(
    vocabulary,
    num_workers=4,
    beam_width=64,
    cutoff_prob=np.log(0.000001),
    cutoff_top_n=40
)

predictions = decoder.decode_batch(logits)
prediction = decoder.decode(logits[0])

Beam Search with LM

logits = [
    [-1.1906, -1.0623, -1.7766, -1.7086],
    [-1.4091, -1.4424, -1.1923, -1.5336],
    [-1.4091, -1.6900, -1.6956, -0.9477],
    [-1.3715, -1.2527, -1.7445, -1.2524],
    [-1.2577, -1.2588, -1.3380, -1.7759]
] # 5x4 TimeSteps x Softmax over Vocabulary (NATURAL LOG !!!)
vocabulary = [' ', 'a', 'b', '_'] # 4
alpha = 2.5 # LM Weight
beta = 0.0 # LM Usage Reward
word_lm_scorer = ctcdecode.WordKenLMScorer('path/to/kenlm', alpha, beta)
decoder = ctcdecode.BeamSearchDecoder(
    vocabulary,
    num_workers=4,
    beam_width=64,
    scorers=[word_lm_scorer],
    cutoff_prob=np.log(0.000001),
    cutoff_top_n=40
)

prediction = decoder.decode(logits) # text (e.g. "a b")

# Batch decoding for multiple utterances
batch = [logits, ....]
predictions = decoder.decode_batch(batch)

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