def _prediction_from_string(self, probabilities, sentence):
pred = Prediction()
pred.labels[:] = self.codec.encode(sentence)
pred.is_voted_result = False
pred.logits = probabilities
for c, l in zip(sentence, pred.labels):
pred.positions.append(
PredictionPosition(chars=[
PredictionCharacter(label=l, char=c, probability=1.0)
]))
return pred
def find_alternatives(self, probabilities, sentence, threshold) -> Prediction:
"""
Find alternatives to the decoded sentence in the logits.
E.g. if a 'c' is decoded in the range 2 to 4, this algorithm will add all characters in the interval [2, 4] to
the output if the confidence of the character is higher than the threshold, respectively.
Parameters
----------
probabilities : array_like
Prediction of the neural net to decode or shape (length x character probability).
The blank index must be 0.
sentence : list of tuple (character index, start pos, end pos)
The decoded sentence (depends on the CTCDecoder).
The position refer to the character position in the logits.
threshold : float
Minimum confidence for alternative characters to be listed.
Returns
-------
a Prediction object
"""
# find alternatives
pred = Prediction()
pred.labels[:] = [c for c, _, _ in sentence]
pred.is_voted_result = False
pred.logits = probabilities
pred.avg_char_probability = 0
for c, start, end in sentence:
p = probabilities[start:end]
p = np.max(p, axis=0)
pos = PredictionPosition(
local_start=start,
local_end=end
)
pred.positions.append(pos)
for label in reversed(sorted(range(len(p)), key=lambda v: p[v])):
if p[label] < threshold and len(pos.chars) > 0:
break
else:
pos.chars.append(PredictionCharacter(
label=label,
probability=p[label],
))
if len(pos.chars) > 0:
pred.avg_char_probability += pos.chars[0].probability
pred.avg_char_probability /= len(pred.positions) if len(pred.positions) > 0 else 1
return pred
def decode(self, probabilities):
"""
Decoding algorithm of the individual CTCDecoder. This abstract function is reimplemented
by the DefaultCTCDecoder and the FuzzyCTCDecoder.
Parameters
----------
probabilities : array_like
Prediction probabilities of the neural net to decode or shape (length x character probability).
The blank index must be 0.
Returns
-------
a Prediction object
"""
return Prediction()