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import torch
import fastwer
import numpy as np
from scipy.special import logsumexp
"""
-------------
CTC decoder
-------------
"""
NINF = -1 * float("inf")
DEFAULT_EMISSION_THRESHOLD = 0.01
def _reconstruct(labels, blank=0):
new_labels = []
# merge same labels
previous = None
for l in labels:
if l != previous:
new_labels.append(l)
previous = l
# delete blank
new_labels = [l for l in new_labels if l != blank]
return new_labels
def beam_search_decode(emission_log_prob, blank=0, **kwargs):
beam_size = kwargs["beam_size"]
emission_threshold = kwargs.get(
"emission_threshold", np.log(DEFAULT_EMISSION_THRESHOLD)
)
length, class_count = emission_log_prob.shape
beams = [([], 0)] # (prefix, accumulated_log_prob)
for t in range(length):
new_beams = []
for prefix, accumulated_log_prob in beams:
for c in range(class_count):
log_prob = emission_log_prob[t, c]
if log_prob < emission_threshold:
continue
new_prefix = prefix + [c]
# log(p1 * p2) = log_p1 + log_p2
new_accu_log_prob = accumulated_log_prob + log_prob
new_beams.append((new_prefix, new_accu_log_prob))
# sorted by accumulated_log_prob
new_beams.sort(key=lambda x: x[1], reverse=True)
beams = new_beams[:beam_size]
# sum up beams to produce labels
total_accu_log_prob = {}
for prefix, accu_log_prob in beams:
labels = tuple(_reconstruct(prefix, blank))
# log(p1 + p2) = logsumexp([log_p1, log_p2])
total_accu_log_prob[labels] = logsumexp(
[accu_log_prob, total_accu_log_prob.get(labels, NINF)]
)
labels_beams = [
(list(labels), accu_log_prob)
for labels, accu_log_prob in total_accu_log_prob.items()
]
labels_beams.sort(key=lambda x: x[1], reverse=True)
labels = labels_beams[0][0]
return labels
def greedy_decode(emission_log_prob, blank=0):
labels = np.argmax(emission_log_prob, axis=-1)
labels = _reconstruct(labels, blank=blank)
return labels
def ctc_decode(
log_probs, which_ctc_decoder="beam_search", label_2_char=None, blank=0, beam_size=25
):
emission_log_probs = np.transpose(log_probs.cpu().numpy(), (1, 0, 2))
# size of emission_log_probs: (batch, length, class)
decoded_list = []
for emission_log_prob in emission_log_probs:
if which_ctc_decoder == "beam_search":
decoded = beam_search_decode(
emission_log_prob, blank=blank, beam_size=beam_size
)
elif which_ctc_decoder == "greedy":
decoded = greedy_decode(emission_log_prob, blank=blank)
else:
print(f"unidentified option for which_ctc_decoder : {which_ctc_decoder}")
sys.exit(0)
if label_2_char:
decoded = [label_2_char[l] for l in decoded]
decoded_list.append(decoded)
return decoded_list
"""
--------------------
Evaluation Metrics
--------------------
"""
def compute_wer_and_cer_for_batch(batch_preds, batch_gts):
cer_batch = fastwer.score(batch_preds, batch_gts, char_level=True)
wer_batch = fastwer.score(batch_preds, batch_gts)
return cer_batch, wer_batch
def compute_wer_and_cer_for_sample(str_pred, str_gt):
cer_sample = fastwer.score_sent(str_pred, str_gt, char_level=True)
wer_sample = fastwer.score_sent(str_pred, str_gt)
return cer_sample, wer_sample
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