def print_result(self, filename, loglikes, words):
logger.info(f"decoding wav file: {str(Path(filename).resolve())}")
if self.verbose:
labels = onehot2int(loglikes).squeeze()
logger.info(f"labels: {' '.join([str(x) for x in labels.tolist()])}")
symbols = [self.decoder.labeler.idx2phone(x) for x in remove_duplicates(labels, blank=0)]
logger.info(f"symbols: {' '.join(symbols)}")
words = words.squeeze()
text = ' '.join([self.decoder.labeler.idx2word(i) for i in words]) \
if words.dim() else '<null output from decoder>'
logger.info(f"decoded text: {text}")
def unit_validate(self, data):
xs, ys, frame_lens, label_lens, filenames, _ = data
if self.use_cuda:
xs = xs.cuda(non_blocking=True)
ys_hat, frame_lens = self.model(xs, frame_lens)
if self.fp16:
ys_hat = ys_hat.float()
# convert likes to ctc labels
hyps = [onehot2int(yh[:s]).squeeze() for yh, s in zip(ys_hat, frame_lens)]
hyps = [remove_duplicates(h, blank=0) for h in hyps]
# slice the targets
pos = torch.cat((torch.zeros((1, ), dtype=torch.long), torch.cumsum(label_lens, dim=0)))
refs = [ys[s:l] for s, l in zip(pos[:-1], pos[1:])]
return hyps, refs
def make_ctc_labels(self):
# find *.phn files
logger.info(f"finding *.phn files under {str(self.target_path)}")
phn_files = [str(x) for x in self.target_path.rglob("*.phn")]
# convert
for phn_file in tqdm(phn_files, ncols=params.NCOLS):
phns = np.loadtxt(phn_file, dtype="int", ndmin=1)
# make ctc labelings by removing duplications
ctcs = np.array([x for x in remove_duplicates(phns)])
# write ctc file
# blank labels will be inserted in warp-ctc loss module,
# so here the target labels have not to contain the blanks interleaved
ctc_file = phn_file.replace("phn", "ctc")
np.savetxt(str(ctc_file), ctcs, "%d")
count_priors(phn_files)