def test_greedy_decoder(data):
vocab_list, _, log_probs_seq1, log_probs_seq2, greedy_result, _ = data
_, bst_result, _ = decoders.ctc_greedy_decoder(log_probs_seq1)
assert ''.join(vocab_list[i] for i in bst_result) == greedy_result[0]
_, bst_result, _ = decoders.ctc_greedy_decoder(log_probs_seq2)
assert ''.join(vocab_list[i] for i in bst_result) == greedy_result[1]
def decode_segment(segment, offset, mode, tokenizer, scorer):
if mode == 'greedy':
result = decoders.ctc_greedy_decoder(segment,
blank=tokenizer.blank_idx)
best_result = result
elif mode == 'lm+fst':
result = decoders.ctc_beam_search_decoder(segment,
lm_scorer=scorer,
blank=tokenizer.blank_idx,
beam_size=200)
best_result = result[0]
else:
raise ValueError('mode must be one of: "greedy" | "lm+fst"')
if mode == 'lm+fst' and not best_result[1]:
return [], "", []
# remove spaces at the end
best_token_ids = np.trim_zeros(np.array(best_result[1]), 'b')
new_l = len(best_token_ids)
frame_ids = np.array(best_result[2])[:new_l]
frame_ids = np.append(frame_ids, frame_ids[-1])
frame_ids += offset
txt_result = ''.join(tokenizer.idx2token(best_result[1]))
word_starts = [0] + list(
frame_ids[np.where(best_token_ids == 0)[0] + 1] * 0.02)
word_ends = list(frame_ids[np.nonzero(best_token_ids == 0)[0] - 1] *
0.02) + [frame_ids[-1] * 0.02]
segment_transcription_json = [{
'transcript': triple[0],
'start': triple[1],
'end': triple[2]
} for triple in zip(txt_result.split(' '), word_starts, word_ends)]
return frame_ids.tolist(), txt_result, segment_transcription_json
def test_real_ctc_decode():
data = np.genfromtxt(os.path.join(data_dir, "rnn_output.csv"),
delimiter=';')[:, :-1]
inputs = torch.as_tensor(data).log_softmax(1)
tokenizer = CharTokenizer(os.path.join(data_dir, 'labels.txt'))
# greedy using beam
result = decoders.ctc_greedy_decoder(inputs, blank=tokenizer.blank_idx)
txt_result = ''.join(tokenizer.idx2token(result[1]))
assert "the fak friend of the fomly hae tC" == txt_result
# default beam decoding
result = decoders.ctc_beam_search_decoder(inputs,
blank=tokenizer.blank_idx,
beam_size=25)
txt_result = ''.join(tokenizer.idx2token(result[0][1]))
# assert "the fak friend of the fomcly hae tC" == txt_result
# lm-based decoding
scorer = KenLMScorer(os.path.join(data_dir, 'bigram.arpa'),
tokenizer,
alpha=2.0,
beta=0,
unit='word')
result = decoders.ctc_beam_search_decoder(inputs,
lm_scorer=scorer,
blank=tokenizer.blank_idx,
beam_size=25)
txt_result = ''.join(tokenizer.idx2token(result[0][1]))
assert "the fake friend of the fomlyhaetC" == txt_result
# lm-based decoding with trie
scorer = KenLMScorer(os.path.join(data_dir, 'bigram.arpa'),
tokenizer,
trie_path=os.path.join(data_dir, 'bigram.fst'),
alpha=2.0,
beta=0,
unit='word')
result = decoders.ctc_beam_search_decoder(inputs,
lm_scorer=scorer,
blank=tokenizer.blank_idx,
beam_size=25)
txt_result = ''.join(tokenizer.idx2token(result[0][1]))
for r in result:
print(tokenizer.idx2token(r[1]), r[0])
assert "the fake friend of the family, like the" == txt_result
def real_ctc_decode(filename, data_dir, frame_segments, mode='lm+fst'):
data = np.load(os.path.join(data_dir, filename + '.npy'))
inputs = torch.as_tensor(data).log_softmax(1)
tokenizer = CharTokenizer('labels.txt')
# greedy using beam
greedy_result = decoders.ctc_greedy_decoder(inputs,
blank=tokenizer.blank_idx)
txt_result_greedy = ''.join(tokenizer.idx2token(greedy_result[1]))
print(txt_result_greedy)
transcription_json = []
scorer = KenLMScorer(os.path.join(data_dir, 'lm.arpa'),
tokenizer,
trie_path=os.path.join(data_dir, filename + '.fst'),
alpha=0.01,
beta=0.0,
unit='word')
print('unmerged segments:', frame_segments)
merged_segments = merge_segments(frame_segments)
print('merged segments:', merged_segments)
transcription_json = []
res = []
t0 = time.time()
if len(frame_segments) < 3 and False:
pass
# result = decoders.ctc_beam_search_decoder(inputs,
# lm_scorer=scorer,
# blank=tokenizer.blank_idx,
# beam_size=300)
# frame_idss = []
# best_result = result[0]
# if not best_result[1]:
# raise Exception
# best_token_ids = np.trim_zeros(np.array(best_result[1]), 'b')
# new_l = len(best_token_ids)
# frame_ids = np.array(best_result[2])[:new_l]
# frame_ids += start
# frame_idss += list(frame_ids)
# frame_idss += [frame_idss[-1]]
# txt_result = ''.join(tokenizer.idx2token(best_result[1]))
# res.append(txt_result)
# best_result = result[0]
# txt_result = ''.join(tokenizer.idx2token(best_result[1]))
#
# word_starts = [0] + list(frame_ids[np.where(best_token_ids == 0)[0] + 1] * 0.02)
# word_ends = list(frame_ids[np.where(best_token_ids == 0)[0] - 1] * 0.02) + [frame_ids[-1] * 0.02]
# transcription_json += [{'transcript': triple[0], 'start': triple[1], 'end': triple[2]}
# for triple in zip(txt_result.split(' '), word_starts, word_ends)]
# for txt in res:
# print(txt)
else:
frame_idss = []
for start, end in tqdm(merged_segments):
result = decoders.ctc_beam_search_decoder(
inputs[start:end, :],
lm_scorer=scorer,
blank=tokenizer.blank_idx,
beam_size=200)
best_result = result[0]
if not best_result[1]:
continue
best_token_ids = np.trim_zeros(np.array(best_result[1]), 'b')
new_l = len(best_token_ids)
frame_ids = np.array(best_result[2])[:new_l]
frame_ids += start
frame_idss += list(frame_ids)
frame_idss += [frame_idss[-1]]
txt_result = ''.join(tokenizer.idx2token(best_result[1]))
res.append(txt_result)
best_result = result[0]
txt_result = ''.join(tokenizer.idx2token(best_result[1]))
word_starts = [0] + list(
frame_ids[np.where(best_token_ids == 0)[0] + 1] * 0.02)
word_ends = list(frame_ids[np.where(best_token_ids == 0)[0] - 1] *
0.02) + [frame_ids[-1] * 0.02]
transcription_json += [{
'transcript': triple[0],
'start': triple[1],
'end': triple[2]
} for triple in zip(txt_result.split(' '), word_starts, word_ends)]
for txt in res:
print(txt)
t1 = time.time()
result_filename = os.path.join(data_dir, filename + '_result.txt')
tlog_filename = os.path.join(data_dir, filename + '_result.json')
with open(result_filename, 'w', encoding='utf8') as f:
print(' '.join(res), file=f)
json.dump(transcription_json,
open(tlog_filename, 'w', encoding='utf8'),
indent=4,
ensure_ascii=False)
return np.array(frame_idss) * 0.02