| | |
|---|
| | | return res_txt, res |
|---|
| | | |
|---|
| | | |
|---|
| | | def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed): |
|---|
| | | def timestamp_sentence(punc_id_list, timestamp_postprocessed, text_postprocessed): |
|---|
| | | punc_list = ['，', '。', '？', '、'] |
|---|
| | | res = [] |
|---|
| | | if text_postprocessed is None: |
|---|
| | | return res |
|---|
| | | if time_stamp_postprocessed is None: |
|---|
| | | if timestamp_postprocessed is None: |
|---|
| | | return res |
|---|
| | | if len(time_stamp_postprocessed) == 0: |
|---|
| | | if len(timestamp_postprocessed) == 0: |
|---|
| | | return res |
|---|
| | | if len(text_postprocessed) == 0: |
|---|
| | | return res |
|---|
| | |
|---|
| | | if punc_id_list is None or len(punc_id_list) == 0: |
|---|
| | | res.append({ |
|---|
| | | 'text': text_postprocessed.split(), |
|---|
| | | "start": time_stamp_postprocessed[0][0], |
|---|
| | | "end": time_stamp_postprocessed[-1][1], |
|---|
| | | 'text_seg': text_postprocessed.split(), |
|---|
| | | "ts_list": time_stamp_postprocessed, |
|---|
| | | "start": timestamp_postprocessed[0][0], |
|---|
| | | "end": timestamp_postprocessed[-1][1], |
|---|
| | | "timestamp": timestamp_postprocessed, |
|---|
| | | }) |
|---|
| | | return res |
|---|
| | | if len(punc_id_list) != len(time_stamp_postprocessed): |
|---|
| | | print(" warning length mistach!!!!!!") |
|---|
| | | if len(punc_id_list) != len(timestamp_postprocessed): |
|---|
| | | logging.warning("length mismatch between punc and timestamp") |
|---|
| | | sentence_text = "" |
|---|
| | | sentence_text_seg = "" |
|---|
| | | ts_list = [] |
|---|
| | | sentence_start = time_stamp_postprocessed[0][0] |
|---|
| | | sentence_end = time_stamp_postprocessed[0][1] |
|---|
| | | sentence_start = timestamp_postprocessed[0][0] |
|---|
| | | sentence_end = timestamp_postprocessed[0][1] |
|---|
| | | texts = text_postprocessed.split() |
|---|
| | | punc_stamp_text_list = list(zip_longest(punc_id_list, time_stamp_postprocessed, texts, fillvalue=None)) |
|---|
| | | punc_stamp_text_list = list(zip_longest(punc_id_list, timestamp_postprocessed, texts, fillvalue=None)) |
|---|
| | | for punc_stamp_text in punc_stamp_text_list: |
|---|
| | | punc_id, time_stamp, text = punc_stamp_text |
|---|
| | | punc_id, timestamp, text = punc_stamp_text |
|---|
| | | # sentence_text += text if text is not None else '' |
|---|
| | | if text is not None: |
|---|
| | | if 'a' <= text[0] <= 'z' or 'A' <= text[0] <= 'Z': |
|---|
| | |
|---|
| | | else: |
|---|
| | | sentence_text += text |
|---|
| | | sentence_text_seg += text + ' ' |
|---|
| | | ts_list.append(time_stamp) |
|---|
| | | ts_list.append(timestamp) |
|---|
| | | |
|---|
| | | punc_id = int(punc_id) if punc_id is not None else 1 |
|---|
| | | sentence_end = time_stamp[1] if time_stamp is not None else sentence_end |
|---|
| | | sentence_end = timestamp[1] if timestamp is not None else sentence_end |
|---|
| | | |
|---|
| | | if punc_id > 1: |
|---|
| | | sentence_text += punc_list[punc_id - 2] |
|---|
| | |
|---|
| | | 'text': sentence_text, |
|---|
| | | "start": sentence_start, |
|---|
| | | "end": sentence_end, |
|---|
| | | "text_seg": sentence_text_seg, |
|---|
| | | "ts_list": ts_list |
|---|
| | | "timestamp": ts_list |
|---|
| | | }) |
|---|
| | | sentence_text = '' |
|---|
| | | sentence_text_seg = '' |
|---|
| | |
|---|
| | | return res |
|---|
| | | |
|---|
| | | |
|---|
| | | # class AverageShiftCalculator(): |
|---|
| | | # def __init__(self): |
|---|
| | | # logging.warning("Calculating average shift.") |
|---|
| | | # def __call__(self, file1, file2): |
|---|
| | | # uttid_list1, ts_dict1 = self.read_timestamps(file1) |
|---|
| | | # uttid_list2, ts_dict2 = self.read_timestamps(file2) |
|---|
| | | # uttid_intersection = self._intersection(uttid_list1, uttid_list2) |
|---|
| | | # res = self.as_cal(uttid_intersection, ts_dict1, ts_dict2) |
|---|
| | | # logging.warning("Average shift of {} and {}: {}.".format(file1, file2, str(res)[:8])) |
|---|
| | | # logging.warning("Following timestamp pair differs most: {}, detail:{}".format(self.max_shift, self.max_shift_uttid)) |
|---|
| | | # |
|---|
| | | # def _intersection(self, list1, list2): |
|---|
| | | # set1 = set(list1) |
|---|
| | | # set2 = set(list2) |
|---|
| | | # if set1 == set2: |
|---|
| | | # logging.warning("Uttid same checked.") |
|---|
| | | # return set1 |
|---|
| | | # itsc = list(set1 & set2) |
|---|
| | | # logging.warning("Uttid differs: file1 {}, file2 {}, lines same {}.".format(len(list1), len(list2), len(itsc))) |
|---|
| | | # return itsc |
|---|
| | | # |
|---|
| | | # def read_timestamps(self, file): |
|---|
| | | # # read timestamps file in standard format |
|---|
| | | # uttid_list = [] |
|---|
| | | # ts_dict = {} |
|---|
| | | # with codecs.open(file, 'r') as fin: |
|---|
| | | # for line in fin.readlines(): |
|---|
| | | # text = '' |
|---|
| | | # ts_list = [] |
|---|
| | | # line = line.rstrip() |
|---|
| | | # uttid = line.split()[0] |
|---|
| | | # uttid_list.append(uttid) |
|---|
| | | # body = " ".join(line.split()[1:]) |
|---|
| | | # for pd in body.split(';'): |
|---|
| | | # if not len(pd): continue |
|---|
| | | # # pdb.set_trace() |
|---|
| | | # char, start, end = pd.lstrip(" ").split(' ') |
|---|
| | | # text += char + ',' |
|---|
| | | # ts_list.append((float(start), float(end))) |
|---|
| | | # # ts_lists.append(ts_list) |
|---|
| | | # ts_dict[uttid] = (text[:-1], ts_list) |
|---|
| | | # logging.warning("File {} read done.".format(file)) |
|---|
| | | # return uttid_list, ts_dict |
|---|
| | | # |
|---|
| | | # def _shift(self, filtered_timestamp_list1, filtered_timestamp_list2): |
|---|
| | | # shift_time = 0 |
|---|
| | | # for fts1, fts2 in zip(filtered_timestamp_list1, filtered_timestamp_list2): |
|---|
| | | # shift_time += abs(fts1[0] - fts2[0]) + abs(fts1[1] - fts2[1]) |
|---|
| | | # num_tokens = len(filtered_timestamp_list1) |
|---|
| | | # return shift_time, num_tokens |
|---|
| | | # |
|---|
| | | # # def as_cal(self, uttid_list, ts_dict1, ts_dict2): |
|---|
| | | # # # calculate average shift between timestamp1 and timestamp2 |
|---|
| | | # # # when characters differ, use edit distance alignment |
|---|
| | | # # # and calculate the error between the same characters |
|---|
| | | # # self._accumlated_shift = 0 |
|---|
| | | # # self._accumlated_tokens = 0 |
|---|
| | | # # self.max_shift = 0 |
|---|
| | | # # self.max_shift_uttid = None |
|---|
| | | # # for uttid in uttid_list: |
|---|
| | | # # (t1, ts1) = ts_dict1[uttid] |
|---|
| | | # # (t2, ts2) = ts_dict2[uttid] |
|---|
| | | # # _align, _align2, _align3 = [], [], [] |
|---|
| | | # # fts1, fts2 = [], [] |
|---|
| | | # # _t1, _t2 = [], [] |
|---|
| | | # # sm = edit_distance.SequenceMatcher(t1.split(','), t2.split(',')) |
|---|
| | | # # s = sm.get_opcodes() |
|---|
| | | # # for j in range(len(s)): |
|---|
| | | # # if s[j][0] == "replace" or s[j][0] == "insert": |
|---|
| | | # # _align.append(0) |
|---|
| | | # # if s[j][0] == "replace" or s[j][0] == "delete": |
|---|
| | | # # _align3.append(0) |
|---|
| | | # # elif s[j][0] == "equal": |
|---|
| | | # # _align.append(1) |
|---|
| | | # # _align3.append(1) |
|---|
| | | # # else: |
|---|
| | | # # continue |
|---|
| | | # # # use s to index t2 |
|---|
| | | # # for a, ts , t in zip(_align, ts2, t2.split(',')): |
|---|
| | | # # if a: |
|---|
| | | # # fts2.append(ts) |
|---|
| | | # # _t2.append(t) |
|---|
| | | # # sm2 = edit_distance.SequenceMatcher(t2.split(','), t1.split(',')) |
|---|
| | | # # s = sm2.get_opcodes() |
|---|
| | | # # for j in range(len(s)): |
|---|
| | | # # if s[j][0] == "replace" or s[j][0] == "insert": |
|---|
| | | # # _align2.append(0) |
|---|
| | | # # elif s[j][0] == "equal": |
|---|
| | | # # _align2.append(1) |
|---|
| | | # # else: |
|---|
| | | # # continue |
|---|
| | | # # # use s2 tp index t1 |
|---|
| | | # # for a, ts, t in zip(_align3, ts1, t1.split(',')): |
|---|
| | | # # if a: |
|---|
| | | # # fts1.append(ts) |
|---|
| | | # # _t1.append(t) |
|---|
| | | # # if len(fts1) == len(fts2): |
|---|
| | | # # shift_time, num_tokens = self._shift(fts1, fts2) |
|---|
| | | # # self._accumlated_shift += shift_time |
|---|
| | | # # self._accumlated_tokens += num_tokens |
|---|
| | | # # if shift_time/num_tokens > self.max_shift: |
|---|
| | | # # self.max_shift = shift_time/num_tokens |
|---|
| | | # # self.max_shift_uttid = uttid |
|---|
| | | # # else: |
|---|
| | | # # logging.warning("length mismatch") |
|---|
| | | # # return self._accumlated_shift / self._accumlated_tokens |
|---|
| | | |
|---|
| | | |
|---|
| | | def convert_external_alphas(alphas_file, text_file, output_file): |
|---|
| | | from funasr.models.paraformer.cif_predictor import cif_wo_hidden |
|---|
| | | with open(alphas_file, 'r') as f1, open(text_file, 'r') as f2, open(output_file, 'w') as f3: |
|---|
| | | for line1, line2 in zip(f1.readlines(), f2.readlines()): |
|---|
| | | line1 = line1.rstrip() |
|---|
| | | line2 = line2.rstrip() |
|---|
| | | assert line1.split()[0] == line2.split()[0] |
|---|
| | | uttid = line1.split()[0] |
|---|
| | | alphas = [float(i) for i in line1.split()[1:]] |
|---|
| | | new_alphas = np.array(remove_chunk_padding(alphas)) |
|---|
| | | new_alphas[-1] += 1e-4 |
|---|
| | | text = line2.split()[1:] |
|---|
| | | if len(text) + 1 != int(new_alphas.sum()): |
|---|
| | | # force resize |
|---|
| | | new_alphas *= (len(text) + 1) / int(new_alphas.sum()) |
|---|
| | | peaks = cif_wo_hidden(torch.Tensor(new_alphas).unsqueeze(0), 1.0-1e-4) |
|---|
| | | if " " in text: |
|---|
| | | text = text.split() |
|---|
| | | else: |
|---|
| | | text = [i for i in text] |
|---|
| | | res_str, _ = ts_prediction_lfr6_standard(new_alphas, peaks[0], text, |
|---|
| | | force_time_shift=-7.0, |
|---|
| | | sil_in_str=False) |
|---|
| | | f3.write("{} {}\n".format(uttid, res_str)) |
|---|
| | | |
|---|
| | | |
|---|
| | | def remove_chunk_padding(alphas): |
|---|
| | | # remove the padding part in alphas if using chunk paraformer for GPU |
|---|
| | | START_ZERO = 45 |
|---|
| | | MID_ZERO = 75 |
|---|
| | | REAL_FRAMES = 360 # for chunk based encoder 10-120-10 and fsmn padding 5 |
|---|
| | | alphas = alphas[START_ZERO:] # remove the padding at beginning |
|---|
| | | new_alphas = [] |
|---|
| | | while True: |
|---|
| | | new_alphas = new_alphas + alphas[:REAL_FRAMES] |
|---|
| | | alphas = alphas[REAL_FRAMES+MID_ZERO:] |
|---|
| | | if len(alphas) < REAL_FRAMES: break |
|---|
| | | return new_alphas |
|---|
| | | |
|---|
| | | SUPPORTED_MODES = ['cal_aas', 'read_ext_alphas'] |
|---|
| | | |
|---|
| | | |
|---|
| | | def main(args): |
|---|
| | | # if args.mode == 'cal_aas': |
|---|
| | | # asc = AverageShiftCalculator() |
|---|
| | | # asc(args.input, args.input2) |
|---|
| | | if args.mode == 'read_ext_alphas': |
|---|
| | | convert_external_alphas(args.input, args.input2, args.output) |
|---|
| | | else: |
|---|
| | | logging.error("Mode {} not in SUPPORTED_MODES: {}.".format(args.mode, SUPPORTED_MODES)) |
|---|
| | | |
|---|
| | | |
|---|
| | | if __name__ == '__main__': |
|---|
| | | parser = argparse.ArgumentParser(description='timestamp tools') |
|---|
| | | parser.add_argument('--mode', |
|---|
| | | default=None, |
|---|
| | | type=str, |
|---|
| | | choices=SUPPORTED_MODES, |
|---|
| | | help='timestamp related toolbox') |
|---|
| | | parser.add_argument('--input', default=None, type=str, help='input file path') |
|---|
| | | parser.add_argument('--output', default=None, type=str, help='output file name') |
|---|
| | | parser.add_argument('--input2', default=None, type=str, help='input2 file path') |
|---|
| | | parser.add_argument('--kaldi-ts-type', |
|---|
| | | default='v2', |
|---|
| | | type=str, |
|---|
| | | choices=['v0', 'v1', 'v2'], |
|---|
| | | help='kaldi timestamp to write') |
|---|
| | | args = parser.parse_args() |
|---|
| | | main(args) |
|---|
| | | |
|---|
