| | |
|---|
| | | align = ctc_forced_align( |
|---|
| | | logits_speech.unsqueeze(0).float(), |
|---|
| | | torch.Tensor(token_int[4:]).unsqueeze(0).long().to(logits_speech.device), |
|---|
| | | (encoder_out_lens - 4).long(), |
|---|
| | | (encoder_out_lens[i] - 4).long(), |
|---|
| | | torch.tensor(len(token_int) - 4).unsqueeze(0).long().to(logits_speech.device), |
|---|
| | | ignore_id=self.ignore_id, |
|---|
| | | ) |
|---|
| | | pred = groupby(align[0, : encoder_out_lens[0]]) |
|---|
| | | pred = groupby(align[0, : encoder_out_lens[i]]) |
|---|
| | | _start = 0 |
|---|
| | | token_id = 0 |
|---|
| | | ts_max = encoder_out_lens[i] - 4 |
|---|
| | |
|---|
| | | |
|---|
| | | def post(self, timestamp): |
|---|
| | | timestamp_new = [] |
|---|
| | | prev_word = None |
|---|
| | | for i, t in enumerate(timestamp): |
|---|
| | | word, start, end = t |
|---|
| | | start = int(start * 1000) |
|---|
| | | end = int(end * 1000) |
|---|
| | | if word == "▁": |
|---|
| | | continue |
|---|
| | | if i == 0: |
|---|
| | | # timestamp_new.append([word, start, end]) |
|---|
| | | timestamp_new.append([int(start * 1000), int(end * 1000)]) |
|---|
| | | elif word.startswith("▁") or len(word) == 1 or not word[1].isalpha(): |
|---|
| | | timestamp_new.append([start, end]) |
|---|
| | | elif word.startswith("▁"): |
|---|
| | | word = word[1:] |
|---|
| | | # timestamp_new.append([word, start, end]) |
|---|
| | | timestamp_new.append([int(start * 1000), int(end * 1000)]) |
|---|
| | | timestamp_new.append([start, end]) |
|---|
| | | elif prev_word.isalpha() and prev_word.isascii() and word.isalpha() and word.isascii(): |
|---|
| | | prev_word += word |
|---|
| | | timestamp_new[-1][1] = end |
|---|
| | | else: |
|---|
| | | # timestamp_new[-1][0] += word |
|---|
| | | timestamp_new[-1][1] = int(end * 1000) |
|---|
| | | timestamp_new.append([start, end]) |
|---|
| | | prev_word = word |
|---|
| | | return timestamp_new |
|---|
| | | |
|---|
| | | def export(self, **kwargs): |
|---|
