| | |
|---|
| | | |
|---|
| | | timestamp = [] |
|---|
| | | tokens = tokenizer.text2tokens(text)[4:] |
|---|
| | | token_back_to_id = tokenizer.tokens2ids(tokens) |
|---|
| | | token_ids = [] |
|---|
| | | for tok_ls in token_back_to_id: |
|---|
| | | if tok_ls: token_ids.extend(tok_ls) |
|---|
| | | else: token_ids.append(124) |
|---|
| | | |
|---|
| | | if len(token_ids) == 0: |
|---|
| | | result_i = {"key": key[i], "text": text} |
|---|
| | | results.append(result_i) |
|---|
| | | continue |
|---|
| | | |
|---|
| | | logits_speech = self.ctc.softmax(encoder_out)[i, 4 : encoder_out_lens[i].item(), :] |
|---|
| | | pred = logits_speech.argmax(-1).cpu() |
|---|
| | | logits_speech[pred == self.blank_id, self.blank_id] = 0 |
|---|
| | | align = ctc_forced_align( |
|---|
| | | logits_speech.unsqueeze(0).float(), |
|---|
| | | torch.Tensor(token_int[4:]).unsqueeze(0).long().to(logits_speech.device), |
|---|
| | | torch.Tensor(token_ids).unsqueeze(0).long().to(logits_speech.device), |
|---|
| | | (encoder_out_lens[i] - 4).long(), |
|---|
| | | torch.tensor(len(token_int) - 4).unsqueeze(0).long().to(logits_speech.device), |
|---|
| | | torch.tensor(len(token_ids)).unsqueeze(0).long().to(logits_speech.device), |
|---|
| | | ignore_id=self.ignore_id, |
|---|
| | | ) |
|---|
| | | pred = groupby(align[0, : encoder_out_lens[i]]) |
|---|
| | |
|---|
| | | elif word.startswith("▁"): |
|---|
| | | word = word[1:] |
|---|
| | | timestamp_new.append([start, end]) |
|---|
| | | elif prev_word.isalpha() and prev_word.isascii() and word.isalpha() and word.isascii(): |
|---|
| | | elif prev_word is not None and prev_word.isalpha() and prev_word.isascii() and word.isalpha() and word.isascii(): |
|---|
| | | prev_word += word |
|---|
| | | timestamp_new[-1][1] = end |
|---|
| | | else: |
|---|
