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Running on Zero
Running on Zero
| import numpy as np | |
| def align_lrc_put_to_front(tokenizer, lrc_start_times, lrc_lines, total_lens): | |
| lrc_text_list = [] | |
| lrc_token = np.zeros(total_lens, dtype=np.int64) | |
| token_start = 0 | |
| for temp in lrc_lines: | |
| # for punct in "，。！？、；：,.!?;:": | |
| # one_line_lrc = one_line_lrc.replace(punct, ",") | |
| # one_line_lrc = one_line_lrc.strip("，。！？、；：,.!?;: ") | |
| for one_line_lrc in temp.split("|"): | |
| lrc_text_list.append(one_line_lrc) | |
| one_line_token = tokenizer.encode(one_line_lrc) | |
| lrc_text_list.append("<SEP>") | |
| one_line_token = one_line_token + [tokenizer.phone2id["<SEP>"]] | |
| one_line_token = np.array(one_line_token) | |
| assert token_start + len(one_line_token) <= len(lrc_token), ( | |
| "lrc_token 的长度超过了 vocal latent" | |
| ) | |
| lrc_token[token_start : token_start + len(one_line_token)] = one_line_token | |
| token_start = token_start + len(one_line_token) | |
| return lrc_token, "".join(lrc_text_list) | |
| def align_lrc_sentence_level( | |
| tokenizer, lrc_start_times, lrc_lines, total_lens, vae_frame_rate | |
| ): | |
| # BUG Only the prompt and the two segments to be generated have start timestamps, the generated content and the prompt do not contain anything like <SEP>. | |
| lrc_text_list = [] | |
| lrc_token = np.zeros(total_lens, dtype=np.int64) | |
| token_start = 0 | |
| for lrc_start_time, one_line_lrc in zip(lrc_start_times, lrc_lines): | |
| one_line_lrc = one_line_lrc.replace("|", " ") | |
| for punct in "，。！？、；：,.!?;:": | |
| one_line_lrc = one_line_lrc.replace(punct, ",") | |
| one_line_lrc = one_line_lrc.strip("，。！？、；：,.!?;: ") | |
| lrc_text_list.append(one_line_lrc) | |
| one_line_token = tokenizer.encode(one_line_lrc) | |
| lrc_text_list.append("<SEP>") | |
| one_line_token = one_line_token + [tokenizer.phone2id["<SEP>"]] | |
| one_line_token = np.array(one_line_token) | |
| timestamp_cal_start_frame = int(lrc_start_time * vae_frame_rate) | |
| # Handling Postponement Situations | |
| timestamp_cal_start_frame = max(timestamp_cal_start_frame, token_start) | |
| assert timestamp_cal_start_frame + len(one_line_token) <= len(lrc_token), ( | |
| "The length of the lrc_token exceeds that of the vocal latent" | |
| ) | |
| lrc_token[ | |
| timestamp_cal_start_frame : timestamp_cal_start_frame + len(one_line_token) | |
| ] = one_line_token | |
| token_start = timestamp_cal_start_frame + len(one_line_token) | |
| return lrc_token, "".join(lrc_text_list) | |