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| | | import json |
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| | | import time |
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| | | import copy |
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| | | import torch |
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| | | import hydra |
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| | | import random |
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| | | import string |
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| | | import logging |
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| | | import os.path |
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| | | import numpy as np |
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| | | from tqdm import tqdm |
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| | | from omegaconf import DictConfig, OmegaConf, ListConfig |
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| | | |
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| | | from funasr.register import tables |
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| | | from funasr.utils.load_utils import load_bytes |
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| | | from funasr.utils.vad_utils import slice_padding_audio_samples |
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| | | from funasr.train_utils.set_all_random_seed import set_all_random_seed |
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| | | from funasr.train_utils.load_pretrained_model import load_pretrained_model |
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| | | from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank |
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| | | from funasr.utils.load_utils import load_audio_text_image_video |
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| | | from funasr.utils.timestamp_tools import timestamp_sentence |
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| | | from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk |
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| | | try: |
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| | | if self.punc_model is not None: |
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| | | self.punc_kwargs.update(cfg) |
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| | | punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, disable_pbar=True, **cfg) |
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| | | import copy; raw_text = copy.copy(result["text"]) |
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| | | raw_text = copy.copy(result["text"]) |
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| | | result["text"] = punc_res[0]["text"] |
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| | | else: |
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| | | raw_text = None |
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| | | |
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| | | # speaker embedding cluster after resorted |
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| | | if self.spk_model is not None and kwargs.get('return_spk_res', True): |
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| | | if raw_text is None: |
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| | | logging.error("Missing punc_model, which is required by spk_model.") |
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| | | all_segments = sorted(all_segments, key=lambda x: x[0]) |
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| | | spk_embedding = result['spk_embedding'] |
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| | | labels = self.cb_model(spk_embedding.cpu(), oracle_num=kwargs.get('preset_spk_num', None)) |
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| | |
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| | | if self.spk_mode == 'vad_segment': # recover sentence_list |
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| | | sentence_list = [] |
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| | | for res, vadsegment in zip(restored_data, vadsegments): |
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| | | if 'timestamp' not in res: |
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| | | logging.error("Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \ |
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| | | and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\ |
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| | | can predict timestamp, and speaker diarization relies on timestamps.") |
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| | | sentence_list.append({"start": vadsegment[0],\ |
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| | | "end": vadsegment[1], |
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| | | "sentence": res['raw_text'], |
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| | | "sentence": res['text'], |
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| | | "timestamp": res['timestamp']}) |
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| | | elif self.spk_mode == 'punc_segment': |
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| | | if 'timestamp' not in result: |
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| | | logging.error("Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \ |
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| | | and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\ |
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| | | can predict timestamp, and speaker diarization relies on timestamps.") |
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| | | sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \ |
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| | | result['timestamp'], \ |
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| | | result['raw_text']) |
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| | | raw_text) |
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| | | distribute_spk(sentence_list, sv_output) |
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| | | result['sentence_info'] = sentence_list |
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| | | elif kwargs.get("sentence_timestamp", False): |
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| | | sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \ |
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| | | result['timestamp'], \ |
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| | | result['raw_text']) |
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| | | raw_text) |
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| | | result['sentence_info'] = sentence_list |
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| | | if "spk_embedding" in result: del result['spk_embedding'] |
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| | | |
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