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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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| | | vad_kwargs = kwargs.get("vad_model_revision", None) |
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| | | if vad_model is not None: |
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| | | logging.info("Building VAD model.") |
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| | | vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs} |
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| | | vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs, "device": kwargs["device"]} |
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| | | vad_model, vad_kwargs = self.build_model(**vad_kwargs) |
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| | | |
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| | | # if punc_model is not None, build punc model else None |
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| | | punc_kwargs = kwargs.get("punc_model_revision", None) |
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| | | if punc_model is not None: |
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| | | logging.info("Building punc model.") |
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| | | punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs} |
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| | | punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs, "device": kwargs["device"]} |
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| | | punc_model, punc_kwargs = self.build_model(**punc_kwargs) |
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| | | |
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| | | # if spk_model is not None, build spk model else None |
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| | | spk_kwargs = kwargs.get("spk_model_revision", None) |
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| | | if spk_model is not None: |
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| | | logging.info("Building SPK model.") |
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| | | spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs} |
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| | | spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs, "device": kwargs["device"]} |
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| | | spk_model, spk_kwargs = self.build_model(**spk_kwargs) |
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| | | self.cb_model = ClusterBackend() |
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| | | self.cb_model = ClusterBackend().to(kwargs["device"]) |
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| | | spk_mode = kwargs.get("spk_mode", 'punc_segment') |
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| | | if spk_mode not in ["default", "vad_segment", "punc_segment"]: |
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| | | logging.error("spk_mode should be one of default, vad_segment and punc_segment.") |
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| | | self.punc_kwargs = punc_kwargs |
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| | | self.spk_model = spk_model |
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| | | self.spk_kwargs = spk_kwargs |
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| | | self.model_path = kwargs.get("model_path", "./") |
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| | | self.model_path = kwargs.get("model_path") |
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| | | def build_model(self, **kwargs): |
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| | | set_all_random_seed(kwargs.get("seed", 0)) |
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| | | |
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| | | device = kwargs.get("device", "cuda") |
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| | | if not torch.cuda.is_available() or kwargs.get("ngpu", 0): |
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| | | if not torch.cuda.is_available() or kwargs.get("ngpu", 0) == 0: |
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| | | device = "cpu" |
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| | | kwargs["batch_size"] = 1 |
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| | | kwargs["device"] = device |
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| | | asr_result_list = [] |
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| | | num_samples = len(data_list) |
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| | | disable_pbar = kwargs.get("disable_pbar", False) |
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| | | pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True) if not disable_pbar else None |
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| | | pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None |
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| | | time_speech_total = 0.0 |
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| | | time_escape_total = 0.0 |
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| | | for beg_idx in range(0, num_samples, batch_size): |
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| | | for _b in range(len(speech_j)): |
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| | | vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, |
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| | | sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, |
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| | | speech_j[_b]]] |
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| | | np.array(speech_j[_b])]] |
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| | | segments = sv_chunk(vad_segments) |
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| | | all_segments.extend(segments) |
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| | | speech_b = [i[2] for i in segments] |
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| | | |
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| | | end_asr_total = time.time() |
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| | | time_escape_total_per_sample = end_asr_total - beg_asr_total |
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| | | pbar_sample.update(1) |
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| | | pbar_sample.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, " |
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| | | f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, " |
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| | | f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}") |
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| | | result[k] = restored_data[j][k] |
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| | | else: |
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| | | result[k] = torch.cat([result[k], restored_data[j][k]], dim=0) |
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| | | elif k == 'text': |
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| | | elif k == 'raw_text': |
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| | | if k not in result: |
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| | | result[k] = restored_data[j][k] |
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| | | else: |
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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, **cfg) |
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| | | result["text_with_punc"] = punc_res[0]["text"] |
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| | | result["text"] = punc_res[0]["text"] |
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| | | |
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| | | # speaker embedding cluster after resorted |
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| | | if self.spk_model is not None: |
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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, oracle_num=self.preset_spk_num) |
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| | | labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num) |
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| | | del result['spk_embedding'] |
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| | | sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu()) |
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| | | if self.spk_mode == 'vad_segment': |
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| | | for res, vadsegment in zip(restored_data, vadsegments): |
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| | | sentence_list.append({"start": vadsegment[0],\ |
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| | | "end": vadsegment[1], |
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| | | "sentence": res['text'], |
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| | | "sentence": res['raw_text'], |
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| | | "timestamp": res['timestamp']}) |
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| | | else: # punc_segment |
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| | | sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \ |
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| | | result['timestamp'], \ |
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| | | result['text']) |
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| | | result['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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