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| import time | |
| import logging | |
| from tqdm import tqdm | |
| from funasr_detach.register import tables | |
| from funasr_detach.download.download_from_hub import download_model | |
| from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank | |
| from funasr_detach.auto.auto_model import prepare_data_iterator | |
| from funasr_detach.auto.auto_model import prepare_data_iterator | |
| class AutoFrontend: | |
| def __init__(self, **kwargs): | |
| assert "model" in kwargs | |
| if "model_conf" not in kwargs: | |
| logging.info( | |
| "download models from model hub: {}".format( | |
| kwargs.get("model_hub", "ms") | |
| ) | |
| ) | |
| kwargs = download_model(**kwargs) | |
| # build frontend | |
| frontend = kwargs.get("frontend", None) | |
| if frontend is not None: | |
| frontend_class = tables.frontend_classes.get(frontend) | |
| frontend = frontend_class(**kwargs["frontend_conf"]) | |
| self.frontend = frontend | |
| if "frontend" in kwargs: | |
| del kwargs["frontend"] | |
| self.kwargs = kwargs | |
| def __call__(self, input, input_len=None, kwargs=None, **cfg): | |
| kwargs = self.kwargs if kwargs is None else kwargs | |
| kwargs.update(cfg) | |
| key_list, data_list = prepare_data_iterator(input, input_len=input_len) | |
| batch_size = kwargs.get("batch_size", 1) | |
| device = kwargs.get("device", "cpu") | |
| if device == "cpu": | |
| batch_size = 1 | |
| meta_data = {} | |
| result_list = [] | |
| num_samples = len(data_list) | |
| pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True) | |
| time0 = time.perf_counter() | |
| for beg_idx in range(0, num_samples, batch_size): | |
| end_idx = min(num_samples, beg_idx + batch_size) | |
| data_batch = data_list[beg_idx:end_idx] | |
| key_batch = key_list[beg_idx:end_idx] | |
| # extract fbank feats | |
| time1 = time.perf_counter() | |
| audio_sample_list = load_audio_text_image_video( | |
| data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000) | |
| ) | |
| time2 = time.perf_counter() | |
| meta_data["load_data"] = f"{time2 - time1:0.3f}" | |
| speech, speech_lengths = extract_fbank( | |
| audio_sample_list, | |
| data_type=kwargs.get("data_type", "sound"), | |
| frontend=self.frontend, | |
| **kwargs, | |
| ) | |
| time3 = time.perf_counter() | |
| meta_data["extract_feat"] = f"{time3 - time2:0.3f}" | |
| meta_data["batch_data_time"] = ( | |
| speech_lengths.sum().item() | |
| * self.frontend.frame_shift | |
| * self.frontend.lfr_n | |
| / 1000 | |
| ) | |
| speech.to(device=device), speech_lengths.to(device=device) | |
| batch = {"input": speech, "input_len": speech_lengths, "key": key_batch} | |
| result_list.append(batch) | |
| pbar.update(1) | |
| description = f"{meta_data}, " | |
| pbar.set_description(description) | |
| time_end = time.perf_counter() | |
| pbar.set_description(f"time escaped total: {time_end - time0:0.3f}") | |
| return result_list | |