import json
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import time
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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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from funasr.register import tables
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from funasr.utils.load_utils import load_bytes
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from funasr.download.file import download_from_url
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from funasr.download.download_from_hub import download_model
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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.timestamp_tools import timestamp_sentence
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from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
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from funasr.models.campplus.cluster_backend import ClusterBackend
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def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
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"""
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:param input:
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:param input_len:
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:param data_type:
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:param frontend:
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:return:
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"""
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data_list = []
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key_list = []
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filelist = [".scp", ".txt", ".json", ".jsonl"]
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chars = string.ascii_letters + string.digits
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if isinstance(data_in, str) and data_in.startswith('http'): # url
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data_in = download_from_url(data_in)
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if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
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_, file_extension = os.path.splitext(data_in)
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file_extension = file_extension.lower()
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if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
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with open(data_in, encoding='utf-8') as fin:
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for line in fin:
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
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lines = json.loads(line.strip())
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data = lines["source"]
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key = data["key"] if "key" in data else key
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else: # filelist, wav.scp, text.txt: id \t data or data
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lines = line.strip().split(maxsplit=1)
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data = lines[1] if len(lines)>1 else lines[0]
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key = lines[0] if len(lines)>1 else key
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data_list.append(data)
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key_list.append(key)
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else:
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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data_list = [data_in]
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key_list = [key]
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elif isinstance(data_in, (list, tuple)):
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if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
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data_list_tmp = []
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for data_in_i, data_type_i in zip(data_in, data_type):
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key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
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data_list_tmp.append(data_list_i)
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data_list = []
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for item in zip(*data_list_tmp):
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data_list.append(item)
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else:
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# [audio sample point, fbank, text]
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data_list = data_in
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key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
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else: # raw text; audio sample point, fbank; bytes
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if isinstance(data_in, bytes): # audio bytes
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data_in = load_bytes(data_in)
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if key is None:
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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data_list = [data_in]
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key_list = [key]
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return key_list, data_list
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class AutoModel:
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def __init__(self, **kwargs):
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tables.print()
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model, kwargs = self.build_model(**kwargs)
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# if vad_model is not None, build vad model else None
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vad_model = kwargs.get("vad_model", None)
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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, "device": kwargs["device"]}
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vad_model, vad_kwargs = self.build_model(**vad_kwargs)
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# if punc_model is not None, build punc model else None
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punc_model = kwargs.get("punc_model", 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, "device": kwargs["device"]}
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punc_model, punc_kwargs = self.build_model(**punc_kwargs)
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# if spk_model is not None, build spk model else None
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spk_model = kwargs.get("spk_model", 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, "device": kwargs["device"]}
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spk_model, spk_kwargs = self.build_model(**spk_kwargs)
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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.spk_mode = spk_mode
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self.preset_spk_num = kwargs.get("preset_spk_num", None)
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if self.preset_spk_num:
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logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
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logging.warning("Many to print when using speaker model...")
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self.kwargs = kwargs
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self.model = model
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self.vad_model = vad_model
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self.vad_kwargs = vad_kwargs
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self.punc_model = punc_model
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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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def build_model(self, **kwargs):
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assert "model" in kwargs
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if "model_conf" not in kwargs:
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logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
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kwargs = download_model(**kwargs)
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set_all_random_seed(kwargs.get("seed", 0))
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device = kwargs.get("device", "cuda")
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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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if kwargs.get("ncpu", None):
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torch.set_num_threads(kwargs.get("ncpu"))
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# build tokenizer
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tokenizer = kwargs.get("tokenizer", None)
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if tokenizer is not None:
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tokenizer_class = tables.tokenizer_classes.get(tokenizer)
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tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
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kwargs["tokenizer"] = tokenizer
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kwargs["token_list"] = tokenizer.token_list
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vocab_size = len(tokenizer.token_list)
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else:
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vocab_size = -1
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# build frontend
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frontend = kwargs.get("frontend", None)
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if frontend is not None:
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frontend_class = tables.frontend_classes.get(frontend)
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frontend = frontend_class(**kwargs["frontend_conf"])
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kwargs["frontend"] = frontend
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kwargs["input_size"] = frontend.output_size()
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# build model
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model_class = tables.model_classes.get(kwargs["model"])
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model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
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model.eval()
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model.to(device)
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# init_param
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init_param = kwargs.get("init_param", None)
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if init_param is not None:
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logging.info(f"Loading pretrained params from {init_param}")
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load_pretrained_model(
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model=model,
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path=init_param,
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ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
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oss_bucket=kwargs.get("oss_bucket", None),
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scope_map=kwargs.get("scope_map", None),
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excludes=kwargs.get("excludes", None),
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)
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return model, kwargs
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def __call__(self, *args, **cfg):
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kwargs = self.kwargs
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kwargs.update(cfg)
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res = self.model(*args, kwargs)
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return res
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def generate(self, input, input_len=None, **cfg):
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if self.vad_model is None:
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return self.inference(input, input_len=input_len, **cfg)
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else:
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return self.inference_with_vad(input, input_len=input_len, **cfg)
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def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
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kwargs = self.kwargs if kwargs is None else kwargs
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kwargs.update(cfg)
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model = self.model if model is None else model
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batch_size = kwargs.get("batch_size", 1)
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# if kwargs.get("device", "cpu") == "cpu":
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# batch_size = 1
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key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
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speed_stats = {}
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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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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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end_idx = min(num_samples, beg_idx + batch_size)
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data_batch = data_list[beg_idx:end_idx]
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key_batch = key_list[beg_idx:end_idx]
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batch = {"data_in": data_batch, "key": key_batch}
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if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
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batch["data_in"] = data_batch[0]
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batch["data_lengths"] = input_len
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time1 = time.perf_counter()
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with torch.no_grad():
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results, meta_data = model.inference(**batch, **kwargs)
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time2 = time.perf_counter()
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asr_result_list.extend(results)
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# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
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batch_data_time = meta_data.get("batch_data_time", -1)
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time_escape = time2 - time1
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speed_stats["load_data"] = meta_data.get("load_data", 0.0)
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speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
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speed_stats["forward"] = f"{time_escape:0.3f}"
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speed_stats["batch_size"] = f"{len(results)}"
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speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
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description = (
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f"{speed_stats}, "
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)
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if pbar:
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pbar.update(1)
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pbar.set_description(description)
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time_speech_total += batch_data_time
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time_escape_total += time_escape
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if pbar:
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pbar.update(1)
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pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
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torch.cuda.empty_cache()
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return asr_result_list
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def inference_with_vad(self, input, input_len=None, **cfg):
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# step.1: compute the vad model
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self.vad_kwargs.update(cfg)
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beg_vad = time.time()
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res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
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end_vad = time.time()
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print(f"time cost vad: {end_vad - beg_vad:0.3f}")
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# step.2 compute asr model
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model = self.model
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kwargs = self.kwargs
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kwargs.update(cfg)
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batch_size = int(kwargs.get("batch_size_s", 300))*1000
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batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
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kwargs["batch_size"] = batch_size
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key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
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results_ret_list = []
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time_speech_total_all_samples = 0.0
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beg_total = time.time()
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pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
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for i in range(len(res)):
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key = res[i]["key"]
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vadsegments = res[i]["value"]
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input_i = data_list[i]
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speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
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speech_lengths = len(speech)
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n = len(vadsegments)
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data_with_index = [(vadsegments[i], i) for i in range(n)]
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sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
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results_sorted = []
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if not len(sorted_data):
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logging.info("decoding, utt: {}, empty speech".format(key))
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continue
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if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
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batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
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batch_size_ms_cum = 0
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beg_idx = 0
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beg_asr_total = time.time()
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time_speech_total_per_sample = speech_lengths/16000
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time_speech_total_all_samples += time_speech_total_per_sample
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pbar_sample = tqdm(colour="blue", total=n + 1, dynamic_ncols=True)
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all_segments = []
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for j, _ in enumerate(range(0, n)):
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pbar_sample.update(1)
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batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
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if j < n - 1 and (
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batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
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sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
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continue
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batch_size_ms_cum = 0
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end_idx = j + 1
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speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
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results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, disable_pbar=True, **cfg)
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if self.spk_model is not None:
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# compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
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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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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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spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
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results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
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beg_idx = end_idx
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if len(results) < 1:
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continue
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results_sorted.extend(results)
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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.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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restored_data = [0] * n
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for j in range(n):
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index = sorted_data[j][1]
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restored_data[index] = results_sorted[j]
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result = {}
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# results combine for texts, timestamps, speaker embeddings and others
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# TODO: rewrite for clean code
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for j in range(n):
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for k, v in restored_data[j].items():
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if k.startswith("timestamp"):
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if k not in result:
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result[k] = []
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for t in restored_data[j][k]:
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t[0] += vadsegments[j][0]
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t[1] += vadsegments[j][0]
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result[k].extend(restored_data[j][k])
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elif k == 'spk_embedding':
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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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result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
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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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result[k] += " " + restored_data[j][k]
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else:
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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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result[k] += restored_data[j][k]
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# step.3 compute punc model
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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"] = punc_res[0]["text"]
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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.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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sentence_list = []
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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['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['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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result["key"] = key
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results_ret_list.append(result)
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pbar_total.update(1)
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pbar_total.update(1)
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end_total = time.time()
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time_escape_total_all_samples = end_total - beg_total
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pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
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f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
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f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
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return results_ret_list
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