import argparse
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import logging
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import os
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import sys
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import json
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from pathlib import Path
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from typing import Any
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from typing import List
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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from typing import Dict
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import math
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import numpy as np
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import torch
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from typeguard import check_argument_types
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from typeguard import check_return_type
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.modules.scorers.scorer_interface import BatchScorerInterface
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from funasr.modules.subsampling import TooShortUttError
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from funasr.tasks.vad import VADTask
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.utils import asr_utils, wav_utils, postprocess_utils
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from funasr.models.frontend.wav_frontend import WavFrontend
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header_colors = '\033[95m'
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end_colors = '\033[0m'
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global_asr_language: str = 'zh-cn'
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global_sample_rate: Union[int, Dict[Any, int]] = {
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'audio_fs': 16000,
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'model_fs': 16000
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}
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class Speech2VadSegment:
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"""Speech2VadSegment class
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Examples:
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>>> import soundfile
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>>> speech2segment = Speech2VadSegment("vad_config.yml", "vad.pt")
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>>> audio, rate = soundfile.read("speech.wav")
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>>> speech2segment(audio)
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[[10, 230], [245, 450], ...]
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"""
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def __init__(
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self,
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vad_infer_config: Union[Path, str] = None,
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vad_model_file: Union[Path, str] = None,
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vad_cmvn_file: Union[Path, str] = None,
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device: str = "cpu",
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batch_size: int = 1,
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dtype: str = "float32",
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**kwargs,
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):
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assert check_argument_types()
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# 1. Build vad model
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vad_model, vad_infer_args = VADTask.build_model_from_file(
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vad_infer_config, vad_model_file, device
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)
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frontend = None
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if vad_infer_args.frontend is not None:
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frontend = WavFrontend(cmvn_file=vad_cmvn_file, **vad_infer_args.frontend_conf)
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logging.info("vad_model: {}".format(vad_model))
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logging.info("vad_infer_args: {}".format(vad_infer_args))
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vad_model.to(dtype=getattr(torch, dtype)).eval()
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self.vad_model = vad_model
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self.vad_infer_args = vad_infer_args
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self.device = device
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self.dtype = dtype
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self.frontend = frontend
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self.batch_size = batch_size
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@torch.no_grad()
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def __call__(
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self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
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in_cache: Dict[str, torch.Tensor] = dict()
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) -> Tuple[List[List[int]], Dict[str, torch.Tensor]]:
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"""Inference
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Args:
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speech: Input speech data
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Returns:
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text, token, token_int, hyp
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"""
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assert check_argument_types()
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# Input as audio signal
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if isinstance(speech, np.ndarray):
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speech = torch.tensor(speech)
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if self.frontend is not None:
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self.frontend.filter_length_max = math.inf
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fbanks, fbanks_len = self.frontend.forward_fbank(speech, speech_lengths)
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feats, feats_len = self.frontend.forward_lfr_cmvn(fbanks, fbanks_len)
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fbanks = to_device(fbanks, device=self.device)
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feats = to_device(feats, device=self.device)
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feats_len = feats_len.int()
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else:
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raise Exception("Need to extract feats first, please configure frontend configuration")
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# b. Forward Encoder streaming
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t_offset = 0
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step = min(feats_len.max(), 6000)
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segments = [[]] * self.batch_size
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for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
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if t_offset + step >= feats_len - 1:
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step = feats_len - t_offset
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is_final = True
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else:
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is_final = False
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batch = {
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"feats": feats[:, t_offset:t_offset + step, :],
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"waveform": speech[:, t_offset * 160:min(speech.shape[-1], (t_offset + step - 1) * 160 + 400)],
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"is_final": is_final,
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"in_cache": in_cache
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}
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# a. To device
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batch = to_device(batch, device=self.device)
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segments_part, in_cache = self.vad_model(**batch)
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if segments_part:
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for batch_num in range(0, self.batch_size):
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segments[batch_num] += segments_part[batch_num]
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return fbanks, segments
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def inference(
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batch_size: int,
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ngpu: int,
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log_level: Union[int, str],
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data_path_and_name_and_type,
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vad_infer_config: Optional[str],
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vad_model_file: Optional[str],
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vad_cmvn_file: Optional[str] = None,
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raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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key_file: Optional[str] = None,
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allow_variable_data_keys: bool = False,
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output_dir: Optional[str] = None,
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dtype: str = "float32",
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seed: int = 0,
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num_workers: int = 1,
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**kwargs,
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):
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inference_pipeline = inference_modelscope(
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batch_size=batch_size,
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ngpu=ngpu,
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log_level=log_level,
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vad_infer_config=vad_infer_config,
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vad_model_file=vad_model_file,
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vad_cmvn_file=vad_cmvn_file,
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key_file=key_file,
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allow_variable_data_keys=allow_variable_data_keys,
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output_dir=output_dir,
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dtype=dtype,
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seed=seed,
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num_workers=num_workers,
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**kwargs,
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)
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return inference_pipeline(data_path_and_name_and_type, raw_inputs)
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def inference_modelscope(
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batch_size: int,
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ngpu: int,
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log_level: Union[int, str],
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# data_path_and_name_and_type,
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vad_infer_config: Optional[str],
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vad_model_file: Optional[str],
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vad_cmvn_file: Optional[str] = None,
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# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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key_file: Optional[str] = None,
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allow_variable_data_keys: bool = False,
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output_dir: Optional[str] = None,
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dtype: str = "float32",
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seed: int = 0,
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num_workers: int = 1,
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**kwargs,
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):
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assert check_argument_types()
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if batch_size > 1:
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raise NotImplementedError("batch decoding is not implemented")
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if ngpu > 1:
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raise NotImplementedError("only single GPU decoding is supported")
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logging.basicConfig(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build speech2vadsegment
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speech2vadsegment_kwargs = dict(
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vad_infer_config=vad_infer_config,
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vad_model_file=vad_model_file,
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vad_cmvn_file=vad_cmvn_file,
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device=device,
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dtype=dtype,
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)
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logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
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speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
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def _forward(
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data_path_and_name_and_type,
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raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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output_dir_v2: Optional[str] = None,
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fs: dict = None,
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param_dict: dict = None
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):
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# 3. Build data-iterator
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if data_path_and_name_and_type is None and raw_inputs is not None:
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if isinstance(raw_inputs, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
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loader = VADTask.build_streaming_iterator(
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data_path_and_name_and_type,
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dtype=dtype,
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batch_size=batch_size,
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key_file=key_file,
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num_workers=num_workers,
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preprocess_fn=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
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collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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)
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finish_count = 0
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file_count = 1
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# 7 .Start for-loop
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# FIXME(kamo): The output format should be discussed about
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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if output_path is not None:
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writer = DatadirWriter(output_path)
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ibest_writer = writer[f"1best_recog"]
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else:
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writer = None
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ibest_writer = None
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vad_results = []
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for keys, batch in loader:
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assert isinstance(batch, dict), type(batch)
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assert all(isinstance(s, str) for s in keys), keys
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_bs = len(next(iter(batch.values())))
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assert len(keys) == _bs, f"{len(keys)} != {_bs}"
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# do vad segment
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_, results = speech2vadsegment(**batch)
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for i, _ in enumerate(keys):
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if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
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results[i] = json.dumps(results[i])
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item = {'key': keys[i], 'value': results[i]}
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vad_results.append(item)
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if writer is not None:
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results[i] = json.loads(results[i])
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ibest_writer["text"][keys[i]] = "{}".format(results[i])
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return vad_results
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return _forward
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="VAD Decoding",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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# Note(kamo): Use '_' instead of '-' as separator.
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# '-' is confusing if written in yaml.
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parser.add_argument(
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"--log_level",
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type=lambda x: x.upper(),
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default="INFO",
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choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
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help="The verbose level of logging",
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)
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parser.add_argument("--output_dir", type=str, required=False)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=0,
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help="The number of gpus. 0 indicates CPU mode",
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)
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parser.add_argument(
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"--gpuid_list",
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type=str,
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default="",
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help="The visible gpus",
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)
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument(
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"--dtype",
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default="float32",
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choices=["float16", "float32", "float64"],
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help="Data type",
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)
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parser.add_argument(
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"--num_workers",
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type=int,
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default=1,
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help="The number of workers used for DataLoader",
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)
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group = parser.add_argument_group("Input data related")
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group.add_argument(
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"--data_path_and_name_and_type",
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type=str2triple_str,
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required=False,
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action="append",
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)
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group.add_argument("--raw_inputs", type=list, default=None)
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# example=[{'key':'EdevDEWdIYQ_0021','file':'/mnt/data/jiangyu.xzy/test_data/speech_io/SPEECHIO_ASR_ZH00007_zhibodaihuo/wav/EdevDEWdIYQ_0021.wav'}])
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group.add_argument("--key_file", type=str_or_none)
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group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
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group = parser.add_argument_group("The model configuration related")
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group.add_argument(
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"--vad_infer_config",
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type=str,
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help="VAD infer configuration",
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)
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group.add_argument(
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"--vad_model_file",
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type=str,
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help="VAD model parameter file",
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)
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group.add_argument(
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"--vad_cmvn_file",
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type=str,
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help="Global cmvn file",
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)
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group = parser.add_argument_group("infer related")
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group.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="The batch size for inference",
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)
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return parser
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def main(cmd=None):
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print(get_commandline_args(), file=sys.stderr)
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parser = get_parser()
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args = parser.parse_args(cmd)
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kwargs = vars(args)
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kwargs.pop("config", None)
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inference(**kwargs)
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if __name__ == "__main__":
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main()
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