#!/usr/bin/env python3
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import argparse
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import logging
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import os
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import sys
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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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import numpy as np
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import torch
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from kaldiio import WriteHelper
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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.utils.cli_utils import get_commandline_args
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from funasr.tasks.sv import SVTask
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from funasr.tasks.asr import ASRTask
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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.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.misc import statistic_model_parameters
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class Speech2Xvector:
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"""Speech2Xvector class
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Examples:
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>>> import soundfile
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>>> speech2xvector = Speech2Xvector("sv_config.yml", "sv.pb")
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>>> audio, rate = soundfile.read("speech.wav")
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>>> speech2xvector(audio)
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[(text, token, token_int, hypothesis object), ...]
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"""
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def __init__(
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self,
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sv_train_config: Union[Path, str] = None,
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sv_model_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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streaming: bool = False,
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embedding_node: str = "resnet1_dense",
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):
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assert check_argument_types()
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# TODO: 1. Build SV model
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sv_model, sv_train_args = SVTask.build_model_from_file(
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config_file=sv_train_config,
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model_file=sv_model_file,
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device=device
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)
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logging.info("sv_model: {}".format(sv_model))
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logging.info("model parameter number: {}".format(statistic_model_parameters(sv_model)))
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logging.info("sv_train_args: {}".format(sv_train_args))
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sv_model.to(dtype=getattr(torch, dtype)).eval()
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self.sv_model = sv_model
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self.sv_train_args = sv_train_args
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self.device = device
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self.dtype = dtype
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self.embedding_node = embedding_node
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@torch.no_grad()
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def calculate_embedding(self, speech: Union[torch.Tensor, np.ndarray]) -> torch.Tensor:
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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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# data: (Nsamples,) -> (1, Nsamples)
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speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
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# lengths: (1,)
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lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
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batch = {"speech": speech, "speech_lengths": lengths}
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# a. To device
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batch = to_device(batch, device=self.device)
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# b. Forward Encoder
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enc, ilens = self.sv_model.encode(**batch)
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# c. Forward Pooling
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pooling = self.sv_model.pooling_layer(enc)
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# d. Forward Decoder
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outputs, embeddings = self.sv_model.decoder(pooling)
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if self.embedding_node not in embeddings:
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raise ValueError("Required embedding node {} not in {}".format(
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self.embedding_node, embeddings.keys()))
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return embeddings[self.embedding_node]
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@torch.no_grad()
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def __call__(
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self, speech: Union[torch.Tensor, np.ndarray],
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ref_speech: Optional[Union[torch.Tensor, np.ndarray]] = None,
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) -> Tuple[torch.Tensor, Union[torch.Tensor, None], Union[torch.Tensor, None]]:
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"""Inference
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Args:
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speech: Input speech data
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ref_speech: Reference speech to compare
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Returns:
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embedding, ref_embedding, similarity_score
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"""
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assert check_argument_types()
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self.sv_model.eval()
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embedding = self.calculate_embedding(speech)
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ref_emb, score = None, None
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if ref_speech is not None:
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ref_emb = self.calculate_embedding(ref_speech)
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score = torch.cosine_similarity(embedding, ref_emb)
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results = (embedding, ref_emb, score)
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assert check_return_type(results)
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return results
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@staticmethod
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def from_pretrained(
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model_tag: Optional[str] = None,
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**kwargs: Optional[Any],
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):
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"""Build Speech2Xvector instance from the pretrained model.
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Args:
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model_tag (Optional[str]): Model tag of the pretrained models.
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Currently, the tags of espnet_model_zoo are supported.
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Returns:
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Speech2Xvector: Speech2Xvector instance.
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"""
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if model_tag is not None:
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try:
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from espnet_model_zoo.downloader import ModelDownloader
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except ImportError:
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logging.error(
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"`espnet_model_zoo` is not installed. "
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"Please install via `pip install -U espnet_model_zoo`."
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)
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raise
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d = ModelDownloader()
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kwargs.update(**d.download_and_unpack(model_tag))
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return Speech2Xvector(**kwargs)
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def inference_modelscope(
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output_dir: Optional[str] = None,
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batch_size: int = 1,
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dtype: str = "float32",
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ngpu: int = 1,
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seed: int = 0,
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num_workers: int = 0,
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log_level: Union[int, str] = "INFO",
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key_file: Optional[str] = None,
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sv_train_config: Optional[str] = "sv.yaml",
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sv_model_file: Optional[str] = "sv.pb",
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model_tag: Optional[str] = None,
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allow_variable_data_keys: bool = True,
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streaming: bool = False,
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embedding_node: str = "resnet1_dense",
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sv_threshold: float = 0.9465,
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param_dict: Optional[dict] = None,
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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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logging.info("param_dict: {}".format(param_dict))
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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 speech2xvector
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speech2xvector_kwargs = dict(
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sv_train_config=sv_train_config,
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sv_model_file=sv_model_file,
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device=device,
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dtype=dtype,
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streaming=streaming,
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embedding_node=embedding_node
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)
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logging.info("speech2xvector_kwargs: {}".format(speech2xvector_kwargs))
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speech2xvector = Speech2Xvector.from_pretrained(
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model_tag=model_tag,
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**speech2xvector_kwargs,
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)
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speech2xvector.sv_model.eval()
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def _forward(
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data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
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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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param_dict: Optional[dict] = None,
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):
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logging.info("param_dict: {}".format(param_dict))
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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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# 3. Build data-iterator
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loader = ASRTask.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=None,
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collate_fn=None,
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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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# 7 .Start for-loop
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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embd_writer, ref_embd_writer, score_writer = None, None, None
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if output_path is not None:
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os.makedirs(output_path, exist_ok=True)
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embd_writer = WriteHelper("ark,scp:{}/xvector.ark,{}/xvector.scp".format(output_path, output_path))
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sv_result_list = []
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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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batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
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embedding, ref_embedding, score = speech2xvector(**batch)
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# Only supporting batch_size==1
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key = keys[0]
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normalized_score = 0.0
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if score is not None:
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score = score.item()
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normalized_score = max(score - sv_threshold, 0.0) / (1.0 - sv_threshold) * 100.0
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item = {"key": key, "value": normalized_score}
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else:
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item = {"key": key, "value": embedding.squeeze(0).cpu().numpy()}
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sv_result_list.append(item)
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if output_path is not None:
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embd_writer(key, embedding[0].cpu().numpy())
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if ref_embedding is not None:
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if ref_embd_writer is None:
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ref_embd_writer = WriteHelper(
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"ark,scp:{}/ref_xvector.ark,{}/ref_xvector.scp".format(output_path, output_path)
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)
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score_writer = open(os.path.join(output_path, "score.txt"), "w")
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ref_embd_writer(key, ref_embedding[0].cpu().numpy())
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score_writer.write("{} {:.6f}\n".format(key, normalized_score))
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if output_path is not None:
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embd_writer.close()
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if ref_embd_writer is not None:
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ref_embd_writer.close()
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score_writer.close()
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return sv_result_list
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return _forward
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def inference(
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output_dir: Optional[str],
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batch_size: int,
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dtype: str,
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ngpu: int,
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seed: int,
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num_workers: int,
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log_level: Union[int, str],
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data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
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key_file: Optional[str],
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sv_train_config: Optional[str],
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sv_model_file: Optional[str],
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model_tag: Optional[str],
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allow_variable_data_keys: bool = True,
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streaming: bool = False,
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embedding_node: str = "resnet1_dense",
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sv_threshold: float = 0.9465,
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**kwargs,
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):
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inference_pipeline = inference_modelscope(
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output_dir=output_dir,
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batch_size=batch_size,
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dtype=dtype,
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ngpu=ngpu,
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seed=seed,
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num_workers=num_workers,
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log_level=log_level,
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key_file=key_file,
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sv_train_config=sv_train_config,
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sv_model_file=sv_model_file,
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model_tag=model_tag,
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allow_variable_data_keys=allow_variable_data_keys,
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streaming=streaming,
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embedding_node=embedding_node,
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sv_threshold=sv_threshold,
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**kwargs,
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)
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return inference_pipeline(data_path_and_name_and_type, raw_inputs=None)
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="Speaker verification/x-vector extraction",
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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("--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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"--sv_train_config",
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type=str,
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help="SV training configuration",
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)
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group.add_argument(
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"--sv_model_file",
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type=str,
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help="SV model parameter file",
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)
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group.add_argument(
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"--sv_threshold",
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type=float,
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default=0.9465,
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help="The threshold for verification"
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)
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group.add_argument(
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"--model_tag",
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type=str,
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help="Pretrained model tag. If specify this option, *_train_config and "
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"*_file will be overwritten",
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)
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parser.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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parser.add_argument("--streaming", type=str2bool, default=False)
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parser.add_argument("--embedding_node", type=str, default="resnet1_dense")
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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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logging.info("args: {}".format(kwargs))
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if args.output_dir is None:
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jobid, n_gpu = 1, 1
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gpuid = args.gpuid_list.split(",")[jobid-1]
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else:
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jobid = int(args.output_dir.split(".")[-1])
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n_gpu = len(args.gpuid_list.split(","))
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gpuid = args.gpuid_list.split(",")[(jobid - 1) % n_gpu]
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
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results_list = inference(**kwargs)
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for results in results_list:
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print("{} {}".format(results["key"], results["value"]))
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if __name__ == "__main__":
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main()
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