# !/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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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 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 soundfile
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import torch
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from scipy.signal import medfilt
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from funasr.bin.diar_infer import Speech2DiarizationSOND, Speech2DiarizationEEND
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from funasr.datasets.iterable_dataset import load_bytes
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from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
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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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def inference_sond(
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diar_train_config: str,
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diar_model_file: str,
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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 = 0,
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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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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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smooth_size: int = 83,
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dur_threshold: int = 10,
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out_format: str = "vad",
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param_dict: Optional[dict] = None,
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mode: str = "sond",
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**kwargs,
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):
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ncpu = kwargs.get("ncpu", 1)
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torch.set_num_threads(ncpu)
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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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# 2a. Build speech2xvec [Optional]
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if mode == "sond_demo" and param_dict is not None and "extract_profile" in param_dict and param_dict[
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"extract_profile"]:
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assert "sv_train_config" in param_dict, "sv_train_config must be provided param_dict."
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assert "sv_model_file" in param_dict, "sv_model_file must be provided in param_dict."
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sv_train_config = param_dict["sv_train_config"]
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sv_model_file = param_dict["sv_model_file"]
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if "model_dir" in param_dict:
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sv_train_config = os.path.join(param_dict["model_dir"], sv_train_config)
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sv_model_file = os.path.join(param_dict["model_dir"], sv_model_file)
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from funasr.bin.sv_infer import 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="resnet1_dense"
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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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# 2b. Build speech2diar
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speech2diar_kwargs = dict(
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diar_train_config=diar_train_config,
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diar_model_file=diar_model_file,
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device=device,
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dtype=dtype,
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streaming=streaming,
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smooth_size=smooth_size,
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dur_threshold=dur_threshold,
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)
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logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs))
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speech2diar = Speech2DiarizationSOND.from_pretrained(
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model_tag=model_tag,
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**speech2diar_kwargs,
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)
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speech2diar.diar_model.eval()
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def output_results_str(results: dict, uttid: str):
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rst = []
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mid = uttid.rsplit("-", 1)[0]
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for key in results:
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results[key] = [(x[0] / 100, x[1] / 100) for x in results[key]]
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if out_format == "vad":
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for spk, segs in results.items():
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rst.append("{} {}".format(spk, segs))
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else:
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template = "SPEAKER {} 0 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>"
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for spk, segs in results.items():
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rst.extend([template.format(mid, st, ed, spk) for st, ed in segs])
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return "\n".join(rst)
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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: List[List[Union[np.ndarray, torch.Tensor, str, bytes]]] = 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, (list, tuple)):
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if not isinstance(raw_inputs[0], List):
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raw_inputs = [raw_inputs]
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assert all([len(example) >= 2 for example in raw_inputs]), \
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"The length of test case in raw_inputs must larger than 1 (>=2)."
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def prepare_dataset():
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for idx, example in enumerate(raw_inputs):
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# read waveform file
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example = [load_bytes(x) if isinstance(x, bytes) else x
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for x in example]
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example = [soundfile.read(x)[0] if isinstance(x, str) else x
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for x in example]
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# convert torch tensor to numpy array
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example = [x.numpy() if isinstance(example[0], torch.Tensor) else x
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for x in example]
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speech = example[0]
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logging.info("Extracting profiles for {} waveforms".format(len(example) - 1))
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profile = [speech2xvector.calculate_embedding(x) for x in example[1:]]
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profile = torch.cat(profile, dim=0)
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yield ["test{}".format(idx)], {"speech": [speech], "profile": [profile]}
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loader = prepare_dataset()
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else:
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raise TypeError("raw_inputs must be a list or tuple in [speech, profile1, profile2, ...] ")
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else:
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# 3. Build data-iterator
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loader = build_streaming_iterator(
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task_name="diar",
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preprocess_args=None,
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data_path_and_name_and_type=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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use_collate_fn=False,
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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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if output_path is not None:
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os.makedirs(output_path, exist_ok=True)
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output_writer = open("{}/result.txt".format(output_path), "w")
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pse_label_writer = open("{}/labels.txt".format(output_path), "w")
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logging.info("Start to diarize...")
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result_list = []
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for idx, (keys, batch) in enumerate(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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results, pse_labels = speech2diar(**batch)
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# Only supporting batch_size==1
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key, value = keys[0], output_results_str(results, keys[0])
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item = {"key": key, "value": value}
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result_list.append(item)
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if output_path is not None:
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output_writer.write(value)
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output_writer.flush()
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pse_label_writer.write("{} {}\n".format(key, " ".join(pse_labels)))
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pse_label_writer.flush()
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if idx % 100 == 0:
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logging.info("Processing {:5d}: {}".format(idx, key))
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if output_path is not None:
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output_writer.close()
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pse_label_writer.close()
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return result_list
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return _forward
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def inference_eend(
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diar_train_config: str,
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diar_model_file: str,
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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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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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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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param_dict: Optional[dict] = None,
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**kwargs,
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):
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ncpu = kwargs.get("ncpu", 1)
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torch.set_num_threads(ncpu)
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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. Build speech2diar
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speech2diar_kwargs = dict(
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diar_train_config=diar_train_config,
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diar_model_file=diar_model_file,
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device=device,
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dtype=dtype,
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)
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logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs))
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speech2diar = Speech2DiarizationEEND.from_pretrained(
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model_tag=model_tag,
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**speech2diar_kwargs,
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)
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speech2diar.diar_model.eval()
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def output_results_str(results: dict, uttid: str):
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rst = []
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mid = uttid.rsplit("-", 1)[0]
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for key in results:
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results[key] = [(x[0] / 100, x[1] / 100) for x in results[key]]
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template = "SPEAKER {} 0 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>"
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for spk, segs in results.items():
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rst.extend([template.format(mid, st, ed, spk) for st, ed in segs])
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return "\n".join(rst)
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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: List[List[Union[np.ndarray, torch.Tensor, str, bytes]]] = 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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# 2. 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[0], "speech", "sound"]
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loader = build_streaming_iterator(
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task_name="diar",
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preprocess_args=None,
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data_path_and_name_and_type=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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)
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# 3. 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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if output_path is not None:
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os.makedirs(output_path, exist_ok=True)
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output_writer = open("{}/result.txt".format(output_path), "w")
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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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results = speech2diar(**batch)
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# post process
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a = results[0][0].cpu().numpy()
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a = medfilt(a, (11, 1))
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rst = []
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for spkid, frames in enumerate(a.T):
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frames = np.pad(frames, (1, 1), 'constant')
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changes, = np.where(np.diff(frames, axis=0) != 0)
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fmt = "SPEAKER {:s} 1 {:7.2f} {:7.2f} <NA> <NA> {:s} <NA>"
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for s, e in zip(changes[::2], changes[1::2]):
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st = s / 10.
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dur = (e - s) / 10.
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rst.append(fmt.format(keys[0], st, dur, "{}_{}".format(keys[0], str(spkid))))
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# Only supporting batch_size==1
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value = "\n".join(rst)
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item = {"key": keys[0], "value": value}
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result_list.append(item)
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if output_path is not None:
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output_writer.write(value)
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output_writer.flush()
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if output_path is not None:
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output_writer.close()
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return result_list
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return _forward
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def inference_launch(mode, **kwargs):
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if mode == "sond":
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return inference_sond(mode=mode, **kwargs)
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elif mode == "sond_demo":
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param_dict = {
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"extract_profile": True,
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"sv_train_config": "sv.yaml",
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"sv_model_file": "sv.pb",
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}
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if "param_dict" in kwargs and kwargs["param_dict"] is not None:
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for key in param_dict:
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if key not in kwargs["param_dict"]:
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kwargs["param_dict"][key] = param_dict[key]
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else:
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kwargs["param_dict"] = param_dict
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return inference_sond(mode=mode, **kwargs)
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elif mode == "eend-ola":
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return inference_eend(mode=mode, **kwargs)
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else:
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logging.info("Unknown decoding mode: {}".format(mode))
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return None
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="Speaker Verification",
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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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"--njob",
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type=int,
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default=1,
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help="The number of jobs for each gpu",
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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=True)
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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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"--diar_train_config",
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type=str,
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help="ASR training configuration",
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)
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group.add_argument(
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"--diar_model_file",
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type=str,
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help="ASR model parameter file",
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)
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group.add_argument(
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"--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.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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group = parser.add_argument_group("The inference configuration 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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group.add_argument(
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"--diar_smooth_size",
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type=int,
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default=121,
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help="The smoothing size for post-processing"
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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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parser.add_argument(
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"--mode",
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type=str,
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default="sond",
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help="The decoding mode",
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)
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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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# set logging messages
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logging.basicConfig(
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level=args.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("Decoding args: {}".format(kwargs))
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# gpu setting
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if args.ngpu > 0:
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jobid = int(args.output_dir.split(".")[-1])
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gpuid = args.gpuid_list.split(",")[(jobid - 1) // args.njob]
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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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inference_pipeline = inference_launch(**kwargs)
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return inference_pipeline(kwargs["data_path_and_name_and_type"])
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if __name__ == "__main__":
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main()
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