#!/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 scipy.signal import medfilt
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from typeguard import check_argument_types
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from funasr.models.frontend.wav_frontend import WavFrontendMel23
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from funasr.tasks.diar import EENDOLADiarTask
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from funasr.torch_utils.device_funcs import to_device
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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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class Speech2Diarization:
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"""Speech2Diarlization class
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Examples:
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>>> import soundfile
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>>> import numpy as np
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>>> speech2diar = Speech2Diarization("diar_sond_config.yml", "diar_sond.pth")
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>>> profile = np.load("profiles.npy")
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>>> audio, rate = soundfile.read("speech.wav")
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>>> speech2diar(audio, profile)
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{"spk1": [(int, int), ...], ...}
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"""
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def __init__(
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self,
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diar_train_config: Union[Path, str] = None,
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diar_model_file: Union[Path, str] = None,
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device: str = "cpu",
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dtype: str = "float32",
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):
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assert check_argument_types()
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# 1. Build Diarization model
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diar_model, diar_train_args = EENDOLADiarTask.build_model_from_file(
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config_file=diar_train_config,
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model_file=diar_model_file,
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device=device
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)
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frontend = None
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if diar_train_args.frontend is not None and diar_train_args.frontend_conf is not None:
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frontend = WavFrontendMel23(**diar_train_args.frontend_conf)
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# set up seed for eda
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np.random.seed(diar_train_args.seed)
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torch.manual_seed(diar_train_args.seed)
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torch.cuda.manual_seed(diar_train_args.seed)
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os.environ['PYTORCH_SEED'] = str(diar_train_args.seed)
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logging.info("diar_model: {}".format(diar_model))
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logging.info("diar_train_args: {}".format(diar_train_args))
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diar_model.to(dtype=getattr(torch, dtype)).eval()
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self.diar_model = diar_model
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self.diar_train_args = diar_train_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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@torch.no_grad()
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def __call__(
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self,
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speech: Union[torch.Tensor, np.ndarray],
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speech_lengths: Union[torch.Tensor, np.ndarray] = None
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):
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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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diarization results
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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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feats, feats_len = self.frontend.forward(speech, speech_lengths)
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feats = to_device(feats, device=self.device)
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feats_len = feats_len.int()
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self.diar_model.frontend = None
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else:
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feats = speech
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feats_len = speech_lengths
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batch = {"speech": feats, "speech_lengths": feats_len}
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batch = to_device(batch, device=self.device)
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results = self.diar_model.estimate_sequential(**batch)
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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 Speech2Diarization 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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Speech2Diarization: Speech2Diarization 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 Speech2Diarization(**kwargs)
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def inference_modelscope(
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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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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. 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 = Speech2Diarization.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", "bytes"]
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loader = EENDOLADiarTask.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=EENDOLADiarTask.build_preprocess_fn(speech2diar.diar_train_args, False),
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collate_fn=EENDOLADiarTask.build_collate_fn(speech2diar.diar_train_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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# 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(
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data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
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diar_train_config: Optional[str],
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diar_model_file: Optional[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 = 1,
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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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**kwargs,
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):
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inference_pipeline = inference_modelscope(
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diar_train_config=diar_train_config,
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diar_model_file=diar_model_file,
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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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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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smooth_size=smooth_size,
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dur_threshold=dur_threshold,
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out_format=out_format,
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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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"--diar_train_config",
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type=str,
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help="diarization 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="diarization model parameter file",
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)
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group.add_argument(
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"--dur_threshold",
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type=int,
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default=10,
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help="The threshold for short segments in number frames"
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)
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parser.add_argument(
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"--smooth_size",
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type=int,
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default=83,
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help="The smoothing window length in number frames"
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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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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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