#!/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 typing import Union, Dict, Any
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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 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 inference_launch(mode, **kwargs):
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if mode == "sond":
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from funasr.bin.sond_inference import inference_modelscope
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return inference_modelscope(**kwargs)
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elif mode == "sond_demo":
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from funasr.bin.sond_inference import inference_modelscope
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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.pth",
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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_modelscope(**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 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_launch(**kwargs)
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if __name__ == "__main__":
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main()
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