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| #!/usr/bin/env python3
| # -*- encoding: utf-8 -*-
| # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
| # MIT License (https://opensource.org/licenses/MIT)
|
| import os
|
| import logging
| import torch
| import numpy as np
| from funasr.utils.download_and_prepare_model import prepare_model
|
| from funasr.utils.types import str2bool
|
| def infer(task_name: str = "asr",
| model: str = None,
| # mode: str = None,
| vad_model: str = None,
| disable_vad: bool = False,
| punc_model: str = None,
| disable_punc: bool = False,
| model_hub: str = "ms",
| cache_dir: str = None,
| **kwargs,
| ):
|
| # set logging messages
| logging.basicConfig(
| level=logging.ERROR,
| )
|
| model, vad_model, punc_model, kwargs = prepare_model(model, vad_model, punc_model, model_hub, cache_dir, **kwargs)
| if task_name == "asr":
| from funasr.bin.asr_inference_launch import inference_launch
|
| inference_pipeline = inference_launch(**kwargs)
| elif task_name == "":
| pipeline = 1
| elif task_name == "":
| pipeline = 2
| elif task_name == "":
| pipeline = 2
|
| def _infer_fn(input, **kwargs):
| data_type = kwargs.get('data_type', 'sound')
| data_path_and_name_and_type = [input, 'speech', data_type]
| raw_inputs = None
| if isinstance(input, torch.Tensor):
| input = input.numpy()
| if isinstance(input, np.ndarray):
| data_path_and_name_and_type = None
| raw_inputs = input
|
| return inference_pipeline(data_path_and_name_and_type, raw_inputs=raw_inputs, **kwargs)
|
| return _infer_fn
|
|
| def main(cmd=None):
| # print(get_commandline_args(), file=sys.stderr)
| from funasr.bin.argument import get_parser
|
| parser = get_parser()
| parser.add_argument('input', help='input file to transcribe')
| parser.add_argument(
| "--task_name",
| type=str,
| default="asr",
| help="The decoding mode",
| )
| parser.add_argument(
| "-m",
| "--model",
| type=str,
| default="paraformer-zh",
| help="The asr mode name",
| )
| parser.add_argument(
| "-v",
| "--vad_model",
| type=str,
| default="fsmn-vad",
| help="vad model name",
| )
| parser.add_argument(
| "-dv",
| "--disable_vad",
| type=str2bool,
| default=False,
| help="",
| )
| parser.add_argument(
| "-p",
| "--punc_model",
| type=str,
| default="ct-punc",
| help="",
| )
| parser.add_argument(
| "-dp",
| "--disable_punc",
| type=str2bool,
| default=False,
| help="",
| )
| parser.add_argument(
| "--batch_size_token",
| type=int,
| default=5000,
| help="",
| )
| parser.add_argument(
| "--batch_size_token_threshold_s",
| type=int,
| default=35,
| help="",
| )
| parser.add_argument(
| "--max_single_segment_time",
| type=int,
| default=5000,
| help="",
| )
| args = parser.parse_args(cmd)
| kwargs = vars(args)
|
| # set logging messages
| logging.basicConfig(
| level=logging.ERROR,
| )
| logging.info("Decoding args: {}".format(kwargs))
|
| # kwargs["ncpu"] = 2 #os.cpu_count()
| kwargs.pop("data_path_and_name_and_type")
| print("args: {}".format(kwargs))
| p = infer(**kwargs)
|
| res = p(**kwargs)
| print(res)
|
|
