update tp_inference

shixian.shi

2023-03-10 ed2370591f6c352b4bb8f065ff8c6d0fc8629227

update tp_inference

 funasr/bin/tp_inference.py

@@ -1,5 +1,6 @@
import argparse
import logging
from optparse import Option
import sys
import json
from pathlib import Path
@@ -11,15 +12,12 @@
from typing import Union
from typing import Dict

import math
import numpy as np
import torch
from typeguard import check_argument_types
from typeguard import check_return_type

from funasr.fileio.datadir_writer import DatadirWriter
from funasr.modules.scorers.scorer_interface import BatchScorerInterface
from funasr.modules.subsampling import TooShortUttError
from funasr.datasets.preprocessor import LMPreprocessor
from funasr.tasks.asr import ASRTaskAligner as ASRTask
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
@@ -28,7 +26,6 @@
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.text.token_id_converter import TokenIDConverter

@@ -191,6 +188,8 @@
        dtype: str = "float32",
        seed: int = 0,
        num_workers: int = 1,
        split_with_space: bool = True,
        seg_dict_file: Optional[str] = None,
        **kwargs,
):
    inference_pipeline = inference_modelscope(
@@ -206,6 +205,8 @@
        dtype=dtype,
        seed=seed,
        num_workers=num_workers,
        split_with_space=split_with_space,
        seg_dict_file=seg_dict_file,
        **kwargs,
    )
    return inference_pipeline(data_path_and_name_and_type, raw_inputs)
@@ -226,6 +227,8 @@
        dtype: str = "float32",
        seed: int = 0,
        num_workers: int = 1,
        split_with_space: bool = True,
        seg_dict_file: Optional[str] = None,
        **kwargs,
):
    assert check_argument_types()
@@ -257,6 +260,19 @@
    logging.info("speechtext2timestamp_kwargs: {}".format(speechtext2timestamp_kwargs))
    speechtext2timestamp = SpeechText2Timestamp(**speechtext2timestamp_kwargs)
    
    preprocessor = LMPreprocessor(
        train=False,
        token_type=speechtext2timestamp.tp_train_args.token_type,
        token_list=speechtext2timestamp.tp_train_args,
        bpemodel=None,
        text_cleaner=None,
        g2p_type=None,
        text_name="text",
        non_linguistic_symbols=speechtext2timestamp.tp_train_args.non_linguistic_symbols,
        split_with_space=split_with_space,
        seg_dict_file=seg_dict_file,
    )
    
    def _forward(
            data_path_and_name_and_type,
            raw_inputs: Union[np.ndarray, torch.Tensor] = None,
@@ -277,7 +293,7 @@
            batch_size=batch_size,
            key_file=key_file,
            num_workers=num_workers,
            preprocess_fn=ASRTask.build_preprocess_fn(speechtext2timestamp.tp_train_args, False),
            preprocess_fn=LMPreprocessor,
            collate_fn=ASRTask.build_collate_fn(speechtext2timestamp.tp_train_args, False),
            allow_variable_data_keys=allow_variable_data_keys,
            inference=True,