update setup (#686)

jmwang66

2023-06-29 98abc0e5ac1a1da0fe1802d9ffb623802fbf0b2f

 funasr/bin/tp_inference_launch.py

@@ -1,91 +1,71 @@
#!/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 argparse
import logging
import os
import sys
from typing import Union, Dict, Any

from funasr.utils import config_argparse
from funasr.utils.cli_utils import get_commandline_args
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none

import argparse
import logging
from optparse import Option
import sys
import json
from pathlib import Path
from typing import Any
from typing import List
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union
from typing import Dict

import numpy as np
import torch
from typeguard import check_argument_types

from funasr.fileio.datadir_writer import DatadirWriter
from funasr.bin.tp_infer import Speech2Timestamp
from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
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.fileio.datadir_writer import DatadirWriter
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import config_argparse
from funasr.utils.cli_utils import get_commandline_args
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.text.token_id_converter import TokenIDConverter
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.bin.tp_infer import Speech2Timestamp


def inference_tp(
    batch_size: int,
    ngpu: int,
    log_level: Union[int, str],
    # data_path_and_name_and_type,
    timestamp_infer_config: Optional[str],
    timestamp_model_file: Optional[str],
    timestamp_cmvn_file: Optional[str] = None,
    # raw_inputs: Union[np.ndarray, torch.Tensor] = None,
    key_file: Optional[str] = None,
    allow_variable_data_keys: bool = False,
    output_dir: Optional[str] = None,
    dtype: str = "float32",
    seed: int = 0,
    num_workers: int = 1,
    split_with_space: bool = True,
    seg_dict_file: Optional[str] = None,
    **kwargs,
        batch_size: int,
        ngpu: int,
        log_level: Union[int, str],
        # data_path_and_name_and_type,
        timestamp_infer_config: Optional[str],
        timestamp_model_file: Optional[str],
        timestamp_cmvn_file: Optional[str] = None,
        # raw_inputs: Union[np.ndarray, torch.Tensor] = None,
        key_file: Optional[str] = None,
        allow_variable_data_keys: bool = False,
        output_dir: Optional[str] = None,
        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()
    ncpu = kwargs.get("ncpu", 1)
    torch.set_num_threads(ncpu)
    

    if batch_size > 1:
        raise NotImplementedError("batch decoding is not implemented")
    if ngpu > 1:
        raise NotImplementedError("only single GPU decoding is supported")
    

    logging.basicConfig(
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )
    

    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"
    # 1. Set random-seed
    set_all_random_seed(seed)
    

    # 2. Build speech2vadsegment
    speechtext2timestamp_kwargs = dict(
        timestamp_infer_config=timestamp_infer_config,
@@ -96,7 +76,7 @@
    )
    logging.info("speechtext2timestamp_kwargs: {}".format(speechtext2timestamp_kwargs))
    speechtext2timestamp = Speech2Timestamp(**speechtext2timestamp_kwargs)
    

    preprocessor = LMPreprocessor(
        train=False,
        token_type=speechtext2timestamp.tp_train_args.token_type,
@@ -109,21 +89,21 @@
        split_with_space=split_with_space,
        seg_dict_file=seg_dict_file,
    )
    

    if output_dir is not None:
        writer = DatadirWriter(output_dir)
        tp_writer = writer[f"timestamp_prediction"]
        # ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
    else:
        tp_writer = None
    

    def _forward(
        data_path_and_name_and_type,
        raw_inputs: Union[np.ndarray, torch.Tensor] = None,
        output_dir_v2: Optional[str] = None,
        fs: dict = None,
        param_dict: dict = None,
        **kwargs
            data_path_and_name_and_type,
            raw_inputs: Union[np.ndarray, torch.Tensor] = None,
            output_dir_v2: Optional[str] = None,
            fs: dict = None,
            param_dict: dict = None,
            **kwargs
    ):
        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
        writer = None
@@ -137,32 +117,31 @@
            if isinstance(raw_inputs, torch.Tensor):
                raw_inputs = raw_inputs.numpy()
            data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
        
        loader = ASRTask.build_streaming_iterator(
            data_path_and_name_and_type,

        loader = build_streaming_iterator(
            task_name="asr",
            preprocess_args=speechtext2timestamp.tp_train_args,
            data_path_and_name_and_type=data_path_and_name_and_type,
            dtype=dtype,
            batch_size=batch_size,
            key_file=key_file,
            num_workers=num_workers,
            preprocess_fn=preprocessor,
            collate_fn=ASRTask.build_collate_fn(speechtext2timestamp.tp_train_args, False),
            allow_variable_data_keys=allow_variable_data_keys,
            inference=True,
        )
        

        tp_result_list = []
        for keys, batch in loader:
            assert isinstance(batch, dict), type(batch)
            assert all(isinstance(s, str) for s in keys), keys
            _bs = len(next(iter(batch.values())))
            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
            

            logging.info("timestamp predicting, utt_id: {}".format(keys))
            _batch = {'speech': batch['speech'],
                      'speech_lengths': batch['speech_lengths'],
                      'text_lengths': batch['text_lengths']}
            us_alphas, us_cif_peak = speechtext2timestamp(**_batch)
            

            for batch_id in range(_bs):
                key = keys[batch_id]
                token = speechtext2timestamp.converter.ids2tokens(batch['text'][batch_id])
@@ -175,8 +154,16 @@
                    tp_writer["tp_time"][key + '#'] = str(ts_list)
                tp_result_list.append(item)
        return tp_result_list
    

    return _forward


def inference_launch(mode, **kwargs):
    if mode == "tp_norm":
        return inference_tp(**kwargs)
    else:
        logging.info("Unknown decoding mode: {}".format(mode))
        return None


def get_parser():
@@ -265,13 +252,6 @@
    return parser


def inference_launch(mode, **kwargs):
    if mode == "tp_norm":
        return inference_tp(**kwargs)
    else:
        logging.info("Unknown decoding mode: {}".format(mode))
        return None

def main(cmd=None):
    print(get_commandline_args(), file=sys.stderr)
    parser = get_parser()
@@ -301,7 +281,6 @@

    inference_pipeline = inference_launch(**kwargs)
    return inference_pipeline(kwargs["data_path_and_name_and_type"])



if __name__ == "__main__":