update setup (#686)

jmwang66

2023-06-29 98abc0e5ac1a1da0fe1802d9ffb623802fbf0b2f

 funasr/bin/tp_inference_launch.py

@@ -1,17 +1,169 @@
#!/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 typing import Optional
from typing import Union

import numpy as np
import torch

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.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


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,
):
    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,
        timestamp_model_file=timestamp_model_file,
        timestamp_cmvn_file=timestamp_cmvn_file,
        device=device,
        dtype=dtype,
    )
    logging.info("speechtext2timestamp_kwargs: {}".format(speechtext2timestamp_kwargs))
    speechtext2timestamp = Speech2Timestamp(**speechtext2timestamp_kwargs)

    preprocessor = LMPreprocessor(
        train=False,
        token_type=speechtext2timestamp.tp_train_args.token_type,
        token_list=speechtext2timestamp.tp_train_args.token_list,
        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,
    )

    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
    ):
        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
        writer = None
        if output_path is not None:
            writer = DatadirWriter(output_path)
            tp_writer = writer[f"timestamp_prediction"]
        else:
            tp_writer = None
        # 3. Build data-iterator
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, torch.Tensor):
                raw_inputs = raw_inputs.numpy()
            data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]

        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,
        )

        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])
                ts_str, ts_list = ts_prediction_lfr6_standard(us_alphas[batch_id], us_cif_peak[batch_id], token,
                                                              force_time_shift=-3.0)
                logging.warning(ts_str)
                item = {'key': key, 'value': ts_str, 'timestamp': ts_list}
                if tp_writer is not None:
                    tp_writer["tp_sync"][key + '#'] = ts_str
                    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():
@@ -100,14 +252,6 @@
    return parser


def inference_launch(mode, **kwargs):
    if mode == "tp_norm":
        from funasr.bin.tp_inference import inference_modelscope
        return inference_modelscope(**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()
@@ -135,7 +279,8 @@
        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
        os.environ["CUDA_VISIBLE_DEVICES"] = gpuid

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


if __name__ == "__main__":