update setup (#686)

jmwang66

2023-06-29 98abc0e5ac1a1da0fe1802d9ffb623802fbf0b2f

 funasr/bin/sv_inference_launch.py

@@ -1,26 +1,12 @@
#!/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
import os
import sys
from pathlib import Path
from typing import Any
from typing import List
from typing import Optional
from typing import Sequence
from typing import Tuple
@@ -29,63 +15,58 @@
import numpy as np
import torch
from kaldiio import WriteHelper
from typeguard import check_argument_types
from typeguard import check_return_type

from funasr.utils.cli_utils import get_commandline_args
from funasr.tasks.sv import SVTask
from funasr.tasks.asr import ASRTask
from funasr.torch_utils.device_funcs import to_device
from funasr.bin.sv_infer import Speech2Xvector
from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
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.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils.misc import statistic_model_parameters
from funasr.bin.sv_infer import Speech2Xvector


def inference_sv(
    output_dir: Optional[str] = None,
    batch_size: int = 1,
    dtype: str = "float32",
    ngpu: int = 1,
    seed: int = 0,
    num_workers: int = 0,
    log_level: Union[int, str] = "INFO",
    key_file: Optional[str] = None,
    sv_train_config: Optional[str] = "sv.yaml",
    sv_model_file: Optional[str] = "sv.pb",
    model_tag: Optional[str] = None,
    allow_variable_data_keys: bool = True,
    streaming: bool = False,
    embedding_node: str = "resnet1_dense",
    sv_threshold: float = 0.9465,
    param_dict: Optional[dict] = None,
    **kwargs,
        output_dir: Optional[str] = None,
        batch_size: int = 1,
        dtype: str = "float32",
        ngpu: int = 1,
        seed: int = 0,
        num_workers: int = 0,
        log_level: Union[int, str] = "INFO",
        key_file: Optional[str] = None,
        sv_train_config: Optional[str] = "sv.yaml",
        sv_model_file: Optional[str] = "sv.pb",
        model_tag: Optional[str] = None,
        allow_variable_data_keys: bool = True,
        streaming: bool = False,
        embedding_node: str = "resnet1_dense",
        sv_threshold: float = 0.9465,
        param_dict: Optional[dict] = 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",
    )
    logging.info("param_dict: {}".format(param_dict))
    

    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"
    

    # 1. Set random-seed
    set_all_random_seed(seed)
    

    # 2. Build speech2xvector
    speech2xvector_kwargs = dict(
        sv_train_config=sv_train_config,
@@ -96,37 +77,33 @@
        embedding_node=embedding_node
    )
    logging.info("speech2xvector_kwargs: {}".format(speech2xvector_kwargs))
    speech2xvector = Speech2Xvector.from_pretrained(
        model_tag=model_tag,
        **speech2xvector_kwargs,
    )
    speech2xvector = Speech2Xvector(**speech2xvector_kwargs)
    speech2xvector.sv_model.eval()
    

    def _forward(
        data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
        raw_inputs: Union[np.ndarray, torch.Tensor] = None,
        output_dir_v2: Optional[str] = None,
        param_dict: Optional[dict] = None,
            data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
            raw_inputs: Union[np.ndarray, torch.Tensor] = None,
            output_dir_v2: Optional[str] = None,
            param_dict: Optional[dict] = None,
    ):
        logging.info("param_dict: {}".format(param_dict))
        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"]
        

        # 3. Build data-iterator
        loader = ASRTask.build_streaming_iterator(
            data_path_and_name_and_type,
        loader = build_streaming_iterator(
            task_name="sv",
            preprocess_args=None,
            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=None,
            collate_fn=None,
            allow_variable_data_keys=allow_variable_data_keys,
            inference=True,
            use_collate_fn=False,
        )
        

        # 7 .Start for-loop
        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
        embd_writer, ref_embd_writer, score_writer = None, None, None
@@ -140,7 +117,7 @@
            _bs = len(next(iter(batch.values())))
            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
            batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
            

            embedding, ref_embedding, score = speech2xvector(**batch)
            # Only supporting batch_size==1
            key = keys[0]
@@ -162,16 +139,24 @@
                        score_writer = open(os.path.join(output_path, "score.txt"), "w")
                    ref_embd_writer(key, ref_embedding[0].cpu().numpy())
                    score_writer.write("{} {:.6f}\n".format(key, normalized_score))
        

        if output_path is not None:
            embd_writer.close()
            if ref_embd_writer is not None:
                ref_embd_writer.close()
                score_writer.close()
        

        return sv_result_list
    

    return _forward


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


def get_parser():
@@ -287,14 +272,6 @@
    )

    return parser


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


def main(cmd=None):