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游雁

2023-11-16 4ace5a95b052d338947fc88809a440ccd55cf6b4

 funasr/bin/sv_infer.py

@@ -1,35 +1,22 @@
#!/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 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

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.build_utils.build_model_from_file import build_model_from_file
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import config_argparse
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


class Speech2Xvector:
    """Speech2Xvector class
@@ -53,13 +40,15 @@
            streaming: bool = False,
            embedding_node: str = "resnet1_dense",
    ):
        assert check_argument_types()

        # TODO: 1. Build SV model
        sv_model, sv_train_args = SVTask.build_model_from_file(
        sv_model, sv_train_args = build_model_from_file(
            config_file=sv_train_config,
            model_file=sv_model_file,
            device=device
            cmvn_file=None,
            device=device,
            task_name="sv",
            mode="sv",
        )
        logging.info("sv_model: {}".format(sv_model))
        logging.info("model parameter number: {}".format(statistic_model_parameters(sv_model)))
@@ -116,7 +105,6 @@
            embedding, ref_embedding, similarity_score

        """
        assert check_argument_types()
        self.sv_model.eval()
        embedding = self.calculate_embedding(speech)
        ref_emb, score = None, None
@@ -125,39 +113,4 @@
            score = torch.cosine_similarity(embedding, ref_emb)

        results = (embedding, ref_emb, score)
        assert check_return_type(results)
        return results

    @staticmethod
    def from_pretrained(
            model_tag: Optional[str] = None,
            **kwargs: Optional[Any],
    ):
        """Build Speech2Xvector instance from the pretrained model.

        Args:
            model_tag (Optional[str]): Model tag of the pretrained models.
                Currently, the tags of espnet_model_zoo are supported.

        Returns:
            Speech2Xvector: Speech2Xvector instance.

        """
        if model_tag is not None:
            try:
                from espnet_model_zoo.downloader import ModelDownloader

            except ImportError:
                logging.error(
                    "`espnet_model_zoo` is not installed. "
                    "Please install via `pip install -U espnet_model_zoo`."
                )
                raise
            d = ModelDownloader()
            kwargs.update(**d.download_and_unpack(model_tag))

        return Speech2Xvector(**kwargs)