Merge pull request #653 from alibaba-damo-academy/dev_wjm_infer

jmwang66

2023-06-20 2ff405b2f4ab899eff9bece232969fbb0c8f0555

 funasr/bin/diar_infer.py

@@ -1,41 +1,28 @@
# -*- encoding: utf-8 -*-
#!/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 collections import OrderedDict
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 collections import OrderedDict
import numpy as np
import soundfile
import torch
from scipy.ndimage import median_filter
from torch.nn import functional as F
from typeguard import check_argument_types
from typeguard import check_return_type

from funasr.utils.cli_utils import get_commandline_args
from funasr.tasks.diar import DiarTask
from funasr.tasks.diar import EENDOLADiarTask
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 scipy.ndimage import median_filter
from funasr.utils.misc import statistic_model_parameters
from funasr.datasets.iterable_dataset import load_bytes
from funasr.models.frontend.wav_frontend import WavFrontendMel23
from funasr.tasks.diar import DiarTask
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.torch_utils.device_funcs import to_device
from funasr.utils.misc import statistic_model_parameters


class Speech2DiarizationEEND:
    """Speech2Diarlization class
@@ -61,10 +48,12 @@
        assert check_argument_types()

        # 1. Build Diarization model
        diar_model, diar_train_args = EENDOLADiarTask.build_model_from_file(
        diar_model, diar_train_args = build_model_from_file(
            config_file=diar_train_config,
            model_file=diar_model_file,
            device=device
            device=device,
            task_name="diar",
            mode="eend-ola",
        )
        frontend = None
        if diar_train_args.frontend is not None and diar_train_args.frontend_conf is not None:
@@ -177,10 +166,12 @@
        assert check_argument_types()

        # TODO: 1. Build Diarization model
        diar_model, diar_train_args = DiarTask.build_model_from_file(
        diar_model, diar_train_args = build_model_from_file(
            config_file=diar_train_config,
            model_file=diar_model_file,
            device=device
            device=device,
            task_name="diar",
            mode="sond",
        )
        logging.info("diar_model: {}".format(diar_model))
        logging.info("model parameter number: {}".format(statistic_model_parameters(diar_model)))
@@ -248,7 +239,7 @@
        ut = logits_idx.shape[1] * self.diar_model.encoder.time_ds_ratio
        logits_idx = F.upsample(
            logits_idx.unsqueeze(1).float(),
            size=(ut, ),
            size=(ut,),
            mode="nearest",
        ).squeeze(1).long()
        logits_idx = logits_idx[0].tolist()
@@ -268,7 +259,7 @@
            if spk not in results:
                results[spk] = []
            if dur > self.dur_threshold:
                results[spk].append((st, st+dur))
                results[spk].append((st, st + dur))

        # sort segments in start time ascending
        for spk in results:
@@ -344,7 +335,3 @@
            kwargs.update(**d.download_and_unpack(model_tag))

        return Speech2DiarizationSOND(**kwargs)