update repo

嘉渊

2023-06-15 4b30f336ee7e3ca405cfa6ff96d9b3c3e936f767

update repo

 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)





 funasr/bin/diar_inference_launch.py

@@ -1,5 +1,5 @@
# !/usr/bin/env python3
# -*- encoding: utf-8 -*-
#!/usr/bin/env python3
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

@@ -8,47 +8,28 @@
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
from typing import Union

from collections import OrderedDict
import numpy as np
import soundfile
import torch
from torch.nn import functional as F
from typeguard import check_argument_types
from typeguard import check_return_type
from scipy.signal import medfilt
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 typeguard import check_argument_types

from funasr.bin.diar_infer import Speech2DiarizationSOND, Speech2DiarizationEEND
from funasr.datasets.iterable_dataset import load_bytes
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 scipy.ndimage import median_filter
from funasr.utils.misc import statistic_model_parameters
from funasr.datasets.iterable_dataset import load_bytes
from funasr.bin.diar_infer import Speech2DiarizationSOND, Speech2DiarizationEEND


def inference_sond(
        diar_train_config: str,
@@ -94,7 +75,8 @@
    set_all_random_seed(seed)

    # 2a. Build speech2xvec [Optional]
    if mode == "sond_demo" and param_dict is not None and "extract_profile" in param_dict and param_dict["extract_profile"]:
    if mode == "sond_demo" and param_dict is not None and "extract_profile" in param_dict and param_dict[
        "extract_profile"]:
        assert "sv_train_config" in param_dict, "sv_train_config must be provided param_dict."
        assert "sv_model_file" in param_dict, "sv_model_file must be provided in param_dict."
        sv_train_config = param_dict["sv_train_config"]
@@ -139,7 +121,7 @@
        rst = []
        mid = uttid.rsplit("-", 1)[0]
        for key in results:
            results[key] = [(x[0]/100, x[1]/100) for x in results[key]]
            results[key] = [(x[0] / 100, x[1] / 100) for x in results[key]]
        if out_format == "vad":
            for spk, segs in results.items():
                rst.append("{} {}".format(spk, segs))
@@ -176,7 +158,7 @@
                        example = [x.numpy() if isinstance(example[0], torch.Tensor) else x
                                   for x in example]
                        speech = example[0]
                        logging.info("Extracting profiles for {} waveforms".format(len(example)-1))
                        logging.info("Extracting profiles for {} waveforms".format(len(example) - 1))
                        profile = [speech2xvector.calculate_embedding(x) for x in example[1:]]
                        profile = torch.cat(profile, dim=0)
                        yield ["test{}".format(idx)], {"speech": [speech], "profile": [profile]}
@@ -186,16 +168,15 @@
                raise TypeError("raw_inputs must be a list or tuple in [speech, profile1, profile2, ...] ")
        else:
            # 3. Build data-iterator
            loader = DiarTask.build_streaming_iterator(
                data_path_and_name_and_type,
            loader = build_streaming_iterator(
                task_name="diar",
                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
@@ -234,6 +215,7 @@
        return result_list

    return _forward


def inference_eend(
        diar_train_config: str,
@@ -306,16 +288,14 @@
            if isinstance(raw_inputs, torch.Tensor):
                raw_inputs = raw_inputs.numpy()
            data_path_and_name_and_type = [raw_inputs[0], "speech", "sound"]
        loader = EENDOLADiarTask.build_streaming_iterator(
            data_path_and_name_and_type,
        loader = build_streaming_iterator(
            task_name="diar",
            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=EENDOLADiarTask.build_preprocess_fn(speech2diar.diar_train_args, False),
            collate_fn=EENDOLADiarTask.build_collate_fn(speech2diar.diar_train_args, False),
            allow_variable_data_keys=allow_variable_data_keys,
            inference=True,
        )

        # 3. Start for-loop
@@ -362,8 +342,6 @@
    return _forward




def inference_launch(mode, **kwargs):
    if mode == "sond":
        return inference_sond(mode=mode, **kwargs)
@@ -386,6 +364,7 @@
        logging.info("Unknown decoding mode: {}".format(mode))
        return None


def get_parser():
    parser = config_argparse.ArgumentParser(
        description="Speaker Verification",

 funasr/build_utils/build_model_from_file.py

@@ -72,6 +72,8 @@
            model.load_state_dict(model_dict)
        else:
            model_dict = torch.load(model_file, map_location=device)
    if task_name == "diar" and mode == "sond":
        model_dict = fileter_model_dict(model_dict, model.state_dict())
    model.load_state_dict(model_dict)
    if model_name_pth is not None and not os.path.exists(model_name_pth):
        torch.save(model_dict, model_name_pth)
@@ -85,7 +87,7 @@
        ckpt,
        mode,
):
    assert mode == "paraformer" or mode == "uniasr"
    assert mode == "paraformer" or mode == "uniasr" or mode == "sond"
    logging.info("start convert tf model to torch model")
    from funasr.modules.streaming_utils.load_fr_tf import load_tf_dict
    var_dict_tf = load_tf_dict(ckpt)
@@ -113,7 +115,7 @@
        # stride_conv
        var_dict_torch_update_local = model.stride_conv.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
    else:
    elif mode == "paraformer":
        # encoder
        var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
@@ -126,5 +128,38 @@
        # bias_encoder
        var_dict_torch_update_local = model.clas_convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
    else:
        if model.encoder is not None:
            var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # speaker encoder
        if model.speaker_encoder is not None:
            var_dict_torch_update_local = model.speaker_encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # cd scorer
        if model.cd_scorer is not None:
            var_dict_torch_update_local = model.cd_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # ci scorer
        if model.ci_scorer is not None:
            var_dict_torch_update_local = model.ci_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # decoder
        if model.decoder is not None:
            var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)

    return var_dict_torch_update

def fileter_model_dict(src_dict: dict, dest_dict: dict):
    from collections import OrderedDict
    new_dict = OrderedDict()
    for key, value in src_dict.items():
        if key in dest_dict:
            new_dict[key] = value
        else:
            logging.info("{} is no longer needed in this model.".format(key))
    for key, value in dest_dict.items():
        if key not in new_dict:
            logging.warning("{} is missed in checkpoint.".format(key))
    return new_dict

 funasr/build_utils/build_streaming_iterator.py

@@ -17,6 +17,7 @@
        mc: bool = False,
        dtype: str = np.float32,
        num_workers: int = 1,
        use_collate_fn: bool = True,
        ngpu: int = 0,
        train: bool=False,
) -> DataLoader:
@@ -30,7 +31,9 @@
        preprocess_fn = None

    # collate
    if task_name in ["punc", "lm"]:
    if not use_collate_fn:
        collate_fn = None
    elif task_name in ["punc", "lm"]:
        collate_fn = CommonCollateFn(int_pad_value=0)
    else:
        collate_fn = CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)