update ola

speech_asr

2023-03-13 e27de5aa6bd9af2a82e80604978b50aa538493ec

update ola

 funasr/models/e2e_diar_eend_ola.py

@@ -11,7 +11,8 @@
import torch.nn as  nn
from typeguard import check_argument_types

from funasr.modules.eend_ola.encoder import TransformerEncoder
from funasr.models.frontend.wav_frontend import WavFrontendMel23
from funasr.modules.eend_ola.encoder import EENDOLATransformerEncoder
from funasr.modules.eend_ola.encoder_decoder_attractor import EncoderDecoderAttractor
from funasr.modules.eend_ola.utils.power import generate_mapping_dict
from funasr.torch_utils.device_funcs import force_gatherable
@@ -34,12 +35,13 @@


class DiarEENDOLAModel(AbsESPnetModel):
    """CTC-attention hybrid Encoder-Decoder model"""
    """EEND-OLA diarization model"""

    def __init__(
            self,
            encoder: TransformerEncoder,
            eda: EncoderDecoderAttractor,
            frontend: WavFrontendMel23,
            encoder: EENDOLATransformerEncoder,
            encoder_decoder_attractor: EncoderDecoderAttractor,
            n_units: int = 256,
            max_n_speaker: int = 8,
            attractor_loss_weight: float = 1.0,
@@ -49,8 +51,9 @@
        assert check_argument_types()

        super().__init__()
        self.frontend = frontend
        self.encoder = encoder
        self.eda = eda
        self.encoder_decoder_attractor = encoder_decoder_attractor
        self.attractor_loss_weight = attractor_loss_weight
        self.max_n_speaker = max_n_speaker
        if mapping_dict is None:
@@ -187,16 +190,18 @@
                            shuffle: bool = True,
                            threshold: float = 0.5,
                            **kwargs):
        if self.frontend is not None:
            speech = self.frontend(speech)
        speech = [s[:s_len] for s, s_len in zip(speech, speech_lengths)]
        emb = self.forward_encoder(speech, speech_lengths)
        if shuffle:
            orders = [np.arange(e.shape[0]) for e in emb]
            for order in orders:
                np.random.shuffle(order)
            attractors, probs = self.eda.estimate(
            attractors, probs = self.encoder_decoder_attractor.estimate(
                [e[torch.from_numpy(order).to(torch.long).to(speech[0].device)] for e, order in zip(emb, orders)])
        else:
            attractors, probs = self.eda.estimate(emb)
            attractors, probs = self.encoder_decoder_attractor.estimate(emb)
        attractors_active = []
        for p, att, e in zip(probs, attractors, emb):
            if n_speakers and n_speakers >= 0:

 funasr/modules/eend_ola/encoder.py

@@ -1,5 +1,5 @@
import math
import numpy as np

import torch
import torch.nn.functional as F
from torch import nn
@@ -81,10 +81,16 @@
        return self.dropout(x)


class TransformerEncoder(nn.Module):
    def __init__(self, idim, n_layers, n_units,
                 e_units=2048, h=8, dropout_rate=0.1, use_pos_emb=False):
        super(TransformerEncoder, self).__init__()
class EENDOLATransformerEncoder(nn.Module):
    def __init__(self,
                 idim: int,
                 n_layers: int,
                 n_units: int,
                 e_units: int = 2048,
                 h: int = 8,
                 dropout_rate: float = 0.1,
                 use_pos_emb: bool = False):
        super(EENDOLATransformerEncoder, self).__init__()
        self.lnorm_in = nn.LayerNorm(n_units)
        self.n_layers = n_layers
        self.dropout = nn.Dropout(dropout_rate)

 funasr/tasks/diar.py

@@ -20,19 +20,18 @@
from funasr.datasets.preprocessor import CommonPreprocessor
from funasr.layers.abs_normalize import AbsNormalize
from funasr.layers.global_mvn import GlobalMVN
from funasr.layers.utterance_mvn import UtteranceMVN
from funasr.layers.label_aggregation import LabelAggregate
from funasr.models.ctc import CTC
from funasr.models.encoder.resnet34_encoder import ResNet34Diar, ResNet34SpL2RegDiar
from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
from funasr.models.encoder.opennmt_encoders.self_attention_encoder import SelfAttentionEncoder
from funasr.models.encoder.opennmt_encoders.ci_scorers import DotScorer, CosScorer
from funasr.layers.utterance_mvn import UtteranceMVN
from funasr.models.e2e_diar_sond import DiarSondModel
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.encoder.conformer_encoder import ConformerEncoder
from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
from funasr.models.encoder.opennmt_encoders.ci_scorers import DotScorer, CosScorer
from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
from funasr.models.encoder.opennmt_encoders.self_attention_encoder import SelfAttentionEncoder
from funasr.models.encoder.resnet34_encoder import ResNet34Diar, ResNet34SpL2RegDiar
from funasr.models.encoder.rnn_encoder import RNNEncoder
from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
from funasr.models.encoder.transformer_encoder import TransformerEncoder
@@ -41,17 +40,13 @@
from funasr.models.frontend.fused import FusedFrontends
from funasr.models.frontend.s3prl import S3prlFrontend
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.frontend.wav_frontend import WavFrontendMel23
from funasr.models.frontend.windowing import SlidingWindow
from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
from funasr.models.postencoder.hugging_face_transformers_postencoder import (
    HuggingFaceTransformersPostEncoder,  # noqa: H301
)
from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
from funasr.models.preencoder.linear import LinearProjection
from funasr.models.preencoder.sinc import LightweightSincConvs
from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.models.specaug.specaug import SpecAug
from funasr.models.specaug.specaug import SpecAugLFR
from funasr.modules.eend_ola.encoder import EENDOLATransformerEncoder
from funasr.modules.eend_ola.encoder_decoder_attractor import EncoderDecoderAttractor
from funasr.tasks.abs_task import AbsTask
from funasr.torch_utils.initialize import initialize
from funasr.train.abs_espnet_model import AbsESPnetModel
@@ -70,6 +65,7 @@
        s3prl=S3prlFrontend,
        fused=FusedFrontends,
        wav_frontend=WavFrontend,
        wav_frontend_mel23=WavFrontendMel23,
    ),
    type_check=AbsFrontend,
    default="default",
@@ -126,6 +122,7 @@
        sanm_chunk_opt=SANMEncoderChunkOpt,
        data2vec_encoder=Data2VecEncoder,
        ecapa_tdnn=ECAPA_TDNN,
        eend_ola_transformer=EENDOLATransformerEncoder,
    ),
    type_check=torch.nn.Module,
    default="resnet34",
@@ -176,6 +173,15 @@
    ),
    type_check=torch.nn.Module,
    default="fsmn",
)
# encoder_decoder_attractor is used for EEND-OLA
encoder_decoder_attractor_choices = ClassChoices(
    "encoder_decoder_attractor",
    classes=dict(
        eda=EncoderDecoderAttractor,
    ),
    type_check=torch.nn.Module,
    default="eda",
)


@@ -594,3 +600,294 @@
            var_dict_torch_update.update(var_dict_torch_update_local)

        return var_dict_torch_update


class EENDOLADiarTask(AbsTask):
    # If you need more than 1 optimizer, change this value
    num_optimizers: int = 1

    # Add variable objects configurations
    class_choices_list = [
        # --frontend and --frontend_conf
        frontend_choices,
        # --specaug and --specaug_conf
        model_choices,
        # --encoder and --encoder_conf
        encoder_choices,
        # --speaker_encoder and --speaker_encoder_conf
        encoder_decoder_attractor_choices,
    ]

    # If you need to modify train() or eval() procedures, change Trainer class here
    trainer = Trainer

    @classmethod
    def add_task_arguments(cls, parser: argparse.ArgumentParser):
        group = parser.add_argument_group(description="Task related")

        # NOTE(kamo): add_arguments(..., required=True) can't be used
        # to provide --print_config mode. Instead of it, do as
        # required = parser.get_default("required")
        # required += ["token_list"]

        group.add_argument(
            "--token_list",
            type=str_or_none,
            default=None,
            help="A text mapping int-id to token",
        )
        group.add_argument(
            "--split_with_space",
            type=str2bool,
            default=True,
            help="whether to split text using <space>",
        )
        group.add_argument(
            "--seg_dict_file",
            type=str,
            default=None,
            help="seg_dict_file for text processing",
        )
        group.add_argument(
            "--init",
            type=lambda x: str_or_none(x.lower()),
            default=None,
            help="The initialization method",
            choices=[
                "chainer",
                "xavier_uniform",
                "xavier_normal",
                "kaiming_uniform",
                "kaiming_normal",
                None,
            ],
        )

        group.add_argument(
            "--input_size",
            type=int_or_none,
            default=None,
            help="The number of input dimension of the feature",
        )

        group = parser.add_argument_group(description="Preprocess related")
        group.add_argument(
            "--use_preprocessor",
            type=str2bool,
            default=True,
            help="Apply preprocessing to data or not",
        )
        group.add_argument(
            "--token_type",
            type=str,
            default="char",
            choices=["char"],
            help="The text will be tokenized in the specified level token",
        )
        parser.add_argument(
            "--speech_volume_normalize",
            type=float_or_none,
            default=None,
            help="Scale the maximum amplitude to the given value.",
        )
        parser.add_argument(
            "--rir_scp",
            type=str_or_none,
            default=None,
            help="The file path of rir scp file.",
        )
        parser.add_argument(
            "--rir_apply_prob",
            type=float,
            default=1.0,
            help="THe probability for applying RIR convolution.",
        )
        parser.add_argument(
            "--cmvn_file",
            type=str_or_none,
            default=None,
            help="The file path of noise scp file.",
        )
        parser.add_argument(
            "--noise_scp",
            type=str_or_none,
            default=None,
            help="The file path of noise scp file.",
        )
        parser.add_argument(
            "--noise_apply_prob",
            type=float,
            default=1.0,
            help="The probability applying Noise adding.",
        )
        parser.add_argument(
            "--noise_db_range",
            type=str,
            default="13_15",
            help="The range of noise decibel level.",
        )

        for class_choices in cls.class_choices_list:
            # Append --<name> and --<name>_conf.
            # e.g. --encoder and --encoder_conf
            class_choices.add_arguments(group)

    @classmethod
    def build_collate_fn(
            cls, args: argparse.Namespace, train: bool
    ) -> Callable[
        [Collection[Tuple[str, Dict[str, np.ndarray]]]],
        Tuple[List[str], Dict[str, torch.Tensor]],
    ]:
        assert check_argument_types()
        # NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
        return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)

    @classmethod
    def build_preprocess_fn(
            cls, args: argparse.Namespace, train: bool
    ) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
        assert check_argument_types()
        if args.use_preprocessor:
            retval = CommonPreprocessor(
                train=train,
                token_type=args.token_type,
                token_list=args.token_list,
                bpemodel=None,
                non_linguistic_symbols=None,
                text_cleaner=None,
                g2p_type=None,
                split_with_space=args.split_with_space if hasattr(args, "split_with_space") else False,
                seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
                # NOTE(kamo): Check attribute existence for backward compatibility
                rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
                rir_apply_prob=args.rir_apply_prob
                if hasattr(args, "rir_apply_prob")
                else 1.0,
                noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
                noise_apply_prob=args.noise_apply_prob
                if hasattr(args, "noise_apply_prob")
                else 1.0,
                noise_db_range=args.noise_db_range
                if hasattr(args, "noise_db_range")
                else "13_15",
                speech_volume_normalize=args.speech_volume_normalize
                if hasattr(args, "rir_scp")
                else None,
            )
        else:
            retval = None
        assert check_return_type(retval)
        return retval

    @classmethod
    def required_data_names(
            cls, train: bool = True, inference: bool = False
    ) -> Tuple[str, ...]:
        if not inference:
            retval = ("speech", "profile", "binary_labels")
        else:
            # Recognition mode
            retval = ("speech")
        return retval

    @classmethod
    def optional_data_names(
            cls, train: bool = True, inference: bool = False
    ) -> Tuple[str, ...]:
        retval = ()
        assert check_return_type(retval)
        return retval

    @classmethod
    def build_model(cls, args: argparse.Namespace):
        assert check_argument_types()

        # 1. frontend
        if args.input_size is None or args.frontend == "wav_frontend_mel23":
            # Extract features in the model
            frontend_class = frontend_choices.get_class(args.frontend)
            if args.frontend == 'wav_frontend':
                frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
            else:
                frontend = frontend_class(**args.frontend_conf)
            input_size = frontend.output_size()
        else:
            # Give features from data-loader
            args.frontend = None
            args.frontend_conf = {}
            frontend = None
            input_size = args.input_size

        # 2. Encoder
        encoder_class = encoder_choices.get_class(args.encoder)
        encoder = encoder_class(input_size=input_size, **args.encoder_conf)

        # 3. EncoderDecoderAttractor
        encoder_decoder_attractor_class = encoder_decoder_attractor_choices.get_class(args.encoder_decoder_attractor)
        encoder_decoder_attractor = encoder_decoder_attractor_class(**args.encoder_decoder_attractor_conf)

        # 9. Build model
        model_class = model_choices.get_class(args.model)
        model = model_class(
            frontend=frontend,
            encoder=encoder,
            encoder_decoder_attractor=encoder_decoder_attractor,
            **args.model_conf,
        )

        # 10. Initialize
        if args.init is not None:
            initialize(model, args.init)

        assert check_return_type(model)
        return model

    # ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
    @classmethod
    def build_model_from_file(
            cls,
            config_file: Union[Path, str] = None,
            model_file: Union[Path, str] = None,
            cmvn_file: Union[Path, str] = None,
            device: str = "cpu",
    ):
        """Build model from the files.

        This method is used for inference or fine-tuning.

        Args:
            config_file: The yaml file saved when training.
            model_file: The model file saved when training.
            cmvn_file: The cmvn file for front-end
            device: Device type, "cpu", "cuda", or "cuda:N".

        """
        assert check_argument_types()
        if config_file is None:
            assert model_file is not None, (
                "The argument 'model_file' must be provided "
                "if the argument 'config_file' is not specified."
            )
            config_file = Path(model_file).parent / "config.yaml"
        else:
            config_file = Path(config_file)

        with config_file.open("r", encoding="utf-8") as f:
            args = yaml.safe_load(f)
        args = argparse.Namespace(**args)
        model = cls.build_model(args)
        if not isinstance(model, AbsESPnetModel):
            raise RuntimeError(
                f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
            )
        if model_file is not None:
            if device == "cuda":
                device = f"cuda:{torch.cuda.current_device()}"
            checkpoint = torch.load(model_file, map_location=device)
            if "state_dict" in checkpoint.keys():
                model.load_state_dict(checkpoint["state_dict"])
            else:
                model.load_state_dict(checkpoint)
        model.to(device)
        return model, args