FunASR/funasr/tasks/sv.py

"""
Author: Speech Lab, Alibaba Group, China
"""

import argparse
import logging
import os
from pathlib import Path
from typing import Callable
from typing import Collection
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union

import numpy as np
import torch
import yaml

from funasr.datasets.collate_fn import CommonCollateFn
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.models.e2e_asr import ASRModel
from funasr.models.decoder.abs_decoder import AbsDecoder
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.encoder.rnn_encoder import RNNEncoder
from funasr.models.encoder.resnet34_encoder import ResNet34, ResNet34_SP_L2Reg
from funasr.models.pooling.statistic_pooling import StatisticPooling
from funasr.models.decoder.sv_decoder import DenseDecoder
from funasr.models.e2e_sv import ESPnetSVModel
from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.models.frontend.default import DefaultFrontend
from funasr.models.frontend.fused import FusedFrontends
from funasr.models.frontend.s3prl import S3prlFrontend
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.tasks.abs_task import AbsTask
from funasr.torch_utils.initialize import initialize
from funasr.models.base_model import FunASRModel
from funasr.train.class_choices import ClassChoices
from funasr.train.trainer import Trainer
from funasr.utils.types import float_or_none
from funasr.utils.types import int_or_none
from funasr.utils.types import str2bool
from funasr.utils.types import str_or_none
from funasr.models.frontend.wav_frontend import WavFrontend

frontend_choices = ClassChoices(
    name="frontend",
    classes=dict(
        default=DefaultFrontend,
        sliding_window=SlidingWindow,
        s3prl=S3prlFrontend,
        fused=FusedFrontends,
        wav_frontend=WavFrontend,
    ),
    type_check=AbsFrontend,
    default="default",
)
specaug_choices = ClassChoices(
    name="specaug",
    classes=dict(
        specaug=SpecAug,
    ),
    type_check=AbsSpecAug,
    default=None,
    optional=True,
)
normalize_choices = ClassChoices(
    "normalize",
    classes=dict(
        global_mvn=GlobalMVN,
        utterance_mvn=UtteranceMVN,
    ),
    type_check=AbsNormalize,
    default=None,
    optional=True,
)
model_choices = ClassChoices(
    "model",
    classes=dict(
        espnet=ESPnetSVModel,
    ),
    type_check=FunASRModel,
    default="espnet",
)
preencoder_choices = ClassChoices(
    name="preencoder",
    classes=dict(
        sinc=LightweightSincConvs,
        linear=LinearProjection,
    ),
    type_check=AbsPreEncoder,
    default=None,
    optional=True,
)
encoder_choices = ClassChoices(
    "encoder",
    classes=dict(
        resnet34=ResNet34,
        resnet34_sp_l2reg=ResNet34_SP_L2Reg,
        rnn=RNNEncoder,
    ),
    type_check=AbsEncoder,
    default="resnet34",
)
postencoder_choices = ClassChoices(
    name="postencoder",
    classes=dict(
        hugging_face_transformers=HuggingFaceTransformersPostEncoder,
    ),
    type_check=AbsPostEncoder,
    default=None,
    optional=True,
)
pooling_choices = ClassChoices(
    name="pooling_type",
    classes=dict(
        statistic=StatisticPooling,
    ),
    type_check=torch.nn.Module,
    default="statistic",
)
decoder_choices = ClassChoices(
    "decoder",
    classes=dict(
        dense=DenseDecoder,
    ),
    type_check=AbsDecoder,
    default="dense",
)


class SVTask(AbsTask):
    # If you need more than one optimizers, change this value
    num_optimizers: int = 1

    # Add variable objects configurations
    class_choices_list = [
        # --frontend and --frontend_conf
        frontend_choices,
        # --specaug and --specaug_conf
        specaug_choices,
        # --normalize and --normalize_conf
        normalize_choices,
        # --model and --model_conf
        model_choices,
        # --preencoder and --preencoder_conf
        preencoder_choices,
        # --encoder and --encoder_conf
        encoder_choices,
        # --postencoder and --postencoder_conf
        postencoder_choices,
        # --pooling and --pooling_conf
        pooling_choices,
        # --decoder and --decoder_conf
        decoder_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 speaker name",
        )
        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",
        )
        parser.add_argument(
            "--cleaner",
            type=str_or_none,
            choices=[None, "tacotron", "jaconv", "vietnamese"],
            default=None,
            help="Apply text cleaning",
        )
        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(
            "--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]],
    ]:
        # 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]]]:
        if args.use_preprocessor:
            retval = CommonPreprocessor(
                train=train,
                token_type=None,
                token_list=None,
                bpemodel=None,
                non_linguistic_symbols=None,
                text_cleaner=args.cleaner,
                g2p_type=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
        return retval

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

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

    @classmethod
    def build_model(cls, args: argparse.Namespace) -> ESPnetSVModel:
        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]

            # Overwriting token_list to keep it as "portable".
            args.token_list = list(token_list)
        elif isinstance(args.token_list, (tuple, list)):
            token_list = list(args.token_list)
        else:
            raise RuntimeError("token_list must be str or list")
        vocab_size = len(token_list)
        logging.info(f"Speaker number: {vocab_size}")

        # 1. frontend
        if args.input_size is None:
            # Extract features in the model
            frontend_class = frontend_choices.get_class(args.frontend)
            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. Data augmentation for spectrogram
        if args.specaug is not None:
            specaug_class = specaug_choices.get_class(args.specaug)
            specaug = specaug_class(**args.specaug_conf)
        else:
            specaug = None

        # 3. Normalization layer
        if args.normalize is not None:
            normalize_class = normalize_choices.get_class(args.normalize)
            normalize = normalize_class(**args.normalize_conf)
        else:
            normalize = None

        # 4. Pre-encoder input block
        # NOTE(kan-bayashi): Use getattr to keep the compatibility
        if getattr(args, "preencoder", None) is not None:
            preencoder_class = preencoder_choices.get_class(args.preencoder)
            preencoder = preencoder_class(**args.preencoder_conf)
            input_size = preencoder.output_size()
        else:
            preencoder = None

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

        # 6. Post-encoder block
        # NOTE(kan-bayashi): Use getattr to keep the compatibility
        encoder_output_size = encoder.output_size()
        if getattr(args, "postencoder", None) is not None:
            postencoder_class = postencoder_choices.get_class(args.postencoder)
            postencoder = postencoder_class(
                input_size=encoder_output_size, **args.postencoder_conf
            )
            encoder_output_size = postencoder.output_size()
        else:
            postencoder = None

        # 7. Pooling layer
        pooling_class = pooling_choices.get_class(args.pooling_type)
        pooling_dim = (2, 3)
        eps = 1e-12
        if hasattr(args, "pooling_type_conf"):
            if "pooling_dim" in args.pooling_type_conf:
                pooling_dim = args.pooling_type_conf["pooling_dim"]
            if "eps" in args.pooling_type_conf:
                eps = args.pooling_type_conf["eps"]
        pooling_layer = pooling_class(
            pooling_dim=pooling_dim,
            eps=eps,
        )
        if args.pooling_type == "statistic":
            encoder_output_size *= 2

        # 8. Decoder
        decoder_class = decoder_choices.get_class(args.decoder)
        decoder = decoder_class(
            vocab_size=vocab_size,
            encoder_output_size=encoder_output_size,
            **args.decoder_conf,
        )

        # 7. Build model
        try:
            model_class = model_choices.get_class(args.model)
        except AttributeError:
            model_class = model_choices.get_class("espnet")
        model = model_class(
            vocab_size=vocab_size,
            token_list=token_list,
            frontend=frontend,
            specaug=specaug,
            normalize=normalize,
            preencoder=preencoder,
            encoder=encoder,
            postencoder=postencoder,
            pooling_layer=pooling_layer,
            decoder=decoder,
            **args.model_conf,
        )

        # FIXME(kamo): Should be done in model?
        # 8. Initialize
        if args.init is not None:
            initialize(model, args.init)

        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".

        """
        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)
        if cmvn_file is not None:
            args["cmvn_file"] = cmvn_file
        args = argparse.Namespace(**args)
        model = cls.build_model(args)
        if not isinstance(model, FunASRModel):
            raise RuntimeError(
                f"model must inherit {FunASRModel.__name__}, but got {type(model)}"
            )
        model.to(device)
        model_dict = dict()
        model_name_pth = None
        if model_file is not None:
            logging.info("model_file is {}".format(model_file))
            if device == "cuda":
                device = f"cuda:{torch.cuda.current_device()}"
            model_dir = os.path.dirname(model_file)
            model_name = os.path.basename(model_file)
            if "model.ckpt-" in model_name or ".bin" in model_name:
                if ".bin" in model_name:
                    model_name_pth = os.path.join(model_dir, model_name.replace('.bin', '.pb'))
                else:
                    model_name_pth = os.path.join(model_dir, "{}.pb".format(model_name))
                if os.path.exists(model_name_pth):
                    logging.info("model_file is load from pth: {}".format(model_name_pth))
                    model_dict = torch.load(model_name_pth, map_location=device)
                else:
                    model_dict = cls.convert_tf2torch(model, model_file)
                model.load_state_dict(model_dict)
            else:
                model_dict = torch.load(model_file, map_location=device)
        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)
            logging.info("model_file is saved to pth: {}".format(model_name_pth))

        return model, args

    @classmethod
    def convert_tf2torch(
            cls,
            model,
            ckpt,
    ):
        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)
        var_dict_torch = model.state_dict()
        var_dict_torch_update = dict()
        # speech 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)
        # pooling layer
        var_dict_torch_update_local = model.pooling_layer.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        # decoder
        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